- W świecie przesyconym treściami wideo, każda sekunda ma potencjał, by stać się niezapomnianym ujęciem.
- PerfectFrameAI to narzędzie wykorzystujące sztuczną inteligencję do analizowania materiałów wideo
- i automatycznego zapisywania najładniejszych klatek.
-
-
👋 How to Contribute
@@ -540,7 +465,7 @@
📜 License
- PerfectFrameAI is licensed under the GNU General Public License v3.0.
+ PerfectFrameAI is licensed under the Apache License 2.0.
See the LICENSE file for more information.
diff --git a/config.py b/config.py
deleted file mode 100644
index c5960e1..0000000
--- a/config.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""
-Main configuration dataclass for extractor service manager tool.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-from dataclasses import dataclass
-from pathlib import Path
-
-BASE_DIRECTORY = Path(__file__).resolve().parent
-
-
-@dataclass
-class Config:
- """
- Configuration settings for the extractor service management tool.
-
- Attributes:
- service_name (str): Name of the managing service.
- dockerfile (str): Path to the managing service dockerfile.
- port (int): Default port for the service in docker and host.
- volume_input_directory (str): Default input directory in the container.
- Note: It must be the same as default in schemas.py in service.
- volume_output_directory (str): Default output directory in the container.
- Note: It must be the same as default in schemas.py in service.
- input_directory (str): Directory with input for the extraction process.
- output_directory (str): Directory where extraction process output will be saved.
- """
-
- service_name: str = "extractor_service"
- dockerfile: str = str(BASE_DIRECTORY / "extractor_service")
- port: int = 8100
- volume_input_directory: str = "/app/input_directory"
- volume_output_directory: str = "/app/output_directory"
- input_directory: str = str(BASE_DIRECTORY / "input_directory")
- output_directory: str = str(BASE_DIRECTORY / "output_directory")
diff --git a/docker-compose.yaml b/docker-compose.yaml
index 54dd2f9..52b1aae 100644
--- a/docker-compose.yaml
+++ b/docker-compose.yaml
@@ -1,12 +1,26 @@
services:
- extractor_service:
+ perfectframe:
build:
- context: ./extractor_service
+ context: .
dockerfile: Dockerfile
ports:
- "8100:8100"
volumes:
- - "./input_directory:/app/input_directory"
- - "./output_directory:/app/output_directory"
+ - "${INPUT_DIR:-./input_directory}:/app/input_directory"
+ - "${OUTPUT_DIR:-./output_directory}:/app/output_directory"
working_dir: /app
- entrypoint: [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8100" ]
+ entrypoint: [ "uvicorn", "perfectframe.app:app", "--host", "0.0.0.0", "--port", "8100" ]
+
+ # GPU-enabled version (use with --profile gpu)
+ perfectframe_gpu:
+ extends:
+ service: perfectframe
+ profiles:
+ - gpu
+ deploy:
+ resources:
+ reservations:
+ devices:
+ - driver: nvidia
+ count: all
+ capabilities: [gpu]
diff --git a/extractor_service/.dockerignore b/extractor_service/.dockerignore
deleted file mode 100644
index e6af855..0000000
--- a/extractor_service/.dockerignore
+++ /dev/null
@@ -1,2 +0,0 @@
-.pytest_cache/
-__pycache__/
diff --git a/extractor_service/Dockerfile b/extractor_service/Dockerfile
deleted file mode 100644
index 7dc542f..0000000
--- a/extractor_service/Dockerfile
+++ /dev/null
@@ -1,48 +0,0 @@
-FROM python:3.12-slim
-
-LABEL authors="BKDDFS"
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
- ffmpeg \
- build-essential \
- yasm \
- libx264-dev \
- libx265-dev \
- libavcodec-dev \
- libavformat-dev \
- libavdevice-dev \
- libavutil-dev \
- libswscale-dev \
- libavfilter-dev \
- pkg-config \
- libgl1 \
- libglib2.0-0 && \
- rm -rf /var/lib/apt/lists/*
-
-# Set cashe for ai model
-VOLUME /root/.cache/huggingface
-
-# Set working directory
-WORKDIR /app
-
-# Copy the requirements file
-COPY requirements.txt .
-
-# Install the dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Set environment variables
-ENV NVIDIA_VISIBLE_DEVICES=all
-ENV NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
-ENV TF_CPP_MIN_LOG_LEVEL=3
-ENV DOCKER_ENV=1
-
-# Copy the source code into the container
-COPY . .
-
-# Expose the port
-EXPOSE 8100
-
-# Run the application
-ENTRYPOINT [ "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8100" ]
\ No newline at end of file
diff --git a/extractor_service/app/dependencies.py b/extractor_service/app/dependencies.py
deleted file mode 100644
index f62d931..0000000
--- a/extractor_service/app/dependencies.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""
-This module provides dependency management for extractors using FastAPI's dependency injection.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-from dataclasses import dataclass
-from typing import Type
-
-from fastapi import Depends
-
-from .image_evaluators import InceptionResNetNIMA
-from .image_processors import OpenCVImage
-from .video_processors import OpenCVVideo
-
-
-@dataclass
-class ExtractorDependencies:
- """
- Data class to hold dependencies for the extractor.
-
- Attributes:
- image_processor (Type[OpenCVImage]): Processor for image processing.
- video_processor (Type[OpenCVVideo]): Processor for video processing.
- evaluator (Type[InceptionResNetNIMA]): Evaluator for image quality.
- """
-
- image_processor: Type[OpenCVImage]
- video_processor: Type[OpenCVVideo]
- evaluator: Type[InceptionResNetNIMA]
-
-
-def get_image_processor() -> Type[OpenCVImage]:
- """
- Provides the image processor dependency.
-
- Returns:
- Type[OpenCVImage]: The image processor class.
- """
- return OpenCVImage
-
-
-def get_video_processor() -> Type[OpenCVVideo]:
- """
- Provides the video processor dependency.
-
- Returns:
- Type[OpenCVVideo]: The video processor class.
- """
- return OpenCVVideo
-
-
-def get_evaluator() -> Type[InceptionResNetNIMA]:
- """
- Provides the image evaluator dependency.
-
- Returns:
- Type[InceptionResNetNIMA]: The image evaluator class.
- """
- return InceptionResNetNIMA
-
-
-def get_extractor_dependencies(
- image_processor=Depends(get_image_processor),
- video_processor=Depends(get_video_processor),
- evaluator=Depends(get_evaluator),
-) -> ExtractorDependencies:
- """
- Provides the dependencies required for the extractor.
-
- Args:
- image_processor (Type[OpenCVImage], optional): Dependency injection for image processor.
- video_processor (Type[OpenCVVideo], optional): Dependency injection for video processor.
- evaluator (Type[InceptionResNetNIMA], optional): Dependency injection for image evaluator.
-
- Returns:
- ExtractorDependencies: All necessary dependencies for the extractor.
- """
- return ExtractorDependencies(
- image_processor=image_processor,
- video_processor=video_processor,
- evaluator=evaluator,
- )
diff --git a/extractor_service/app/extractor_manager.py b/extractor_service/app/extractor_manager.py
deleted file mode 100644
index f11dc40..0000000
--- a/extractor_service/app/extractor_manager.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-This module provides manager class for running extractors and
-managing extraction process lifecycle.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-
-from fastapi import BackgroundTasks, HTTPException
-
-from .dependencies import ExtractorDependencies
-from .extractors import Extractor, ExtractorFactory
-from .schemas import ExtractorConfig
-
-logger = logging.getLogger(__name__)
-
-
-class ExtractorManager:
- """
- This class orchestrates extractors, ensuring that only one extractor is active at once,
- maintaining system stability.
- """
-
- _active_extractor = None
-
- @classmethod
- def get_active_extractor(cls) -> str:
- """
- Getter for class active extractor.
-
- Returns:
- str: Active extractor name.
- """
- return cls._active_extractor
-
- @classmethod
- def start_extractor(
- cls,
- extractor_name: str,
- background_tasks: BackgroundTasks,
- config: ExtractorConfig,
- dependencies: ExtractorDependencies,
- ) -> str:
- """
- Initializes the extractor class and runs the extraction process in the background.
-
- Args:
- extractor_name (str): The name of the extractor that will be used.
- background_tasks (BackgroundTasks): A FastAPI tool for running tasks in background.
- config (ExtractorConfig): A Pydantic model with extractor configuration.
- dependencies(ExtractorDependencies): Dependencies that will be used in extractor.
-
- Returns:
- str: Endpoint feedback message with started extractor name.
- """
- cls._check_is_already_extracting()
- extractor = ExtractorFactory.create_extractor(extractor_name, config, dependencies)
- background_tasks.add_task(cls.__run_extractor, extractor, extractor_name)
- message = f"'{extractor_name}' started."
- return message
-
- @classmethod
- def __run_extractor(cls, extractor: Extractor, extractor_name: str) -> None:
- """
- Run extraction process and clean after it's done.
-
- Args:
- extractor (Extractor): Extractor that will be used for extraction.
- extractor_name (str): The name of the extractor that will be used.
- """
- try:
- cls._active_extractor = extractor_name
- extractor.process()
- finally:
- cls._active_extractor = None
-
- @classmethod
- def _check_is_already_extracting(cls) -> None:
- """
- Checks if some extractor is already active and raises an HTTPException if so.
-
- Raises:
- HTTPException: If extractor is already active to prevent concurrent extractions.
- """
- if cls._active_extractor:
- error_message = (
- f"Extractor '{cls._active_extractor}' is already running. "
- f"You can run only one extractor at the same time. "
- f"Wait until the extractor is done before run next process."
- )
- logger.error(error_message)
- raise HTTPException(status_code=409, detail=error_message)
diff --git a/extractor_service/app/extractors.py b/extractor_service/app/extractors.py
deleted file mode 100644
index 65b7a19..0000000
--- a/extractor_service/app/extractors.py
+++ /dev/null
@@ -1,366 +0,0 @@
-"""
-This module provides:
- - Extractor: Abstract class for creating extractors.
- - ExtractorFactory: Factory for getting extractors by their names.
- - Extractors:
- - BestFramesExtractor: For extracting best frames from all videos from any directory.
- - TopImagesExtractor: For extracting images with top percent evaluating from any directory.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import gc
-import logging
-from abc import ABC, abstractmethod
-from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
-from typing import Type
-
-import numpy as np
-
-from .dependencies import ExtractorDependencies
-from .image_evaluators import ImageEvaluator
-from .image_processors import ImageProcessor
-from .schemas import ExtractorConfig
-from .video_processors import VideoProcessor
-
-logger = logging.getLogger(__name__)
-
-
-class Extractor(ABC):
- """Abstract class for creating extractors."""
-
- class EmptyInputDirectoryError(Exception):
- """Error appear when extractor can't get any input to extraction."""
-
- def __init__(
- self,
- config: ExtractorConfig,
- image_processor: Type[ImageProcessor],
- video_processor: Type[VideoProcessor],
- image_evaluator_class: Type[ImageEvaluator],
- ) -> None:
- """
- Initializes the manager with the given extractor configuration.
-
- Args:
- config (ExtractorConfig): A Pydantic model with configuration
- parameters for the extractor.
- image_processor (Type[ImageProcessor]): The class for processing images.
- video_processor (Type[VideoProcessor]): The class for processing videos.
- image_evaluator_class (Type[ImageEvaluator]): The class for evaluating images.
- """
- self._config = config
- self._image_processor = image_processor
- self._video_processor = video_processor
- self._image_evaluator_class = image_evaluator_class
- self._image_evaluator = None
-
- @abstractmethod
- def process(self) -> None:
- """Abstract main method for extraction process implementation."""
-
- def _get_image_evaluator(self) -> ImageEvaluator:
- """
- Initializes one of image evaluators (currently NIMA) and
- adds it to extractor instance parameters.
-
- Returns:
- PyIQA: Image evaluator class instance for evaluating images.
- """
- self._image_evaluator = self._image_evaluator_class(self._config)
- return self._image_evaluator
-
- def _list_input_directory_files(self, extensions: tuple[str, ...], prefix: str | None = None) -> list[Path]:
- """
- List all files with given extensions except files with given filename prefix form
- config input directory.
-
- Args:
- extensions (tuple): Searched files extensions.
- prefix (str | None): Excluded files filename prefix. Default is None.
-
- Returns:
- list[Path]: All matching files list.
- """
- directory = self._config.input_directory
- entries = directory.iterdir()
- files = [
- entry
- for entry in entries
- if entry.is_file() and entry.suffix in extensions and (prefix is None or not entry.name.startswith(prefix))
- ]
- if not files:
- prefix = prefix if prefix else "Prefix not provided"
- error_massage = (
- f"Files with extensions '{extensions}' and without prefix '{prefix}' "
- f"not found in folder: {directory}."
- f"\n-->HINT: You probably don't have input or you haven't changed prefixes. "
- f"\nCheck input directory."
- )
- logger.error(error_massage)
- raise self.EmptyInputDirectoryError(error_massage)
- logger.info("Directory '%s' files listed.", str(directory))
- logger.debug("Listed file paths: %s", files)
- return files
-
- def _evaluate_images(self, normalized_images: np.ndarray) -> np.array:
- """
- Rating all images in provided images batch using already initialized image evaluator.
-
- Args:
- normalized_images (list[np.ndarray]): Already normalized images for evaluating.
-
- Returns:
- np.array: Array with images scores in given images order.
- """
- scores = np.array(self._image_evaluator.evaluate_images(normalized_images))
- return scores
-
- def _read_images(self, paths: list[Path]) -> list[np.ndarray]:
- """
- Read all images from given paths synonymously.
-
- Args:
- paths (list[Path]): List of images paths.
-
- Returns:
- list[np.ndarray]: List of images in numpy ndarrays.
- """
- with ThreadPoolExecutor() as executor:
- images = []
- futures = [
- executor.submit(
- self._image_processor.read_image,
- path,
- )
- for path in paths
- ]
- for future in futures:
- image = future.result()
- if image is not None:
- images.append(image)
- return images
-
- def _save_images(self, images: list[np.ndarray]) -> None:
- """
- Save all images in config output directory synonymously.
-
- Args:
- images (list[np.ndarray]): List of images in numpy ndarrays.
- """
- with ThreadPoolExecutor() as executor:
- futures = [
- executor.submit(
- self._image_processor.save_image,
- image,
- self._config.output_directory,
- self._config.images_output_format,
- )
- for image in images
- ]
- for future in futures:
- future.result()
-
- def _normalize_images(self, images: list[np.ndarray], target_size: tuple[int, int]) -> np.ndarray:
- """
- Normalize all images in given list to target size for further operations.
-
- Args:
- images (list[np.ndarray]): List of np.ndarray images to normalize.
- target_size (tuple[int, int]): Images will be normalized to this size.
-
- Returns:
- np.ndarray: All images as a one numpy array.
- """
- normalized_images = self._image_processor.normalize_images(images, target_size)
- return normalized_images
-
- @staticmethod
- def _add_prefix(prefix: str, path: Path) -> Path:
- """
- Adds prefix to file filename.
-
- Args:
- prefix (str): Prefix that will be added.
- path (Path): Path to file that filename will be changed.
-
- Returns:
- Path: Path of the file with new filename.
- """
- new_path = path.parent / f"{prefix}{path.name}"
- path.rename(new_path)
- logger.debug("Prefix '%s' added to file '%s'. New path: %s", prefix, path, new_path)
- return new_path
-
- @staticmethod
- def _signal_readiness_for_shutdown() -> None:
- """
- Contains the logic for sending a signal externally that the service has completed
- the process and can be safely shut down.
- """
- logger.info("Service ready for shutdown")
-
-
-class ExtractorFactory:
- """Extractor factory for getting extractors class by their names."""
-
- @staticmethod
- def create_extractor(
- extractor_name: str,
- config: ExtractorConfig,
- dependencies: ExtractorDependencies,
- ) -> Extractor:
- """
- Match extractor class by its name and return its class.
-
- Args:
- extractor_name (str): Name of the extractor.
- config (ExtractorConfig): A Pydantic model with extractor configuration.
- dependencies(ExtractorDependencies): Dependencies that will be used in extractor.
-
- Returns:
- Extractor: Chosen extractor class.
- """
- match extractor_name:
- case "best_frames_extractor":
- return BestFramesExtractor(
- config,
- dependencies.image_processor,
- dependencies.video_processor,
- dependencies.evaluator,
- )
- case "top_images_extractor":
- return TopImagesExtractor(
- config,
- dependencies.image_processor,
- dependencies.video_processor,
- dependencies.evaluator,
- )
- case _:
- error_massage = f"Provided unknown extractor name: {extractor_name}"
- logger.error(error_massage)
- raise ValueError(error_massage)
-
-
-class BestFramesExtractor(Extractor):
- """Extractor for extracting best frames from videos in any input directory."""
-
- def process(self) -> None:
- """
- Rate all videos in given config input directory and
- extract best visually frames from every video.
- """
- logger.info(
- "Starting frames extraction process from '%s'.",
- self._config.input_directory,
- )
- videos_paths = self._list_input_directory_files(
- self._config.video_extensions, self._config.processed_video_prefix
- )
- if self._config.all_frames is False: # evaluator won't be used if all frames
- self._get_image_evaluator()
- for video_path in videos_paths:
- self._extract_best_frames(video_path)
- self._add_prefix(self._config.processed_video_prefix, video_path)
- logger.info("Frames extraction has finished for video: %s", video_path)
- logger.info("Extraction process finished. All frames extracted.")
- self._signal_readiness_for_shutdown()
-
- def _extract_best_frames(self, video_path: Path) -> None:
- """
- Extract best visually frames from given video.
-
- Args:
- video_path (Path): Path of the video that will be extracted.
- """
- frames_batch_generator = self._video_processor.get_next_frames(video_path, self._config.batch_size)
- for frames in frames_batch_generator:
- if not frames:
- continue
- logger.debug("Frames batch generated.")
- if not self._config.all_frames:
- frames = self._get_best_frames(frames)
- self._save_images(frames)
- del frames
- gc.collect()
-
- def _get_best_frames(self, frames: list[np.ndarray]) -> list[np.ndarray]:
- """
- Splits images batch for comparing groups and select best image for each group.
-
- Args:
- frames (list[np.ndarray]): Batch of images in numpy ndarray.
-
- Returns:
- list[np.ndarray]: Best images list.
- """
- normalized_images = self._normalize_images(frames, self._config.target_image_size)
- scores = self._evaluate_images(normalized_images)
- del normalized_images
-
- best_frames = []
- group_size = self._config.compering_group_size
- groups = np.array_split(scores, np.arange(group_size, len(scores), group_size))
- for index, group in enumerate(groups):
- best_index = np.argmax(group)
- global_index = index * group_size + best_index
- best_frames.append(frames[global_index])
- logger.info("Best frames selected(%s).", len(best_frames))
- return best_frames
-
-
-class TopImagesExtractor(Extractor):
- """Images extractor for extracting top percent of images in config input directory."""
-
- def process(self) -> None:
- """
- Rate all images in given config input directory and
- extract images that are in top percent of images visually.
- """
- images_paths = self._list_input_directory_files(self._config.images_extensions)
- self._get_image_evaluator()
- for batch_index in range(0, len(images_paths), self._config.batch_size):
- batch = images_paths[batch_index : batch_index + self._config.batch_size]
- images = self._read_images(batch)
- normalized_images = self._normalize_images(images, self._config.target_image_size)
- scores = self._evaluate_images(normalized_images)
- top_images = self._get_top_percent_images(images, scores, self._config.top_images_percent)
- self._save_images(top_images)
- logger.info(
- "Extraction process finished. All top images extracted from directory: %s.",
- self._config.input_directory,
- )
- self._signal_readiness_for_shutdown()
-
- @staticmethod
- def _get_top_percent_images(images: list[np.ndarray], scores: np.array, top_percent: float) -> list[np.ndarray]:
- """
- Returns images that have scores in the top percent of all scores.
-
- Args:
- images (list[np.ndarray]): Batch of images in numpy ndarray.
- scores (np.array): Array with images scores with images batch order.
- top_percent (float): The top percentage of scores to include (e.g. 80 for top 80%).
-
- Returns:
- list[np.ndarray]: Top images from given images batch.
- """
- threshold = np.percentile(scores, top_percent)
- top_images = [img for img, score in zip(images, scores) if score >= threshold]
- logger.info("Top images selected(%s).", len(top_images))
- return top_images
diff --git a/extractor_service/app/image_evaluators.py b/extractor_service/app/image_evaluators.py
deleted file mode 100644
index fe4d20a..0000000
--- a/extractor_service/app/image_evaluators.py
+++ /dev/null
@@ -1,274 +0,0 @@
-"""
-This module provides abstract class for creating image evaluators and image evaluators.
-Image evaluators:
- - InceptionResNetNIMA: NIMA model with helper classes.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-import numpy as np
-import requests
-import tensorflow as tf
-from tensorflow import convert_to_tensor
-from tensorflow.keras import Model
-from tensorflow.keras.layers import Dense, Dropout
-
-from .schemas import ExtractorConfig
-
-logger = logging.getLogger(__name__)
-
-
-class ImageEvaluator(ABC):
- """Abstract class for creating image evaluators."""
-
- @abstractmethod
- def __init__(self, config: ExtractorConfig) -> None:
- """
- Initialize the image evaluator with the provided configuration.
-
- Args:
- config (ExtractorConfig): Configuration from user.
- """
-
- @abstractmethod
- def evaluate_images(self, images: np.ndarray) -> list[float]:
- """
- Evaluates images batch and returns it.
-
- Args:
- images (list[np.ndarray]): Batch of images that will be evaluated.
-
- Returns:
- list[float]: List of images' scores.
- """
-
- @staticmethod
- def _check_scores(images: list[np.ndarray], scores: list[float]) -> None:
- """
- Check if the lengths of the images and scores lists match.
-
- Args:
- images (list[np.ndarray]): List of images.
- scores (list[float]): List of scores.
- """
- images_list_length = len(images)
- scores_list_length = len(scores)
- logger.debug("Scores: %s", scores)
- if images_list_length == scores_list_length:
- logger.debug("Scores and images lists length: %s", images_list_length)
- else:
- logger.warning("Scores and images lists lengths don't match!")
- logger.debug("Images list length: %s", images_list_length)
- logger.debug("Scores list length: %s", scores_list_length)
-
-
-class InceptionResNetNIMA(ImageEvaluator):
- """
- NeuralImageAssessment model based image evaluator.
- It uses NIMA for evaluating aesthetics of images.
- """
-
- def __init__(self, config: ExtractorConfig) -> None:
- """
- Initialize the Neural Image Assessment with the provided configuration.
-
- Args:
- config (ExtractorConfig): Configuration object for the image evaluator.
- """
- self._model = _ResNetModel.get_model(config)
-
- def evaluate_images(self, images: np.ndarray) -> list[float]:
- """
- Evaluate a batch of images using the NIMA model, and return the results.
-
- Args:
- images (np.ndarray): Batch of numpy ndarray images to be evaluated.
-
- Returns:
- list[float]: List of scores corresponding to the input images.
- """
- logger.info("Evaluating images...")
- tensor = convert_to_tensor(images)
- batch_size = images.shape[0]
- predictions = self._model.predict(tensor, batch_size=batch_size, verbose=0)
- weights = _ResNetModel.get_prediction_weights()
- scores = [self._calculate_weighted_mean(prediction, weights) for prediction in predictions]
- self._check_scores(images, scores)
- logger.info("Images batch evaluated.")
- return scores
-
- @staticmethod
- def _calculate_weighted_mean(prediction: np.array, weights: np.array = None) -> float:
- """
- Calculate the weighted mean of the prediction to get final image score.
- For example model InceptionResNetV2 returns 10 prediction scores for each image.
- We want to calculate weighted mean from that classification scores to calculate
- image final score. First classification score is less important and last is most.
-
- Args:
- prediction (np.array): Array of classification scores.
-
- Returns:
- float: Weighted mean of the prediction.
- """
- if weights is None:
- weights = np.ones_like(prediction) # Default weights, equally distribute importance
- weighted_mean = np.sum(prediction * weights) / np.sum(weights)
- return weighted_mean
-
-
-class _NIMAModel(ABC):
- """
- Abstract base class for the NIMA models. Uses a singleton pattern
- to manage a unique instance of the models.
- This is helper class for NeuralImageAssessment class.
- """
-
- class DownloadingModelWeightsError(Exception):
- """Error raised when there's an issue with downloading model weights."""
-
- _config = None
- _model = None
-
- @classmethod
- def reset(cls) -> None:
- """Resets class for using new model and config."""
- cls._model = None
- cls._config = None
-
- @classmethod
- def get_model(cls, config: ExtractorConfig) -> Model:
- """
- Get the NIMA model instance, downloading the weights if necessary.
-
- Args:
- config (ExtractorConfig): Configuration object for the model.
-
- Returns:
- Model: NIMA model instance.
- """
- if cls._model is None:
- cls._config = config
- model_weights_path = cls._get_model_weights()
- cls._model = cls._create_model(model_weights_path)
- return cls._model
-
- @classmethod
- @abstractmethod
- def _create_model(cls, model_weights_path: Path) -> Model:
- """
- Create the NIMA model with the provided weights.
-
- Args:
- model_weights_path (Path): Path to the model weights.
-
- Returns:
- Model: NIMA model instance.
- """
-
- @classmethod
- def _get_model_weights(cls) -> Path:
- """
- Get the path to the model weights, downloading them if necessary.
-
- Returns:
- Path: Path to the model weights.
- """
- model_weights_directory = cls._config.weights_directory
- logger.info(
- "Searching for model weights in weights directory: %s",
- model_weights_directory,
- )
- model_weights_path = Path(model_weights_directory) / cls._config.weights_filename
- if not model_weights_path.is_file():
- logger.debug(
- "Can't find model weights in weights directory: %s",
- model_weights_directory,
- )
- cls._download_model_weights(model_weights_path)
- else:
- logger.debug("Model weights loaded from: %s", model_weights_path)
- return model_weights_path
-
- @classmethod
- def _download_model_weights(cls, weights_path: Path, timeout: int = 10) -> None:
- """
- Download the model weights from the specified URL.
-
- Args:
- weights_path (Path): Path to save the downloaded weights.
- timeout (int): Timeout for the request in seconds.
-
- Raises:
- cls.DownloadingModelWeightsError: If there's an issue downloading the weights.
- """
- url = f"{cls._config.weights_repo_url}{cls._config.weights_filename}"
- logger.debug("Downloading model weights from ulr: %s", url)
- response = requests.get(url, allow_redirects=True, timeout=timeout)
- if response.status_code == 200:
- weights_path.parent.mkdir(parents=True, exist_ok=True)
- weights_path.write_bytes(response.content)
- logger.debug("Model weights downloaded and saved to %s", weights_path)
- else:
- error_message = f"Failed to download the weights: HTTP status code {response.status_code}"
- logger.error(error_message)
- raise cls.DownloadingModelWeightsError(error_message)
-
-
-class _ResNetModel(_NIMAModel):
- """
- Implements the specific InceptionResNetV2-based NIMA model.
- This is helper class for NeuralImageAssessment class.
- """
-
- _prediction_weights = np.arange(1, 11)
- _input_shape = (224, 224, 3)
- _dropout_rate = 0.75
- _num_classes = 10
-
- @classmethod
- def get_prediction_weights(cls):
- """
- Getter for prediction weights.
- Weights are for calculating weighted mean from model predictions.
- """
- return cls._prediction_weights
-
- @classmethod
- def _create_model(cls, model_weights_path: Path) -> Model:
- """
- Create the InceptionResNetV2-based NIMA model with the provided weights.
-
- Args:
- model_weights_path (Path): Path to the model weights.
-
- Returns:
- Model: NIMA model instance.
- """
- base_model = tf.keras.applications.InceptionResNetV2(
- input_shape=cls._input_shape, include_top=False, pooling="avg", weights=None
- )
- processed_output = Dropout(cls._dropout_rate)(base_model.output)
- final_output = Dense(cls._num_classes, activation="softmax")(processed_output)
- model = Model(inputs=base_model.input, outputs=final_output)
- model.load_weights(model_weights_path)
- logger.debug("Model loaded successfully.")
- return model
diff --git a/extractor_service/app/image_processors.py b/extractor_service/app/image_processors.py
deleted file mode 100644
index d031880..0000000
--- a/extractor_service/app/image_processors.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""
-This module provides abstract class for creating image processors and image processors.
-Image processors:
- - OpenCVImage: using OpenCV library to manage operations on images.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-import uuid
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-import cv2
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-
-class ImageProcessor(ABC):
- """Abstract class for creating image processors used for managing image operations."""
