How Does OpenCV's find_squares() Function Detect Squares in an Image?

OpenCV C /Obj-C: Detecting a Sheet of Paper/Square Detection

Detecting squares in an image is a common task that arises in various image processing applications. In OpenCV, the find_squares() function provides a robust approach for identifying squares within an image.

The find_squares() function operates by iteratively examining each color channel (blue, green, and red) within the input image. For each channel:

1. Image Blurring: The input image is first blurred using a median filter to enhance edge detection.
2. Thresholding: Multiple threshold levels are applied to create binary images, which enhance the visibility of edges.
3. Contour Detection: Canny edge detection is employed to identify contours within the binary images.
4. Contour Approximation: The detected contours are approximated using the Douglas-Peucker algorithm to simplify their representation.
5. Square Selection: The approximated contours are then tested to determine if they meet the criteria for being a square. This includes criteria such as having four sides, a convex shape, and an area that exceeds a defined threshold.
6. Cosine Angle Test: To further refine the detection, a cosine angle test is performed to eliminate contours that do not resemble rectangles or squares. Only contours with a low maximum cosine value are considered as potential squares.
7. Output: The function returns a vector containing the identified squares, each represented as a list of points.

By following these steps, the find_squares() function can effectively detect squares within an image. The resulting output can be used for further processing tasks, such as document processing or object recognition.

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