opencv face_recognition检测视频中的人脸并截取人脸

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