-
- @staticmethod
- @abstractmethod
- def read_image(image_path: Path) -> np.ndarray:
- """
- Read image from given path and convert it to np.ndarray.
-
- Args:
- image_path (Path): Path to image that will be read.
-
- Returns:
- np.ndarray: Image in numpy ndarray.
- """
-
- @classmethod
- @abstractmethod
- def save_image(cls, image: np.ndarray, output_directory: Path, output_extension: str) -> Path:
- """
- Save given image in given path in given extension.
-
- Args:
- image (np.ndarray): Numpy ndarray image that will be saved.
- output_directory (Path): Path where images will be saved.
- output_extension (str): Extension with image will be saved.
-
- Returns:
- Path: Path where image was saved.
- """
-
- @staticmethod
- @abstractmethod
- def normalize_images(images: list[np.ndarray], target_size: tuple[int, int]) -> np.array:
- """
- Resize a batch of images and convert them to a normalized numpy array.
-
- Args:
- images (list[np.ndarray]): List of numpy ndarray images to be normalized.
- target_size (tuple | None): Target size to which the images will be resized.
- Default is (224, 224).
-
- Returns:
- np.ndarray: Normalized numpy array containing the resized images.
- """
-
-
-class OpenCVImage(ImageProcessor):
- """Image processor implementation using OpenCV library."""
-
- @staticmethod
- def read_image(image_path: Path) -> np.ndarray | None:
- """
- Read image from given path and convert it to np.ndarray.
-
- Args:
- image_path (Path): Path to image that will be read.
-
- Returns:
- np.ndarray: Image in numpy ndarray.
- """
- image = cv2.imread(str(image_path))
- if not isinstance(image, np.ndarray):
- logger.warning(
- "Can't read image. OpenCV reading not returns np.ndarray for image path: %s",
- str(image_path),
- )
- return None
- logger.debug("Image '%s' has successfully read.", image_path)
- return image
-
- @classmethod
- def save_image(cls, image: np.ndarray, output_directory: Path, output_extension: str) -> Path:
- """
- Save given image in given path with given extension.
-
- Args:
- image (np.ndarray): Numpy ndarray image that will be saved.
- output_directory (Path): Path where images will be saved.
- output_extension (str): Extension with image will be saved.
-
- Returns:
- Path: Path where image was saved.
- """
- filename = cls._generate_filename()
- image_path = output_directory / f"{filename}{output_extension}"
- cv2.imwrite(str(image_path), image)
- logger.debug("Image saved at '%s'.", image_path)
- return image_path
-
- @staticmethod
- def _generate_filename() -> str:
- """
- Generate filename for images using uuid library.
-
- Returns:
- str: Generated filename.
- """
- filename = f"image_{uuid.uuid4()}"
- return filename
-
- @staticmethod
- def normalize_images(images: list[np.ndarray], target_size: tuple[int, int]) -> np.array:
- """
- Resize a batch of images and convert them to a normalized numpy array.
-
- Args:
- images (list[np.ndarray]): List of numpy ndarray images to be normalized.
- target_size (tuple | None): Target size to which the images will be resized.
-
- Returns:
- np.ndarray: Normalized numpy array containing the resized images.
- """
- batch_images = []
- logger.debug("Normalizing images...")
- for img in images:
- img_resized = cv2.resize(img, target_size, interpolation=cv2.INTER_LANCZOS4)
- img_rgb = cv2.cvtColor(img_resized, cv2.COLOR_BGR2RGB)
- batch_images.append(img_rgb)
- img_array = np.array(batch_images, dtype=np.float32) / 255.0
- return img_array
diff --git a/extractor_service/app/schemas.py b/extractor_service/app/schemas.py
deleted file mode 100644
index fc84ef5..0000000
--- a/extractor_service/app/schemas.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-This module defines Pydantic models and validators.
-Models:
- - ExtractorConfig: Model containing the extractors configuration parameters.
- - Message: Model for encapsulating messages returned by the application.
- - ExtractorStatus: Model representing the status of the working extractor in the system.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-from pathlib import Path
-
-from pydantic import BaseModel, DirectoryPath
-
-logger = logging.getLogger(__name__)
-
-
-class ExtractorConfig(BaseModel):
- """
- A Pydantic model containing the extractors configuration parameters.
-
- Attributes:
- input_directory (DirectoryPath): Input directory path containing entries for extraction.
- By default, it sets value for docker container volume.
- output_directory (DirectoryPath): Output directory path for extraction results.
- By default, it sets value for docker container volume.
- video_extensions (tuple[str]): Supported videos' extensions in service for reading videos.
- images_extensions (tuple[str]): Supported images' extensions in service for reading images.
- processed_video_prefix (str): Prefix will be added to processed video after extraction.
- batch_size (int): Maximum number of images processed in a single batch.
- compering_group_size (int): Images group number to compare for finding the best one.
- top_images_percent (float): Percentage threshold to determine the top images.
- images_output_format (str): Format for saving output images, e.g., '.jpg', '.png'.
- target_image_size (tuple[int, int]): Images will be normalized to this size.
- weights_directory (Path | str): Directory path where model weights are stored.
- weights_filename (str): The filename of the model weights file to be loaded.
- weights_repo_url (str): URL to the repository where model weights can be downloaded.
- all_frames (bool): It changes best_frames_extractor -> frames_extractor.
- If Ture best_frames_extractor returns all frames without filtering/evaluation.
- """
-
- input_directory: DirectoryPath = Path("/app/input_directory")
- output_directory: DirectoryPath = Path("/app/output_directory")
- video_extensions: tuple[str] = (
- ".mp4",
- ".mov",
- ".webm",
- ".mkv",
- ".avi",
- ) # add more containers here
- images_extensions: tuple[str] = (
- ".jpg",
- ".jpeg",
- ".png",
- ".webp",
- ) # add more containers here
- processed_video_prefix: str = "frames_extracted_"
- batch_size: int = 100
- compering_group_size: int = 5
- top_images_percent: float = 90.0
- images_output_format: str = ".jpg"
- target_image_size: tuple[int, int] = (224, 224)
- weights_directory: Path | str = Path.home() / ".cache" / "huggingface"
- weights_filename: str = "weights.h5"
- weights_repo_url: str = "https://huggingface.co/BKDDFS/nima_weights/resolve/main/"
- all_frames: bool = False
-
-
-class Message(BaseModel):
- """
- A Pydantic model for encapsulating messages returned by the application.
-
- Attributes:
- message (str): The message content.
- """
-
- message: str
-
-
-class ExtractorStatus(BaseModel):
- """
- A Pydantic model representing the status of the currently working extractor in the system.
-
- Attributes:
- active_extractor (str): The name of the currently active extractor.
- """
-
- active_extractor: str | None
diff --git a/extractor_service/app/video_processors.py b/extractor_service/app/video_processors.py
deleted file mode 100644
index 6759896..0000000
--- a/extractor_service/app/video_processors.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""
-This module provides abstract class for creating video processors and video processors.
-Video processors:
- - OpenCVVideo: using OpenCV library to manage operations on videos.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-from abc import ABC, abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Generator
-
-import cv2
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-
-class VideoProcessor(ABC):
- """Abstract class for creating video processors used for managing video operations."""
-
- @classmethod
- @abstractmethod
- def get_next_frames(cls, video_path: Path, batch_size: int) -> Generator[list[np.ndarray], None, None]:
- """
- Abstract generator method to generate batches of frames from a video file.
-
- Args:
- video_path (Path): Path for video from which frames will be read.
- batch_size (int): Number of frames to include in each batch.
-
- Returns:
- Generator: Generator yielding batches of frames as lists of numpy ndarrays.
-
- Yields:
- list[np.ndarray]: A batch of video frames.
- """
-
-
-class OpenCVVideo(VideoProcessor):
- """Video processor based on OpenCV with FFMPEG extension."""
-
- class CantOpenVideoCapture(Exception):
- """Exception raised when the video file cannot be opened."""
-
- class VideoCaptureClosed(Exception):
- """Exception raised when the video capture is prematurely closed."""
-
- @staticmethod
- @contextmanager
- def _video_capture(video_path: Path) -> cv2.VideoCapture:
- """
- Get and release a video capture object.
-
- Args:
- video_path (str): Path to the video file to be opened.
-
- Yields:
- cv2.VideoCapture: OpenCV video capture object.
-
- Raises:
- CantOpenVideoCapture: If the video file cannot be opened.
- """
- video_cap = cv2.VideoCapture(str(video_path))
- try:
- if not video_cap.isOpened():
- error_massage = f"Can't open video file: {video_path}"
- logger.error(error_massage)
- raise OpenCVVideo.CantOpenVideoCapture(error_massage)
- logger.debug("Creating video capture.")
- yield video_cap
- finally:
- video_cap.release()
-
- @classmethod
- def get_next_frames(cls, video_path: Path, batch_size: int) -> Generator[list[np.ndarray], None, None]:
- """
- Generates batches of frames from the specified video using OpenCV.
-
- Args:
- video_path (Path): Path for video from which frames will be read.
- batch_size (int): Maximum number of frames per batch.
-
- Returns:
- Generator: Generator yielding batches of frames as lists of numpy ndarrays.
-
- Yields:
- list[np.ndarray]: A batch of video frames.
- """
- with cls._video_capture(video_path) as video:
- frame_rate = cls._get_video_attribute(video, cv2.CAP_PROP_FPS, "frame rate")
- total_frames = cls._get_video_attribute(video, cv2.CAP_PROP_FRAME_COUNT, "total frames")
- frames_batch = []
- logger.info("Getting frames batch...")
- for frame_index in range(0, total_frames, frame_rate):
- frame = cls._read_next_frame(video, frame_index)
- frames_batch.append(frame)
- logger.debug("Frame appended to frames batch.")
- if len(frames_batch) == batch_size:
- logger.info("Got full frames batch.")
- yield frames_batch
- frames_batch = []
- if frames_batch:
- logger.info("Returning last frames batch.")
- yield frames_batch
-
- @classmethod
- def _read_next_frame(cls, video: cv2.VideoCapture, frame_index: int) -> np.ndarray | None:
- """
- Reads frame with specified index from provided video.
-
- Args:
- video: Video capture object from which frame will be taken.
- frame_index (int): Place of the frame in video among other frames measured in indexes.
-
- Returns:
- np.ndarray: Decoded frame.
- """
- cls._check_video_capture(video)
- video.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
- success, frame = video.read()
- if not success:
- logger.warning("Couldn't read frame with index: %s", frame_index)
- return None
- return frame
-
- @classmethod
- def _get_video_attribute(cls, video: cv2.VideoCapture, attribute_id: int, display_name: str) -> int:
- """
- Retrieves a specified attribute value from the video capture object and validates it.
-
- Args:
- attribute_id (int): OpenCV video capture ID of the attribute to retrieve.
- display_name (str): Descriptive name of the attribute for logging purposes.
-
- Returns:
- int: The value of the requested attribute, validated to be a positive integer.
-
- Raises:
- ValueError: If the retrieved value is invalid.
- """
- cls._check_video_capture(video)
- attribute_value = video.get(attribute_id)
- logger.debug("Got input video %s: %s", display_name, attribute_value)
- if attribute_value <= 0:
- error_message = f"Invalid {display_name} retrieved: {attribute_value}."
- logger.error(error_message)
- raise ValueError(error_message)
- attribute = int(round(attribute_value))
- return attribute
-
- @staticmethod
- def _check_video_capture(video: cv2.VideoCapture) -> None:
- """
- Checks is video capture object still available for future operations.
-
- Args:
- video (cv2.VideoCapture): Video capture object that will be checked.
-
- Raises:
- ValueError: If the video capture object is not opened.
- """
- if not video.isOpened():
- error_message = (
- "Invalid video capture object or object not opened. Probably video capture closed at some point."
- )
- logger.error(error_message)
- raise ValueError(error_message)
diff --git a/extractor_service/main.py b/extractor_service/main.py
deleted file mode 100644
index 4311d5c..0000000
--- a/extractor_service/main.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""
-This module defines a FastAPI web application for managing image extractors.
-
-Endpoints:
- GET /status:
- For checking is some extractor already running.
- POST /extractors/{extractor_name}:
- For running chosen extractor.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-import os
-import sys
-
-import uvicorn
-from fastapi import BackgroundTasks, Depends, FastAPI
-
-if os.getenv("DOCKER_ENV"):
- from app.dependencies import ExtractorDependencies, get_extractor_dependencies
- from app.extractor_manager import ExtractorManager
- from app.schemas import ExtractorConfig, ExtractorStatus, Message
-else:
- from .app.dependencies import ExtractorDependencies, get_extractor_dependencies
- from .app.extractor_manager import ExtractorManager
- from .app.schemas import ExtractorConfig, ExtractorStatus, Message
-
-logging.basicConfig(
- level=logging.INFO,
- format="%(asctime)s - %(levelname)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- handlers=[logging.StreamHandler(sys.stdout)],
-)
-logger = logging.getLogger(__name__)
-
-app = FastAPI()
-
-
-@app.get("/v2/status")
-def get_extractors_status() -> ExtractorStatus:
- """
- Checks is some extractor already running on service.
-
- Returns:
- ExtractorStatus: Contains the name of the currently active extractor.
- """
- return ExtractorStatus(active_extractor=ExtractorManager.get_active_extractor())
-
-
-@app.post("/v2/extractors/{extractor_name}")
-def run_extractor(
- extractor_name: str,
- background_tasks: BackgroundTasks,
- config: ExtractorConfig = ExtractorConfig(),
- dependencies: ExtractorDependencies = Depends(get_extractor_dependencies),
-) -> Message:
- """
- Runs provided extractor.
-
- Args:
- extractor_name (str): The name of the extractor that will be used.
- background_tasks (BackgroundTasks): A FastAPI tool for running tasks in background.
- dependencies(ExtractorDependencies): Dependencies that will be used in extractor.
- config (ExtractorConfig): A Pydantic model with extractor configuration.
-
- Returns:
- Message: Contains the operation status.
- """
- message = ExtractorManager.start_extractor(extractor_name, background_tasks, config, dependencies)
- return Message(message=message)
-
-
-if __name__ == "__main__":
- uvicorn.run("main:app", host="localhost", port=8100, reload=True)
diff --git a/extractor_service/requirements.txt b/extractor_service/requirements.txt
deleted file mode 100644
index 9480db5..0000000
--- a/extractor_service/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-fastapi==0.115.6
-uvicorn==0.34.0
-opencv-python==4.11.0.86
-tensorflow==2.18.0
diff --git a/perfectframe/__init__.py b/perfectframe/__init__.py
new file mode 100644
index 0000000..1b8e80e
--- /dev/null
+++ b/perfectframe/__init__.py
@@ -0,0 +1 @@
+"""PerfectFrameAI - AI tool for finding the most aesthetic frames in a video."""
diff --git a/perfectframe/app.py b/perfectframe/app.py
new file mode 100644
index 0000000..1cf27e0
--- /dev/null
+++ b/perfectframe/app.py
@@ -0,0 +1,43 @@
+"""Define a FastAPI web application for managing image extractors."""
+
+import logging
+import sys
+from typing import Annotated
+
+from fastapi import BackgroundTasks, Depends, FastAPI
+
+from perfectframe.dependencies import Dependencies, get_dependencies
+from perfectframe.extractor_manager import ExtractorManager
+from perfectframe.schemas import ExtractorName, ExtractorStatus, Message
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s - %(levelname)s - %(message)s",
+ datefmt="%Y-%m-%d %H:%M:%S",
+ handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+
+@app.get("/health")
+def health_check() -> dict[str, str]:
+ """Health check endpoint for container health monitoring."""
+ return {"status": "healthy"}
+
+
+@app.get("/v2/status")
+def get_extractors_status() -> ExtractorStatus:
+ """Check if some extractor is already running on service."""
+ return ExtractorStatus(active_extractor=ExtractorManager.get_active_extractor())
+
+
+@app.post("/v2/extractors/{extractor_name}")
+def run_extractor(
+ extractor_name: ExtractorName,
+ background_tasks: BackgroundTasks,
+ dependencies: Annotated[Dependencies, Depends(get_dependencies)],
+) -> Message:
+ """Run the provided extractor."""
+ return ExtractorManager.start_extractor(extractor_name, background_tasks, dependencies)
diff --git a/perfectframe/dependencies.py b/perfectframe/dependencies.py
new file mode 100644
index 0000000..d72686f
--- /dev/null
+++ b/perfectframe/dependencies.py
@@ -0,0 +1,28 @@
+"""Provide dependency management for extractors using FastAPI's dependency injection."""
+
+from dataclasses import dataclass
+
+from perfectframe.image_evaluators import NIMAEvaluator
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import ExtractorConfig
+from perfectframe.video_processors import OpenCVVideo
+
+
+@dataclass
+class Dependencies:
+ """Data class to hold dependencies for the extractor."""
+
+ image_processor: type[OpenCVImage]
+ video_processor: type[OpenCVVideo]
+ evaluator: type[NIMAEvaluator]
+ config: ExtractorConfig
+
+
+def get_dependencies(config: ExtractorConfig = ExtractorConfig()) -> Dependencies:
+ """Return all dependencies required for the extractor."""
+ return Dependencies(
+ image_processor=OpenCVImage,
+ video_processor=OpenCVVideo,
+ evaluator=NIMAEvaluator,
+ config=config,
+ )
diff --git a/perfectframe/extractor_manager.py b/perfectframe/extractor_manager.py
new file mode 100644
index 0000000..41245ff
--- /dev/null
+++ b/perfectframe/extractor_manager.py
@@ -0,0 +1,63 @@
+"""Provide manager class for running extractors and managing extraction process lifecycle."""
+
+import logging
+import threading
+
+from fastapi import BackgroundTasks, HTTPException
+
+from perfectframe.dependencies import Dependencies
+from perfectframe.extractors import Extractor, ExtractorFactory
+from perfectframe.schemas import ExtractorName, Message
+
+logger = logging.getLogger(__name__)
+
+
+class ExtractorManager:
+ """Orchestrate extractors, ensuring that only one extractor is active at once."""
+
+ _active_extractor: ExtractorName | None = None
+ _lock = threading.Lock()
+
+ @classmethod
+ def get_active_extractor(cls) -> ExtractorName | None:
+ """Return the active extractor name."""
+ with cls._lock:
+ return cls._active_extractor
+
+ @classmethod
+ def start_extractor(
+ cls,
+ extractor_name: ExtractorName,
+ background_tasks: BackgroundTasks,
+ dependencies: Dependencies,
+ ) -> Message:
+ """Initialize the extractor class and run the extraction process in the background."""
+ with cls._lock:
+ cls._check_is_already_extracting()
+ cls._active_extractor = extractor_name
+ extractor = ExtractorFactory.create_extractor(extractor_name, dependencies)
+ background_tasks.add_task(cls.__run_extractor, extractor)
+ return Message(message=f"'{extractor_name.value}' started.")
+
+ @classmethod
+ def __run_extractor(cls, extractor: Extractor) -> None:
+ """Run extraction process and clean after it's done."""
+ try:
+ extractor.process()
+ except Exception:
+ logger.exception("Extraction failed with error")
+ finally:
+ with cls._lock:
+ cls._active_extractor = None
+
+ @classmethod
+ def _check_is_already_extracting(cls) -> None:
+ """Check if some extractor is already active and raise an HTTPException if so."""
+ if cls._active_extractor:
+ error_message = (
+ f"Extractor '{cls._active_extractor.value}' is already running. "
+ f"You can run only one extractor at the same time. "
+ f"Wait until the extractor is done before run next process."
+ )
+ logger.error(error_message)
+ raise HTTPException(status_code=409, detail=error_message)
diff --git a/perfectframe/extractors.py b/perfectframe/extractors.py
new file mode 100644
index 0000000..5640d19
--- /dev/null
+++ b/perfectframe/extractors.py
@@ -0,0 +1,268 @@
+"""Provide extractor classes for video and image processing.
+
+- Extractor: Abstract class for creating extractors.
+- ExtractorFactory: Factory for getting extractors by their names.
+- Extractors:
+ - BestFramesExtractor: For extracting best frames from all videos from any directory.
+ - TopImagesExtractor: For extracting images with top percent evaluating from any directory.
+"""
+
+import gc
+import logging
+from abc import ABC, abstractmethod
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import numpy as np
+
+from perfectframe.dependencies import Dependencies
+from perfectframe.image_evaluators import ImageEvaluator
+from perfectframe.image_processors import ImageProcessor
+from perfectframe.schemas import (
+ ExtractorConfig,
+ ExtractorName,
+ ImageExtension,
+ ImageResolution,
+ Images,
+ ImagesBatch,
+ ScoresArray,
+ VideoExtension,
+)
+from perfectframe.video_processors import VideoProcessor
+
+logger = logging.getLogger(__name__)
+
+
+class Extractor(ABC):
+ """Abstract class for creating extractors."""
+
+ class EmptyInputDirectoryError(Exception):
+ """Error appear when extractor can't get any input to extraction."""
+
+ def __init__(
+ self,
+ config: ExtractorConfig,
+ image_processor: type[ImageProcessor],
+ video_processor: type[VideoProcessor],
+ image_evaluator_class: type[ImageEvaluator],
+ ) -> None:
+ """Initialize the manager with the given extractor configuration."""
+ self._config = config
+ self._image_processor = image_processor
+ self._video_processor = video_processor
+ self._image_evaluator_class = image_evaluator_class
+ self._image_evaluator = None
+
+ @abstractmethod
+ def process(self) -> None:
+ """Abstract main method for extraction process implementation."""
+
+ def _get_image_evaluator(self) -> ImageEvaluator:
+ """Initialize an image evaluator and add it to extractor instance parameters."""
+ self._image_evaluator = self._image_evaluator_class(self._config)
+ return self._image_evaluator
+
+ def _list_input_directory_files(
+ self,
+ extensions: type[VideoExtension] | type[ImageExtension],
+ prefix: str | None = None,
+ ) -> list[Path]:
+ """List all files with given extensions except files with given filename prefix.
+
+ Args:
+ extensions: Enum class defining valid file extensions.
+ prefix: Excluded files filename prefix.
+
+ Returns:
+ All matching files list.
+ """
+ directory = self._config.input_directory
+ entries = directory.iterdir()
+ files = [
+ entry
+ for entry in entries
+ if entry.is_file()
+ and extensions.contains(entry.suffix)
+ and (prefix is None or not entry.name.startswith(prefix))
+ ]
+ if not files:
+ prefix = prefix if prefix else "Prefix not provided"
+ error_message = (
+ f"Files with extensions '{extensions}' and without prefix '{prefix}' "
+ f"not found in folder: {directory}."
+ f"\n-->HINT: You probably don't have input or you haven't changed prefixes. "
+ f"\nCheck input directory."
+ )
+ logger.error(error_message)
+ raise self.EmptyInputDirectoryError(error_message)
+ logger.info("Directory '%s' files listed.", str(directory))
+ logger.debug("Listed file paths: %s", files)
+ return files
+
+ def _evaluate_images(self, normalized_images: ImagesBatch) -> ScoresArray:
+ """Rate all images in provided images batch using already initialized image evaluator."""
+ if self._image_evaluator is None:
+ msg = "_image_evaluator must be initialized before calling _evaluate_images"
+ raise RuntimeError(msg)
+ return np.array(self._image_evaluator.evaluate_images(normalized_images))
+
+ def _read_images(self, images_paths: list[Path]) -> Images:
+ """Read all images from given paths synchronously."""
+ with ThreadPoolExecutor() as executor:
+ images = []
+ futures = [
+ executor.submit(
+ self._image_processor.read_image,
+ image_path,
+ )
+ for image_path in images_paths
+ ]
+ for future in futures:
+ image = future.result()
+ if image is not None:
+ images.append(image)
+ return images
+
+ def _save_images(self, images: Images) -> None:
+ """Save all images in config output directory synchronously."""
+ with ThreadPoolExecutor() as executor:
+ futures = [
+ executor.submit(
+ self._image_processor.save_image,
+ image,
+ self._config.output_directory,
+ self._config.images_output_format,
+ )
+ for image in images
+ ]
+ for future in futures:
+ future.result()
+
+ def _normalize_images(self, images: Images, target_size: ImageResolution) -> ImagesBatch:
+ """Normalize all images in given list to target size for further operations."""
+ return self._image_processor.normalize_images(images, target_size)
+
+ @staticmethod
+ def _add_prefix(prefix: str, file_path: Path) -> Path:
+ """Add prefix to file filename."""
+ new_path = file_path.parent / f"{prefix}{file_path.name}"
+ file_path.rename(new_path)
+ logger.debug("Prefix '%s' added to file '%s'. New path: %s", prefix, file_path, new_path)
+ return new_path
+
+ @staticmethod
+ def _signal_readiness_for_shutdown() -> None:
+ """Signal externally that the service has completed the process and can be shut down."""
+ logger.info("Service ready for shutdown")
+
+
+class ExtractorFactory:
+ """Extractor factory for getting extractors class by their names."""
+
+ @staticmethod
+ def create_extractor(extractor_name: ExtractorName, dependencies: Dependencies) -> Extractor:
+ """Match extractor class by its name and return its class."""
+ match extractor_name:
+ case ExtractorName.BEST_FRAMES:
+ return BestFramesExtractor(
+ dependencies.config,
+ dependencies.image_processor,
+ dependencies.video_processor,
+ dependencies.evaluator,
+ )
+ case ExtractorName.TOP_IMAGES:
+ return TopImagesExtractor(
+ dependencies.config,
+ dependencies.image_processor,
+ dependencies.video_processor,
+ dependencies.evaluator,
+ )
+
+
+class BestFramesExtractor(Extractor):
+ """Extractor for extracting best frames from videos in any input directory."""
+
+ def process(self) -> None:
+ """Rate all videos in config input directory and extract best frames from every video."""
+ logger.info(
+ "Starting frames extraction process from '%s'.",
+ self._config.input_directory,
+ )
+ videos_paths = self._list_input_directory_files(
+ VideoExtension, self._config.processed_video_prefix
+ )
+ if self._config.all_frames is False: # evaluator won't be used if all frames
+ self._get_image_evaluator()
+ for video_path in videos_paths:
+ self._extract_best_frames(video_path)
+ self._add_prefix(self._config.processed_video_prefix, video_path)
+ logger.info("Frames extraction has finished for video: %s", video_path)
+ logger.info("Extraction process finished. All frames extracted.")
+ self._signal_readiness_for_shutdown()
+
+ def _extract_best_frames(self, video_path: Path) -> None:
+ """Extract best visually frames from given video."""
+ frames_batch_generator = self._video_processor.get_next_frames(
+ video_path, self._config.batch_size
+ )
+ for frames in frames_batch_generator:
+ if not frames:
+ continue
+ logger.debug("Frames batch generated.")
+ frames_to_save = (
+ self._get_best_frames(frames) if not self._config.all_frames else frames
+ )
+ self._save_images(frames_to_save)
+ del frames_to_save
+ gc.collect()
+
+ def _get_best_frames(self, frames: Images) -> Images:
+ """Split images batch into comparing groups and select best image for each group."""
+ normalized_images = self._normalize_images(frames, self._config.input_size)
+ scores = self._evaluate_images(normalized_images)
+ del normalized_images
+
+ best_frames = []
+ group_size = self._config.comparing_group_size
+ groups = np.array_split(scores, np.arange(group_size, len(scores), group_size))
+ for index, group in enumerate(groups):
+ best_index = np.argmax(group)
+ global_index = index * group_size + best_index
+ best_frames.append(frames[global_index])
+ logger.info("Best frames selected(%s).", len(best_frames))
+ return best_frames
+
+
+class TopImagesExtractor(Extractor):
+ """Images extractor for extracting top percent of images in config input directory."""
+
+ def process(self) -> None:
+ """Rate all images in config input directory and extract top percent images."""
+ images_paths = self._list_input_directory_files(ImageExtension)
+ self._get_image_evaluator()
+ for batch_index in range(0, len(images_paths), self._config.batch_size):
+ batch = images_paths[batch_index : batch_index + self._config.batch_size]
+ images = self._read_images(batch)
+ normalized_images = self._normalize_images(images, self._config.input_size)
+ scores = self._evaluate_images(normalized_images)
+ top_images = self._get_top_percent_images(
+ images, scores, self._config.top_images_percent
+ )
+ self._save_images(top_images)
+ logger.info(
+ "Extraction process finished. All top images extracted from directory: %s.",
+ self._config.input_directory,
+ )
+ self._signal_readiness_for_shutdown()
+
+ @staticmethod
+ def _get_top_percent_images(
+ images: Images,
+ scores: ScoresArray,
+ top_percent: float,
+ ) -> Images:
+ """Return images that have scores in the top percent of all scores."""
