Darknet using OpenCV in python

darknet_opencv_camera.py

import cv2
import numpy as np

# Load YOLO
net = cv2.dnn.readNet("./yolov2.weights", "./cfg/yolov2.cfg")
classes = []
with open("./data/coco.names", "r") as f:
    classes = [line.strip() for line in f]

# Set up OpenCV video capture
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("http://<IP>:8080/video")

while True:
    # Read a frame from the camera
    ret, frame = cap.read()

    # Create a blob from the frame
    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)

    # Get the output layer names
    output_layer_names = net.getUnconnectedOutLayersNames()

    # Run forward pass to get output from output layer
    outputs = net.forward(output_layer_names)

    # Process each output
    for output in outputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]

            if confidence > 0.5:
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                width = int(detection[2] * frame.shape[1])
                height = int(detection[3] * frame.shape[0])

                # Calculate top-left corner of bounding box
                x = int(center_x - width / 2)
                y = int(center_y - height / 2)

                # Draw bounding box and label on the frame
                cv2.rectangle(frame, (x, y), (x + width, y + height), (0, 255, 0), 2)
                label = f"{classes[class_id]}: {confidence:.2f}"
                cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow("YOLO Object Detection", frame)

    # Break the loop when the 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close the window
cap.release()
cv2.destroyAllWindows()