Social Distance Detection

import cv2
import numpy as np
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
def calculate_distance(point1, point2):
    return np.linalg.norm(np.array(point1) - np.array(point2))
def social_distance_detection(video_source=0):
    cap = cv2.VideoCapture(video_source)

    while True:
        # Read frame from the video source
        ret, frame = cap.read()
        height, width, _ = frame.shape
        
        # Detecting objects
        blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
        net.setInput(blob)
        outputs = net.forward(output_layers)

        boxes = []
        confidences = []
        class_ids = []
        for output in outputs:
            for detection in output:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                
                # Filter only person class (ID 0 for COCO dataset)
                if confidence > 0.5 and class_id == 0:
                    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)

        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
        detected_points = []
        for i in range(len(boxes)):
            if i in indexes:
                x, y, w, h = boxes[i]
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                cx, cy = x + w // 2, y + h // 2
                detected_points.append((cx, cy))
                cv2.putText(frame, "Person", (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

        for i in range(len(detected_points)):
            for j in range(i + 1, len(detected_points)):
                dist = calculate_distance(detected_points[i], detected_points[j])
                if dist < 100:  # Distance threshold (e.g., 100 pixels)
                    cv2.putText(frame, "Maintain distance!", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        cv2.imshow("Social Distance Detection", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    social_distance_detection()