+ threshold = np.percentile(scores, top_percent)
+ top_images = [img for img, score in zip(images, scores, strict=True) if score >= threshold]
+ logger.info("Top images selected(%s).", len(top_images))
+ return top_images
diff --git a/perfectframe/image_evaluators.py b/perfectframe/image_evaluators.py
new file mode 100644
index 0000000..d16ea3b
--- /dev/null
+++ b/perfectframe/image_evaluators.py
@@ -0,0 +1,126 @@
+"""Provide abstract class for creating image evaluators and implementations.
+
+Image evaluators:
+ - NIMAEvaluator: NIMA-based image evaluator using ONNX runtime.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+import requests
+
+from perfectframe.schemas import (
+ ExtractorConfig,
+ ImagesBatch,
+ NIMAModelOutput,
+ Score,
+ Scores,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class ImageEvaluator(ABC):
+ """Abstract class for creating image evaluators."""
+
+ @abstractmethod
+ def __init__(self, config: ExtractorConfig) -> None:
+ """Initialize the image evaluator with the provided configuration."""
+
+ @abstractmethod
+ def evaluate_images(self, images: ImagesBatch) -> Scores:
+ """Evaluate images batch and return scores."""
+
+ @staticmethod
+ def _check_scores(images: ImagesBatch, scores: Scores) -> None:
+ """Check if the lengths of the images and scores lists match."""
+ images_list_length = len(images)
+ scores_list_length = len(scores)
+ logger.debug("Scores: %s", scores)
+ if images_list_length == scores_list_length:
+ logger.debug("Scores and images lists length: %s", images_list_length)
+ else:
+ logger.warning("Scores and images lists lengths don't match!")
+ logger.debug("Images list length: %s", images_list_length)
+ logger.debug("Scores list length: %s", scores_list_length)
+
+
+class NIMAEvaluator(ImageEvaluator):
+ """NIMA-based image evaluator using ONNX runtime."""
+
+ class ModelWeightsDownloadError(Exception):
+ """Error raised when there's an issue with downloading model weights."""
+
+ _prediction_weights = np.arange(1, 11)
+
+ def __init__(self, config: ExtractorConfig) -> None:
+ """Initialize the NIMA evaluator with the provided configuration."""
+ model_path = self._get_model_path(config)
+ self._session = ort.InferenceSession(str(model_path))
+ self._input_name = self._session.get_inputs()[0].name
+
+ def evaluate_images(self, images: ImagesBatch) -> Scores:
+ """Evaluate a batch of images using the NIMA model."""
+ logger.info("Evaluating images...")
+ predictions = self._session.run(None, {self._input_name: images.astype(np.float32)})[0]
+ if not isinstance(predictions, np.ndarray):
+ return []
+ scores = [self._calculate_weighted_mean(p) for p in predictions]
+ self._check_scores(images, scores)
+ logger.info("Images batch evaluated.")
+ return scores
+
+ def _calculate_weighted_mean(self, prediction: NIMAModelOutput) -> Score:
+ """Calculate the weighted mean of the prediction to get final image score.
+
+ For example model InceptionResNetV2 returns 10 prediction scores for each image. We want to
+ calculate weighted mean from that classification scores to calculate image final score.
+ First classification score is less important and last is most.
+ """
+ return np.sum(prediction * self._prediction_weights) / np.sum(self._prediction_weights)
+
+ @classmethod
+ def _get_model_path(cls, config: ExtractorConfig) -> Path:
+ """Get the path to the ONNX model, downloading it if necessary."""
+ model_weights_directory = config.weights_directory
+ logger.info(
+ "Searching for model weights in weights directory: %s",
+ model_weights_directory,
+ )
+ model_weights_path = Path(model_weights_directory) / config.weights_filename
+ if not model_weights_path.is_file():
+ logger.debug(
+ "Can't find model weights in weights directory: %s",
+ model_weights_directory,
+ )
+ cls._download_model_weights(model_weights_path, config)
+ else:
+ logger.debug("Model weights loaded from: %s", model_weights_path)
+ return model_weights_path
+
+ @classmethod
+ def _download_model_weights(
+ cls, weights_path: Path, config: ExtractorConfig, timeout: int = 10
+ ) -> None:
+ """Download the model weights from the specified URL."""
+ url = f"{config.weights_repo_url}{config.weights_filename}"
+ logger.debug("Downloading model weights from url: %s", url)
+ try:
+ response = requests.get(url, allow_redirects=True, timeout=timeout)
+ except requests.RequestException as e:
+ error_message = f"Network error while downloading model weights: {e}"
+ logger.exception(error_message)
+ raise cls.ModelWeightsDownloadError(error_message) from e
+ if response.ok:
+ weights_path.parent.mkdir(parents=True, exist_ok=True)
+ weights_path.write_bytes(response.content)
+ logger.debug("Model weights downloaded and saved to %s", weights_path)
+ else:
+ error_message = (
+ f"Failed to download the weights: HTTP status code {response.status_code}"
+ )
+ logger.error(error_message)
+ raise cls.ModelWeightsDownloadError(error_message)
diff --git a/perfectframe/image_processors.py b/perfectframe/image_processors.py
new file mode 100644
index 0000000..90dfc69
--- /dev/null
+++ b/perfectframe/image_processors.py
@@ -0,0 +1,85 @@
+"""Provide abstract class for creating image processors and implementations.
+
+Image processors:
+ - OpenCVImage: using OpenCV library to manage operations on images.
+"""
+
+import logging
+import uuid
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+import cv2
+import numpy as np
+
+from perfectframe.schemas import Image, ImageExtension, ImageResolution, Images, ImagesBatch
+
+logger = logging.getLogger(__name__)
+
+
+class ImageProcessor(ABC):
+ """Abstract class for creating image processors used for managing image operations."""
+
+ @staticmethod
+ @abstractmethod
+ def read_image(image_path: Path) -> Image | None:
+ """Read image from given path and convert it to np.ndarray."""
+
+ @classmethod
+ @abstractmethod
+ def save_image(
+ cls, image: Image, output_directory: Path, output_extension: ImageExtension
+ ) -> Path:
+ """Save given image in given path in given extension."""
+
+ @staticmethod
+ @abstractmethod
+ def normalize_images(images: Images, target_size: ImageResolution) -> ImagesBatch:
+ """Resize a batch of images and convert them to a normalized numpy array."""
+
+
+class OpenCVImage(ImageProcessor):
+ """Image processor implementation using OpenCV library."""
+
+ @staticmethod
+ def read_image(image_path: Path) -> Image | None:
+ """Read image from given path and convert it to np.ndarray."""
+ image = cv2.imread(str(image_path))
+ if not isinstance(image, np.ndarray):
+ logger.warning(
+ "Can't read image. OpenCV reading not returns np.ndarray for image path: %s",
+ str(image_path),
+ )
+ return None
+ logger.debug("Image '%s' has successfully read.", image_path)
+ return image
+
+ @classmethod
+ def save_image(
+ cls, image: Image, output_directory: Path, output_extension: ImageExtension
+ ) -> Path:
+ """Save given image in given path with given extension."""
+ filename = cls._generate_filename()
+ image_path = output_directory / f"{filename}{output_extension.value}"
+ success = cv2.imwrite(str(image_path), image)
+ if not success:
+ logger.error("Failed to save image at '%s'", image_path)
+ else:
+ logger.debug("Image saved at '%s'.", image_path)
+ return image_path
+
+ @staticmethod
+ def _generate_filename() -> str:
+ """Generate filename for images using uuid library."""
+ return f"image_{uuid.uuid4()}"
+
+ @staticmethod
+ def normalize_images(images: Images, target_size: ImageResolution) -> ImagesBatch:
+ """Resize a batch of images and convert them to a normalized numpy array."""
+ batch_images = []
+ logger.debug("Normalizing images...")
+ for img in images:
+ img_resized = cv2.resize(img, target_size, interpolation=cv2.INTER_LANCZOS4)
+ img_rgb = cv2.cvtColor(img_resized, cv2.COLOR_BGR2RGB)
+ batch_images.append(img_rgb)
+ return np.array(batch_images, dtype=np.float32) / 255.0
diff --git a/perfectframe/schemas.py b/perfectframe/schemas.py
new file mode 100644
index 0000000..1264a88
--- /dev/null
+++ b/perfectframe/schemas.py
@@ -0,0 +1,157 @@
+"""Define Pydantic models and validators."""
+
+import logging
+from enum import Enum
+from pathlib import Path
+from typing import NamedTuple
+
+import numpy as np
+from pydantic import BaseModel, DirectoryPath
+
+
+class ImageResolution(NamedTuple):
+ """Resolution of an image in pixels (width x height)."""
+
+ width: int
+ height: int
+
+
+type Image = np.ndarray
+"""Single image as numpy array."""
+
+type Images = list[Image]
+"""List of images."""
+
+type ImagesBatch = np.ndarray
+"""Batch of images as single numpy array for batch processing."""
+
+type ScoresArray = np.ndarray
+"""Array of aesthetic scores for images."""
+
+type Score = float
+"""Single aesthetic score for an image."""
+
+type Scores = list[Score]
+"""List of aesthetic scores for images."""
+
+type NIMAModelOutput = np.ndarray
+"""NIMA model output: probability distribution over 10 aesthetic rating classes.
+
+The model outputs 10 values representing the probability that an image
+belongs to each aesthetic rating class (1-10). For example:
+[0.01, 0.02, 0.03, 0.05, 0.15, 0.25, 0.25, 0.15, 0.07, 0.02]
+means 1% chance for rating 1, 2% for rating 2, ..., 25% for rating 7, etc.
+"""
+
+type RatingScale = np.ndarray
+"""Rating scale values [1, 2, 3, ..., 10] for calculating weighted mean.
+
+Used to compute expected aesthetic rating from NIMAModelOutput:
+final_score = sum(output * scale) / sum(scale)
+"""
+
+
+class ExtractorName(str, Enum):
+ """Available extractor names."""
+
+ BEST_FRAMES = "best_frames_extractor"
+ TOP_IMAGES = "top_images_extractor"
+
+
+class FileExtension(str, Enum):
+ """Base class for file extension enums."""
+
+ @classmethod
+ def contains(cls, value: str) -> bool:
+ """Check if value is a valid extension."""
+ return value in cls._value2member_map_
+
+
+class ImageExtension(FileExtension):
+ """Supported image file extensions."""
+
+ JPG = ".jpg"
+ JPEG = ".jpeg"
+ PNG = ".png"
+ WEBP = ".webp"
+ TIF = ".tif"
+ TIFF = ".tiff"
+ BMP = ".bmp"
+ GIF = ".gif"
+
+
+class VideoExtension(FileExtension):
+ """Supported video file extensions."""
+
+ MP4 = ".mp4"
+ MOV = ".mov"
+ WEBM = ".webm"
+ MKV = ".mkv"
+ AVI = ".avi"
+ WMV = ".wmv"
+ FLV = ".flv"
+ M4V = ".m4v"
+
+
+logger = logging.getLogger(__name__)
+
+
+class ExtractorConfig(BaseModel):
+ """A Pydantic model containing the extractors configuration parameters."""
+
+ input_directory: DirectoryPath = Path("/app/input_directory")
+ """Input directory path containing entries for extraction.
+
+ By default, it sets value for docker container volume.
+ """
+
+ output_directory: DirectoryPath = Path("/app/output_directory")
+ """Output directory path for extraction results.
+
+ By default, it sets value for docker container volume.
+ """
+
+ processed_video_prefix: str = "frames_extracted_"
+ """Prefix will be added to processed video after extraction."""
+
+ batch_size: int = 100
+ """Maximum number of images processed in a single batch."""
+
+ comparing_group_size: int = 5
+ """Images group number to compare for finding the best one."""
+
+ top_images_percent: float = 90.0
+ """Percentage threshold to determine the top images."""
+
+ images_output_format: ImageExtension = ImageExtension.JPG
+ """Format for saving output images."""
+
+ input_size: ImageResolution = ImageResolution(224, 224)
+ """Images will be normalized to this resolution for model input."""
+
+ weights_directory: Path | str = Path.home() / ".cache" / "huggingface"
+ """Directory path where model weights are stored."""
+
+ weights_filename: str = "weights.onnx"
+ """The filename of the model weights file to be loaded."""
+
+ weights_repo_url: str = "https://huggingface.co/BKDDFS/nima_weights/resolve/main/"
+ """URL to the repository where model weights can be downloaded."""
+
+ all_frames: bool = False
+ """It changes best_frames_extractor -> frames_extractor.
+
+ If True best_frames_extractor returns all frames without filtering/evaluation.
+ """
+
+
+class Message(BaseModel):
+ """A Pydantic model for encapsulating messages returned by the application."""
+
+ message: str
+
+
+class ExtractorStatus(BaseModel):
+ """A Pydantic model representing the status of the currently working extractor in the system."""
+
+ active_extractor: ExtractorName | None
diff --git a/perfectframe/video_processors.py b/perfectframe/video_processors.py
new file mode 100644
index 0000000..36958b6
--- /dev/null
+++ b/perfectframe/video_processors.py
@@ -0,0 +1,106 @@
+"""Provide abstract class for creating video processors and video processors.
+
+Video processors:
+ - OpenCVVideo: using OpenCV library to manage operations on videos.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Generator
+from contextlib import contextmanager
+from pathlib import Path
+
+import cv2
+
+from perfectframe.schemas import Image, Images
+
+logger = logging.getLogger(__name__)
+
+
+class VideoProcessor(ABC):
+ """Abstract class for creating video processors used for managing video operations."""
+
+ class _Error(Exception):
+ """Video processor error."""
+
+ @classmethod
+ @abstractmethod
+ def get_next_frames(cls, video_path: Path, frames_batch_size: int) -> Generator[Images]:
+ """Abstract generator method to generate batches of frames from a video file."""
+
+
+class OpenCVVideo(VideoProcessor):
+ """Video processor based on OpenCV with FFMPEG extension."""
+
+ @staticmethod
+ @contextmanager
+ def _video_capture(video_path: Path) -> Generator[cv2.VideoCapture]:
+ """Get and release a video capture object."""
+ video_cap = cv2.VideoCapture(str(video_path))
+ try:
+ if not video_cap.isOpened():
+ error_message = f"Can't open video file: {video_path}"
+ logger.error(error_message)
+ raise OpenCVVideo._Error(error_message)
+ logger.debug("Creating video capture.")
+ yield video_cap
+ finally:
+ video_cap.release()
+
+ @classmethod
+ def get_next_frames(cls, video_path: Path, frames_batch_size: int) -> Generator[Images]:
+ """Generate batches of frames from the specified video using OpenCV."""
+ with cls._video_capture(video_path) as video:
+ frame_rate = cls._get_video_property(video, cv2.CAP_PROP_FPS, "frame rate")
+ total_frames = cls._get_video_property(video, cv2.CAP_PROP_FRAME_COUNT, "total frames")
+ frames_batch: Images = []
+ logger.info("Getting frames batch...")
+ for frame_index in range(0, total_frames, frame_rate):
+ frame = cls._read_next_frame(video, frame_index)
+ if frame is None:
+ continue
+ frames_batch.append(frame)
+ logger.debug("Frame appended to frames batch.")
+ if len(frames_batch) == frames_batch_size:
+ logger.info("Got full frames batch.")
+ yield frames_batch
+ frames_batch = []
+ if frames_batch:
+ logger.info("Returning last frames batch.")
+ yield frames_batch
+
+ @classmethod
+ def _read_next_frame(cls, video: cv2.VideoCapture, frame_index: int) -> Image | None:
+ """Read frame with specified index from provided video."""
+ cls._check_video_capture(video)
+ video.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
+ success, frame = video.read()
+ if not success:
+ logger.warning("Couldn't read frame with index: %s", frame_index)
+ return None
+ return frame
+
+ @classmethod
+ def _get_video_property(
+ cls, video: cv2.VideoCapture, property_id: int, property_name: str
+ ) -> int:
+ """Retrieve a specified property value from the video capture object and validate it."""
+ cls._check_video_capture(video)
+ property_value = video.get(property_id)
+ logger.debug("Got input video %s: %s", property_name, property_value)
+ if property_value <= 0:
+ error_message = f"Invalid {property_name} retrieved: {property_value}."
+ logger.error(error_message)
+ raise ValueError(error_message)
+ return round(property_value)
+
+ @staticmethod
+ def _check_video_capture(video: cv2.VideoCapture) -> None:
+ """Check if video capture object is still available for future operations."""
+ if not video.isOpened():
+ error_message = (
+ "Invalid video capture object or object not opened. "
+ "Probably video capture closed at some point."
+ )
+ logger.error(error_message)
+ raise ValueError(error_message)
diff --git a/pyproject.toml b/pyproject.toml
index d0bf314..b3bc8d0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,33 +5,70 @@ description = "AI tool for finding the most aesthetic frames in a video. 🎞️
authors = [
{name = "Bartłomiej Flis", email = "Bartekdawidflis@gmail.com"}
]
-license = {text = "GPL-3.0"}
+license = {text = "Apache-2.0"}
readme = "README.md"
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
dependencies = [
- "fastapi==0.115.6",
- "uvicorn==0.34.0",
- "opencv-python==4.11.0.86",
- "requests==2.32.2",
- "tensorflow==2.18.0",
+ "fastapi==0.128.0", # API endpoints
+ "uvicorn==0.40.0", # Server
+ "opencv-python==4.13.0.90", # Frame extraction
+ "requests==2.32.5", # Model download
+ "onnxruntime==1.23.2; sys_platform == 'darwin' or (sys_platform == 'linux' and platform_machine == 'aarch64')",
+ "onnxruntime-gpu==1.23.2; sys_platform == 'win32' or (sys_platform == 'linux' and platform_machine == 'x86_64')",
+ "numpy==2.4.1", # Image batch processing
]
[dependency-groups]
dev = [
- "ruff>=0.9.2",
- "pre-commit>=4.0.1",
+ "ruff>=0.14.14", # Linter and formatter
+ "ty>=0.0.13", # Type checker
+ "pre-commit>=4.5.1", # Git hooks manager
+ "docformatter>=1.7.5", # Docstring formatter
+ "detect-secrets>=1.5.0", # Secret detection
]
test = [
- "pytest>=8.3.4",
- "pytest-cov>=5.0.0",
- "pytest-order>=1.2.1",
- "docker>=7.1.0",
- "httpx>=0.28.1",
+ "pytest>=9.0.2", # Testing framework
+ "pytest-cov>=7.0.0", # Coverage plugin
+ "pytest-mock>=3.14.0", # Mocking fixture
+ "pytest-order>=1.3.0", # Test ordering
+ "pytest-timeout>=2.3.1", # Timeout plugin
+ "docker>=7.1.0", # Docker SDK
+ "httpx>=0.28.1", # HTTP client
+ "testcontainers>=4.14.0", # Docker containers for e2e
]
[tool.ruff]
-line-length = 120
+line-length = 100
+target-version = "py313"
+exclude = [".git", "__pycache__"]
[tool.ruff.lint]
-per-file-ignores = {"**/conftest.py"=["F401"]}
+select = ["ALL"]
+extend-ignore = [
+ "D105", # missing docstring in magic method (redundant)
+ "D107", # missing docstring in __init__ (redundant)
+ "FA102", # Missing `from __future__ import annotations` (not needed for Python 3.11+)
+ "COM812", # trailing-comma-missing (ruff format handles differently)
+]
+
+[tool.ruff.lint.per-file-ignores]
+"{**/app.py,**/dependencies.py}" = ["B008"] # FastAPI Depends() in function arguments
+"{tests/**,**/conftest.py}" = [
+ "S", # security rules (annoying in tests)
+ "ANN", # type annotations (not useful in tests)
+ "D", # docstrings (self-descriptive test names)
+ "SLF001", # private member access (testing internals)
+ "PLR0913", # too many arguments (pytest fixtures)
+ "INP001", # implicit namespace package
+]
+"**/conftest.py" = ["F401", "F405"] # unused/star imports OK
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.flake8-tidy-imports.banned-api]
+"unittest.mock".msg = "Use pytest-mock's 'mocker' fixture instead"
+
+[tool.ty.environment]
+python-version = "3.13"
diff --git a/quick_demo_cpu.bat b/quick_demo_cpu.bat
index ed63939..b593379 100644
--- a/quick_demo_cpu.bat
+++ b/quick_demo_cpu.bat
@@ -1,4 +1,12 @@
@echo off
-echo Starting demo...
-python start.py best_frames_extractor --cpu
-pause
+echo Starting PerfectFrameAI (CPU mode)...
+docker-compose up --build -d
+echo Waiting for service to start...
+timeout /t 60 /nobreak >nul
+echo Calling best_frames_extractor...
+curl -X POST http://localhost:8100/v2/extractors/best_frames_extractor
+echo.
+echo Results will appear in output_directory/
+echo Press any key to stop the service...
+pause >nul
+docker-compose down
diff --git a/quick_demo_gpu.bat b/quick_demo_gpu.bat
index 5885379..a13cf27 100644
--- a/quick_demo_gpu.bat
+++ b/quick_demo_gpu.bat
@@ -1,4 +1,12 @@
@echo off
-echo Starting demo...
-python start.py best_frames_extractor
-pause
+echo Starting PerfectFrameAI (GPU mode)...
+docker-compose --profile gpu up --build -d
+echo Waiting for service to start...
+timeout /t 60 /nobreak >nul
+echo Calling best_frames_extractor...
+curl -X POST http://localhost:8100/v2/extractors/best_frames_extractor
+echo.
+echo Results will appear in output_directory/
+echo Press any key to stop the service...
+pause >nul
+docker-compose --profile gpu down
diff --git a/service_manager/docker_manager.py b/service_manager/docker_manager.py
deleted file mode 100644
index 3742f48..0000000
--- a/service_manager/docker_manager.py
+++ /dev/null
@@ -1,286 +0,0 @@
-"""
-I built a custom Docker manager because I wanted to simplify and accelerate the process of
-launching the service using a script as much as possible. Therefore,
-I didn’t want to use any external libraries in this part of the project.
-
-This module defines a DockerManager class to handle Docker operations like building images,
-managing container lifecycle, and monitoring container logs.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import logging
-import subprocess
-import sys
-from typing import Optional
-
-logger = logging.getLogger(__name__)
-
-
-class DockerManager:
- """
- Manages Docker containers and images, including operations like building, starting,
- stopping, and logging containers.
- """
-
- class ServiceShutdownSignal(Exception):
- """Exception raised when the service signals it is ready to be shut down."""
-
- def __init__(
- self,
- container_name: str,
- input_dir: str,
- output_dir: str,
- port: int,
- force_build: bool,
- cpu_only: bool,
- ) -> None:
- """
- Initialize the DockerManager with specific parameters for container and image management.
-
- Args:
- container_name (str): Name of the Docker container.
- input_dir (str): Path to the directory for input data volumes.
- output_dir (str): Path to the directory for output data volumes.
- port (int): Port number to expose from the container.
- """
- self._container_name = container_name
- self._image_name = f"{self._container_name}_image"
- self._input_directory = input_dir
- self._output_directory = output_dir
- self._port = port
- self._force_build = force_build
- self._cpu_only = cpu_only
- self.__log_input()
-
- @property
- def image_name(self):
- """
- Returns the name of the image.
-
- Returns:
- str: The name of the image.
- """
- return self._image_name
-
- def __log_input(self) -> None:
- """Log user input if debugging."""
- logger.debug("container_name: %s", self._container_name)
- logger.debug("image_name: %s", self._image_name)
- logger.debug("Input directory from user: %s", self._input_directory)
- logger.debug("Output directory from user: %s", self._output_directory)
- logger.debug("Port from user: %s", self._port)
- logger.debug("Force build: %s", self._force_build)
- logger.debug("CPU only: %s", self._cpu_only)
-
- @property
- def docker_image_existence(self) -> bool:
- """
- Checks if the Docker image exists.
-
- This property calls a method that checks for the existence of the Docker
- image associated with this instance.
-
- Returns:
- bool: True if the Docker image exists, False otherwise.
- """
- return self._check_image_exists()
-
- def _check_image_exists(self) -> bool:
- """
- Checks whether the Docker image already exists in the system.
-
- Returns:
- bool: True if the image exists, False otherwise.
- """
- command = ["docker", "images", "-q", self._image_name]
- process_output = subprocess.run(command, capture_output=True, text=True, check=True).stdout.strip()
- is_exists = process_output != ""
- return is_exists
-
- def build_image(self, dockerfile_path: str) -> None:
- """
- Builds a Docker image from a Dockerfile located in a subdirectory.
-
- Args:
- dockerfile_path (str): Path to the Dockerfile.
- """
- if not self.docker_image_existence or self._force_build:
- logging.info("Building Docker image...")
- command = ["docker", "build", "-t", self._image_name, dockerfile_path]
- subprocess.run(command, check=True)
- else:
- logger.info("Image is already created. Using existing one.")
-
- @property
- def container_status(self) -> str:
- """
- Retrieves the current status of the Docker container.
-
- Returns:
- str: Container status.
- """
- return self._check_container_status()
-
- def _check_container_status(self) -> Optional[str]:
- """
- Check the status of the container.
-
- Returns:
- str: The status of the container.
- """
- command = [
- "docker",
- "inspect",
- "--format='{{.State.Status}}'",
- self._container_name,
- ]
- result = subprocess.run(command, capture_output=True, text=True, check=False)
- if result.returncode == 0:
- return result.stdout.strip().replace("'", "")
- return None
-
- def deploy_container(
- self,
- container_port: int,
- container_input_directory: str,
- container_output_directory: str,
- ) -> None:
- """Deploys or starts the Docker container based on its current status.
-
- Args:
- container_port (int): Port to expose on the Docker container.
- container_input_directory (str): Directory inside the container for input data.
- container_output_directory (str): Directory inside the container for output data.
- """
- status = self.container_status
- if status is None:
- logging.info("No existing container found. Running a new container.")
- self._run_container(container_port, container_input_directory, container_output_directory)
- elif self._force_build:
- logging.info("Force rebuild initiated.")
- if status in ["running", "paused"]:
- self._stop_container()
- self._delete_container()
- self._run_container(container_port, container_input_directory, container_output_directory)
- elif status in ["exited", "created"]:
- self._start_container()
- elif status == "running":
- logging.info("Container is already running.")
- else:
- logging.warning(
- "Container in unsupported status: %s. Fix container on your own.",
- status,
- )
-
- def _start_container(self) -> None:
- """Start the container if it exists but stopped."""
- logging.info("Starting the existing container...")
- command = ["docker", "start", self._container_name]
- subprocess.run(command, check=True)
-
- def _run_container(
- self,
- container_port: int,
- container_input_directory: str,
- container_output_directory: str,
- ) -> None:
- """
- Runs a new Docker container using the configured parameters.
-
- Args:
- container_port (int): Port to expose on the Docker container.
- container_input_directory (str): Directory inside the container for input data.
- container_output_directory (str): Directory inside the container for output data.
- """
- logging.info("Running a new container...")
- command = [
- "docker",
- "run",
- "--name",
- self._container_name,
- "--restart",
- "unless-stopped",
- "-d",
- "-p",
- f"{self._port}:{container_port}",
- "-v",
- f"{self._input_directory}:{container_input_directory}",
- "-v",
- f"{self._output_directory}:{container_output_directory}",
- ]
- if not self._cpu_only:
- command.extend(["--gpus", "all"])
- command.append(self._image_name)
- subprocess.run(command, check=True)
-
- def follow_container_logs(self) -> None:
- """Starts following the logs of the running Docker container."""
- try:
- process = self._run_log_process()
- for line in iter(process.stdout.readline, ""):
- sys.stdout.write(line)
- if "Service ready for shutdown" in line:
- raise self.ServiceShutdownSignal("Service has signaled readiness for shutdown.")
- except KeyboardInterrupt:
- logger.info("Process stopped by user.")
- except self.ServiceShutdownSignal:
- logger.info("Service has signaled readiness for shutdown.")
- finally:
- self.__stop_log_process(process)
-
- def _run_log_process(self) -> subprocess.Popen:
- """Initiates the process to follow Docker container logs.
-
- Returns:
- subprocess.Popen: The process object for the log following command.
- """
- logger.info("Following logs for %s...", self._container_name)
- command = ["docker", "logs", "-f", "--since", "1s", self._container_name]
- process = subprocess.Popen(
- command,
- stdout=subprocess.PIPE,
- stderr=subprocess.STDOUT,
- text=True,
- encoding="utf-8",
- )
- return process
-
- def __stop_log_process(self, process: subprocess.Popen) -> None:
- """Terminates the log following process and stops the container.
-
- Args:
- process (subprocess.Popen): The process object for the log following command.
