Code for Non-Maximum Suppression with OpenCV and Python Tutorial

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nms_tutorial.py

import cv2 import numpy as np image = cv2.imread("images/1.jpg") # Take a copy of the original image to draw the bounding boxes after NMS image_copy = image.copy() # Dummy data: boxes in format (xmin, ymin, xmax, ymax), scores, and threshold boxes = np.array([[245, 305, 575, 490], # Box 1 coordinates [235, 300, 485, 515], # Box 2 coordinates, overlaps with Box 1 [305, 270, 540, 500],]) # Box 3 coordinates, overlaps with Box 1 confidence_scores = np.array([0.9, 0.8, 0.6]) threshold = 0.5 # Draw the bounding boxes on the image for xmin, ymin, xmax, ymax in boxes: cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 0, 255), 3) cv2.imshow("Before NMS", image) cv2.waitKey(0) # Apply non-maximum suppression indices = cv2.dnn.NMSBoxes(bboxes=boxes, scores=confidence_scores, score_threshold=0.7, nms_threshold=threshold) # Filter out the boxes based on the NMS result filtered_boxes = [boxes[i] for i in indices.flatten()] # Draw the filtered boxes on the image for xmin, ymin, xmax, ymax in filtered_boxes: cv2.rectangle(image_copy, (xmin, ymin), (xmax, ymax), (0, 255, 0), 3) cv2.imshow("After NMS", image_copy) cv2.waitKey(0) print("Filtered Boxes:", filtered_boxes)