How to efficiently find white circular areas in high-resolution images?

Accurately locate white circular targets in high-resolution images

When processing ultra-high resolution images (such as 9000x7000 pixels), it is crucial to quickly and accurately identify the white circular areas in it. This article is based on Python and OpenCV libraries and provides an optimization solution to effectively solve such image processing problems.

The original code is directly applied to high-resolution images with inefficiency. Therefore, we need to optimize the processing process and improve detection accuracy and speed.

Detailed explanation of optimization strategy

1. Image size adjustment: To reduce the computational complexity, the image is first scaled. Use the cv2.resize() function to resize the image, for example, reduce the image to one-tenth of the original image.

    src = cv2.imread(image_path)
   scale_factor = 0.1
   resized_image = cv2.resize(src, None, fx=scale_factor, fy=scale_factor)

2. Grayscale conversion and threshold segmentation: convert the scaled image into a grayscale map and use threshold segmentation to extract the white area. This step enhances the contrast of the target area.

    gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
   _, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)

3. Morphological processing: Use morphological closed operation ( cv2.MORPH_CLOSE ) to connect the small gaps in the white area to form a complete circular contour, improving the reliability of detection.

    kernel = np.ones((5, 5), np.uint8)
   closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

4. HoughCircle Transform: Use HoughCircle Transform ( cv2.HoughCircles ) to detect circles in an image. The parameters need to be adjusted according to actual conditions to achieve the best detection effect.

    circles = cv2.HoughCircles(closing, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)
   If circles is not None:
       circles = np.uint16(np.around(circles))
       for i in circles[0, :]:
           cv2.circle(resized_image, (i[0], i[1]), i[2], (0, 255, 0), 2)
           cv2.circle(resized_image, (i[0], i[1]), 2, (0, 0, 255), 3)

5. Results are displayed: Finally, the processed image is displayed and the detected circular area is marked.

    cv2.imshow("Detected Circles", resized_image)
   cv2.waitKey(0)
   cv2.destroyAllWindows()

Through the above steps, we can efficiently and accurately identify white circular areas in high-resolution images. It should be noted that the threshold and the parameters of the Hough transform need to be fine-tuned according to the specific image to obtain the best results.

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