- """
- logger.info("Following container logs stopped.")
- process.terminate()
- process.wait()
- self._stop_container()
-
- def _stop_container(self) -> None:
- """Stops the running Docker container."""
- logger.info("Stopping container %s...", self._container_name)
- command = ["docker", "stop", self._container_name]
- subprocess.run(command, check=True, capture_output=True)
- logger.info("Container stopped.")
-
- def _delete_container(self) -> None:
- """Deletes the Docker container."""
- logger.info("Deleting container %s...", self._container_name)
- command = ["docker", "rm", self._container_name]
- subprocess.run(command, check=True, capture_output=True)
- logger.info("Container deleted.")
diff --git a/service_manager/service_initializer.py b/service_manager/service_initializer.py
deleted file mode 100644
index 8b86cb1..0000000
--- a/service_manager/service_initializer.py
+++ /dev/null
@@ -1,126 +0,0 @@
-"""
-This module provide tool for starting extractor service.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import argparse
-import json
-import logging
-import time
-from http.client import RemoteDisconnected
-from pathlib import Path
-from typing import Union
-from urllib.request import Request, urlopen
-
-logger = logging.getLogger(__name__)
-
-
-class ServiceInitializer:
- """
- Handles command-line input and manages the setup and
- execution of Docker-based image processing tasks.
- """
-
- def __init__(self, user_input: argparse.Namespace) -> None:
- """Initializes the service initializer by taking and validating user input."""
- self._input_directory = self._check_directory(user_input.input_dir)
- self._output_directory = self._check_directory(user_input.output_dir)
- self._extractor_name = user_input.extractor_name
- self._port = user_input.port
- self._all_frames = user_input.all_frames
-
- @staticmethod
- def _check_directory(directory: str) -> Path:
- """
- Validates if the provided directory path is an actual directory.
-
- Args:
- directory (str): The directory path to validate.
-
- Returns:
- Path: The validated directory as a Path object.
-
- Raises:
- NotADirectoryError: If the provided path is not a directory.
- """
- directory = Path(directory)
- if not directory.is_dir():
- error_massage = f"Invalid directory path: {str(directory)}"
- logger.error(error_massage)
- raise NotADirectoryError(error_massage)
- return directory
-
- def run_extractor(self, extractor_url: Union[str, None] = None) -> None:
- """Send POST request to local port extractor service to start chosen extractor."""
- if extractor_url is None:
- extractor_url = f"http://localhost:{self._port}/v2/extractors/{self._extractor_name}"
- json_data = {"all_frames": self._all_frames}
- req = Request(
- extractor_url,
- method="POST",
- data=json.dumps(json_data).encode("utf-8"),
- headers={"Content-Type": "application/json"},
- )
- start_time = time.time()
- while True:
- if self._try_to_run_extractor(req, start_time):
- break
-
- def _try_to_run_extractor(self, req: Request, start_time: float, timeout: int = 60) -> bool:
- """
- Attempts to send a request to the extractor service
- and handles service availability and timeouts.
-
- Args:
- req (Request): The request object to send.
- start_time (float): The timestamp at the start of the operation for timeout management.
- timeout (int): Maximum time in seconds to wait for the service to become available.
-
- Returns:
- bool: True if the service response as expected, False otherwise.
- """
- try:
- with urlopen(req) as response:
- if response.status == 200:
- response_body = response.read()
- response_body = json.loads(response_body.decode("utf-8"))
- message = response_body.get("message", "No message returned")
- logger.info("Response from server: %s", message)
- return True
- except RemoteDisconnected:
- logger.info("Waiting for service to be available...")
- self.__check_timeout(start_time, timeout)
- time.sleep(3)
- return False
-
- @staticmethod
- def __check_timeout(start_time: float, timeout: int) -> None:
- """
- Checks if the operation has timed out based on the start time and specified timeout.
-
- Args:
- start_time (float): The start time of the operation.
- timeout (int): The maximum allowable duration for the operation.
-
- Raises:
- TimeoutError: If the current time exceeds the start time by the timeout duration.
- """
- if time.time() - start_time > timeout:
- error_massage = "Timed out waiting for service to respond."
- logger.error(error_massage)
- raise TimeoutError(error_massage)
diff --git a/start.py b/start.py
deleted file mode 100644
index 4aca55b..0000000
--- a/start.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""
-This module provide script for starting extraction process with
-given arguments in fast and easy way.
-LICENSE
-=======
-Copyright (C) 2024 Bartłomiej Flis
-
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-GNU General Public License for more details.
-
-You should have received a copy of the GNU General Public License
-along with this program. If not, see
.
-"""
-
-import argparse
-import logging
-
-from config import Config
-from service_manager.docker_manager import DockerManager
-from service_manager.service_initializer import ServiceInitializer
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def main() -> None:
- """Script for starting extractor service and extraction process."""
- user_input = parse_args()
- service = ServiceInitializer(user_input)
- docker = DockerManager(
- Config.service_name,
- user_input.input_dir,
- user_input.output_dir,
- user_input.port,
- user_input.build,
- user_input.cpu,
- )
- docker.build_image(Config.dockerfile)
- docker.deploy_container(Config.port, Config.volume_input_directory, Config.volume_output_directory)
- service.run_extractor()
- docker.follow_container_logs()
- logger.info("Process stopped.")
-
-
-def parse_args() -> argparse.Namespace:
- """
- Parses command line arguments from user for extractor service.
-
- Returns:
- argparse.Namespace: Arguments from user.
- """
- parser = argparse.ArgumentParser(
- description="Tool to manage and execute image processing tasks within a Docker container."
- )
- parser.add_argument(
- "extractor_name",
- choices=["best_frames_extractor", "top_images_extractor"],
- help="Name of extractor to run.",
- )
- parser.add_argument(
- "--input_dir",
- "-i",
- default=Config.input_directory,
- help="Full path to the extractors input directory.",
- )
- parser.add_argument(
- "--output_dir",
- "-o",
- default=Config.output_directory,
- help="Full path to the extractors output directory.",
- )
- parser.add_argument(
- "--port",
- "-p",
- type=int,
- default=Config.port,
- help="Port to expose the service on the host.",
- )
- parser.add_argument(
- "--build",
- "-b",
- action="store_true",
- help="Forces the Docker image to be rebuilt if set to true.",
- )
- parser.add_argument(
- "--all_frames",
- action="store_true",
- help="Returning all frames every second without filtering. "
- "For best_frames_extractor - does nothing with others.",
- )
- parser.add_argument("--cpu", action="store_true", help="Turn on cpu-only mode.")
- args = parser.parse_args()
- return args
-
-
-if __name__ == "__main__":
- main()
diff --git a/tests/common.py b/tests/common.py
index 84a165f..03b55a7 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -5,6 +5,10 @@
import pytest
+from perfectframe.dependencies import Dependencies, get_dependencies
+from perfectframe.extractors import BestFramesExtractor
+from perfectframe.schemas import ExtractorConfig, ImageExtension
+
@pytest.fixture(scope="session")
def files_dir():
@@ -22,7 +26,7 @@ def top_images_dir(files_dir):
@pytest.fixture
-def setup_top_images_extractor_env(files_dir, top_images_dir) -> tuple[Path, Path]:
+def setup_top_images_extractor_env(files_dir, top_images_dir):
assert files_dir.is_dir()
if top_images_dir.is_dir():
@@ -38,7 +42,7 @@ def setup_top_images_extractor_env(files_dir, top_images_dir) -> tuple[Path, Pat
@pytest.fixture
-def setup_best_frames_extractor_env(files_dir, best_frames_dir) -> tuple[Path, Path, Path]:
+def setup_best_frames_extractor_env(files_dir, best_frames_dir):
video_filename = "test_video.mp4"
expected_video_path = files_dir / f"frames_extracted_{video_filename}"
video_path = files_dir / video_filename
@@ -57,3 +61,28 @@ def setup_best_frames_extractor_env(files_dir, best_frames_dir) -> tuple[Path, P
gitkeep_file = best_frames_dir / ".gitkeep"
gitkeep_file.touch()
assert gitkeep_file.exists()
+
+
+@pytest.fixture(scope="package")
+def config(files_dir, best_frames_dir) -> ExtractorConfig:
+ return ExtractorConfig(
+ input_directory=files_dir,
+ output_directory=best_frames_dir,
+ images_output_format=ImageExtension.JPG,
+ processed_video_prefix="done_",
+ )
+
+
+@pytest.fixture(scope="package")
+def dependencies(config) -> Dependencies:
+ return get_dependencies(config)
+
+
+@pytest.fixture(scope="package")
+def extractor(dependencies):
+ return BestFramesExtractor(
+ dependencies.config,
+ dependencies.image_processor,
+ dependencies.video_processor,
+ dependencies.evaluator,
+ )
diff --git a/extractor_service/__init__.py b/tests/e2e/__init__.py
similarity index 100%
rename from extractor_service/__init__.py
rename to tests/e2e/__init__.py
diff --git a/tests/extractor_service/e2e/best_frames_extractor_api_test.py b/tests/e2e/best_frames_extractor_api_test.py
similarity index 62%
rename from tests/extractor_service/e2e/best_frames_extractor_api_test.py
rename to tests/e2e/best_frames_extractor_api_test.py
index 51a9d57..20257b4 100644
--- a/tests/extractor_service/e2e/best_frames_extractor_api_test.py
+++ b/tests/e2e/best_frames_extractor_api_test.py
@@ -1,7 +1,3 @@
-# import pytest
-
-
-# @pytest.mark.skip(reason="Test time-consuming and dependent on hardware performance")
def test_best_frames_extractor_api(client, setup_best_frames_extractor_env):
input_directory, output_directory, expected_video_path = setup_best_frames_extractor_env
extractor_name = "best_frames_extractor"
@@ -12,10 +8,14 @@ def test_best_frames_extractor_api(client, setup_best_frames_extractor_env):
response = client.post(f"/v2/extractors/{extractor_name}", json=config)
- assert response.status_code == 200
+ assert response.is_success
assert response.json()["message"] == f"'{extractor_name}' started."
found_best_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.suffix == ".jpg"
+ file
+ for file in output_directory.iterdir()
+ if file.name.startswith("image_") and file.suffix == ".jpg"
]
- assert len(found_best_frame_files) > 0, "No files meeting the criteria were found in output_directory"
+ assert len(found_best_frame_files) > 0, (
+ "No files meeting the criteria were found in output_directory"
+ )
assert expected_video_path.is_file(), "Video file name was not changed as expected"
diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py
new file mode 100644
index 0000000..033ba44
--- /dev/null
+++ b/tests/e2e/conftest.py
@@ -0,0 +1,119 @@
+"""E2E test fixtures using testcontainers."""
+
+import os
+import shutil
+import time
+from pathlib import Path
+
+import pytest
+import requests
+from fastapi.testclient import TestClient
+from testcontainers.compose import DockerCompose
+
+from perfectframe.app import app
+from tests.common import (
+ best_frames_dir,
+ config,
+ files_dir,
+ setup_best_frames_extractor_env,
+ setup_top_images_extractor_env,
+ top_images_dir,
+)
+
+PROJECT_ROOT = Path(__file__).parent.parent.parent
+TEST_FILES_DIR = Path(__file__).parent.parent / "test_files"
+
+
+@pytest.fixture(scope="package")
+def client():
+ with TestClient(app) as client:
+ yield client
+
+
+def wait_for_health(base_url: str, timeout: int = 120, interval: float = 0.5) -> bool:
+ """Wait for health endpoint to return 200."""
+ start_time = time.time()
+ while time.time() - start_time < timeout:
+ try:
+ response = requests.get(f"{base_url}/health", timeout=5)
+ if response.ok:
+ return True
+ except requests.exceptions.RequestException:
+ pass
+ time.sleep(interval)
+ return False
+
+
+def wait_for_extraction_complete(base_url: str, timeout: int = 300, interval: float = 0.5) -> bool:
+ """Wait for extraction to complete by polling /v2/status endpoint."""
+ start_time = time.time()
+ while time.time() - start_time < timeout:
+ try:
+ response = requests.get(f"{base_url}/v2/status", timeout=5)
+ if response.ok:
+ status = response.json()
+ if status.get("active_extractor") is None:
+ return True
+ except requests.exceptions.RequestException:
+ pass
+ time.sleep(interval)
+ return False
+
+
+def cleanup_output_dir(output_dir: Path) -> None:
+ """Remove all image files from output directory."""
+ for f in output_dir.glob("image_*.jpg"):
+ f.unlink()
+
+
+@pytest.fixture(scope="package")
+def extractor_service(tmp_path_factory):
+ """Start extractor service using docker-compose."""
+ input_dir = tmp_path_factory.mktemp("input")
+ output_dir = tmp_path_factory.mktemp("output")
+
+ # Make directories writable by container's non-root user (uid=1000)
+ input_dir.chmod(0o777)
+ output_dir.chmod(0o777)
+
+ # Copy test video to input (reset name if it was processed by another test)
+ test_video = TEST_FILES_DIR / "test_video.mp4"
+ processed_video = TEST_FILES_DIR / "frames_extracted_test_video.mp4"
+ if processed_video.exists() and not test_video.exists():
+ processed_video.rename(test_video)
+ if test_video.exists():
+ shutil.copy(test_video, input_dir / "test_video.mp4")
+
+ # Copy test image to input (for top_images_extractor)
+ test_image = TEST_FILES_DIR / "image_3e4aa2ce-7f83-45fd-b56f-e3bed645224e.jpg"
+ if test_image.exists():
+ shutil.copy(test_image, input_dir / "test_image.jpg")
+
+ compose = DockerCompose(
+ context=str(PROJECT_ROOT),
+ compose_file_name="docker-compose.yaml",
+ env_file=None,
+ build=True,
+ )
+ # Set environment variables for volumes
+ os.environ["INPUT_DIR"] = str(input_dir)
+ os.environ["OUTPUT_DIR"] = str(output_dir)
+
+ compose.start()
+
+ # Wait for health endpoint
+ base_url = "http://localhost:8100"
+ if not wait_for_health(base_url):
+ compose.stop()
+ pytest.fail("Service did not become healthy in time")
+
+ yield {
+ "input_dir": input_dir,
+ "output_dir": output_dir,
+ "base_url": base_url,
+ }
+
+ compose.stop()
+ # Clean up environment variables
+ os.environ.pop("INPUT_DIR", None)
+ os.environ.pop("OUTPUT_DIR", None)
diff --git a/tests/e2e/docker_best_frames_extractor_test.py b/tests/e2e/docker_best_frames_extractor_test.py
new file mode 100644
index 0000000..9dfa116
--- /dev/null
+++ b/tests/e2e/docker_best_frames_extractor_test.py
@@ -0,0 +1,38 @@
+"""E2E test for best_frames_extractor using testcontainers."""
+
+import requests
+
+from tests.e2e.conftest import cleanup_output_dir, wait_for_extraction_complete
+
+
+def test_best_frames_extractor(extractor_service):
+ """Test best_frames_extractor endpoint via docker-compose service."""
+ base_url = extractor_service["base_url"]
+ input_dir = extractor_service["input_dir"]
+ output_dir = extractor_service["output_dir"]
+
+ # Cleanup and verify empty
+ cleanup_output_dir(output_dir)
+ assert len(list(output_dir.glob("image_*.jpg"))) == 0, "Output dir not empty"
+
+ # Call extractor API
+ response = requests.post(
+ f"{base_url}/v2/extractors/best_frames_extractor",
+ json={"all_frames": False},
+ timeout=30,
+ )
+
+ assert response.ok
+ assert "started" in response.json().get("message", "").lower()
+
+ # Wait for extraction to complete
+ extraction_completed = wait_for_extraction_complete(base_url, timeout=300)
+ assert extraction_completed, "Extraction did not complete within timeout"
+
+ # Verify output files were created
+ output_files = list(output_dir.glob("image_*.jpg"))
+ assert len(output_files) > 0, "No output files were created"
+
+ # Verify video file was renamed (processed)
+ expected_video_path = input_dir / "frames_extracted_test_video.mp4"
+ assert expected_video_path.is_file(), "Video file was not renamed after processing"
diff --git a/tests/e2e/docker_top_images_extractor_test.py b/tests/e2e/docker_top_images_extractor_test.py
new file mode 100644
index 0000000..c5fd0e7
--- /dev/null
+++ b/tests/e2e/docker_top_images_extractor_test.py
@@ -0,0 +1,38 @@
+"""E2E test for top_images_extractor using testcontainers."""
+
+import requests
+
+from tests.e2e.conftest import cleanup_output_dir, wait_for_extraction_complete
+
+
+def test_top_images_extractor(extractor_service):
+ """Test top_images_extractor endpoint via docker-compose service."""
+ base_url = extractor_service["base_url"]
+ input_dir = extractor_service["input_dir"]
+ output_dir = extractor_service["output_dir"]
+
+ # Verify input image exists
+ input_image = input_dir / "test_image.jpg"
+ assert input_image.is_file(), "Test image not found in input directory"
+
+ # Cleanup and verify empty
+ cleanup_output_dir(output_dir)
+ assert len(list(output_dir.glob("image_*.jpg"))) == 0, "Output dir not empty"
+
+ # Call extractor API
+ response = requests.post(
+ f"{base_url}/v2/extractors/top_images_extractor",
+ json={},
+ timeout=30,
+ )
+
+ assert response.ok
+ assert "started" in response.json().get("message", "").lower()
+
+ # Wait for extraction to complete
+ extraction_completed = wait_for_extraction_complete(base_url, timeout=300)
+ assert extraction_completed, "Extraction did not complete within timeout"
+
+ # Verify output files were created
+ output_files = list(output_dir.glob("image_*.jpg"))
+ assert len(output_files) > 0, "No output files were created"
diff --git a/tests/extractor_service/e2e/frames_extractor_test.py b/tests/e2e/frames_extractor_test.py
similarity index 63%
rename from tests/extractor_service/e2e/frames_extractor_test.py
rename to tests/e2e/frames_extractor_test.py
index 119b39d..9dfaa08 100644
--- a/tests/extractor_service/e2e/frames_extractor_test.py
+++ b/tests/e2e/frames_extractor_test.py
@@ -1,7 +1,3 @@
-# import pytest
-
-
-# @pytest.mark.skip(reason="Test time-consuming and dependent on hardware performance")
def test_frames_extractor_api(client, setup_best_frames_extractor_env):
input_directory, output_directory, expected_video_path = setup_best_frames_extractor_env
extractor_name = "best_frames_extractor"
@@ -13,10 +9,14 @@ def test_frames_extractor_api(client, setup_best_frames_extractor_env):
response = client.post(f"/v2/extractors/{extractor_name}", json=config)
- assert response.status_code == 200
+ assert response.is_success
assert response.json()["message"] == f"'{extractor_name}' started."
found_best_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.suffix == ".jpg"
+ file
+ for file in output_directory.iterdir()
+ if file.name.startswith("image_") and file.suffix == ".jpg"
]
- assert len(found_best_frame_files) > 0, "No files meeting the criteria were found in output_directory"
+ assert len(found_best_frame_files) > 0, (
+ "No files meeting the criteria were found in output_directory"
+ )
assert expected_video_path.is_file(), "Video file name was not changed as expected"
diff --git a/tests/extractor_service/e2e/top_images_extractor_api_test.py b/tests/e2e/top_images_extractor_api_test.py
similarity index 57%
rename from tests/extractor_service/e2e/top_images_extractor_api_test.py
rename to tests/e2e/top_images_extractor_api_test.py
index b6a6f58..6fb58d5 100644
--- a/tests/extractor_service/e2e/top_images_extractor_api_test.py
+++ b/tests/e2e/top_images_extractor_api_test.py
@@ -1,7 +1,3 @@
-# import pytest
-
-
-# @pytest.mark.skip(reason="Test time-consuming and dependent on hardware performance")
def test_top_images_extractor_api(client, setup_top_images_extractor_env):
input_directory, output_directory = setup_top_images_extractor_env
extractor_name = "top_images_extractor"
@@ -12,9 +8,13 @@ def test_top_images_extractor_api(client, setup_top_images_extractor_env):
response = client.post(f"/v2/extractors/{extractor_name}", json=config)
- assert response.status_code == 200
+ assert response.is_success
assert response.json()["message"] == f"'{extractor_name}' started."
found_top_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.name.endswith(".jpg")
+ file
+ for file in output_directory.iterdir()
+ if file.name.startswith("image_") and file.name.endswith(".jpg")
]
- assert len(found_top_frame_files) > 0, "No files meeting the criteria were found in output_directory"
+ assert len(found_top_frame_files) > 0, (
+ "No files meeting the criteria were found in output_directory"
+ )
diff --git a/tests/extractor_service/__init__.py b/tests/extractor_service/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/extractor_service/common.py b/tests/extractor_service/common.py
deleted file mode 100644
index aae3659..0000000
--- a/tests/extractor_service/common.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""Common fixtures for all conftest files."""
-
-import pytest
-
-from extractor_service.app.dependencies import (
- ExtractorDependencies,
- get_evaluator,
- get_image_processor,
- get_video_processor,
-)
-from extractor_service.app.extractors import BestFramesExtractor
-from extractor_service.app.schemas import ExtractorConfig
-
-
-@pytest.fixture(scope="package")
-def dependencies():
- image_processor = get_image_processor()
- video_processor = get_video_processor()
- evaluator = get_evaluator()
-
- return ExtractorDependencies(
- image_processor=image_processor,
- video_processor=video_processor,
- evaluator=evaluator,
- )
-
-
-@pytest.fixture(scope="package")
-def extractor(config, dependencies):
- extractor = BestFramesExtractor(
- config,
- dependencies.image_processor,
- dependencies.video_processor,
- dependencies.evaluator,
- )
- return extractor
-
-
-@pytest.fixture(scope="package")
-def config(files_dir, best_frames_dir) -> ExtractorConfig:
- config = ExtractorConfig(
- input_directory=files_dir,
- output_directory=best_frames_dir,
- images_output_format=".jpg",
- video_extensions=(".mp4",),
- processed_video_prefix="done_",
- )
- return config
diff --git a/tests/extractor_service/e2e/__init__.py b/tests/extractor_service/e2e/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/extractor_service/e2e/conftest.py b/tests/extractor_service/e2e/conftest.py
deleted file mode 100644
index eb9e9e2..0000000
--- a/tests/extractor_service/e2e/conftest.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import pytest
-from fastapi.testclient import TestClient
-
-from extractor_service.main import app, run_extractor
-from tests.common import (
- best_frames_dir,
- files_dir,
- setup_best_frames_extractor_env,
- setup_top_images_extractor_env,
- top_images_dir,
-)
-from tests.extractor_service.common import config
-
-
-@pytest.fixture(scope="package")
-def client():
- with TestClient(app) as client:
- yield client
diff --git a/tests/extractor_service/integration/__init__.py b/tests/extractor_service/integration/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/extractor_service/integration/manager_and_fastapi_integration_test.py b/tests/extractor_service/integration/manager_and_fastapi_integration_test.py
deleted file mode 100644
index 0b72f71..0000000
--- a/tests/extractor_service/integration/manager_and_fastapi_integration_test.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from fastapi import BackgroundTasks
-from starlette.testclient import TestClient
-
-from extractor_service.app.extractor_manager import ExtractorManager
-from extractor_service.main import app
-
-client = TestClient(app)
-
-
-def test_extractor_start_and_stop(config, dependencies):
- extractor_name = "best_frames_extractor"
- background_tasks = BackgroundTasks()
-
- response = ExtractorManager.start_extractor(extractor_name, background_tasks, config, dependencies)
-
- assert response == f"'{extractor_name}' started."
- assert ExtractorManager.get_active_extractor() is None
diff --git a/tests/extractor_service/unit/__init__.py b/tests/extractor_service/unit/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/extractor_service/unit/best_frames_extractor_test.py b/tests/extractor_service/unit/best_frames_extractor_test.py
deleted file mode 100644
index 504e3f7..0000000
--- a/tests/extractor_service/unit/best_frames_extractor_test.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import logging
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import numpy as np
-import pytest
-
-from extractor_service.app.extractors import BestFramesExtractor
-from extractor_service.app.image_evaluators import InceptionResNetNIMA
-from extractor_service.app.image_processors import OpenCVImage
-from extractor_service.app.video_processors import OpenCVVideo
-
-
-@pytest.fixture
-def all_frames_extractor(extractor):
- extractor._config.all_frames = True
- yield extractor
- extractor._config.all_frames = False
-
-
-@pytest.fixture(scope="function")
-def extractor(config):
- extractor = BestFramesExtractor(config, OpenCVImage, OpenCVVideo, InceptionResNetNIMA)
- return extractor
-
-
-def test_process(extractor, caplog, config):
- test_videos = ["/fake/directory/video1.mp4", "/fake/directory/video2.mp4"]
- test_frames = ["frame1", "frame2"]
- extractor._list_input_directory_files = MagicMock(return_value=test_videos)
- extractor._get_image_evaluator = MagicMock()
- extractor._extract_best_frames = MagicMock(return_value=test_frames)
- extractor._add_prefix = MagicMock()
- extractor._signal_readiness_for_shutdown = MagicMock()
-
- with caplog.at_level(logging.INFO):
- extractor.process()
-
- extractor._list_input_directory_files.assert_called_once_with(
- config.video_extensions, config.processed_video_prefix
- )
- extractor._get_image_evaluator.assert_called_once()
- assert extractor._extract_best_frames.call_count == len(test_videos)
- assert extractor._add_prefix.call_count == len(test_videos)
- extractor._signal_readiness_for_shutdown.assert_called_once()
- for video in test_videos:
- extractor._add_prefix.assert_any_call(config.processed_video_prefix, video)
- extractor._extract_best_frames.assert_any_call(video)
- assert f"Frames extraction has finished for video: {video}" in caplog.text
- assert f"Starting frames extraction process from '{config.input_directory}'." in caplog.text
-
-
-def test_process_if_all_frames(extractor, caplog, config, all_frames_extractor):
- test_videos = ["/fake/directory/video1.mp4", "/fake/directory/video2.mp4"]
- test_frames = ["frame1", "frame2"]
- extractor._list_input_directory_files = MagicMock(return_value=test_videos)
- extractor._get_image_evaluator = MagicMock()
- extractor._extract_best_frames = MagicMock(return_value=test_frames)
- extractor._add_prefix = MagicMock()
- extractor._signal_readiness_for_shutdown = MagicMock()
-
- with caplog.at_level(logging.INFO):
- extractor.process()
-
- extractor._list_input_directory_files.assert_called_once_with(
- config.video_extensions, config.processed_video_prefix
- )
- extractor._get_image_evaluator.assert_not_called()
- assert not extractor._image_evaluator
- assert extractor._extract_best_frames.call_count == len(test_videos)
- assert extractor._add_prefix.call_count == len(test_videos)
- extractor._signal_readiness_for_shutdown.assert_called_once()
- for video in test_videos:
- extractor._add_prefix.assert_any_call(config.processed_video_prefix, video)
- extractor._extract_best_frames.assert_any_call(video)
- assert f"Frames extraction has finished for video: {video}" in caplog.text
- assert f"Starting frames extraction process from '{config.input_directory}'." in caplog.text
-
-
-@patch("extractor_service.app.extractors.gc.collect")
-@patch.object(BestFramesExtractor, "_get_best_frames")
-@patch.object(BestFramesExtractor, "_save_images")
-@patch.object(OpenCVVideo, "get_next_frames")
-def test_extract_best_frames(mock_generator, mock_save, mock_get, mock_collect, extractor):
- video_path = MagicMock(spec=Path)
-
- batch_1 = [f"frame{i}" for i in range(5)]
- batch_2 = []
- batch_3 = [f"frame{i}" for i in range(5)]
- mock_generator.return_value = iter([batch_1, batch_2, batch_3])
-
- mock_get.side_effect = [batch_1, batch_3]
-
- extractor._extract_best_frames(video_path)
-
- assert not extractor._config.all_frames
- mock_generator.assert_called_once_with(video_path, extractor._config.batch_size)
- assert mock_get.call_count == 2
- for batch in [batch_1, batch_3]:
- mock_save.assert_called_with(batch)
- assert mock_collect.call_count == 2
-
-
-@patch("extractor_service.app.extractors.gc.collect")
-@patch.object(BestFramesExtractor, "_get_best_frames")
-@patch.object(BestFramesExtractor, "_save_images")
-@patch.object(OpenCVVideo, "get_next_frames")
-def test_extract_all_frames(mock_generator, mock_save, mock_get, mock_collect, all_frames_extractor):
- video_path = MagicMock(spec=Path)
-
- batch_1 = [f"frame{i}" for i in range(5)]
- batch_2 = []
- batch_3 = [f"frame{i}" for i in range(5)]
- mock_generator.return_value = iter([batch_1, batch_2, batch_3])
-
- all_frames_extractor._extract_best_frames(video_path)
-
- assert all_frames_extractor._config.all_frames
- mock_generator.assert_called_once_with(video_path, all_frames_extractor._config.batch_size)
- assert mock_get.assert_not_called
- for batch in [batch_1, batch_3]:
- mock_save.assert_called_with(batch)
- assert mock_collect.call_count == 2
-
-
-@patch.object(BestFramesExtractor, "_normalize_images")
-@patch.object(BestFramesExtractor, "_evaluate_images")
-def test_get_best_frames(mock_evaluate, mock_normalize, caplog, extractor, config):
- frames = [f"frames{i}" for i in range(10)]
- scores = np.array([7, 2, 9, 3, 8, 5, 10, 1, 4, 6])
- normalized_images = [MagicMock() for _ in range(10)]
- mock_normalize.return_value = normalized_images
- mock_evaluate.return_value = scores
- expected_best_images = [frames[2], frames[6]]
-
- with caplog.at_level(logging.INFO):
- best_images = extractor._get_best_frames(frames)
-
- mock_evaluate.assert_called_once_with(normalized_images)
- mock_normalize.assert_called_once_with(frames, config.target_image_size)
- assert best_images == expected_best_images
- assert f"Best frames selected({len(expected_best_images)})." in caplog.text
diff --git a/tests/extractor_service/unit/conftest.py b/tests/extractor_service/unit/conftest.py
deleted file mode 100644
index 1a32383..0000000
--- a/tests/extractor_service/unit/conftest.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import pytest
-
-from extractor_service.app.extractors import BestFramesExtractor
-from extractor_service.app.schemas import ExtractorConfig
-from tests.common import best_frames_dir, files_dir
-from tests.extractor_service.common import config, dependencies, extractor
diff --git a/tests/extractor_service/unit/dependencies_test.py b/tests/extractor_service/unit/dependencies_test.py
deleted file mode 100644
index 9058d19..0000000
--- a/tests/extractor_service/unit/dependencies_test.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from extractor_service.app.dependencies import (
- ExtractorDependencies,
- get_evaluator,
- get_extractor_dependencies,
- get_image_processor,
- get_video_processor,
-)
-from extractor_service.app.image_evaluators import InceptionResNetNIMA
-from extractor_service.app.image_processors import OpenCVImage
-from extractor_service.app.video_processors import OpenCVVideo
-
-
-def test_get_image_processor():
- assert get_image_processor() == OpenCVImage
-
-
-def test_get_video_processor():
- assert get_video_processor() == OpenCVVideo
-
-
-def test_get_evaluator():
- assert get_evaluator() == InceptionResNetNIMA
-
-
-def test_get_extractor_dependencies():
- dependencies = get_extractor_dependencies(
- image_processor=get_image_processor(),
- video_processor=get_video_processor(),
- evaluator=get_evaluator(),
- )
-
- assert isinstance(dependencies, ExtractorDependencies)
- assert dependencies.image_processor == OpenCVImage
- assert dependencies.video_processor == OpenCVVideo
- assert dependencies.evaluator == InceptionResNetNIMA
diff --git a/tests/extractor_service/unit/extractor_manager_test.py b/tests/extractor_service/unit/extractor_manager_test.py
deleted file mode 100644
index d89f8e4..0000000
--- a/tests/extractor_service/unit/extractor_manager_test.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-from fastapi import BackgroundTasks, HTTPException
-
-from extractor_service.app.extractor_manager import ExtractorManager
-from extractor_service.app.extractors import ExtractorFactory
-
-
-def test_get_active_extractor():
- assert ExtractorManager.get_active_extractor() is None
-
-
-@patch.object(ExtractorFactory, "create_extractor")
-@patch.object(ExtractorManager, "_check_is_already_extracting")
-def test_start_extractor(mock_checking, mock_create_extractor, config, dependencies):
- extractor_name = "some_extractor"
- mock_extractor = MagicMock()
- mock_background_tasks = MagicMock(spec=BackgroundTasks)
- mock_create_extractor.return_value = mock_extractor
-
- message = ExtractorManager.start_extractor(extractor_name, mock_background_tasks, config, dependencies)
-
- mock_checking.assert_called_once()
- mock_create_extractor.assert_called_once_with(extractor_name, config, dependencies)
- mock_background_tasks.add_task.assert_called_once_with(
- ExtractorManager._ExtractorManager__run_extractor,
- mock_extractor,
- extractor_name,
- )
- expected_message = f"'{extractor_name}' started."
