mediapiple opencv识别视频人脸
拼命_小李 人气:01、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()
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