Python uses mediaple+opencv to recognize video faces

1、 install

pip install mediapipe

2、 Code implementation

# -*- 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
# Size, thickness and color of points and lines for drawing face portrait （ The default is white ）
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()

3、 effect

3、 to update mediapiple+threadpool+opencv The efficiency of image face acquisition is higher than 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()