- assert message == expected_message, "The return message does not match expected."
-
-
-@patch("extractor_service.app.extractors.BestFramesExtractor")
-def test_run_extractor(mock_extractor):
- extractor_name = "some_extractor"
-
- ExtractorManager._ExtractorManager__run_extractor(mock_extractor, extractor_name)
-
- mock_extractor.process.assert_called_once()
-
-
-def test_check_is_already_evaluating_true():
- test_extractor = "active_extractor"
- ExtractorManager._active_extractor = test_extractor
- expected_error_massage = (
- f"Extractor '{test_extractor}' is already running. "
- f"You can run only one extractor at the same time. "
- f"Wait until the extractor is done before run next process."
- )
-
- with pytest.raises(HTTPException, match=expected_error_massage) as exc_info:
- ExtractorManager._check_is_already_extracting()
-
- assert exc_info.value.status_code == 409
diff --git a/tests/extractor_service/unit/image_evaluators_test.py b/tests/extractor_service/unit/image_evaluators_test.py
deleted file mode 100644
index 300bad3..0000000
--- a/tests/extractor_service/unit/image_evaluators_test.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import logging
-from unittest.mock import MagicMock, call, patch
-
-import numpy as np
-import pytest
-
-from extractor_service.app.image_evaluators import InceptionResNetNIMA, _ResNetModel
-
-
-@pytest.fixture
-def evaluator():
- with patch.object(_ResNetModel, "get_model", return_value=MagicMock()):
- evaluator = InceptionResNetNIMA(MagicMock())
- return evaluator
-
-
-@patch.object(_ResNetModel, "get_model")
-def test_evaluator_initialization(mock_get_model, config):
- test_model = "some_model"
- mock_get_model.return_value = test_model
-
- instance = InceptionResNetNIMA(config)
-
- mock_get_model.assert_called_once()
- assert instance._model == test_model
-
-
-@patch("extractor_service.app.image_evaluators.convert_to_tensor")
-@patch.object(InceptionResNetNIMA, "_calculate_weighted_mean")
-@patch.object(InceptionResNetNIMA, "_check_scores")
-def test_evaluate_images(mock_check, mock_calculate, mock_convert_to_tensor, evaluator, caplog):
- fake_images = MagicMock(spec=np.ndarray)
- fake_images.shape = (3, 2, 2)
- tensor = "some_tensor"
- predictions = [1.0, 2.0, 3.0]
- expected_scores = [10.0, 20.0, 30.0]
- mock_convert_to_tensor.return_value = tensor
- mock_calculate.side_effect = expected_scores
- evaluator._model.predict.return_value = predictions
-
- with caplog.at_level(logging.INFO):
- result = evaluator.evaluate_images(fake_images)
-
- mock_convert_to_tensor.assert_called_once_with(fake_images)
- evaluator._model.predict.assert_called_once_with(tensor, batch_size=fake_images.shape[0], verbose=0)
- mock_calculate.assert_has_calls(
- [call(prediction, _ResNetModel._prediction_weights) for prediction in predictions],
- any_order=True,
- )
- mock_check.assert_called_once()
- assert "Evaluating images..." in caplog.text
- assert "Images batch evaluated." in caplog.text
- assert result == expected_scores
-
-
-def test_calculate_weighted_mean_with_default_weights(evaluator):
- prediction = np.array([10, 20, 30])
- expected_weighted_mean = np.mean(prediction) # Since default weights are equal
-
- calculated_mean = evaluator._calculate_weighted_mean(prediction)
-
- assert np.isclose(calculated_mean, expected_weighted_mean)
-
-
-def test_calculate_weighted_mean_with_custom_weights(evaluator):
- prediction = np.array([10, 20, 30])
- weights = np.array([1, 2, 3])
- expected_weighted_mean = np.sum(prediction * weights) / np.sum(weights)
-
- calculated_mean = evaluator._calculate_weighted_mean(prediction, weights)
-
- assert np.isclose(calculated_mean, expected_weighted_mean)
-
-
-@pytest.mark.parametrize("score_len, images_len", ((1, 1), (1, 2)))
-def test_check_scores(score_len, images_len, evaluator, caplog):
- scores = [MagicMock(spec=np.ndarray) for _ in range(score_len)]
- images = [MagicMock(spec=float) for _ in range(images_len)]
- with caplog.at_level(logging.DEBUG):
- evaluator._check_scores(images, scores)
-
- assert f"Scores: {scores}" in caplog.text
- if score_len == images_len:
- assert f"Scores and images lists length: {score_len}" in caplog.text
- else:
- assert "Scores and images lists lengths don't match!" in caplog.text
- assert f"Images list length: {images_len}" in caplog.text
- assert f"Scores list length: {score_len}" in caplog.text
diff --git a/tests/extractor_service/unit/image_processors_test.py b/tests/extractor_service/unit/image_processors_test.py
deleted file mode 100644
index 2ee1845..0000000
--- a/tests/extractor_service/unit/image_processors_test.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import logging
-import uuid
-from pathlib import Path
-from unittest.mock import MagicMock, call, patch
-
-import cv2
-import numpy as np
-
-from extractor_service.app.image_processors import OpenCVImage
-
-
-@patch.object(cv2, "imread")
-def test_read_image(mock_imread, caplog):
- mock_path = Path("some/path/to/image.jpg")
- expected_image = MagicMock(spec=np.ndarray)
- mock_imread.return_value = expected_image
-
- with caplog.at_level(logging.DEBUG):
- result = OpenCVImage.read_image(mock_path)
-
- assert result == expected_image
- mock_imread.assert_called_once_with(str(mock_path))
- assert f"Image '{mock_path}' has successfully read." in caplog.text
-
-
-@patch.object(cv2, "imread")
-def test_read_image_invalid_image(mock_imread, caplog):
- mock_path = Path("some/path/to/image.jpg")
- mock_imread.return_value = None
-
- with caplog.at_level(logging.WARNING):
- result = OpenCVImage.read_image(mock_path)
-
- assert result is None
- mock_imread.assert_called_once_with(str(mock_path))
- assert (f"Can't read image. OpenCV reading not returns np.ndarray for image path: {str(mock_path)}") in caplog.text
-
-
-@patch.object(uuid, "uuid4")
-@patch.object(cv2, "imwrite")
-def test_save_image(mock_imwrite, mock_uuid, caplog):
- file_name = "some_filename"
- mock_uuid.return_value = file_name
- fake_image = MagicMock(spec=np.ndarray)
- output_directory = Path("/fake/directory")
- output_format = ".jpg"
- expected_path = output_directory / f"image_{file_name}{output_format}"
-
- with caplog.at_level(logging.DEBUG):
- image_path = OpenCVImage.save_image(fake_image, output_directory, output_format)
-
- mock_imwrite.assert_called_once_with(str(expected_path), fake_image)
- assert image_path == expected_path, "The returned path does not match the expected path."
- assert f"Image saved at '{expected_path}'." in caplog.text
-
-
-@patch.object(cv2, "resize")
-@patch.object(cv2, "cvtColor")
-@patch.object(np, "array")
-def test_normalize_images(mock_array, mock_cvt, mock_resize, caplog):
- images_num = 3
- target_size = (112, 112)
- batch_images = [MagicMock(spec=np.ndarray) for _ in range(images_num)]
- resized_images = [MagicMock(spec=np.ndarray) for _ in range(images_num)]
- expected_images = [MagicMock(spec=np.ndarray) for _ in range(images_num)]
- mock_resize.side_effect = resized_images
- mock_cvt.side_effect = expected_images
- mock_array.return_value = np.array(expected_images, dtype=np.float32) / 255.0
-
- result = OpenCVImage.normalize_images(batch_images, target_size)
-
- calls = [call(image, target_size, interpolation=cv2.INTER_LANCZOS4) for image in batch_images]
- mock_resize.assert_has_calls(calls, any_order=True)
- calls = [call(image, cv2.COLOR_BGR2RGB) for image in resized_images]
- mock_cvt.assert_has_calls(calls, any_order=True)
- np.testing.assert_array_equal(result, mock_array.return_value)
diff --git a/tests/extractor_service/unit/nima_models_test.py b/tests/extractor_service/unit/nima_models_test.py
deleted file mode 100644
index 0d5a00d..0000000
--- a/tests/extractor_service/unit/nima_models_test.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import logging
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import numpy as np
-import pytest
-
-from extractor_service.app.image_evaluators import _ResNetModel
-
-
-@pytest.fixture(autouse=True)
-def reset_resnet_model():
- _ResNetModel.reset()
- yield
- _ResNetModel.reset()
-
-
-def test_get_prediction_weights():
- result = _ResNetModel.get_prediction_weights()
-
- assert result is _ResNetModel._prediction_weights
-
-
-@patch("extractor_service.app.image_evaluators.tf.keras.applications.InceptionResNetV2")
-@patch("extractor_service.app.image_evaluators.Dropout")
-@patch("extractor_service.app.image_evaluators.Dense")
-@patch("extractor_service.app.image_evaluators.Model")
-def test_create_model(mock_model, mock_dense, mock_dropout, mock_resnet, caplog):
- model_weights_path = Path("/fake/path/to/weights.h5")
- model_inputs = "mock_input"
- model_outputs = "mock_output"
- processed_output = "mock_processed_output"
- final_output = "mock_final_output"
-
- mock_base_model_instance = MagicMock()
- mock_resnet.return_value = mock_base_model_instance
- mock_base_model_instance.output = model_outputs
- mock_base_model_instance.input = model_inputs
- mock_dropout_instance = MagicMock()
- mock_dropout.return_value = mock_dropout_instance
- mock_dropout_instance.return_value = processed_output
- mock_dense_instance = MagicMock()
- mock_dense.return_value = mock_dense_instance
- mock_dense_instance.return_value = final_output
- mock_model_instance = MagicMock()
- mock_model.return_value = mock_model_instance
- mock_model_instance.load_weights = MagicMock()
-
- with caplog.at_level(logging.DEBUG):
- model = _ResNetModel._create_model(model_weights_path)
-
- mock_resnet.assert_called_once_with(input_shape=(224, 224, 3), include_top=False, pooling="avg", weights=None)
- mock_dropout.assert_called_once_with(_ResNetModel._dropout_rate)
- mock_dense.assert_called_once_with(_ResNetModel._num_classes, activation="softmax")
- mock_model.assert_called_once_with(inputs=model_inputs, outputs=final_output)
- mock_model_instance.load_weights.assert_called_once_with(model_weights_path)
- assert "Model loaded successfully." in caplog.text
- assert model == mock_model_instance
-
-
-def test_class_arguments():
- model = _ResNetModel
- assert model._config is None
- assert model._model is None
- assert list(model._prediction_weights) == list(np.arange(1, 11))
- assert model._input_shape == (224, 224, 3)
- assert np.isclose(model._dropout_rate, 0.75, rtol=1e-9)
- assert model._num_classes == 10
-
-
-def test_reset(config):
- model = "some_model"
- _ResNetModel._model = model
- _ResNetModel._config = config
-
- _ResNetModel.reset()
-
- assert _ResNetModel._model is None
- assert _ResNetModel._config is None
-
-
-@pytest.mark.parametrize("had_model", (True, False))
-@patch.object(_ResNetModel, "_get_model_weights")
-@patch.object(_ResNetModel, "_create_model")
-def test_get_model(mock_create, mock_get_weights, had_model, config):
- weights = "some_weights"
- model = "some_model"
- mock_get_weights.return_value = weights
- mock_create.return_value = model
-
- assert _ResNetModel._model is None
-
- if had_model:
- _ResNetModel._model = model
-
- result = _ResNetModel.get_model(config)
-
- if had_model:
- mock_get_weights.assert_not_called()
- mock_create.assert_not_called()
- assert _ResNetModel._config != config
- assert result == model
- else:
- mock_get_weights.assert_called_once()
- mock_create.assert_called_once_with(weights)
- assert _ResNetModel._config == config
- assert _ResNetModel._model == result
- assert result == model
-
-
-@pytest.mark.parametrize("file_exists", (True, False))
-@patch.object(Path, "is_file")
-@patch.object(_ResNetModel, "_download_model_weights")
-def test_get_model_weights(mock_download, mock_is_file, file_exists, caplog):
- mock_is_file.return_value = file_exists
- test_directory = "/fake/directory"
- test_filename = "weights.h5"
- _ResNetModel._config = MagicMock(weights_directory=test_directory, weights_filename=test_filename)
- expected_path = Path(test_directory) / test_filename
-
- with caplog.at_level(logging.DEBUG):
- result = _ResNetModel._get_model_weights()
-
- assert f"Searching for model weights in weights directory: {test_directory}" in caplog.text
- if file_exists:
- assert f"Model weights loaded from: {expected_path}" in caplog.text
- mock_download.assert_not_called()
- else:
- assert f"Can't find model weights in weights directory: {test_directory}" in caplog.text
- mock_download.assert_called_once_with(expected_path)
- assert result == expected_path
-
-
-@pytest.mark.parametrize("status_code", (200, 404))
-@patch.object(Path, "write_bytes")
-@patch("extractor_service.app.image_evaluators.requests.get")
-@patch.object(Path, "mkdir")
-def test_download_model_weights_success(mock_mkdir, mock_get, mock_write_bytes, status_code, caplog):
- test_url = "https://example.com/weights.h5"
- test_path = Path("/fake/path/to/weights.h5")
- _ResNetModel._config = MagicMock(weights_repo_url="https://example.com/", weights_filename="weights.h5")
- weights_data = b"weights data"
- timeout = 12
-
- mock_response = MagicMock()
- mock_response.status_code = status_code
- mock_response.content = weights_data
- mock_get.return_value = mock_response
-
- if status_code == 200:
- with caplog.at_level(logging.DEBUG):
- _ResNetModel._download_model_weights(test_path, timeout)
- mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
- mock_write_bytes.assert_called_once_with(weights_data)
- assert f"Model weights downloaded and saved to {test_path}" in caplog.text
- else:
- error_message = f"Failed to download the weights: HTTP status code {status_code}"
- with (
- caplog.at_level(logging.DEBUG),
- pytest.raises(_ResNetModel.DownloadingModelWeightsError, match=error_message),
- ):
- _ResNetModel._download_model_weights(test_path, timeout)
- assert "Failed to download the weights: HTTP status code 404" in caplog.text
- assert f"Downloading model weights from ulr: {test_url}" in caplog.text
- mock_get.assert_called_once_with(test_url, allow_redirects=True, timeout=timeout)
diff --git a/tests/extractor_service/unit/schemas_test.py b/tests/extractor_service/unit/schemas_test.py
deleted file mode 100644
index 79ed0a1..0000000
--- a/tests/extractor_service/unit/schemas_test.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-from pydantic import ValidationError
-
-from extractor_service.app.schemas import ExtractorConfig, ExtractorStatus, Message
-
-
-def test_config_default():
- with patch.object(Path, "is_dir", return_value=True):
- config = ExtractorConfig()
- assert config.input_directory == Path("/app/input_directory")
- assert config.output_directory == Path("/app/output_directory")
- assert config.video_extensions == (".mp4", ".mov", ".webm", ".mkv", ".avi")
- assert config.images_extensions == (".jpg", ".jpeg", ".png", ".webp")
- assert config.processed_video_prefix == "frames_extracted_"
- assert isinstance(config.compering_group_size, int)
- assert isinstance(config.batch_size, int)
- assert isinstance(config.top_images_percent, float)
- assert config.images_output_format == ".jpg"
- assert config.weights_directory == Path.home() / ".cache" / "huggingface"
- assert config.weights_filename == "weights.h5"
- assert config.weights_repo_url == "https://huggingface.co/BKDDFS/nima_weights/resolve/main/"
- assert config.all_frames is False
-
-
-def test_request_data_validation_failure_output():
- mock_directory = r"C:\invalid_dir"
- with pytest.raises(ValidationError):
- ExtractorConfig(input_directory=mock_directory)
-
-
-def test_str_directory():
- mock_directory = str(Path.cwd())
- config = ExtractorConfig(input_directory=mock_directory)
- assert isinstance(config.input_directory, Path)
-
-
-def test_extractor_status():
- status = ExtractorStatus(active_extractor=None)
- assert status.active_extractor is None
-
- mock_status = "BestFramesExtractor"
- status = ExtractorStatus(active_extractor=mock_status)
- assert status.active_extractor == mock_status
-
-
-def test_message():
- mock_massage = "Test message"
- msg = Message(message=mock_massage)
- assert msg.message == mock_massage
diff --git a/tests/extractor_service/unit/top_images_extractor_test.py b/tests/extractor_service/unit/top_images_extractor_test.py
deleted file mode 100644
index 6798f18..0000000
--- a/tests/extractor_service/unit/top_images_extractor_test.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import logging
-from unittest.mock import MagicMock, call, patch
-
-import numpy as np
-import pytest
-
-from extractor_service.app.extractors import TopImagesExtractor
-from extractor_service.app.image_evaluators import InceptionResNetNIMA
-from extractor_service.app.image_processors import OpenCVImage
-from extractor_service.app.video_processors import OpenCVVideo
-
-
-@pytest.fixture()
-def extractor(config):
- extractor = TopImagesExtractor(config, OpenCVImage, OpenCVVideo, InceptionResNetNIMA)
- return extractor
-
-
-@patch.object(OpenCVImage, "read_image")
-@patch.object(TopImagesExtractor, "_normalize_images")
-def test_process_with_images(mock_normalize, mock_read_image, extractor, caplog, config):
- # Setup
- test_images = [
- "/fake/directory/image1.jpg",
- "/fake/directory/image2.jpg",
- "/fake/directory/image3.jpg",
- ]
- test_ratings = [10, 20, 30]
- best_image = ["image3.jpg"]
-
- # Mock internal methods
- extractor._list_input_directory_files = MagicMock(return_value=test_images)
- extractor._get_image_evaluator = MagicMock()
- extractor._evaluate_images = MagicMock(return_value=test_ratings)
- extractor._get_top_percent_images = MagicMock(return_value=best_image)
- extractor._save_images = MagicMock()
- extractor._signal_readiness_for_shutdown = MagicMock()
-
- # Call
- with caplog.at_level(logging.INFO):
- extractor.process()
-
- # Check that the internal methods were called as expected
- extractor._list_input_directory_files.assert_called_once_with(extractor._config.images_extensions)
- mock_read_image.assert_has_calls([call(path) for path in test_images])
- mock_normalize.assert_called_once_with([mock_read_image.return_value] * 3, extractor._config.target_image_size)
- extractor._evaluate_images.assert_called_once_with(mock_normalize.return_value)
- extractor._get_top_percent_images.assert_called_once_with(
- [mock_read_image.return_value] * 3,
- test_ratings,
- extractor._config.top_images_percent,
- )
- extractor._save_images.assert_called_once_with(best_image)
-
- # Check logging
- expected_massage = (
- f"Extraction process finished. All top images extracted from directory: {config.input_directory}."
- )
- assert expected_massage in caplog.text
- extractor._signal_readiness_for_shutdown.assert_called_once()
-
-
-def test_get_top_percent_images(extractor, caplog):
- images = [MagicMock(spec=np.ndarray) for _ in range(5)]
- ratings = np.array([55, 70, 85, 40, 20])
- top_percent = 70
- expected_images = [images[1], images[2]]
-
- with caplog.at_level(logging.INFO):
- selected_images = extractor._get_top_percent_images(images, ratings, top_percent)
-
- assert selected_images == expected_images, "The selected images do not match the expected top percent images."
- assert f"Top images selected({len(expected_images)})." in caplog.text
diff --git a/tests/extractor_service/unit/video_processors_test.py b/tests/extractor_service/unit/video_processors_test.py
deleted file mode 100644
index 35eba64..0000000
--- a/tests/extractor_service/unit/video_processors_test.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import logging
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import cv2
-import pytest
-
-from extractor_service.app.video_processors import OpenCVVideo
-
-TOTAL_FRAMES_ATTR = "total frames"
-
-
-@patch.object(cv2, "VideoCapture")
-def test_get_video_capture_success(mock_cap):
- test_path = MagicMock(spec=Path)
- mock_video = MagicMock()
- mock_video.isOpened.return_value = True
- mock_cap.return_value = mock_video
-
- with OpenCVVideo._video_capture(test_path) as video:
- assert video.isOpened() is True
-
- mock_video.release.assert_called_once()
-
-
-@patch.object(cv2, "VideoCapture")
-def test_get_video_capture_failure(mock_cap):
- test_path = MagicMock(spec=Path)
- mock_video = MagicMock()
- mock_video.isOpened.return_value = False
- mock_cap.return_value = mock_video
-
- with pytest.raises(OpenCVVideo.CantOpenVideoCapture):
- with OpenCVVideo._video_capture(test_path):
- # No additional operations are needed here, we are just testing the exception
- pass
-
- mock_video.release.assert_called_once()
-
-
-@pytest.fixture
-def mock_video():
- video = MagicMock()
- video.get.return_value = 30
- video.read.side_effect = [
- (True, "frame1"),
- (True, "frame2"),
- (True, "frame3"),
- (False, None),
- ]
- return video
-
-
-@pytest.mark.parametrize(
- "batch_size, expected_num_batches",
- [
- (1, 3),
- (2, 2),
- (3, 1),
- ],
-)
-@patch.object(OpenCVVideo, "_video_capture")
-@patch.object(OpenCVVideo, "_get_video_attribute")
-@patch.object(OpenCVVideo, "_read_next_frame")
-def test_get_next_video_frames(
- mock_read,
- mock_get_attribute,
- mock_video_cap,
- batch_size,
- expected_num_batches,
- caplog,
-):
- frame_rate_attr = "frame rate"
- video_path = MagicMock()
- mock_video = MagicMock()
- frames_number = 3
- mock_get_attribute.side_effect = (
- lambda video, attribute_id, value_name: frames_number if TOTAL_FRAMES_ATTR in value_name else 1
- )
- mock_video_cap.return_value.__enter__.return_value = mock_video
- mock_read.side_effect = lambda video, idx: f"frame{idx // 30}"
-
- with caplog.at_level(logging.DEBUG):
- frames_generator = OpenCVVideo.get_next_frames(video_path, batch_size)
- batches = list(frames_generator)
-
- assert len(batches) == expected_num_batches, "Number of batches does not match expected"
- for batch in batches:
- assert len(batch) <= batch_size, "Batch size is larger than expected"
- assert mock_video_cap.called
- assert mock_get_attribute.call_count == 2
- mock_get_attribute.assert_any_call(mock_video, cv2.CAP_PROP_FPS, frame_rate_attr)
- mock_get_attribute.assert_any_call(mock_video, cv2.CAP_PROP_FRAME_COUNT, TOTAL_FRAMES_ATTR)
- assert mock_read.call_count == 3
-
- assert "Frame appended to frames batch." in caplog.text
- assert "Got full frames batch." in caplog.text
- if batch_size % frames_number and frames_number > expected_num_batches * batch_size:
- assert "Returning last frames batch." in caplog.text
-
-
-@pytest.mark.parametrize("read_return", ((True, "frame"), (False, None)))
-@patch.object(OpenCVVideo, "_check_video_capture")
-def test_read_next_frame(mock_check_cap, read_return, caplog):
- mock_cap = MagicMock(spec=cv2.VideoCapture)
- mock_cap.read = MagicMock(return_value=read_return)
- test_frame_index = 1
- with caplog.at_level(logging.WARNING):
- result = OpenCVVideo._read_next_frame(mock_cap, test_frame_index)
-
- mock_check_cap.assert_called_once_with(mock_cap)
- mock_cap.set.assert_called_once_with(cv2.CAP_PROP_POS_FRAMES, test_frame_index)
- mock_cap.read.assert_called_once()
- if read_return[0] is True:
- assert result == "frame"
- else:
- assert result is None
- assert f"Couldn't read frame with index: {test_frame_index}" in caplog.text
-
-
-@patch.object(OpenCVVideo, "_check_video_capture")
-def test_get_video_attribute(mock_check_cap, caplog):
- mock_cap = MagicMock(spec=cv2.VideoCapture)
- attribute_id = cv2.CAP_PROP_FRAME_COUNT
- value_name = TOTAL_FRAMES_ATTR
- total_frames = 24.6
- mock_cap.get.return_value = total_frames
-
- with caplog.at_level(logging.DEBUG):
- result = OpenCVVideo._get_video_attribute(mock_cap, attribute_id, value_name)
-
- mock_check_cap.assert_called_once_with(mock_cap)
- assert f"Got input video {value_name}: {total_frames}" in caplog.text
- assert result == 25
-
-
-@patch.object(OpenCVVideo, "_check_video_capture")
-def test_get_video_attribute_invalid(mock_check_cap, caplog):
- mock_cap = MagicMock(spec=cv2.VideoCapture)
- attribute_id = cv2.CAP_PROP_FRAME_COUNT
- value_name = TOTAL_FRAMES_ATTR
- total_frames = -24.6
- mock_cap.get.return_value = total_frames
- expected_message = f"Invalid {value_name} retrieved: {total_frames}."
