使用OpenCV检测小圆圈(图像质量差)

import cv2

def preprocess_image(image_path):
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    _, thresholded = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return thresholded

def detect_circles(image):
    circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)
    return circles

def visualize_circles(image, circles):
    for circle in circles[0]:
        cv2.circle(image, (circle[0], circle[1]), circle[2], (0, 255, 0), 2)
    return image

import cv2

def preprocess_image(image_path):
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    _, thresholded = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return thresholded

def detect_circles(image):
    circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)
    return circles

def visualize_circles(image, circles):
    for circle in circles[0]:
        cv2.circle(image, (circle[0], circle[1]), circle[2], (0, 255, 0), 2)
    return image

def main():
    image_path = "path/to/your/image.jpg"
    preprocessed_image = preprocess_image(image_path)
    circles = detect_circles(preprocessed_image)
    visualized_image = visualize_circles(cv2.imread(image_path), circles)
    cv2.imshow("Detected Circles", visualized_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()