opencv face_recognition检测视频中的人脸并截取人脸 - 好文

import face_recognition import cv2 # This is a demo of running face recognition
on a video file and saving the results to a new video file. # # PLEASE NOTE:
This example requires OpenCV (the `cv2` library) to be installed only to read
from your webcam. # OpenCV is *not* required to use the face_recognition
library. It's only required if you want to run this # specific demo. If you
have trouble installing it, try any of the other demos that don't require it
instead. # Open the input movie file input_movie =
cv2.VideoCapture("hamilton_clip.mp4") length =
int(input_movie.get(cv2.CAP_PROP_FRAME_COUNT)) # Create an output movie file
(make sure resolution/frame rate matches input video!) fourcc =
cv2.VideoWriter_fourcc(*'XVID') output_movie = cv2.VideoWriter('output.avi',
fourcc, 29.97, (640, 360)) # Load some sample pictures and learn how to
recognize them. lmm_image =
face_recognition.load_image_file("lin-manuel-miranda.png") lmm_face_encoding =
face_recognition.face_encodings(lmm_image)[0] al_image =
face_recognition.load_image_file("alex-lacamoire.png") al_face_encoding =
face_recognition.face_encodings(al_image)[0] known_faces = [ lmm_face_encoding,
al_face_encoding ] # Initialize some variables face_locations = []
face_encodings = [] face_names = [] frame_number = 0 import datetime starttime
= datetime.datetime.now() print(starttime) while True: # Grab a single frame of
video ret, frame = input_movie.read() frame_number += 1 # Quit when the input
video file ends if not ret: break # Convert the image from BGR color (which
OpenCV uses) to RGB color (which face_recognition uses) rgb_frame = frame[:, :,
::-1] # Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_frame) face_encodings =
face_recognition.face_encodings(rgb_frame, face_locations) face_names = [] for
face_encoding in face_encodings: # See if the face is a match for the known
face(s) match = face_recognition.compare_faces(known_faces, face_encoding,
tolerance=0.50) # If you had more than 2 faces, you could make this logic a lot
prettier # but I kept it simple for the demo name = None if match[0]: name =
"Lin-Manuel Miranda" elif match[1]: name = "Alex Lacamoire"
face_names.append(name) # Label the results for (top, right, bottom, left),
name in zip(face_locations, face_names): if not name: continue # Draw a box
around the face cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255),
2) #crop the frame #cropImg = cv2.rectangle(frame, (left, top), (right,
bottom), (0, 0, 255), 2) #cv2.imwrite('test.png',cv2.rectangle(frame, (left,
top), (right, bottom), (0, 0, 255), 2)) #print('test.png') #image = frame[y -
10: y + h + 10, x - 10: x + w + 10] #cv2.imwrite(img_name,
image,[int(cv2.IMWRITE_PNG_COMPRESSION), 9]) #cv2.rectangle(frame, (x - 10, y -
10), (x + w + 10, y + h + 10), color, 2)         #if there is a face in
frame,intercept the face from frame and save to image         testImg =
frame[top:bottom,left:right]         #output the img to local
        cv2.imwrite('test.png', testImg,[int(cv2.IMWRITE_PNG_COMPRESSION), 9])
#cv2.imwrite('test.png',cv2.rectangle(frame, (left, top), (right, bottom), (0,
0, 255), 2)) #cv2.imwrite('test.png',cropImg) #cv2.imshow(cropImg)
print("test.png") # Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 25), (right, bottom), (0, 0, 255),
cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left + 6,
bottom - 6), font, 0.5, (255, 255, 255), 1) # Write the resulting image to the
output video file #print("Writing frame {} / {}".format(frame_number, length))
output_movie.write(frame) # All done! endtime = datetime.datetime.now()
print(endtime) print((endtime - starttime).seconds) input_movie.release()
cv2.destroyAllWindows()

#从视频帧中截取人脸保存截取的人脸图片

#if there is a face in frame,intercept the face from frame and save to image
testImg = frame[top:bottom,left:right] #output the img to local
cv2.imwrite('test.png', testImg,[int(cv2.IMWRITE_PNG_COMPRESSION), 9])
find_faces_in_picture.py通过pil_image的截取的人脸并本地保存

find_faces_in_picture_cv.py通过cv2的方式保存识别的人脸

facerec_from_video_file.py从本地视频中截取人脸

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