-
- with (
- caplog.at_level(logging.ERROR),
- pytest.raises(ValueError, match=expected_message),
- ):
- OpenCVVideo._get_video_attribute(mock_cap, attribute_id, value_name)
-
- mock_check_cap.assert_called_once_with(mock_cap)
- assert expected_message in caplog.text
-
-
-def test_check_video_capture(caplog):
- mock_cap = MagicMock(spec=cv2.VideoCapture)
- mock_cap.isOpened.return_value = False
- error_message = "Invalid video capture object or object not opened. Probably video capture closed at some point."
-
- with caplog.at_level(logging.ERROR), pytest.raises(ValueError, match=error_message):
- OpenCVVideo._check_video_capture(mock_cap)
-
- assert error_message in caplog.text
diff --git a/extractor_service/app/__init__.py b/tests/integration/__init__.py
similarity index 100%
rename from extractor_service/app/__init__.py
rename to tests/integration/__init__.py
diff --git a/tests/extractor_service/integration/best_frames_extrator_test.py b/tests/integration/best_frames_extrator_test.py
similarity index 65%
rename from tests/extractor_service/integration/best_frames_extrator_test.py
rename to tests/integration/best_frames_extrator_test.py
index 1c78d2f..9d24720 100644
--- a/tests/extractor_service/integration/best_frames_extrator_test.py
+++ b/tests/integration/best_frames_extrator_test.py
@@ -1,7 +1,5 @@
-# import pytest
-
-from extractor_service.app.extractors import BestFramesExtractor
-from extractor_service.app.schemas import ExtractorConfig
+from perfectframe.extractors import BestFramesExtractor
+from perfectframe.schemas import ExtractorConfig
# @pytest.mark.skip(reason="Test time-consuming and dependent on hardware performance")
@@ -18,7 +16,11 @@ def test_best_frames_extractor(setup_best_frames_extractor_env, dependencies):
extractor.process()
found_best_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.name.endswith(".jpg")
+ file
+ for file in output_directory.iterdir()
+ if file.name.startswith("image_") and file.name.endswith(".jpg")
]
- assert len(found_best_frame_files) > 0, "No files meeting the criteria were found in output_directory"
+ assert len(found_best_frame_files) > 0, (
+ "No files meeting the criteria were found in output_directory"
+ )
assert expected_video_path.is_file(), "Video file name was not changed as expected"
diff --git a/tests/extractor_service/integration/conftest.py b/tests/integration/conftest.py
similarity index 55%
rename from tests/extractor_service/integration/conftest.py
rename to tests/integration/conftest.py
index 40ad66f..7c8cb21 100644
--- a/tests/extractor_service/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -1,11 +1,13 @@
import pytest
-from extractor_service.app.extractors import BestFramesExtractor
+from perfectframe.extractors import BestFramesExtractor
from tests.common import (
best_frames_dir,
+ config,
+ dependencies,
+ extractor,
files_dir,
setup_best_frames_extractor_env,
setup_top_images_extractor_env,
top_images_dir,
)
-from tests.extractor_service.common import config, dependencies, extractor
diff --git a/tests/extractor_service/integration/extractor_and_evaluator_integration_test.py b/tests/integration/extractor_and_evaluator_integration_test.py
similarity index 69%
rename from tests/extractor_service/integration/extractor_and_evaluator_integration_test.py
rename to tests/integration/extractor_and_evaluator_integration_test.py
index 3b8b5c1..25a07bd 100644
--- a/tests/extractor_service/integration/extractor_and_evaluator_integration_test.py
+++ b/tests/integration/extractor_and_evaluator_integration_test.py
@@ -1,8 +1,9 @@
import numpy as np
+import onnxruntime as ort
import pytest
-from tensorflow.keras import Model
-from extractor_service.app.image_evaluators import InceptionResNetNIMA
+from perfectframe.image_evaluators import NIMAEvaluator
+from perfectframe.schemas import ImageExtension
@pytest.mark.order(1) # this test must be first because of hugging face limitations
@@ -14,16 +15,16 @@ def test_get_image_evaluator_download_weights_and_create_model(extractor, config
evaluator = extractor._get_image_evaluator()
- assert isinstance(evaluator, InceptionResNetNIMA)
- assert isinstance(evaluator._model, Model)
+ assert isinstance(evaluator, NIMAEvaluator)
+ assert isinstance(evaluator._session, ort.InferenceSession)
assert weights_path.exists()
def test_evaluate_images(extractor, config):
- files = extractor._list_input_directory_files(config.images_extensions)
+ files = extractor._list_input_directory_files(ImageExtension)
images = extractor._read_images(files)
extractor._get_image_evaluator()
- normalized_images = extractor._normalize_images(images, config.target_image_size)
+ normalized_images = extractor._normalize_images(images, config.input_size)
result = extractor._evaluate_images(normalized_images)
assert isinstance(result, np.ndarray)
diff --git a/tests/extractor_service/integration/extractor_and_image_processor_integration_test.py b/tests/integration/extractor_and_image_processor_integration_test.py
similarity index 67%
rename from tests/extractor_service/integration/extractor_and_image_processor_integration_test.py
rename to tests/integration/extractor_and_image_processor_integration_test.py
index f7e3721..7a50761 100644
--- a/tests/extractor_service/integration/extractor_and_image_processor_integration_test.py
+++ b/tests/integration/extractor_and_image_processor_integration_test.py
@@ -2,16 +2,18 @@
import numpy as np
+from perfectframe.schemas import ImageExtension
-def test_list_directory_files(config, extractor):
- files = extractor._list_input_directory_files(config.images_extensions)
+
+def test_list_directory_files(extractor):
+ files = extractor._list_input_directory_files(ImageExtension)
assert isinstance(files, list)
for file in files:
assert isinstance(file, Path)
-def test_read_images(config, extractor):
- files = extractor._list_input_directory_files(config.images_extensions)
+def test_read_images(extractor):
+ files = extractor._list_input_directory_files(ImageExtension)
images = extractor._read_images(files)
assert isinstance(images, list)
for image in images:
@@ -23,7 +25,7 @@ def test_save_images(extractor, config, setup_best_frames_extractor_env):
files = list(config.output_directory.iterdir())
assert not files
- files = extractor._list_input_directory_files(config.images_extensions)
+ files = extractor._list_input_directory_files(ImageExtension)
images = extractor._read_images(files)
extractor._save_images(images)
diff --git a/tests/extractor_service/integration/extractor_and_video_processor_integration_test.py b/tests/integration/extractor_and_video_processor_integration_test.py
similarity index 62%
rename from tests/extractor_service/integration/extractor_and_video_processor_integration_test.py
rename to tests/integration/extractor_and_video_processor_integration_test.py
index 9cd33e2..7c5185e 100644
--- a/tests/extractor_service/integration/extractor_and_video_processor_integration_test.py
+++ b/tests/integration/extractor_and_video_processor_integration_test.py
@@ -1,8 +1,15 @@
-def test_extract_best_frames(extractor, config, setup_best_frames_extractor_env):
+from perfectframe.schemas import VideoExtension
+
+
+def test_extract_best_frames(extractor, setup_best_frames_extractor_env):
input_dir, output_dir, _ = setup_best_frames_extractor_env
entries = list(input_dir.iterdir())
assert len(entries) > 0, "None entries in files_dir found"
- videos = [entry for entry in entries if entry.is_file() and entry.suffix in config.video_extensions]
+ videos = [
+ entry
+ for entry in entries
+ if entry.is_file() and entry.suffix in [e.value for e in VideoExtension]
+ ]
assert len(list(videos)) > 0, "None videos in files_dir found"
assert not any(output_dir.iterdir()), "Output dir has entries before test"
diff --git a/tests/integration/manager_and_fastapi_integration_test.py b/tests/integration/manager_and_fastapi_integration_test.py
new file mode 100644
index 0000000..8a95fd4
--- /dev/null
+++ b/tests/integration/manager_and_fastapi_integration_test.py
@@ -0,0 +1,19 @@
+from fastapi import BackgroundTasks
+from starlette.testclient import TestClient
+
+from perfectframe.app import app
+from perfectframe.extractor_manager import ExtractorManager
+from perfectframe.schemas import ExtractorName, Message
+
+client = TestClient(app)
+
+
+def test_extractor_start_and_stop(dependencies):
+ extractor_name = ExtractorName.BEST_FRAMES
+ background_tasks = BackgroundTasks()
+
+ response = ExtractorManager.start_extractor(extractor_name, background_tasks, dependencies)
+
+ assert response == Message(message=f"'{extractor_name.value}' started.")
+ assert ExtractorManager.get_active_extractor() == extractor_name
+ ExtractorManager._active_extractor = None
diff --git a/tests/extractor_service/integration/top_images_extractor_test.py b/tests/integration/top_images_extractor_test.py
similarity index 60%
rename from tests/extractor_service/integration/top_images_extractor_test.py
rename to tests/integration/top_images_extractor_test.py
index aaed2f5..02a8a95 100644
--- a/tests/extractor_service/integration/top_images_extractor_test.py
+++ b/tests/integration/top_images_extractor_test.py
@@ -1,5 +1,5 @@
-from extractor_service.app.extractors import TopImagesExtractor
-from extractor_service.app.schemas import ExtractorConfig
+from perfectframe.extractors import TopImagesExtractor
+from perfectframe.schemas import ExtractorConfig
# @pytest.mark.skip(reason="Test time-consuming and dependent on hardware performance")
@@ -16,6 +16,10 @@ def test_top_frames_extractor(setup_top_images_extractor_env, dependencies):
selector.process()
found_top_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.name.endswith(".jpg")
+ file
+ for file in output_directory.iterdir()
+ if file.name.startswith("image_") and file.name.endswith(".jpg")
]
- assert len(found_top_frame_files) > 0, "No files meeting the criteria were found in output_directory"
+ assert len(found_top_frame_files) > 0, (
+ "No files meeting the criteria were found in output_directory"
+ )
diff --git a/tests/service_manager/__init__.py b/tests/service_manager/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/service_manager/e2e/__init__.py b/tests/service_manager/e2e/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/service_manager/e2e/best_frames_extractor_test.py b/tests/service_manager/e2e/best_frames_extractor_test.py
deleted file mode 100644
index ff96bb3..0000000
--- a/tests/service_manager/e2e/best_frames_extractor_test.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os
-import subprocess
-import sys
-
-import pytest
-
-
-@pytest.mark.skipif("CI" in os.environ, reason="Test skipped in GitHub Actions.")
-def test_best_frames_extractor(setup_best_frames_extractor_env, start_script_path):
- input_directory, output_directory, expected_video_path = setup_best_frames_extractor_env
- command = [
- sys.executable,
- str(start_script_path),
- "best_frames_extractor",
- "--input_dir",
- str(input_directory),
- "--output_dir",
- str(output_directory),
- "--build",
- "--cpu",
- ]
-
- subprocess.run(command)
-
- found_best_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.suffix == ".jpg"
- ]
- assert len(found_best_frame_files) > 0, "No files meeting the criteria were found in output_directory"
- assert expected_video_path.is_file(), "Video file name was not changed as expected"
diff --git a/tests/service_manager/e2e/conftest.py b/tests/service_manager/e2e/conftest.py
deleted file mode 100644
index 4380589..0000000
--- a/tests/service_manager/e2e/conftest.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from pathlib import Path
-
-import pytest
-
-from tests.common import (
- best_frames_dir,
- files_dir,
- setup_best_frames_extractor_env,
- setup_top_images_extractor_env,
- top_images_dir,
-)
-
-
-@pytest.fixture(scope="module")
-def start_script_path():
- base_path = Path(__file__).parent.parent.parent.parent
- print(base_path)
- start_script_path = base_path / "start.py"
- return start_script_path
diff --git a/tests/service_manager/e2e/top_images_extractor_test.py b/tests/service_manager/e2e/top_images_extractor_test.py
deleted file mode 100644
index 70aee31..0000000
--- a/tests/service_manager/e2e/top_images_extractor_test.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import subprocess
-import sys
-
-import pytest
-
-
-@pytest.mark.skipif("CI" in os.environ, reason="Test skipped in GitHub Actions.")
-def test_top_images_extractor(setup_top_images_extractor_env, start_script_path):
- input_directory, output_directory = setup_top_images_extractor_env
- command = [
- sys.executable,
- str(start_script_path),
- "top_images_extractor",
- "--input_dir",
- input_directory,
- "--output_dir",
- output_directory,
- "--build",
- "--cpu",
- ]
-
- subprocess.run(command)
-
- found_top_frame_files = [
- file for file in output_directory.iterdir() if file.name.startswith("image_") and file.name.endswith(".jpg")
- ]
- assert len(found_top_frame_files) > 0, "No files meeting the criteria were found in output_directory"
diff --git a/tests/service_manager/integration/__init__.py b/tests/service_manager/integration/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/service_manager/integration/conftest.py b/tests/service_manager/integration/conftest.py
deleted file mode 100644
index 787f71c..0000000
--- a/tests/service_manager/integration/conftest.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import docker
-import pytest
-
-from config import Config
-from service_manager.docker_manager import DockerManager
-
-
-@pytest.fixture(scope="package")
-def config():
- config = Config()
- return config
-
-
-@pytest.fixture(scope="module")
-def manager(config):
- manager = DockerManager(
- config.service_name,
- config.input_directory,
- config.output_directory,
- config.port,
- False,
- True,
- )
- return manager
-
-
-@pytest.fixture
-def client():
- client = docker.from_env()
- return client
diff --git a/tests/service_manager/integration/docker_container_test.py b/tests/service_manager/integration/docker_container_test.py
deleted file mode 100644
index 248dd02..0000000
--- a/tests/service_manager/integration/docker_container_test.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import docker
-import pytest
-
-COMMAND = "sleep 300"
-
-
-@pytest.fixture
-def image(client, manager, config):
- image_name = "image_name"
- image = client.images.pull("busybox")
- image.tag(image_name)
- manager._image_name = image_name
- yield image
- client.images.remove(image_name, force=True)
-
-
-@pytest.fixture
-def cleanup_container(client, manager, config):
- try:
- container = client.containers.get(manager._container_name)
- container.remove(force=True)
- except docker.errors.NotFound:
- pass
- yield
- try:
- container = client.containers.get(manager._container_name)
- container.remove(force=True)
- except docker.errors.NotFound:
- pass
-
-
-def test_run_container(manager, config, client, cleanup_container, image):
- manager._run_container(config.port, config.volume_input_directory, config.volume_output_directory)
-
- container = client.containers.get(manager._container_name)
- container.reload()
- assert container.status == "running" or container.status == "restarting"
- assert container.attrs["Config"]["Image"] == manager.image_name
- port_binding = container.attrs["HostConfig"]["PortBindings"]
- assert "8100/tcp" in port_binding
- assert port_binding["8100/tcp"][0]["HostPort"] == str(config.port)
- assert f"{manager._input_directory}:{config.volume_input_directory}" in container.attrs["HostConfig"]["Binds"]
- assert f"{manager._output_directory}:{config.volume_output_directory}" in container.attrs["HostConfig"]["Binds"]
-
-
-def test_start_container(manager, cleanup_container, client, image):
- container = client.containers.create(image, command=COMMAND, detach=True, name=manager._container_name)
- assert container.status == "created"
- manager._start_container()
- container.reload()
- assert container.status == "running"
-
-
-def test_stop_container(manager, cleanup_container, client, image):
- container = client.containers.create(image, command=COMMAND, detach=True, name=manager._container_name)
- assert container.status == "created"
- container.start()
- container.reload()
- assert container.status == "running"
- manager._stop_container()
- container.reload()
- assert container.status == "exited"
-
-
-def test_delete_container(manager, cleanup_container, client, image):
- container = client.containers.create(image, command=COMMAND, detach=True, name=manager._container_name)
- assert container.status == "created"
- manager._delete_container()
- with pytest.raises(docker.errors.NotFound):
- client.containers.get(manager._container_name)
-
-
-def test_container_status(manager, cleanup_container, client, image):
- container = client.containers.create(image, command=COMMAND, detach=True, name=manager._container_name)
- assert container.status == "created"
- assert manager.container_status == "created"
- container.start()
- container.reload()
- assert container.status == "running"
- assert manager.container_status == "running"
-
-
-def test_run_log_process(manager, cleanup_container, client, image):
- client.containers.run(
- image,
- command="sh -c 'while true; do date; done'",
- detach=True,
- name=manager._container_name,
- )
- log_process = manager._run_log_process()
- assert log_process.poll() is None, "Log process should be running"
- output = []
- try:
- for _ in range(5):
- line = log_process.stdout.readline()
- output.append(line)
- assert line, "Log line should not be empty"
- finally:
- log_process.terminate()
- log_process.wait()
- assert len(output) >= 5, "Should have read at least 5 lines of logs"
diff --git a/tests/service_manager/integration/docker_image_test.py b/tests/service_manager/integration/docker_image_test.py
deleted file mode 100644
index 7d387eb..0000000
--- a/tests/service_manager/integration/docker_image_test.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import docker
-import pytest
-
-from config import Config
-
-
-@pytest.fixture
-def cleanup_docker_image(manager, client):
- image_name = manager.image_name
- try:
- client.images.remove(image_name, force=True)
- except docker.errors.ImageNotFound:
- pass
-
- yield
-
- try:
- client.images.remove(image_name, force=True)
- except docker.errors.ImageNotFound:
- pass
-
-
-def test_build_image_and_docker_image_existence(cleanup_docker_image, manager, client):
- manager.build_image(Config.dockerfile)
-
- try:
- client.images.get(manager.image_name)
- except docker.errors.ImageNotFound:
- pytest.fail("Image was not built.")
-
- result = manager.docker_image_existence
- assert result is True
-
-
-def test_docker_image_existence(cleanup_docker_image, manager):
- result = manager.docker_image_existence
- assert result is False
diff --git a/tests/service_manager/integration/service_initializer_test.py b/tests/service_manager/integration/service_initializer_test.py
deleted file mode 100644
index e9ca8f6..0000000
--- a/tests/service_manager/integration/service_initializer_test.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import logging
-from pathlib import Path
-
-import pytest
-
-from service_manager.service_initializer import ServiceInitializer
-
-
-def test_directory_check_valid(tmp_path):
- assert ServiceInitializer._check_directory(str(tmp_path)) == tmp_path
-
-
-def test_check_invalid_directory(caplog):
- invalid_directory = Path("/invalid/input")
- error_massage = f"Invalid directory path: {str(invalid_directory)}"
-
- with pytest.raises(NotADirectoryError), caplog.at_level(logging.ERROR):
- ServiceInitializer._check_directory(str(invalid_directory))
-
- assert error_massage in caplog.text, "Invalid logging."
diff --git a/tests/service_manager/unit/__init__.py b/tests/service_manager/unit/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/service_manager/unit/conftest.py b/tests/service_manager/unit/conftest.py
deleted file mode 100644
index fd0aa1b..0000000
--- a/tests/service_manager/unit/conftest.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import pytest
-
-from config import Config
-
-
-@pytest.fixture(scope="package")
-def config():
- return Config()
diff --git a/tests/service_manager/unit/docker_manager_test.py b/tests/service_manager/unit/docker_manager_test.py
deleted file mode 100644
index 27fe9a7..0000000
--- a/tests/service_manager/unit/docker_manager_test.py
+++ /dev/null
@@ -1,327 +0,0 @@
-import logging
-import subprocess
-from unittest.mock import MagicMock, PropertyMock, call, patch
-
-import pytest
-
-from service_manager.docker_manager import DockerManager
-
-LOG_LINE_1 = "log line 1\n"
-LOG_LINE_2 = "log line 2\n"
-
-
-def test_docker_manager_init(caplog, config):
- image_name = f"{config.service_name}_image"
- expected_logs = (
- f"container_name: {config.service_name}",
- f"image_name: {image_name}",
- f"Input directory from user: {config.input_directory}",
- f"Output directory from user: {config.output_directory}",
- f"Port from user: {config.port}",
- "Force build: False",
- "CPU only: False",
- )
-
- with caplog.at_level(logging.DEBUG):
- docker = DockerManager(
- config.service_name,
- config.input_directory,
- config.output_directory,
- config.port,
- False,
- False,
- )
-
- assert docker._container_name == config.service_name
- assert docker._image_name == image_name
- assert docker._input_directory == config.input_directory
- assert docker._output_directory == config.output_directory
- assert docker._port == config.port
- assert docker._force_build is False
- assert docker._cpu_only is False
- for message in expected_logs:
- assert message in caplog.text, f"Expected phrase not found in logs: {message}"
-
-
-@pytest.fixture(scope="function")
-def docker(config):
- docker = DockerManager(
- config.service_name,
- config.input_directory,
- config.output_directory,
- config.port,
- False,
- False,
- )
- return docker
-
-
-@pytest.fixture(name="mock_run")
-def mock_subprocess_run():
- with patch("service_manager.docker_manager.subprocess.run") as mock_run:
- yield mock_run
-
-
-@pytest.mark.parametrize("mock_image, is_exists", (("some_image", True), ("", False)))
-def test_check_image_exists(mock_image, is_exists, docker, mock_run):
- expected_command = ["docker", "images", "-q", docker._image_name]
-
- mock_run.return_value = MagicMock(stdout=mock_image)
- assert docker.docker_image_existence is is_exists
- mock_run.assert_called_with(expected_command, capture_output=True, text=True, check=True)
-
-
-@patch.object(DockerManager, "_check_image_exists")
-def test_build_image(mock_check_image_exists, docker, mock_run, caplog, config):
- mock_check_image_exists.return_value = False
- expected_command = ["docker", "build", "-t", docker._image_name, config.dockerfile]
-
- docker.build_image(config.dockerfile)
-
- mock_run.assert_called_once_with(expected_command, check=True)
-
-
-@patch.object(DockerManager, "_check_image_exists")
-def test_build_image_when_image_exists_and_not_force_build(mock_check_image_exists, docker, mock_run, caplog, config):
- mock_check_image_exists.return_value = True
-
- with caplog.at_level(logging.INFO):
- docker.build_image(config.dockerfile)
-
- mock_run.assert_not_called()
- assert "Image is already created. Using existing one." in caplog.text
-
-
-@patch.object(DockerManager, "_check_image_exists")
-def test_build_image_when_image_exists_and_force_build(mock_check_image_exists, docker, mock_run, caplog, config):
- mock_check_image_exists.return_value = True
- docker._force_build = True
-
- with caplog.at_level(logging.INFO):
- docker.build_image(config.dockerfile)
-
- mock_run.assert_called()
- assert "Image is already created. Using existing one." not in caplog.text
- assert "Building Docker image..." in caplog.text
-
-
-@pytest.mark.parametrize("code, output, status", ((1, "", None), (0, "'running'", "running")))
-def test_container_status(code, output, status, docker, mock_run):
- command_output = MagicMock()
- command_output.returncode = code
- command_output.stdout = output
- mock_run.return_value = command_output
- expected_command = [
- "docker",
- "inspect",
- "--format='{{.State.Status}}'",
- docker._container_name,
- ]
-
- result_status = docker.container_status
-
- mock_run.assert_called_once_with(expected_command, capture_output=True, text=True, check=False)
- assert status == result_status
-
-
-@pytest.mark.parametrize("build", (True, False))
-@pytest.mark.parametrize("status", ("exited", None, "running", "dead", "created"))
-@patch.object(DockerManager, "_stop_container")
-@patch.object(DockerManager, "_delete_container")
-@patch.object(DockerManager, "_run_container")
-@patch.object(DockerManager, "_start_container")
-@patch.object(DockerManager, "container_status", new_callable=PropertyMock)
-def test_deploy_container(
- mock_status,
- mock_start,
- mock_run,
- mock_delete,
- mock_stop,
- status,
- build,
- docker,
- caplog,
- config,
-):
- container_input_directory = "/container_input_directory/"
- container_output_directory = "/container_output_directory/"
- mock_status.return_value = status
- deploy_container_args = (
- config.port,
- container_input_directory,
- container_output_directory,
- )
- docker._force_build = build
-
- with caplog.at_level(logging.INFO):
- docker.deploy_container(*deploy_container_args)
-
- if status is None:
- assert "No existing container found. Running a new container." in caplog.text
- mock_start.assert_not_called()
- mock_stop.assert_not_called()
- mock_delete.assert_not_called()
- mock_run.assert_called_once_with(*deploy_container_args)
- elif build:
- assert "Force rebuild initiated." in caplog.text
- if status in ["running", "paused"]:
- mock_stop.assert_called_once()
- else:
- mock_stop.assert_not_called()
- mock_delete.assert_called_once()
- mock_run.assert_called_once_with(*deploy_container_args)
- elif status in ["exited", "created"]:
- mock_start.assert_called_once()
- mock_run.assert_not_called()
- elif status == "running":
- assert "Container is already running." in caplog.text
- mock_start.assert_not_called()
- mock_run.assert_not_called()
- else:
- assert "Container in unsupported status: dead. Fix container on your own." in caplog.text
- mock_start.assert_not_called()
- mock_run.assert_not_called()
-
-
-def test_start_container_success(docker, mock_run, caplog):
- mock_subprocess_run.return_value = MagicMock()
- expected_command = ["docker", "start", docker._container_name]
- with caplog.at_level(logging.INFO):
- docker._start_container()
-
- mock_run.assert_called_once_with(expected_command, check=True)
- assert "Starting the existing container..." in caplog.text
-
-
-@pytest.mark.parametrize("cpu", (True, False))
-def test_run_container(docker, mock_run, config, caplog, cpu):
- expected_command = [
- "docker",
- "run",
- "--name",
- docker._container_name,
- "--restart",
- "unless-stopped",
- "-d",
- "-p",
- f"{docker._port}:{config.port}",
- "-v",
- f"{docker._input_directory}:{config.input_directory}",
- "-v",
- f"{docker._output_directory}:{config.input_directory}",
- ]
- if not cpu:
- expected_command.extend(["--gpus", "all"])
- expected_command.append(docker._image_name)
- try:
- if cpu:
- docker._cpu_only = True
- with caplog.at_level(logging.INFO):
- docker._run_container(config.port, config.input_directory, config.input_directory)
- finally:
- docker._cpu_only = False
-
- mock_run.assert_called_once_with(expected_command, check=True)
- assert "Running a new container..." in caplog.text
-
-
-@patch.object(subprocess, "Popen", autospec=True)
-def test_run_log_process(mock_popen, docker, caplog):
- command = ["docker", "logs", "-f", "--since", "1s", docker._container_name]
-
- with caplog.at_level(logging.INFO):
- result = docker._run_log_process()
-
- mock_popen.assert_called_once_with(
- command,
- stdout=subprocess.PIPE,
- stderr=subprocess.STDOUT,
- text=True,
- encoding="utf-8",
- )
- assert result
- assert f"Following logs for {docker._container_name}" in caplog.text
-
-
-def test_stop_container_success(docker, mock_run, caplog):
- expected_command = ["docker", "stop", docker._container_name]
-
- with caplog.at_level(logging.INFO):
- docker._stop_container()
-
- mock_run.assert_called_once_with(expected_command, check=True, capture_output=True)
- assert f"Stopping container {docker._container_name}..." in caplog.text
- assert "Container stopped." in caplog.text
-
-
-def test_delete_container_success(docker, mock_run, caplog):
- expected_command = ["docker", "rm", docker._container_name]
-
- with caplog.at_level(logging.INFO):
- docker._delete_container()
-
- mock_run.assert_called_once_with(expected_command, check=True, capture_output=True)
- assert f"Deleting container {docker._container_name}..." in caplog.text
- assert "Container deleted." in caplog.text
-
-
-@patch("service_manager.docker_manager.sys.stdout.write")
-@patch.object(DockerManager, "_run_log_process")
-@patch.object(DockerManager, "_stop_container")
-def test_follow_container_logs_stopped_by_user(mock_stop, mock_run_log, mock_stdout, docker, caplog):
- mock_process = MagicMock()
- mock_process.stdout.readline.side_effect = [
- LOG_LINE_1,
- LOG_LINE_2,
- KeyboardInterrupt(),
- ]
- mock_run_log.return_value = mock_process
- mock_process.terminate = MagicMock()
- mock_process.wait = MagicMock()
-
- with (
- caplog.at_level(logging.INFO),
- patch.object(subprocess, "Popen", autospec=True),
- ):
- docker.follow_container_logs()
-
- mock_run_log.assert_called_once()
- mock_process.terminate.assert_called_once()
- mock_process.wait.assert_called_once()
- mock_stop.assert_called_once()
-
- calls = [call(LOG_LINE_1), call(LOG_LINE_2)]
- mock_stdout.assert_has_calls(calls, any_order=True)
- assert "Process stopped by user." in caplog.text
- assert "Following container logs stopped." in caplog.text
-
-
-@patch("service_manager.docker_manager.sys.stdout.write")
-@patch.object(DockerManager, "_run_log_process")
-@patch.object(DockerManager, "_stop_container")
-def test_follow_container_logs_stopped_automatically(mock_stop, mock_run_log, mock_stdout, docker, caplog):
- mock_process = MagicMock()
- mock_process.stdout.readline.side_effect = [
- LOG_LINE_1,
- LOG_LINE_2,
- DockerManager.ServiceShutdownSignal(),
- ]
- mock_run_log.return_value = mock_process
- mock_process.terminate = MagicMock()
- mock_process.wait = MagicMock()
-
- with (
- caplog.at_level(logging.INFO),
- patch.object(subprocess, "Popen", autospec=True),
- ):
- docker.follow_container_logs()
-
- mock_run_log.assert_called_once()
- mock_process.terminate.assert_called_once()
- mock_process.wait.assert_called_once()
- mock_stop.assert_called_once()
-
- calls = [call(LOG_LINE_1), call(LOG_LINE_2)]
- mock_stdout.assert_has_calls(calls, any_order=True)
- assert "Service has signaled readiness for shutdown." in caplog.text
- assert "Following container logs stopped." in caplog.text
diff --git a/tests/service_manager/unit/service_initializer_test.py b/tests/service_manager/unit/service_initializer_test.py
deleted file mode 100644
index 0a45d7b..0000000
--- a/tests/service_manager/unit/service_initializer_test.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import argparse
-import json
-import logging
-import time
-import urllib.request
-from http.client import RemoteDisconnected
-from pathlib import Path
-from unittest import mock
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from service_manager import service_initializer
-from service_manager.service_initializer import ServiceInitializer
-
-ALL_FRAMES = False
-
-
-@pytest.fixture
-def service(config):
- user_input = MagicMock(
- spec=argparse.Namespace,
- extractor_name=config.service_name,
- input_dir=config.input_directory,
- output_dir=config.output_directory,
- port=config.port,
- all_frames=ALL_FRAMES,
- )
- with patch.object(ServiceInitializer, "_check_directory"):
- initializer = ServiceInitializer(user_input)
- return initializer
-
-
-@pytest.mark.parametrize(
- "arg_set",
- (
- {
- "extractor_name": "best_frames_extractor",
- "input": "/valid/input",
- "output": "/valid/output",
- "port": 8000,
- },
- {
- "extractor_name": "top_images_extractor",
- "input": "/another/input",
- "output": "/another/output",
- "port": 9000,
- },
- ),
-)
-@patch.object(ServiceInitializer, "_check_directory")
-def test_start_various_args(mock_check_directory, arg_set):
- user_input = MagicMock(
- spec=argparse.Namespace,
- extractor_name=arg_set["extractor_name"],
- input_dir=arg_set["input"],
- output_dir=arg_set["output"],
- port=arg_set["port"],
- all_frames=ALL_FRAMES,
- )
- mock_check_directory.side_effect = lambda x: x
-
- service = ServiceInitializer(user_input)
-
- assert service._extractor_name == arg_set["extractor_name"]
- assert service._input_directory == arg_set["input"]
- assert service._output_directory == arg_set["output"]
- assert service._port == arg_set["port"]
- assert service._all_frames == ALL_FRAMES
- mock_check_directory.assert_any_call(arg_set["input"])
- mock_check_directory.assert_any_call(arg_set["output"])
-
-
-def test_check_valid_directory():
- valid_directory = "/valid/input"
-
- with patch("pathlib.Path.is_dir"):
- result = ServiceInitializer._check_directory(valid_directory)
-
- assert result
- assert isinstance(result, Path)
-
-
-@patch.object(time, "time")
-def test_run_extractor_post_request(mock_time, service):
- test_url = f"http://localhost:{service._port}/v2/extractors/{service._extractor_name}"
- test_method = "POST"
- start_time = 100
- mock_time.side_effect = [start_time, start_time + 1, start_time + 2, start_time + 3]
- mock_try = MagicMock(side_effect=[False, False, True])
- service._try_to_run_extractor = mock_try
-
- service.run_extractor()
-
- assert mock_try.call_count == 3
- mock_try.assert_any_call(mock.ANY, start_time)
- last_call = mock_try.call_args
- request_obj = last_call[0][0]
- assert request_obj.method == test_method
- assert request_obj.full_url == test_url
- request_data = json.loads(request_obj.data.decode("utf-8"))
- assert request_data["all_frames"] is False
-
-
-@pytest.fixture
-def mock_urlopen():
- with patch.object(service_initializer, "urlopen") as mock_urlopen:
- yield mock_urlopen
-
-
-@pytest.fixture(scope="module")
-def mock_request():
- return MagicMock(spec=urllib.request.Request)
-
-
-def test_try_to_run_extractor_success(mock_urlopen, service, caplog, mock_request):
- mock_response = MagicMock()
- mock_response.status = 200
- mock_message = "Success"
- response_content = json.dumps({"message": mock_message}).encode("utf-8")
- mock_response.read.return_value = response_content
- mock_urlopen.return_value.__enter__.return_value = mock_response
-
- with caplog.at_level(logging.INFO):
- result = service._try_to_run_extractor(mock_request, time.time())
-
- mock_urlopen.assert_called_once_with(mock_request)
- assert result is True
- mock_response.read.assert_called_once()
- assert f"Response from server: {mock_message}" in caplog.text
-
-
-@patch.object(time, "sleep")
-def test_try_to_run_extractor_remote_disconnected(mock_sleep, mock_urlopen, service, caplog, mock_request):
- mock_urlopen.side_effect = RemoteDisconnected
- with caplog.at_level(logging.INFO):
- result = service._try_to_run_extractor(mock_request, time.time())
-
- mock_sleep.assert_called_with(3)
- mock_urlopen.assert_called_once()
- assert result is False
- assert "Waiting for service to be available..." in caplog.text
-
-
-@patch.object(time, "time", return_value=3)
-def test_try_to_run_extractor_timeout(mock_time, mock_urlopen, service, caplog, mock_request):
- mock_urlopen.side_effect = RemoteDisconnected
- error_massage = "Timed out waiting for service to respond."
