interactive_threshold.py

import cv2
import numpy as np

# Callback function for trackbars (it does nothing but is required by createTrackbar)
def nothing(x):
    pass

# Function to display the filtered image based on current parameters
def interactive_circle_detection(image_path):
    # Load the image
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Unable to load image {image_path}")
        return
    
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Create a window to display the image
    cv2.namedWindow('Interactive Circle Detection')

    # Set a fixed resolution for the window
    fixed_width = 2560  # Desired width
    fixed_height = 1440  # Desired height

    # Create trackbars for all adjustable parameters
    cv2.createTrackbar('Threshold', 'Interactive Circle Detection', 180, 255, nothing)  # Binary Threshold
    cv2.createTrackbar('Gaussian Blur', 'Interactive Circle Detection', 9, 50, nothing)  # Gaussian blur kernel size
    cv2.createTrackbar('Canny Min', 'Interactive Circle Detection', 50, 255, nothing)  # Canny min threshold
    cv2.createTrackbar('Canny Max', 'Interactive Circle Detection', 150, 255, nothing)  # Canny max threshold
    cv2.createTrackbar('Min Circularity', 'Interactive Circle Detection', 70, 100, nothing)  # Min Circularity (0.70 to 1.30)
    cv2.createTrackbar('Max Circularity', 'Interactive Circle Detection', 130, 150, nothing)  # Max Circularity

    # Loop to update the image based on the trackbar positions
    while True:
        # Get the current positions of the trackbars
        threshold_value = cv2.getTrackbarPos('Threshold', 'Interactive Circle Detection')
        blur_value = cv2.getTrackbarPos('Gaussian Blur', 'Interactive Circle Detection')
        canny_min = cv2.getTrackbarPos('Canny Min', 'Interactive Circle Detection')
        canny_max = cv2.getTrackbarPos('Canny Max', 'Interactive Circle Detection')
        min_circularity = cv2.getTrackbarPos('Min Circularity', 'Interactive Circle Detection') / 100.0
        max_circularity = cv2.getTrackbarPos('Max Circularity', 'Interactive Circle Detection') / 100.0

        # Ensure blur_value is odd (Gaussian blur kernel size must be odd)
        if blur_value % 2 == 0:
            blur_value += 1

        # Apply Gaussian blur
        blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0)

        # Apply binary thresholding
        _, thresh = cv2.threshold(blurred, threshold_value, 255, cv2.THRESH_BINARY)

        # Apply Canny edge detection
        edges = cv2.Canny(thresh, canny_min, canny_max)

        # Find contours
        contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        # Create a copy of the image to draw on
        output_image = image.copy()

        # Loop over the contours and calculate circularity
        for contour in contours:
            area = cv2.contourArea(contour)
            perimeter = cv2.arcLength(contour, True)
            if perimeter == 0:
                continue

            # Calculate circularity
            circularity = 4 * np.pi * (area / (perimeter ** 2))

            # Check if the contour is circular within the selected range
            if min_circularity < circularity < max_circularity:
                (x, y), radius = cv2.minEnclosingCircle(contour)

                # Draw the circle on the output image
                center = (int(x), int(y))
                radius = int(radius)
                cv2.circle(output_image, center, radius, (0, 255, 0), 2)  # Green circle
                cv2.circle(output_image, center, 5, (0, 0, 255), -1)  # Red center

        # Resize the output image to the fixed resolution
        output_image_resized = cv2.resize(output_image, (fixed_width, fixed_height))

        # Display the resized image with the detected circle(s)
        cv2.imshow('Interactive Circle Detection', output_image_resized)

        # Press 'q' to exit the loop
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Close all windows
    cv2.destroyAllWindows()

if __name__ == "__main__":
    img_path = r'C:\Users\thepr\yolo\datasets\For Test\2024_09_19_06_36_30_166.tiff'  # Path to your image
    interactive_circle_detection(img_path)