MTCNN 人脸检测

draw/MTCNN_person.py

@@ -0,0 +1,26 @@
+#coding=utf-8
+from mtcnn.mtcnn import MTCNN
+import cv2
+
+# 初始化MTCNN人脸检测器
+face_detector = MTCNN()
+
+def detector(file):
+    # 读取图像
+    img = cv2.cvtColor(cv2.imread(file), cv2.COLOR_BGR2RGB)
+    # 进行人脸检测
+    faces = face_detector.detect_faces(img)
+
+    print(len(faces))
+    # # 在图像中绘制人脸框和关键点
+    for face in faces:
+        x, y, w, h = face['box']
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        for key, value in face['keypoints'].items():
+            cv2.circle(img, value, 2, (0, 255, 0), -1)
+    return faces
+
+# # 显示结果
+# cv2.imshow('Detected Faces', img)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()

draw/requirement.txt

@@ -0,0 +1,4 @@
+opencv-python-headless
+tensorflow
+opencv-python
+mtcnn

draw/web_index.py

@@ -0,0 +1,57 @@
+import time
+
+from flask import Flask, request
+import json
+import MTCNN_person
+
+app = Flask(__name__)
+
+@app.route('/')
+def hello_world():
+    return 'Hello World!'
+
+import requests
+#图片链接
+headers ={
+    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
+}
+dir = 'C:\\Users\\yufen\\Pictures\\face\\'
+def do_download(url):
+    url1 = url.split('?')[0]
+    name = url1.split('/')[-1]
+    r = requests.get(url, headers=headers)
+    # 下载图片
+    # 二进制数据需要用r.content 进行提取
+    # 将图片放在‘图库’文件夹下，‘图库’是文件夹的名称，将图片放入该文件夹中，该文件夹与py文件在同一目录下
+    f = open(dir + name, 'wb')
+    f.write(r.content)
+    f.close()
+    return name
+
+@app.route('/download')
+def download():
+    url = request.args.get('url')
+    time1 = int(round(time.time() * 1000))
+    print('info=%s' % url)
+    do_download(url)
+    time2 = int(round(time.time() * 1000))
+    print('over %s from %s' % (time2 - time1, time1))
+    return 'Hello World!'
+
+@app.route('/face')
+def face():
+    info = request.args.get('url')
+    time1 = int(round(time.time() * 1000))
+    print('info=%s' % info)
+    name = do_download(info)
+    time2 = int(round(time.time() * 1000))
+    faces = MTCNN_person.detector(dir + name)
+    time3 = int(round(time.time() * 1000))
+    print('over %s from %s' % (time3 - time1, time1))
+    return json.dumps(faces)
+
+
+if __name__ == '__main__':
+    app.run(port=5000)
+    # do_download(url='https://img.bbztx.com/article/live150178/team217182/20230913192631_tmp_9fbaaecf98fd1a3e91a6571f744f4b34511300d6c5a5b358.jpg?x-oss-process=image/resize,h_1000,m_lfit')
+    # MTCNN_person.detector('C:\\Users\\yufen\\Pictures\\face\\20230913192631_tmp_9fbaaecf98fd1a3e91a6571f744f4b34511300d6c5a5b358.jpg')