- start_time = 1
- with (
- caplog.at_level(logging.ERROR),
- pytest.raises(TimeoutError, match=error_massage),
- ):
- service._try_to_run_extractor(mock_request, start_time, 1)
-
- mock_urlopen.assert_called_once()
- mock_time.assert_any_call()
- assert error_massage in caplog.text
diff --git a/tests/test_files/frames_extracted_test_video.mp4 b/tests/test_files/test_video.mp4
similarity index 100%
rename from tests/test_files/frames_extracted_test_video.mp4
rename to tests/test_files/test_video.mp4
diff --git a/service_manager/__init__.py b/tests/unit/__init__.py
similarity index 100%
rename from service_manager/__init__.py
rename to tests/unit/__init__.py
diff --git a/tests/unit/app_test.py b/tests/unit/app_test.py
new file mode 100644
index 0000000..4be5edb
--- /dev/null
+++ b/tests/unit/app_test.py
@@ -0,0 +1,40 @@
+from fastapi import BackgroundTasks
+
+from perfectframe.app import get_extractors_status, health_check, run_extractor
+from perfectframe.extractor_manager import ExtractorManager
+from perfectframe.schemas import ExtractorName, Message
+
+
+def test_health_check():
+ result = health_check()
+
+ assert result == {"status": "healthy"}
+
+
+def test_get_extractors_status(mocker):
+ mocker.patch.object(
+ ExtractorManager, "get_active_extractor", return_value=ExtractorName.BEST_FRAMES
+ )
+
+ result = get_extractors_status()
+
+ assert result.active_extractor == ExtractorName.BEST_FRAMES
+
+
+def test_run_extractor(mocker, dependencies):
+ expected_message = Message(message="'best_frames_extractor' started.")
+ mock_start = mocker.patch.object(
+ ExtractorManager, "start_extractor", return_value=expected_message
+ )
+ mock_background_tasks = mocker.MagicMock(spec=BackgroundTasks)
+
+ result = run_extractor(
+ extractor_name=ExtractorName.BEST_FRAMES,
+ background_tasks=mock_background_tasks,
+ dependencies=dependencies,
+ )
+
+ mock_start.assert_called_once_with(
+ ExtractorName.BEST_FRAMES, mock_background_tasks, dependencies
+ )
+ assert result == expected_message
diff --git a/tests/unit/best_frames_extractor_test.py b/tests/unit/best_frames_extractor_test.py
new file mode 100644
index 0000000..e156544
--- /dev/null
+++ b/tests/unit/best_frames_extractor_test.py
@@ -0,0 +1,143 @@
+import logging
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from perfectframe.extractors import BestFramesExtractor
+from perfectframe.image_evaluators import NIMAEvaluator
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import VideoExtension
+from perfectframe.video_processors import OpenCVVideo
+
+
+@pytest.fixture
+def all_frames_extractor(extractor):
+ extractor._config.all_frames = True
+ yield extractor
+ extractor._config.all_frames = False
+
+
+@pytest.fixture
+def extractor(config):
+ return BestFramesExtractor(config, OpenCVImage, OpenCVVideo, NIMAEvaluator)
+
+
+def test_process(mocker, extractor, caplog, config):
+ test_videos = ["/fake/directory/video1.mp4", "/fake/directory/video2.mp4"]
+ test_frames = ["frame1", "frame2"]
+ extractor._list_input_directory_files = mocker.MagicMock(return_value=test_videos)
+ extractor._get_image_evaluator = mocker.MagicMock()
+ extractor._extract_best_frames = mocker.MagicMock(return_value=test_frames)
+ extractor._add_prefix = mocker.MagicMock()
+ extractor._signal_readiness_for_shutdown = mocker.MagicMock()
+
+ with caplog.at_level(logging.INFO):
+ extractor.process()
+
+ extractor._list_input_directory_files.assert_called_once_with(
+ VideoExtension, config.processed_video_prefix
+ )
+ extractor._get_image_evaluator.assert_called_once()
+ assert extractor._extract_best_frames.call_count == len(test_videos)
+ assert extractor._add_prefix.call_count == len(test_videos)
+ extractor._signal_readiness_for_shutdown.assert_called_once()
+ for video in test_videos:
+ extractor._add_prefix.assert_any_call(config.processed_video_prefix, video)
+ extractor._extract_best_frames.assert_any_call(video)
+ assert f"Frames extraction has finished for video: {video}" in caplog.text
+ assert f"Starting frames extraction process from '{config.input_directory}'." in caplog.text
+
+
+def test_process_if_all_frames(mocker, all_frames_extractor, caplog, config):
+ test_videos = ["/fake/directory/video1.mp4", "/fake/directory/video2.mp4"]
+ test_frames = ["frame1", "frame2"]
+ all_frames_extractor._list_input_directory_files = mocker.MagicMock(return_value=test_videos)
+ all_frames_extractor._get_image_evaluator = mocker.MagicMock()
+ all_frames_extractor._extract_best_frames = mocker.MagicMock(return_value=test_frames)
+ all_frames_extractor._add_prefix = mocker.MagicMock()
+ all_frames_extractor._signal_readiness_for_shutdown = mocker.MagicMock()
+
+ with caplog.at_level(logging.INFO):
+ all_frames_extractor.process()
+
+ all_frames_extractor._list_input_directory_files.assert_called_once_with(
+ VideoExtension, config.processed_video_prefix
+ )
+ all_frames_extractor._get_image_evaluator.assert_not_called()
+ assert not all_frames_extractor._image_evaluator
+ assert all_frames_extractor._extract_best_frames.call_count == len(test_videos)
+ assert all_frames_extractor._add_prefix.call_count == len(test_videos)
+ all_frames_extractor._signal_readiness_for_shutdown.assert_called_once()
+ for video in test_videos:
+ all_frames_extractor._add_prefix.assert_any_call(config.processed_video_prefix, video)
+ all_frames_extractor._extract_best_frames.assert_any_call(video)
+ assert f"Frames extraction has finished for video: {video}" in caplog.text
+ assert f"Starting frames extraction process from '{config.input_directory}'." in caplog.text
+
+
+def test_extract_best_frames(mocker, extractor):
+ mock_generator = mocker.patch.object(OpenCVVideo, "get_next_frames")
+ mock_save = mocker.patch.object(BestFramesExtractor, "_save_images")
+ mock_get = mocker.patch.object(BestFramesExtractor, "_get_best_frames")
+ mock_collect = mocker.patch("perfectframe.extractors.gc.collect")
+ video_path = mocker.MagicMock(spec=Path)
+
+ batch_1 = [f"frame{i}" for i in range(5)]
+ batch_2 = []
+ batch_3 = [f"frame{i}" for i in range(5)]
+ mock_generator.return_value = iter([batch_1, batch_2, batch_3])
+
+ mock_get.side_effect = [batch_1, batch_3]
+
+ extractor._extract_best_frames(video_path)
+
+ expected_call_count = 2
+ assert not extractor._config.all_frames
+ mock_generator.assert_called_once_with(video_path, extractor._config.batch_size)
+ assert mock_get.call_count == expected_call_count
+ for batch in [batch_1, batch_3]:
+ mock_save.assert_called_with(batch)
+ assert mock_collect.call_count == expected_call_count
+
+
+def test_extract_all_frames(mocker, all_frames_extractor):
+ mock_generator = mocker.patch.object(OpenCVVideo, "get_next_frames")
+ mock_save = mocker.patch.object(BestFramesExtractor, "_save_images")
+ mock_get = mocker.patch.object(BestFramesExtractor, "_get_best_frames")
+ mock_collect = mocker.patch("perfectframe.extractors.gc.collect")
+ video_path = mocker.MagicMock(spec=Path)
+
+ batch_1 = [f"frame{i}" for i in range(5)]
+ batch_2 = []
+ batch_3 = [f"frame{i}" for i in range(5)]
+ mock_generator.return_value = iter([batch_1, batch_2, batch_3])
+
+ all_frames_extractor._extract_best_frames(video_path)
+
+ expected_call_count = 2
+ assert all_frames_extractor._config.all_frames
+ mock_generator.assert_called_once_with(video_path, all_frames_extractor._config.batch_size)
+ mock_get.assert_not_called()
+ for batch in [batch_1, batch_3]:
+ mock_save.assert_called_with(batch)
+ assert mock_collect.call_count == expected_call_count
+
+
+def test_get_best_frames(mocker, caplog, extractor, config):
+ mock_normalize = mocker.patch.object(BestFramesExtractor, "_normalize_images")
+ mock_evaluate = mocker.patch.object(BestFramesExtractor, "_evaluate_images")
+ frames = [f"frames{i}" for i in range(10)]
+ scores = np.array([7, 2, 9, 3, 8, 5, 10, 1, 4, 6])
+ normalized_images = [mocker.MagicMock() for _ in range(10)]
+ mock_normalize.return_value = normalized_images
+ mock_evaluate.return_value = scores
+ expected_best_images = [frames[2], frames[6]]
+
+ with caplog.at_level(logging.INFO):
+ best_images = extractor._get_best_frames(frames)
+
+ mock_evaluate.assert_called_once_with(normalized_images)
+ mock_normalize.assert_called_once_with(frames, config.input_size)
+ assert best_images == expected_best_images
+ assert f"Best frames selected({len(expected_best_images)})." in caplog.text
diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py
new file mode 100644
index 0000000..9f596ca
--- /dev/null
+++ b/tests/unit/conftest.py
@@ -0,0 +1,5 @@
+import pytest
+
+from perfectframe.extractors import BestFramesExtractor
+from perfectframe.schemas import ExtractorConfig
+from tests.common import best_frames_dir, config, dependencies, extractor, files_dir
diff --git a/tests/unit/dependencies_test.py b/tests/unit/dependencies_test.py
new file mode 100644
index 0000000..0f29f48
--- /dev/null
+++ b/tests/unit/dependencies_test.py
@@ -0,0 +1,24 @@
+from perfectframe.dependencies import Dependencies, get_dependencies
+from perfectframe.image_evaluators import NIMAEvaluator
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import ExtractorConfig
+from perfectframe.video_processors import OpenCVVideo
+
+
+def test_get_dependencies():
+ dependencies = get_dependencies()
+
+ assert isinstance(dependencies, Dependencies)
+ assert dependencies.image_processor == OpenCVImage
+ assert dependencies.video_processor == OpenCVVideo
+ assert dependencies.evaluator == NIMAEvaluator
+ assert isinstance(dependencies.config, ExtractorConfig)
+
+
+def test_get_dependencies_with_custom_config():
+ custom_config = ExtractorConfig(batch_size=100)
+
+ dependencies = get_dependencies(custom_config)
+
+ assert dependencies.config == custom_config
+ assert dependencies.config.batch_size == custom_config.batch_size
diff --git a/tests/unit/extractor_manager_test.py b/tests/unit/extractor_manager_test.py
new file mode 100644
index 0000000..1e1edbc
--- /dev/null
+++ b/tests/unit/extractor_manager_test.py
@@ -0,0 +1,70 @@
+import http
+
+import pytest
+from fastapi import BackgroundTasks, HTTPException
+
+from perfectframe.extractor_manager import ExtractorManager
+from perfectframe.extractors import ExtractorFactory
+from perfectframe.schemas import ExtractorName, Message
+
+
+def test_get_active_extractor():
+ assert ExtractorManager.get_active_extractor() is None
+
+
+def test_start_extractor(mocker, dependencies):
+ mock_checking = mocker.patch.object(ExtractorManager, "_check_is_already_extracting")
+ mock_create_extractor = mocker.patch.object(ExtractorFactory, "create_extractor")
+ extractor_name = ExtractorName.BEST_FRAMES
+ mock_extractor = mocker.MagicMock()
+ mock_background_tasks = mocker.MagicMock(spec=BackgroundTasks)
+ mock_create_extractor.return_value = mock_extractor
+
+ message = ExtractorManager.start_extractor(extractor_name, mock_background_tasks, dependencies)
+
+ mock_checking.assert_called_once()
+ assert ExtractorManager._active_extractor == extractor_name
+ mock_create_extractor.assert_called_once_with(extractor_name, dependencies)
+ mock_background_tasks.add_task.assert_called_once_with(
+ ExtractorManager._ExtractorManager__run_extractor,
+ mock_extractor,
+ )
+ expected_message = Message(message=f"'{extractor_name.value}' started.")
+ assert message == expected_message, "The return message does not match expected."
+ ExtractorManager._active_extractor = None
+
+
+def test_run_extractor(mocker):
+ mock_extractor = mocker.patch("perfectframe.extractors.BestFramesExtractor")
+
+ ExtractorManager._ExtractorManager__run_extractor(mock_extractor)
+
+ mock_extractor.process.assert_called_once()
+
+
+def test_run_extractor_logs_exception_on_failure(mocker, caplog):
+ mock_extractor = mocker.MagicMock()
+ mock_extractor.process.side_effect = RuntimeError("Test error")
+
+ with caplog.at_level("ERROR"):
+ ExtractorManager._ExtractorManager__run_extractor(mock_extractor)
+
+ mock_extractor.process.assert_called_once()
+ assert "Extraction failed with error" in caplog.text
+ assert ExtractorManager._active_extractor is None
+
+
+def test_check_is_already_evaluating_true():
+ test_extractor = ExtractorName.BEST_FRAMES
+ ExtractorManager._active_extractor = test_extractor
+ expected_error_message = (
+ f"Extractor '{test_extractor.value}' is already running. "
+ f"You can run only one extractor at the same time. "
+ f"Wait until the extractor is done before run next process."
+ )
+
+ with pytest.raises(HTTPException, match=expected_error_message) as exc_info:
+ ExtractorManager._check_is_already_extracting()
+
+ assert exc_info.value.status_code == http.HTTPStatus.CONFLICT
+ ExtractorManager._active_extractor = None
diff --git a/tests/extractor_service/unit/extractor_test.py b/tests/unit/extractor_test.py
similarity index 50%
rename from tests/extractor_service/unit/extractor_test.py
rename to tests/unit/extractor_test.py
index 97bb502..11368a8 100644
--- a/tests/extractor_service/unit/extractor_test.py
+++ b/tests/unit/extractor_test.py
@@ -1,16 +1,16 @@
import logging
from pathlib import Path
-from unittest.mock import MagicMock, patch
import numpy as np
import pytest
-from extractor_service.app.extractors import (
+from perfectframe.extractors import (
BestFramesExtractor,
ExtractorFactory,
TopImagesExtractor,
)
-from extractor_service.app.image_processors import OpenCVImage
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import ExtractorName, VideoExtension
def test_extractor_initialization(config, dependencies):
@@ -25,9 +25,9 @@ def test_extractor_initialization(config, dependencies):
assert extractor._image_evaluator is None
-def test_get_image_evaluator(extractor, config):
+def test_get_image_evaluator(mocker, extractor, config):
expected = "value"
- mock_class = MagicMock(return_value=expected)
+ mock_class = mocker.MagicMock(return_value=expected)
extractor._image_evaluator_class = mock_class
result = extractor._get_image_evaluator()
@@ -39,11 +39,11 @@ def test_get_image_evaluator(extractor, config):
)
-def test_evaluate_images(extractor):
- test_input = MagicMock(spec=np.ndarray)
+def test_evaluate_images(mocker, extractor):
+ test_input = mocker.MagicMock(spec=np.ndarray)
expected = "expected"
- extractor._image_evaluator = MagicMock()
- extractor._image_evaluator.evaluate_images = MagicMock()
+ extractor._image_evaluator = mocker.MagicMock()
+ extractor._image_evaluator.evaluate_images = mocker.MagicMock()
extractor._image_evaluator.evaluate_images.return_value = expected
result = extractor._evaluate_images(test_input)
@@ -52,11 +52,19 @@ def test_evaluate_images(extractor):
assert result == expected
-@pytest.mark.parametrize("image", ("some_image", None))
-@patch.object(OpenCVImage, "read_image", return_value=None)
-@patch("extractor_service.app.extractors.ThreadPoolExecutor")
-def test_read_images(mock_executor, mock_read_image, image, extractor):
- mock_paths = [MagicMock(spec=Path) for _ in range(3)]
+def test_evaluate_images_raises_when_evaluator_not_initialized(mocker, extractor):
+ test_input = mocker.MagicMock(spec=np.ndarray)
+ extractor._image_evaluator = None
+
+ with pytest.raises(RuntimeError, match="_image_evaluator must be initialized"):
+ extractor._evaluate_images(test_input)
+
+
+@pytest.mark.parametrize("image", ["some_image", None])
+def test_read_images(mocker, image, extractor):
+ mock_executor = mocker.patch("perfectframe.extractors.ThreadPoolExecutor")
+ mock_read_image = mocker.patch.object(OpenCVImage, "read_image", return_value=None)
+ mock_paths = [mocker.MagicMock(spec=Path) for _ in range(3)]
mock_executor.return_value.__enter__.return_value = mock_executor
mock_executor.submit.return_value.result.return_value = image
calls = [((mock_read_image, path),) for path in mock_paths]
@@ -72,10 +80,10 @@ def test_read_images(mock_executor, mock_read_image, image, extractor):
assert not result
-@patch.object(OpenCVImage, "read_image", return_value=None)
-@patch("extractor_service.app.extractors.ThreadPoolExecutor")
-def test_save_images(mock_executor, mock_save_image, extractor, config):
- images = [MagicMock(spec=np.ndarray) for _ in range(3)]
+def test_save_images(mocker, extractor, config):
+ mock_executor = mocker.patch("perfectframe.extractors.ThreadPoolExecutor")
+ mocker.patch.object(OpenCVImage, "read_image", return_value=None)
+ images = [mocker.MagicMock(spec=np.ndarray) for _ in range(3)]
mock_executor.return_value.__enter__.return_value = mock_executor
mock_executor.submit.return_value.result.return_value = None
calls = [
@@ -97,63 +105,59 @@ def test_save_images(mock_executor, mock_save_image, extractor, config):
assert mock_executor.submit.return_value.result.call_count == len(images)
-@patch.object(OpenCVImage, "normalize_images")
-def test_normalize_images(mock_normalize, extractor, config):
- images = [MagicMock() for _ in range(3)]
+def test_normalize_images(mocker, extractor, config):
+ mock_normalize = mocker.patch.object(OpenCVImage, "normalize_images")
+ images = [mocker.MagicMock() for _ in range(3)]
- extractor._normalize_images(images, config.target_image_size)
+ extractor._normalize_images(images, config.input_size)
- mock_normalize.assert_called_once_with(images, config.target_image_size)
+ mock_normalize.assert_called_once_with(images, config.input_size)
-@patch.object(Path, "iterdir")
-@patch.object(Path, "is_file")
-def test_list_input_directory_files(mock_is_file, mock_iterdir, extractor, caplog, config):
- mock_files = [Path("/fake/directory/file1.txt"), Path("/fake/directory/file2.log")]
- mock_extensions = (".txt", ".log")
+def test_list_input_directory_files(mocker, extractor, caplog, config):
+ mock_iterdir = mocker.patch.object(Path, "iterdir")
+ mock_is_file = mocker.patch.object(Path, "is_file")
+ mock_files = [Path("/fake/directory/file1.mp4"), Path("/fake/directory/file2.mov")]
mock_iterdir.return_value = mock_files
mock_is_file.return_value = True
with caplog.at_level(logging.DEBUG):
- result = extractor._list_input_directory_files(mock_extensions, None)
+ result = extractor._list_input_directory_files(VideoExtension, None)
assert result == mock_files
assert f"Directory '{config.input_directory}' files listed." in caplog.text
assert f"Listed file paths: {mock_files}" in caplog.text
-@patch.object(Path, "iterdir")
-def test_list_input_directory_files_no_files_found(mock_iterdir, extractor, caplog, config):
+def test_list_input_directory_files_no_files_found(mocker, extractor, caplog):
+ mock_iterdir = mocker.patch.object(Path, "iterdir")
mock_files = []
- mock_extensions = (".txt", ".log")
mock_iterdir.return_value = mock_files
- error_massage = (
- f"Files with extensions '{mock_extensions}' and "
- f"without prefix 'Prefix not provided' not found in folder: {config.input_directory}."
- f"\n-->HINT: You probably don't have input or you haven't changed prefixes. "
- f"\nCheck input directory."
