Random_anna.mp4

height, width, channels = frame.shape

cv2.imshow("Image", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break random_anna.mp4

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

# 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) import cv2 class_ids = [] confidences = []

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.

import cv2

class_ids = [] confidences = [] boxes = [] for out in outs: for detection in out: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > 0.5 and classes[class_id] == "person": # Filter by class and confidence # Object detected center_x = int(detection[0] * width) center_y = int(detection[1] * height) w = int(detection[2] * width) h = int(detection[3] * height) # Rectangle coordinates x = int(center_x - w / 2) y = int(center_y - h / 2) boxes.append([x, y, w, h]) confidences.append(float(confidence)) class_ids.append(class_id)