6 code Foundations of Computer Vision Feature Detection and Matching скачать в хорошем качестве

6 code Foundations of Computer Vision Feature Detection and Matching

This code demonstrates the implementation of local feature detection, description, and matching—essential techniques for tasks like image stitching and object recognition. 1. Harris Corner Detection Harris Corner Detection identifies points of interest (corners) based on intense intensity changes in all directions. cv.cvtColor(img, cv.COLOR_BGR2RGB): Converts the image from BGR (OpenCV default) to RGB for correct display in Matplotlib. gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY): Converts the image to grayscale, as feature detection operates on intensity rather than color. cv.cornerHarris(gray, 3, 3, 0.1): Computes the Harris response. blockSize (3): The size of the neighborhood considered for detection. ksize (3): Aperture parameter for the Sobel derivative used to find gradients. k (0.1): Harris detector free parameter. cv.dilate: Thickens the detected corner points to make them visible for visualization. 2. SIFT (Scale-Invariant Feature Transform) SIFT is a more robust detector and descriptor that remains effective even if the image is scaled or rotated. sift = cv.SIFT_create(): Initializes the SIFT detector object. kp1, des1 = sift.detectAndCompute(img1, None): detect: Finds keypoints (locations). compute: Generates descriptors (mathematical "fingerprints" of the area around the keypoint). Keypoint visualization: The circles denote the location and scale of the feature, while the radius direction denotes its orientation. 3. Feature Matching (BFMatcher) Matching connects similar descriptors between two different images. cv.BFMatcher(cv.NORM_L2, crossCheck=True): Initializes the Brute-Force Matcher using the L2 norm (Euclidean distance) to measure similarity between descriptors. bf.match(des1, des2): Finds the best match for each descriptor in the first set from the second set. sorted(matches, key=lambda x:x.distance): Sorts matches by distance (lower distance means the descriptors are more similar/better matches). cv.drawMatches: Visualizes the connections between the top matched keypoints across the two images. 4. Advanced Matching with KNN and Ratio Test To improve accuracy and reduce false positives, the code implements a ratio test. bf.knnMatch(des1, des2, k=2): Finds the two nearest neighbors for each descriptor. if m.distance lt 0.6 * n.distance: The Lowe's Ratio Test. It only accepts a match if the best match is significantly closer than the second-best match, effectively filtering out ambiguous points.