- )
with (
pytest.raises(BestFramesExtractor.EmptyInputDirectoryError),
caplog.at_level(logging.ERROR),
):
- extractor._list_input_directory_files(mock_extensions)
+ extractor._list_input_directory_files(VideoExtension)
- assert error_massage in caplog.text
+ assert "not found in folder" in caplog.text
+ assert "without prefix 'Prefix not provided'" in caplog.text
-def test_add_prefix(extractor, caplog):
+def test_add_prefix(mocker, extractor, caplog):
+ mock_rename = mocker.patch("pathlib.Path.rename")
test_prefix = "prefix_"
test_path = Path("test_path/file.mp4")
test_new_path = Path("test_path/prefix_file.mp4")
- expected_massage = f"Prefix '{test_prefix}' added to file '{test_path}'. New path: {test_new_path}"
+ expected_message = (
+ f"Prefix '{test_prefix}' added to file '{test_path}'. New path: {test_new_path}"
+ )
- with patch("pathlib.Path.rename") as mock_rename, caplog.at_level(logging.DEBUG):
+ with caplog.at_level(logging.DEBUG):
result = extractor._add_prefix(test_prefix, test_path)
- mock_rename.assert_called_once_with(test_new_path)
- assert expected_massage in caplog.text
+ mock_rename.assert_called_once_with(test_new_path)
+ assert expected_message in caplog.text
assert result == test_new_path
@@ -164,25 +168,12 @@ def test_signal_readiness_for_shutdown(extractor, caplog):
@pytest.mark.parametrize(
- "extractor_name, extractor",
- (
- ("best_frames_extractor", BestFramesExtractor),
- ("top_images_extractor", TopImagesExtractor),
- ),
+ ("extractor_name", "extractor_class"),
+ [
+ (ExtractorName.BEST_FRAMES, BestFramesExtractor),
+ (ExtractorName.TOP_IMAGES, TopImagesExtractor),
+ ],
)
-def test_create_extractor_known_extractors(extractor_name, extractor, config, dependencies):
- extractor_instance = ExtractorFactory.create_extractor(extractor_name, config, dependencies)
- assert isinstance(extractor_instance, extractor)
-
-
-def test_create_extractor_unknown_extractor_raises(caplog, config, dependencies):
- unknown_extractor_name = "unknown_extractor"
- expected_massage = f"Provided unknown extractor name: {unknown_extractor_name}"
-
- with (
- pytest.raises(ValueError, match=expected_massage),
- caplog.at_level(logging.ERROR),
- ):
- ExtractorFactory.create_extractor(unknown_extractor_name, config, dependencies)
-
- assert expected_massage in caplog.text
+def test_create_extractor_known_extractors(extractor_name, extractor_class, dependencies):
+ extractor_instance = ExtractorFactory.create_extractor(extractor_name, dependencies)
+ assert isinstance(extractor_instance, extractor_class)
diff --git a/tests/unit/image_evaluators_test.py b/tests/unit/image_evaluators_test.py
new file mode 100644
index 0000000..11e89a9
--- /dev/null
+++ b/tests/unit/image_evaluators_test.py
@@ -0,0 +1,97 @@
+import logging
+
+import numpy as np
+import pytest
+
+from perfectframe.image_evaluators import NIMAEvaluator
+
+
+@pytest.fixture
+def evaluator(mocker):
+ mocker.patch.object(NIMAEvaluator, "_get_model_path", return_value="/fake/path/model.onnx")
+ mock_session = mocker.patch("perfectframe.image_evaluators.ort.InferenceSession")
+ mock_session_instance = mocker.MagicMock()
+ mock_session_instance.get_inputs.return_value = [mocker.MagicMock(name="input")]
+ mock_session.return_value = mock_session_instance
+ return NIMAEvaluator(mocker.MagicMock())
+
+
+def test_evaluator_initialization(mocker, config):
+ mock_get_path = mocker.patch.object(NIMAEvaluator, "_get_model_path")
+ mock_session = mocker.patch("perfectframe.image_evaluators.ort.InferenceSession")
+ test_path = "/some/path/model.onnx"
+ mock_get_path.return_value = test_path
+ mock_session_instance = mocker.MagicMock()
+ mock_input = mocker.MagicMock()
+ mock_input.name = "input"
+ mock_session_instance.get_inputs.return_value = [mock_input]
+ mock_session.return_value = mock_session_instance
+
+ instance = NIMAEvaluator(config)
+
+ mock_get_path.assert_called_once_with(config)
+ mock_session.assert_called_once_with(test_path)
+ assert instance._session == mock_session_instance
+ assert instance._input_name == "input"
+
+
+def test_evaluate_images(mocker, evaluator, caplog):
+ mock_calculate = mocker.patch.object(NIMAEvaluator, "_calculate_weighted_mean")
+ mock_check = mocker.patch.object(NIMAEvaluator, "_check_scores")
+ fake_images = mocker.MagicMock(spec=np.ndarray)
+ fake_images.shape = (3, 2, 2)
+ fake_images.astype.return_value = fake_images
+ predictions = np.array([[0.1] * 10, [0.2] * 10, [0.3] * 10])
+ expected_scores = [10.0, 20.0, 30.0]
+ mock_calculate.side_effect = expected_scores
+ evaluator._session.run.return_value = [predictions]
+
+ with caplog.at_level(logging.INFO):
+ result = evaluator.evaluate_images(fake_images)
+
+ predictions_count = 3
+ fake_images.astype.assert_called_once_with(np.float32)
+ evaluator._session.run.assert_called_once_with(None, {evaluator._input_name: fake_images})
+ assert mock_calculate.call_count == predictions_count
+ for i, call_args in enumerate(mock_calculate.call_args_list):
+ np.testing.assert_array_equal(call_args[0][0], predictions[i])
+ mock_check.assert_called_once()
+ assert "Evaluating images..." in caplog.text
+ assert "Images batch evaluated." in caplog.text
+ assert result == expected_scores
+
+
+def test_evaluate_images_returns_empty_list_when_predictions_not_ndarray(mocker, evaluator):
+ fake_images = mocker.MagicMock(spec=np.ndarray)
+ fake_images.astype.return_value = fake_images
+ evaluator._session.run.return_value = [None]
+
+ result = evaluator.evaluate_images(fake_images)
+
+ assert result == []
+
+
+def test_calculate_weighted_mean(evaluator):
+ prediction = np.array([0.1] * 10) # 10 values to match _prediction_weights
+ weights = NIMAEvaluator._prediction_weights
+ expected_weighted_mean = np.sum(prediction * weights) / np.sum(weights)
+
+ calculated_mean = evaluator._calculate_weighted_mean(prediction)
+
+ assert np.isclose(calculated_mean, expected_weighted_mean)
+
+
+@pytest.mark.parametrize(("score_len", "images_len"), [(1, 1), (1, 2)])
+def test_check_scores(mocker, score_len, images_len, evaluator, caplog):
+ scores = [mocker.MagicMock(spec=np.ndarray) for _ in range(score_len)]
+ images = [mocker.MagicMock(spec=float) for _ in range(images_len)]
+ with caplog.at_level(logging.DEBUG):
+ evaluator._check_scores(images, scores)
+
+ assert f"Scores: {scores}" in caplog.text
+ if score_len == images_len:
+ assert f"Scores and images lists length: {score_len}" in caplog.text
+ else:
+ assert "Scores and images lists lengths don't match!" in caplog.text
+ assert f"Images list length: {images_len}" in caplog.text
+ assert f"Scores list length: {score_len}" in caplog.text
diff --git a/tests/unit/image_processors_test.py b/tests/unit/image_processors_test.py
new file mode 100644
index 0000000..cbbbb98
--- /dev/null
+++ b/tests/unit/image_processors_test.py
@@ -0,0 +1,99 @@
+import logging
+import uuid
+from pathlib import Path
+from unittest.mock import call # noqa: TID251
+
+import cv2
+import numpy as np
+
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import ImageExtension, ImageResolution
+
+
+def test_read_image(mocker, caplog):
+ mock_imread = mocker.patch.object(cv2, "imread")
+ mock_path = Path("some/path/to/image.jpg")
+ expected_image = mocker.MagicMock(spec=np.ndarray)
+ mock_imread.return_value = expected_image
+
+ with caplog.at_level(logging.DEBUG):
+ result = OpenCVImage.read_image(mock_path)
+
+ assert result == expected_image
+ mock_imread.assert_called_once_with(str(mock_path))
+ assert f"Image '{mock_path}' has successfully read." in caplog.text
+
+
+def test_read_image_invalid_image(mocker, caplog):
+ mock_imread = mocker.patch.object(cv2, "imread")
+ mock_path = Path("some/path/to/image.jpg")
+ mock_imread.return_value = None
+
+ with caplog.at_level(logging.WARNING):
+ result = OpenCVImage.read_image(mock_path)
+
+ assert result is None
+ mock_imread.assert_called_once_with(str(mock_path))
+ assert (
+ f"Can't read image. OpenCV reading not returns np.ndarray for image path: {mock_path!s}"
+ ) in caplog.text
+
+
+def test_save_image(mocker, caplog):
+ mock_imwrite = mocker.patch.object(cv2, "imwrite")
+ mock_imwrite.return_value = True
+ mock_uuid = mocker.patch.object(uuid, "uuid4")
+ file_name = "some_filename"
+ mock_uuid.return_value = file_name
+ fake_image = mocker.MagicMock(spec=np.ndarray)
+ output_directory = Path("/fake/directory")
+ output_format = ImageExtension.JPG
+ expected_path = output_directory / f"image_{file_name}{output_format.value}"
+
+ with caplog.at_level(logging.DEBUG):
+ image_path = OpenCVImage.save_image(fake_image, output_directory, output_format)
+
+ mock_imwrite.assert_called_once_with(str(expected_path), fake_image)
+ assert image_path == expected_path, "The returned path does not match the expected path."
+ assert f"Image saved at '{expected_path}'." in caplog.text
+
+
+def test_save_image_logs_error_on_failure(mocker, caplog):
+ mock_imwrite = mocker.patch.object(cv2, "imwrite")
+ mock_imwrite.return_value = False
+ mock_uuid = mocker.patch.object(uuid, "uuid4")
+ file_name = "some_filename"
+ mock_uuid.return_value = file_name
+ fake_image = mocker.MagicMock(spec=np.ndarray)
+ output_directory = Path("/fake/directory")
+ output_format = ImageExtension.JPG
+ expected_path = output_directory / f"image_{file_name}{output_format.value}"
+
+ with caplog.at_level(logging.ERROR):
+ image_path = OpenCVImage.save_image(fake_image, output_directory, output_format)
+
+ mock_imwrite.assert_called_once_with(str(expected_path), fake_image)
+ assert image_path == expected_path
+ assert f"Failed to save image at '{expected_path}'" in caplog.text
+
+
+def test_normalize_images(mocker):
+ mock_resize = mocker.patch.object(cv2, "resize")
+ mock_cvt = mocker.patch.object(cv2, "cvtColor")
+ mock_array = mocker.patch.object(np, "array")
+ images_num = 3
+ target_size = ImageResolution(112, 112)
+ batch_images = [mocker.MagicMock(spec=np.ndarray) for _ in range(images_num)]
+ resized_images = [mocker.MagicMock(spec=np.ndarray) for _ in range(images_num)]
+ expected_images = [mocker.MagicMock(spec=np.ndarray) for _ in range(images_num)]
+ mock_resize.side_effect = resized_images
+ mock_cvt.side_effect = expected_images
+ mock_array.return_value = np.array(expected_images, dtype=np.float32) / 255.0
+
+ result = OpenCVImage.normalize_images(batch_images, target_size)
+
+ calls = [call(image, target_size, interpolation=cv2.INTER_LANCZOS4) for image in batch_images]
+ mock_resize.assert_has_calls(calls, any_order=True)
+ calls = [call(image, cv2.COLOR_BGR2RGB) for image in resized_images]
+ mock_cvt.assert_has_calls(calls, any_order=True)
+ np.testing.assert_array_equal(result, mock_array.return_value)
diff --git a/tests/unit/nima_models_test.py b/tests/unit/nima_models_test.py
new file mode 100644
index 0000000..c2f4df2
--- /dev/null
+++ b/tests/unit/nima_models_test.py
@@ -0,0 +1,93 @@
+import logging
+from http import HTTPStatus
+from pathlib import Path
+
+import numpy as np
+import pytest
+import requests
+
+from perfectframe.image_evaluators import NIMAEvaluator
+
+
+def test_prediction_weights():
+ assert list(NIMAEvaluator._prediction_weights) == list(np.arange(1, 11))
+
+
+@pytest.mark.parametrize("file_exists", [True, False])
+def test_get_model_path(mocker, file_exists, config, caplog):
+ mock_is_file = mocker.patch.object(Path, "is_file")
+ mock_download = mocker.patch.object(NIMAEvaluator, "_download_model_weights")
+ mock_is_file.return_value = file_exists
+ expected_path = Path(config.weights_directory) / config.weights_filename
+
+ with caplog.at_level(logging.DEBUG):
+ result = NIMAEvaluator._get_model_path(config)
+
+ assert (
+ f"Searching for model weights in weights directory: {config.weights_directory}"
+ in caplog.text
+ )
+ if file_exists:
+ assert f"Model weights loaded from: {expected_path}" in caplog.text
+ mock_download.assert_not_called()
+ else:
+ assert (
+ f"Can't find model weights in weights directory: {config.weights_directory}"
+ in caplog.text
+ )
+ mock_download.assert_called_once_with(expected_path, config)
+ assert result == expected_path
+
+
+@pytest.mark.parametrize("status_code", [HTTPStatus.OK, HTTPStatus.NOT_FOUND])
+def test_download_model_weights(mocker, status_code, config, caplog):
+ mock_mkdir = mocker.patch.object(Path, "mkdir")
+ mock_get = mocker.patch("perfectframe.image_evaluators.requests.get")
+ mock_write_bytes = mocker.patch.object(Path, "write_bytes")
+ test_path = Path("/fake/path/to/weights.onnx")
+ test_url = f"{config.weights_repo_url}{config.weights_filename}"
+ weights_data = b"weights data"
+ timeout = 12
+
+ mock_response = mocker.MagicMock()
+ mock_response.ok = status_code == HTTPStatus.OK
+ mock_response.status_code = status_code
+ mock_response.content = weights_data
+ mock_get.return_value = mock_response
+
+ if status_code == HTTPStatus.OK:
+ with caplog.at_level(logging.DEBUG):
+ NIMAEvaluator._download_model_weights(test_path, config, timeout)
+ mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+ mock_write_bytes.assert_called_once_with(weights_data)
+ assert f"Model weights downloaded and saved to {test_path}" in caplog.text
+ else:
+ error_message = f"Failed to download the weights: HTTP status code {status_code}"
+ with (
+ caplog.at_level(logging.DEBUG),
+ pytest.raises(NIMAEvaluator.ModelWeightsDownloadError, match=error_message),
+ ):
+ NIMAEvaluator._download_model_weights(test_path, config, timeout)
+ assert f"Failed to download the weights: HTTP status code {status_code}" in caplog.text
+ assert f"Downloading model weights from url: {test_url}" in caplog.text
+ mock_get.assert_called_once_with(test_url, allow_redirects=True, timeout=timeout)
+
+
+def test_download_model_weights_network_error(mocker, config, caplog):
+ mock_get = mocker.patch("perfectframe.image_evaluators.requests.get")
+ test_path = Path("/fake/path/to/weights.onnx")
+ test_url = f"{config.weights_repo_url}{config.weights_filename}"
+ timeout = 12
+
+ mock_get.side_effect = requests.ConnectionError("Network unreachable")
+
+ with (
+ caplog.at_level(logging.ERROR),
+ pytest.raises(
+ NIMAEvaluator.ModelWeightsDownloadError, match="Network error while downloading"
+ ),
+ ):
+ NIMAEvaluator._download_model_weights(test_path, config, timeout)
+
+ assert "Network error while downloading model weights" in caplog.text
+ mock_get.assert_called_once_with(test_url, allow_redirects=True, timeout=timeout)
diff --git a/tests/unit/schemas_test.py b/tests/unit/schemas_test.py
new file mode 100644
index 0000000..75287bb
--- /dev/null
+++ b/tests/unit/schemas_test.py
@@ -0,0 +1,74 @@
+from pathlib import Path
+
+import pytest
+from pydantic import ValidationError
+
+from perfectframe.schemas import (
+ ExtractorConfig,
+ ExtractorName,
+ ExtractorStatus,
+ ImageExtension,
+ Message,
+ VideoExtension,
+)
+
+
+def test_config_default(mocker):
+ mocker.patch.object(Path, "is_dir", return_value=True)
+ config = ExtractorConfig()
+ assert config.input_directory == Path("/app/input_directory")
+ assert config.output_directory == Path("/app/output_directory")
+ assert config.processed_video_prefix == "frames_extracted_"
+ assert isinstance(config.comparing_group_size, int)
+ assert isinstance(config.batch_size, int)
+ assert isinstance(config.top_images_percent, float)
+ assert config.images_output_format == ImageExtension.JPG
+ assert config.weights_directory == Path.home() / ".cache" / "huggingface"
+ assert config.weights_filename == "weights.onnx"
+ assert config.weights_repo_url == "https://huggingface.co/BKDDFS/nima_weights/resolve/main/"
+ assert config.all_frames is False
+
+
+def test_image_extension_contains():
+ assert ImageExtension.contains(".jpg") is True
+ assert ImageExtension.contains(".jpeg") is True
+ assert ImageExtension.contains(".png") is True
+ assert ImageExtension.contains(".webp") is True
+ assert ImageExtension.contains(".mp4") is False
+ assert ImageExtension.contains(".invalid") is False
+
+
+def test_video_extension_contains():
+ assert VideoExtension.contains(".mp4") is True
+ assert VideoExtension.contains(".mov") is True
+ assert VideoExtension.contains(".webm") is True
+ assert VideoExtension.contains(".mkv") is True
+ assert VideoExtension.contains(".avi") is True
+ assert VideoExtension.contains(".jpg") is False
+ assert VideoExtension.contains(".invalid") is False
+
+
+def test_request_data_validation_failure_output():
+ mock_directory = r"C:\invalid_dir"
+ with pytest.raises(ValidationError):
+ ExtractorConfig(input_directory=mock_directory)
+
+
+def test_str_directory():
+ mock_directory = str(Path.cwd())
+ config = ExtractorConfig(input_directory=mock_directory)
+ assert isinstance(config.input_directory, Path)
+
+
+def test_extractor_status():
+ status = ExtractorStatus(active_extractor=None)
+ assert status.active_extractor is None
+
+ status = ExtractorStatus(active_extractor=ExtractorName.BEST_FRAMES)
+ assert status.active_extractor == ExtractorName.BEST_FRAMES
+
+
+def test_message():
+ mock_message = "Test message"
+ msg = Message(message=mock_message)
+ assert msg.message == mock_message
diff --git a/tests/unit/top_images_extractor_test.py b/tests/unit/top_images_extractor_test.py
new file mode 100644
index 0000000..175e7b7
--- /dev/null
+++ b/tests/unit/top_images_extractor_test.py
@@ -0,0 +1,78 @@
+import logging
+from unittest.mock import call # noqa: TID251
+
+import numpy as np
+import pytest
+
+from perfectframe.extractors import TopImagesExtractor
+from perfectframe.image_evaluators import NIMAEvaluator
+from perfectframe.image_processors import OpenCVImage
+from perfectframe.schemas import ImageExtension
+from perfectframe.video_processors import OpenCVVideo
+
+
+@pytest.fixture
+def extractor(config):
+ return TopImagesExtractor(config, OpenCVImage, OpenCVVideo, NIMAEvaluator)
+
+
+def test_process_with_images(mocker, extractor, caplog, config):
+ mock_read_image = mocker.patch.object(OpenCVImage, "read_image")
+ mock_normalize = mocker.patch.object(TopImagesExtractor, "_normalize_images")
+ # Setup
+ test_images = [
+ "/fake/directory/image1.jpg",
+ "/fake/directory/image2.jpg",
+ "/fake/directory/image3.jpg",
+ ]
+ test_ratings = [10, 20, 30]
+ best_image = ["image3.jpg"]
+
+ # Mock internal methods
+ extractor._list_input_directory_files = mocker.MagicMock(return_value=test_images)
+ extractor._get_image_evaluator = mocker.MagicMock()
+ extractor._evaluate_images = mocker.MagicMock(return_value=test_ratings)
+ extractor._get_top_percent_images = mocker.MagicMock(return_value=best_image)
+ extractor._save_images = mocker.MagicMock()
+ extractor._signal_readiness_for_shutdown = mocker.MagicMock()
+
+ # Call
+ with caplog.at_level(logging.INFO):
+ extractor.process()
+
+ # Check that the internal methods were called as expected
+ extractor._list_input_directory_files.assert_called_once_with(ImageExtension)
+ mock_read_image.assert_has_calls([call(path) for path in test_images], any_order=True)
+ mock_normalize.assert_called_once_with(
+ [mock_read_image.return_value] * 3, extractor._config.input_size
+ )
+ extractor._evaluate_images.assert_called_once_with(mock_normalize.return_value)
+ extractor._get_top_percent_images.assert_called_once_with(
+ [mock_read_image.return_value] * 3,
+ test_ratings,
+ extractor._config.top_images_percent,
+ )
+ extractor._save_images.assert_called_once_with(best_image)
+
+ # Check logging
+ expected_message = (
+ f"Extraction process finished. "
+ f"All top images extracted from directory: {config.input_directory}."
+ )
+ assert expected_message in caplog.text
+ extractor._signal_readiness_for_shutdown.assert_called_once()
+
+
+def test_get_top_percent_images(mocker, extractor, caplog):
+ images = [mocker.MagicMock(spec=np.ndarray) for _ in range(5)]
+ ratings = np.array([55, 70, 85, 40, 20])
+ top_percent = 70
+ expected_images = [images[1], images[2]]
+
+ with caplog.at_level(logging.INFO):
+ selected_images = extractor._get_top_percent_images(images, ratings, top_percent)
+
+ assert selected_images == expected_images, (
+ "The selected images do not match the expected top percent images."
+ )
+ assert f"Top images selected({len(expected_images)})." in caplog.text
diff --git a/tests/unit/video_processors_test.py b/tests/unit/video_processors_test.py
new file mode 100644
index 0000000..b48583a
--- /dev/null
+++ b/tests/unit/video_processors_test.py
@@ -0,0 +1,194 @@
+import logging
+from pathlib import Path
+
+import cv2
+import pytest
+
+from perfectframe.video_processors import OpenCVVideo
+
+TOTAL_FRAMES_PROP = "total frames"
+
+
+def test_get_video_capture_success(mocker):
+ mock_cap = mocker.patch.object(cv2, "VideoCapture")
+ test_path = mocker.MagicMock(spec=Path)
+ mock_video = mocker.MagicMock()
+ mock_video.isOpened.return_value = True
+ mock_cap.return_value = mock_video
+
+ with OpenCVVideo._video_capture(test_path) as video:
+ assert video.isOpened() is True
+
+ mock_video.release.assert_called_once()
+
+
+def test_get_video_capture_failure(mocker):
+ mock_cap = mocker.patch.object(cv2, "VideoCapture")
+ test_path = mocker.MagicMock(spec=Path)
+ mock_video = mocker.MagicMock()
+ mock_video.isOpened.return_value = False
+ mock_cap.return_value = mock_video
+
+ with (
+ pytest.raises(OpenCVVideo._Error),
+ OpenCVVideo._video_capture(test_path),
+ ):
+ # No additional operations are needed here, we are just testing the exception
+ pass
+
+ mock_video.release.assert_called_once()
+
+
+@pytest.fixture
+def mock_video(mocker):
+ video = mocker.MagicMock()
+ video.get.return_value = 30
+ video.read.side_effect = [
+ (True, "frame1"),
+ (True, "frame2"),
+ (True, "frame3"),
+ (False, None),
+ ]
+ return video
+
+
+@pytest.mark.parametrize(
+ ("frames_batch_size", "expected_num_batches"),
+ [
+ (1, 3),
+ (2, 2),
+ (3, 1),
+ ],
+)
+def test_get_next_video_frames(
+ mocker,
+ frames_batch_size,
+ expected_num_batches,
+ caplog,
+):
+ mock_read = mocker.patch.object(OpenCVVideo, "_read_next_frame")
+ mock_get_property = mocker.patch.object(OpenCVVideo, "_get_video_property")
+ mock_video_cap = mocker.patch.object(OpenCVVideo, "_video_capture")
+ frame_rate_attr = "frame rate"
+ video_path = mocker.MagicMock()
+ mock_video = mocker.MagicMock()
+ frames_number = 3
+
+ def get_property_side_effect(_video, _property_id, value_name):
+ return frames_number if TOTAL_FRAMES_PROP in value_name else 1
+
+ mock_get_property.side_effect = get_property_side_effect
+ mock_video_cap.return_value.__enter__.return_value = mock_video
+
+ def read_side_effect(_video, idx):
+ return f"frame{idx // 30}"
+
+ mock_read.side_effect = read_side_effect
+
+ with caplog.at_level(logging.DEBUG):
+ frames_generator = OpenCVVideo.get_next_frames(video_path, frames_batch_size)
+ batches = list(frames_generator)
+
+ expected_property_calls = 2
+ assert len(batches) == expected_num_batches, "Number of batches does not match expected"
+ for batch in batches:
+ assert len(batch) <= frames_batch_size, "Batch size is larger than expected"
+ assert mock_video_cap.called
+ assert mock_get_property.call_count == expected_property_calls
+ mock_get_property.assert_any_call(mock_video, cv2.CAP_PROP_FPS, frame_rate_attr)
+ mock_get_property.assert_any_call(mock_video, cv2.CAP_PROP_FRAME_COUNT, TOTAL_FRAMES_PROP)
+ assert mock_read.call_count == frames_number
+
+ assert "Frame appended to frames batch." in caplog.text
+ assert "Got full frames batch." in caplog.text
+ if (
+ frames_batch_size % frames_number
+ and frames_number > expected_num_batches * frames_batch_size
+ ):
+ assert "Returning last frames batch." in caplog.text
+
+
+def test_get_next_video_frames_skips_none_frames(mocker):
+ mock_read = mocker.patch.object(OpenCVVideo, "_read_next_frame")
+ mock_get_property = mocker.patch.object(OpenCVVideo, "_get_video_property")
+ mock_video_cap = mocker.patch.object(OpenCVVideo, "_video_capture")
+ video_path = mocker.MagicMock()
+ mock_video = mocker.MagicMock()
+
+ mock_get_property.side_effect = lambda _v, _a, name: 2 if "total" in name else 1
+ mock_video_cap.return_value.__enter__.return_value = mock_video
+ mock_read.side_effect = ["frame0", None]
+
+ batches = list(OpenCVVideo.get_next_frames(video_path, 10))
+
+ assert len(batches) == 1
+ assert batches[0] == ["frame0"]
+
+
+@pytest.mark.parametrize("read_return", [(True, "frame"), (False, None)])
+def test_read_next_frame(mocker, read_return, caplog):
+ mock_check_cap = mocker.patch.object(OpenCVVideo, "_check_video_capture")
+ mock_cap = mocker.MagicMock(spec=cv2.VideoCapture)
+ mock_cap.read = mocker.MagicMock(return_value=read_return)
+ test_frame_index = 1
+ with caplog.at_level(logging.WARNING):
+ result = OpenCVVideo._read_next_frame(mock_cap, test_frame_index)
+
+ mock_check_cap.assert_called_once_with(mock_cap)
+ mock_cap.set.assert_called_once_with(cv2.CAP_PROP_POS_FRAMES, test_frame_index)
+ mock_cap.read.assert_called_once()
+ if read_return[0] is True:
+ assert result == "frame"
+ else:
+ assert result is None
+ assert f"Couldn't read frame with index: {test_frame_index}" in caplog.text
+
+
+def test_get_video_property(mocker, caplog):
+ mock_check_cap = mocker.patch.object(OpenCVVideo, "_check_video_capture")
+ mock_cap = mocker.MagicMock(spec=cv2.VideoCapture)
+ property_id = cv2.CAP_PROP_FRAME_COUNT
+ value_name = TOTAL_FRAMES_PROP
+ total_frames = 24.6
+ mock_cap.get.return_value = total_frames
+
+ with caplog.at_level(logging.DEBUG):
+ result = OpenCVVideo._get_video_property(mock_cap, property_id, value_name)
+
+ expected_rounded = 25
+ mock_check_cap.assert_called_once_with(mock_cap)
+ assert f"Got input video {value_name}: {total_frames}" in caplog.text
+ assert result == expected_rounded
+
+
+def test_get_video_property_invalid(mocker, caplog):
+ mock_check_cap = mocker.patch.object(OpenCVVideo, "_check_video_capture")
+ mock_cap = mocker.MagicMock(spec=cv2.VideoCapture)
+ property_id = cv2.CAP_PROP_FRAME_COUNT
+ value_name = TOTAL_FRAMES_PROP
+ total_frames = -24.6
+ mock_cap.get.return_value = total_frames
+ expected_message = f"Invalid {value_name} retrieved: {total_frames}."
+
+ with (
+ caplog.at_level(logging.ERROR),
+ pytest.raises(ValueError, match=expected_message),
+ ):
+ OpenCVVideo._get_video_property(mock_cap, property_id, value_name)
+
+ mock_check_cap.assert_called_once_with(mock_cap)
+ assert expected_message in caplog.text
+
+
+def test_check_video_capture(mocker, caplog):
+ mock_cap = mocker.MagicMock(spec=cv2.VideoCapture)
+ mock_cap.isOpened.return_value = False
+ error_message = (
+ "Invalid video capture object or object not opened. "
+ "Probably video capture closed at some point."
+ )
+
+ with caplog.at_level(logging.ERROR), pytest.raises(ValueError, match=error_message):
+ OpenCVVideo._check_video_capture(mock_cap)
+
+ assert error_message in caplog.text
diff --git a/uv.lock b/uv.lock
index 6b8b06c..f212906 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,33 +1,23 @@
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- "python_full_version == '3.11.*' and sys_platform == 'darwin'",
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- "python_full_version < '3.11' and sys_platform == 'darwin'",
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- "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
-]
+revision = 3
+requires-python = ">=3.11, <3.14"
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