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app.py
from importlib import import_module
import os
from flask import Flask, render_template, Response
# import camera driver
if os.environ.get('CAMERA'):
Camera = import_module('camera_' + os.environ['CAMERA']).Camera
else:
from camera_opencv import Camera
app = Flask(__name__)
@app.route('/')
def index():
"""Video streaming home page."""
return render_template('index.html')
def gen(camera):
"""Video streaming generator function."""
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
@app.route('/video_feed')
def video_feed():
"""Video streaming route. Put this in the src attribute of an img tag."""
return Response(gen(Camera()),
mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
app.run(host='127.0.0.1', threaded=True)
camera_opencv.py
import cv2
from PIL import Image
from base_camera import BaseCamera
class Camera(BaseCamera):
video_source = 0
@staticmethod
def set_video_source(source):
Camera.video_source = source
@staticmethod
def frames():
camera = cv2.VideoCapture(Camera.video_source)
if not camera.isOpened():
raise RuntimeError('Could not start camera.')
while True:
# read current frame
_, img = camera.read()
#2. 人脸检测
#告诉OpenCV使用人脸识别分类器
classfier = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
#识别出人脸后要画的边框的颜色，RGB格式
color = (0, 255, 0)
#将当前帧转换成灰度图像
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
faceRects = classfier.detectMultiScale(grey, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32))
#3. 给识别出的人脸画矩形框
if len(faceRects) > 0: #大于0则检测到人脸
for faceRect in faceRects: #单独框出每一张人脸
x, y, w, h = faceRect
cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)
#4. 显示图像
# encode as a jpeg image and return it
yield cv2.imencode('.jpg', img)[1].tobytes()
./templates/index.html
<html>
<head>
<title>Video Streaming Demonstration</title>
</head>
<body>
<h1>Video Streaming Demonstration</h1>
<img src="{{ url_for('video_feed') }}">
</body>
</html>
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