Random_anna.mp4 -

while video.isOpened(): ret, frame = video.read() if not ret: break

import cv2

height, width, channels = frame.shape

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

# Detecting objects blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False) net.setInput(blob) outs = net.forward(output_layers) random_anna.mp4

cv2.imshow("Image", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break

video.release() cv2.destroyAllWindows() This example focuses on object detection. Depending on your specific needs, you might need to adjust libraries, models, or entirely different approaches. Ensure you have the necessary models and configuration files (like yolov3.weights , yolov3.cfg , and coco.names for the YOLOv3 example) downloaded and properly referenced. while video

font = cv2.FONT_HERSHEY_SIMPLEX colors = np.random.uniform(0, 255, size=(len(classes), 3)) for i in range(len(boxes)): if i in indexes: x, y, w, h = boxes[i] label = str(classes[class_ids[i]]) confidence = str(round(confidences[i], 2)) color = colors[i] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) cv2.putText(frame, label + " " + confidence, (x, y + 20), font, 2, color, 2)