segmentation.py

import cv2
import sys
import os
import argparse
import numpy as np


confThreshold = 0.5  #Confidence threshold
maskThreshold = 0.3  # Mask threshold

# Load names of classes
classesFile = "mscoco_labels.names"
classes = None
with open(classesFile, 'rt') as f:
    classes = f.read().rstrip('\n').split('\n')

 # Load the colors
colorsFile = "colors.txt"
with open(colorsFile, 'rt') as f:
    colorsStr = f.read().rstrip('\n').split('\n')
colors = []
for i in range(len(colorsStr)):
    rgb = colorsStr[i].split(' ')
    color = np.array([float(rgb[0]), float(rgb[1]), float(rgb[2])])
    colors.append(color)

def main():
    # Give the textGraph and weight files for the model
    textGraph = "mask_rcnn_inception_v2_coco_2018_01_28.pbtxt"
    modelWeights = "frozen_inference_graph.pb"
 
    # Load the network
    net = cv2.dnn.readNetFromTensorflow(modelWeights, textGraph)
    net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
    net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

    # Parse command-line arguments
    parser = argparse.ArgumentParser(description='Mask R-CNN Object Detection')
    parser.add_argument('-i', '--image', help='Path to input image')
    parser.add_argument('-v', '--video', help='Path to input video')
    args = parser.parse_args()

    outputFile = "mask_rcnn_out_py.avi"
    if (args.image):
    # Open the image file
        if not os.path.isfile(args.image):
            print("Input image file ", args.image, " doesn't exist")
            sys.exit(1)
        cap = cv2.VideoCapture(args.image)
        outputFile = args.image[:-4]+'_mask_rcnn_out_py.jpg'
    elif (args.video):
        # Open the video file
        if not os.path.isfile(args.video):
            print("Input video file ", args.video, " doesn't exist")
            sys.exit(1)
        cap = cv2.VideoCapture(args.video)
        outputFile = args.video[:-4]+'_mask_rcnn_out_py.avi'
    else:
        cap = cv2.VideoCapture(1)

    # Get the video writer initialized to save the output video
    if (not args.image):
        vid_writer = cv2.VideoWriter(outputFile, cv2.VideoWriter_fourcc('M','J','P','G'), 28, (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
    
    while cv2.waitKey(1) < 0:
 
        # Get frame from the video
        hasFrame, frame = cap.read()
 
        # Stop the program if reached end of video
        if not hasFrame:
            print("Done processing !!!")
            print("Output file is stored as ", outputFile)
            cv2.waitKey(3000)
            break

        # Create a 4D blob from a frame.
        blob = cv2.dnn.blobFromImage(frame, swapRB=True, crop=False)
 
        # Set the input to the network
        net.setInput(blob)
 
        # Run the forward pass to get output from the output layers
        boxes, masks = net.forward(['detection_out_final', 'detection_masks'])
 
        # Extract the bounding box and mask for each of the detected objects
        postprocess(boxes, masks, frame)

        # Put efficiency information.
        t, _ = net.getPerfProfile()
        label = 'Mask-RCNN : Inference time: %.2f ms' % (t * 1000.0 / cv2.getTickFrequency())
        cv2.putText(frame, label, (0, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))

        # Write the frame with the detection boxes
        if (args.image):
            cv2.imwrite(outputFile, frame.astype(np.uint8));
        else:
            vid_writer.write(frame.astype(np.uint8))
        
        winName = "Mask_Rcnn_Output"
 
        cv2.imshow(winName, frame)

def postprocess(boxes, masks, frame):
    # Output size of masks is NxCxHxW where
    # N - number of detected boxes
    # C - number of classes (excluding background)
    # HxW - segmentation shape
    numClasses = masks.shape[1]
    numDetections = boxes.shape[2]
 
    frameH = frame.shape[0]
    frameW = frame.shape[1]

    for i in range(numDetections):
        box = boxes[0, 0, i]
        mask = masks[i]
        score = box[2]
        if score > confThreshold:
            classId = int(box[1])
            # Extract the bounding box
            left = int(frameW * box[3])
            top = int(frameH * box[4])
            right = int(frameW * box[5])
            bottom = int(frameH * box[6])
 
            left = max(0, min(left, frameW - 1))
            top = max(0, min(top, frameH - 1))
            right = max(0, min(right, frameW - 1))
            bottom = max(0, min(bottom, frameH - 1))
 
            # Extract the mask for the object
            classMask = mask[classId]
 
            # Draw bounding box, colorize and show the mask on the image
            drawBox(frame, classId, score, left, top, right, bottom, classMask)

def drawBox(frame, classId, conf, left, top, right, bottom, classMask):
    # Draw a bounding box.
    cv2.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)

    # Print a label of class.
    label = '%.2f' % conf
    if classes:
        assert(classId < len(classes))
        label = '%s:%s' % (classes[classId], label)

    # Display the label at the top of the bounding box
    labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
    top = max(top, labelSize[1])
    cv2.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv2.FILLED)
    cv2.putText(frame, label, (left, top), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)

    # Resize the mask, threshold, color and apply it on the image
    classMask = cv2.resize(classMask, (right - left + 1, bottom - top + 1))
    mask = (classMask > maskThreshold)
    roi = frame[top:bottom+1, left:right+1][mask]
 
    color = colors[classId%len(colors)]
    # Comment the above line and uncomment the two lines below to generate different instance colors
    #colorIndex = random.randint(0, len(colors)-1)
    #color = colors[colorIndex]
 
    frame[top:bottom+1, left:right+1][mask] = ([0.3*color[0], 0.3*color[1], 0.3*color[2]] + 0.7 * roi).astype(np.uint8)
 
    # Draw the contours on the image
    mask = mask.astype(np.uint8)
    contours, hierarchy = cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(frame[top:bottom+1, left:right+1], contours, -1, color, 3, cv2.LINE_8, hierarchy, 100)

 
if __name__ == "__main__":
    main()