stream.py

from flask import Flask, render_template, Response
from imutils.video import VideoStream
import numpy as np
import matplotlib.pyplot as plt
import threading
import argparse
import datetime
import imutils
import time
import cv2

app = Flask(__name__)




@app.route('/')
def index():
    # rendering webpage
    return Response(gen(),
                    mimetype='multipart/x-mixed-replace; boundary=frame')
    return render_template('index.html')
def gen():
    classes = None
    with open('../hello/yolo-coco/coco.names', 'r') as f:
        classes = [line.strip() for line in f.readlines()]
        net = cv2.dnn.readNet('../hello/yolo-coco/yolov3.weights', '../hello/yolo-coco/yolov3.cfg')
    # print("[INFO] loading YOLO from disk...")
    # net.setInput(cv2.dnn.blobFromImage(image, 0.00392, (416,416), (0,0,0), True, crop=False))
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    # outs = net.forward(output_layers)

    vs = cv2.VideoCapture(0)
    # time.sleep(2.0)
    (W, H) = (None, None)

    while True:

        (grabbed, frame) = vs.read()
        if not grabbed:
            break
        
        if W is None or H is None:
            (H, W) = frame.shape[:2]
            
        blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
            swapRB=True, crop=False)
        
        net.setInput(blob)
        start = time.time()
        outs = net.forward(output_layers)
        end = time.time()
		
        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 > 0.1:
                    box = detection[0:4] * np.array([W, H, W, H])
                    (centerX, centerY, width, height) = box.astype("int")
                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))
                    
                    class_ids.append(class_id)
                    confidences.append(float(confidence))
                    boxes.append([x, y, int(width), int(height)])
					

		
        indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.1)
        #check if people detected
        rects = []
        c = []
        for i in indices:
            i = i[0]
            box = boxes[i]
            if class_ids[i]==0:
                label = str(classes[class_id]) 
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])
                rects = cv2.rectangle(frame, (x, y), (x + w, y + h),(0,0,0), 2)
                c.append(rects)
                cv2.putText(frame,label, (x, y - 5),cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0,0,0), 2)
                
        cv2.putText(frame, 'Status : Detecting ', (40,40), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)
        cv2.putText(frame, f'Total Persons : {len(c)}', (40,70), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,0,0), 2)

        ret, op = cv2.imencode('.jpg', frame)
        # op.tobytes()

        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + bytearray(op) + b'\r\n\r\n')
@app.route('/video_feed')
def video_feed():
    return Response(gen(),
                    mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
    # defining server ip address and port
    app.run(host='127.0.0.1', debug=False)