本文的重点内容为opencv+flask+face_recognition
导读
先上开源代码，https://github.com/Kr1s77/flask-video-streaming-recorder 作者通过opencv+flask完成了读取摄像头，并且在HTML页面显示的功能，很多场景都会用到，本文是分享二次开发人脸识别。
环境正常登录
源码的main.py中源码app.run(threaded=True, host="0.0.0.0")，默认了port=5000，端口是可以改变的，如下app.run(threaded=True, host="0.0.0.0",port=5002)
改完之后运行python main.py，打开浏览器访问http://localhost:5002/login 就可以看到登录页面啦！(作者忘了提示这步)
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Username: admin
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Password: admin
加入人脸识别功能
本文用开源的face_recognition（如果没有安装就pip install face_recognition）
只需要修改一个文件：controller/utils/camera.py，下面直接给完整代码
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import cv2
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import threading
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import face_recognition
-
-
class RecordingThread(threading.Thread):
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def __init__(self, name, camera):
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threading.Thread.__init_
_(
self)
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self.name = name
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self.isRunning = True
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self.cap = camera
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fourcc = cv2.VideoWriter_fourcc(*
'MJPG')
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self.out = cv2.VideoWriter(
'./static/video.avi', fourcc,
20.0, (
640,
480))
-
-
def run(self):
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while
self.
isRunning:
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ret, frame =
self.cap.read()
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if
ret:
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self.out.write(frame)
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self.out.release()
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def stop(self):
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self.isRunning = False
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def __del__(self):
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self.out.release()
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class VideoCamera(object):
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def __init__(self):
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# 打开摄像头， 0代表笔记本内置摄像头
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self.cap = cv2.VideoCapture(
0)
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#初始化人脸
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obama_img = face_recognition.load_image_file(
"obama.jpg")
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self.obama_face_encoding = face_recognition.face_encodings(obama_img)[
0]
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self.face_locations = []
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self.face_encodings = []
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self.face_names = []
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self.process_this_frame = True
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# 初始化视频录制环境
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self.is_record = False
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self.out = None
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# 视频录制线程
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self.recordingThread = None
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# 退出程序释放摄像头
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def __del__(self):
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self.cap.release()
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def get_frame(self):
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ret, frame =
self.cap.read()
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if
ret:
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# 将视频帧调整为1/4大小，以加快脸部识别处理
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small_frame = cv2.resize(frame,(
0,
0),fx=
0.
25, fy=
0.
25)
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# 将图像从BGR颜色（OpenCV使用的）转换为RGB颜色（face_recognition使用）
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rgb_small_frame = small_frame[
:,
:,
:
:-
1]
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if
self.
process_this_frame:
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# 查找当前视频帧中的所有面部和脸部编码
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self.face_locations = face_recognition.face_locations(small_frame)
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self.face_encodings = face_recognition.face_encodings(small_frame,
self.face_locations)
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self.face_names = []
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#处理多张人脸的情况
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for
self.face_encoding
in
self.
face_encodings:
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# 查看脸部是否与已知脸部相匹配（S）
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match = face_recognition.compare_faces([
self.obama_face_encoding],
self.face_encoding)
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if match[
0]:
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name =
"obama"
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else:
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name =
"unkonwn"
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self.face_names.append(name)
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self.process_this_frame =
not
self.process_this_frame
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#对有人脸的图进行处理
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for (top, right, bottom, left), name
in zip(
self.face_locations,
self.face_names):
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# 自从我们检测到的框架缩放到1/4尺寸后，缩放后面的位置
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top *=
4
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right *=
4
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bottom *=
4
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left *=
4
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# 在脸上画一个方框
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cv2.rectangle(frame, (left, top), (right, bottom), (
0,
0,
255),
2)
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# 在脸部下面画一个名字
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cv2.rectangle(frame, (left, bottom -
35), (right, bottom), (
0,
0,
255),
2)
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font = cv2.FONT_HERSHEY_DUPLEX
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cv2.putText(frame, name, (left+
6, bottom-
6), font,
1.0, (
255,
255,
255),
1)
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# 因为opencv读取的图片并非jpeg格式，因此要用motion JPEG模式需要先将图片转码成jpg格式图片
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ret, jpeg = cv2.imencode(
'.jpg', frame)
-
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# 视频录制
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if
self.
is_record:
-
if
self.out ==
None:
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fourcc = cv2.VideoWriter_fourcc(*
'MJPG')
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self.out = cv2.VideoWriter(
'./static/video.avi', fourcc,
20.0, (
640,
480))
-
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ret, frame =
self.cap.read()
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if
ret:
-
self.out.write(frame)
-
else:
-
if
self.out !=
None:
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self.out.release()
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self.out = None
-
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return jpeg.tobytes()
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else:
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return None
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def start_record(self):
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self.is_record = True
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self.recordingThread = RecordingThread(
"Video Recording Thread",
self.cap)
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self.recordingThread.start()
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def stop_record(self):
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self.is_record = False
-
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if
self.recordingThread !=
None:
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self.recordingThread.stop()
obama_img = face_recognition.load_image_file("obama.jpg") ，我这里只加了一张奥巴马的图片作为人脸库，这张图片你可以拍一张自己的照片替换，放在目录下就好了，没有照片会报错的。
结尾
加人脸识别的地方，可以改成调用某些平台的AI图片处理API，自行封装哈。
转载：https://blog.csdn.net/liangyuna8787/article/details/106361532
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