Random_anna.mp4 Apr 2026

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

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) random_anna.mp4

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) 2)) color = colors[i] cv2.rectangle(frame

height, width, channels = frame.shape

# 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) (x + w

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

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