python使用mediapiple+opencv识别视频人脸的实现

1、安装

pip install mediapipe

2、代码实现

# -*- coding: utf-8 -*-
"""
@Time    : 2022/3/18 14:43
@Author  : liwei
@Description:
"""
import cv2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh
mp_face_detection = mp.solutions.face_detection
# 绘制人脸画像的点和线的大小粗细及颜色（默认为白色）
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
cap = cv2.VideoCapture("E:\\video\\test\\test.mp4")# , cv2.CAP_DSHOW
# For webcam input:
# cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(
   model_selection=0, min_detection_confidence=0.5) as face_detection:
 while cap.isOpened():
   success, image = cap.read()
   if not success:
     print("Ignoring empty camera frame.")
     # If loading a video, use 'break' instead of 'continue'.
     break

# To improve performance, optionally mark the image as not writeable to
   # pass by reference.
   image.flags.writeable = False
   image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
   results = face_detection.process(image)

# Draw the face detection annotations on the image.
   image.flags.writeable = True
   image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
   if results.detections:
     box = results.detections[0].location_data.relative_bounding_box
     xmin = box.xmin
     ymin = box.ymin
     width = box.width
     height = box.height
     xmax = box.xmin + width
     ymax = ymin + height
     cv2.rectangle(image, (int(xmin * image.shape[1]),int(ymin* image.shape[0])), (int(xmax* image.shape[1]), int(ymax* image.shape[0])), (0, 0, 255), 2)
     # for detection in results.detections:
     #   mp_drawing.draw_detection(image, detection)
   # Flip the image horizontally for a selfie-view display.
   cv2.imshow('MediaPipe Face Detection', cv2.flip(image, 1))
   if cv2.waitKey(5) & 0xFF == 27:
     break
cap.release()

效果

3、更新 mediapiple+threadpool+opencv实现图片人脸采集效率高于dlib

# -*- coding: utf-8 -*-
"""
@Time    : 2022/3/23 13:43
@Author  : liwei
@Description:
"""
import cv2 as cv
import mediapipe as mp
import os
import threadpool
mp_drawing = mp.solutions.drawing_utils
mp_face_mesh = mp.solutions.face_mesh
mp_face_detection = mp.solutions.face_detection

savePath = "E:\\saveImg\\"
basePath = "E:\\img\\clear\\20220301\\"
def cut_face_img(file):
   # print(basePath + file)
   img = cv.imread(basePath + file)
   with mp_face_detection.FaceDetection(
           model_selection=0, min_detection_confidence=0.5) as face_detection:
       img.flags.writeable = False
       image = cv.cvtColor(img, cv.COLOR_RGB2BGR)
       results = face_detection.process(image)
       image = cv.cvtColor(image, cv.COLOR_RGB2BGR)
       image.flags.writeable = True
       if results.detections:
           box = results.detections[0].location_data.relative_bounding_box
           xmin = box.xmin
           ymin = box.ymin
           width = box.width
           height = box.height
           xmax = box.xmin + width
           ymax = ymin + height
           x1, x2, y1, y2 = int(xmax * image.shape[1]), int(xmin * image.shape[1]), int(
               ymax * image.shape[0]), int(ymin * image.shape[0])
           cropped = image[y2:y1, x2:x1]

if cropped.shape[1] > 200:
               cv.imwrite(savePath + file, cropped)
               print(savePath + file)

if __name__ == '__main__':
   data = os.listdir(basePath)
   pool = threadpool.ThreadPool(3)
   requests = threadpool.makeRequests(cut_face_img, data)
   [pool.putRequest(req) for req in requests]
   pool.wait()

来源：https://blog.csdn.net/m0_43432638/article/details/123684319