Detecting Circles in Images: Algorithm Implementation and Visualization

After reading an image, we can perform a series of operations such as binarization to extract all circular shapes present in the image. These circles may vary in size and color. The primary objective is to accurately identify all circular objects and mark them with red circular boundaries. This process requires the implementation of computer vision algorithms and toolkits to process image data efficiently and precisely. For code implementation, we typically use image processing libraries like OpenCV. The workflow includes: 1. Reading the image using functions like cv2.imread() 2. Converting to grayscale with cv2.cvtColor() 3. Applying binarization through thresholding methods like cv2.threshold() 4. Detecting circles using Hough Circle Transform (cv2.HoughCircles()) which employs a voting mechanism to identify circular patterns 5. Drawing detected circles with cv2.circle() using red color parameters (BGR: 0,0,255) 6. Displaying the final result using cv2.imshow() The Hough Circle Transform algorithm works by accumulating votes in a parameter space for circle centers and radii, making it robust against partial occlusion and noise. Proper parameter tuning for minimum/maximum radius and detection sensitivity is crucial for accurate results. Ultimately, the processed image with annotated circles is displayed in a window, providing clear visualization of all identified circular objects with their precise locations and dimensions marked in red.