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参考的：

其中到第7步的时候，face_recognition  提供的源代码不对，提示如下：

gs@nano:~/01_nano/dlib_face_recognition$ python3 dlib_face_recognition.py select timeoutVIDIOC_DQBUF: Resource temporarily unavailableTraceback (most recent call last):  File "dlib_face_recognition.py", line 81, in 
   
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)cv2.error: OpenCV(4.1.0) /home/gs/02_github/nano_build_opencv/opencv/modules/imgproc/src/resize.cpp:3718: error: (-215:Assertion failed) !ssize.empty() in function 'resize'gs@nano:~/01_nano/dlib_face_recognition$
   

经过一翻检查后，发现应该是打开摄像头的代码不对，因此修改下面的代码：

import face_recognitionimport cv2import numpy as np# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the# other example, but it includes some basic performance tweaks to make things run a lot faster:#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)#   2. Only detect faces in every other frame of video.# 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.def gstreamer_pipeline(    capture_width=1280,    capture_height=720,    display_width=1280,    display_height=720,    framerate=60,    flip_method=0,):    return (        "nvarguscamerasrc ! "        "video/x-raw(memory:NVMM), "        "width=(int)%d, height=(int)%d, "        "format=(string)NV12, framerate=(fraction)%d/1 ! "        "nvvidconv flip-method=%d ! "        "video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! "        "videoconvert ! "        "video/x-raw, format=(string)BGR ! appsink"        % (            capture_width,            capture_height,            framerate,            flip_method,            display_width,            display_height,        )    )# Get a reference to webcam #0 (the default one)#video_capture = cv2.VideoCapture(0)video_capture = cv2.VideoCapture(gstreamer_pipeline(flip_method=0), cv2.CAP_GSTREAMER)# Load a sample picture and learn how to recognize it.obama_image = face_recognition.load_image_file("me.png")obama_face_encoding = face_recognition.face_encodings(obama_image)[0]# Load a second sample picture and learn how to recognize it.biden_image = face_recognition.load_image_file("biden.jpg")biden_face_encoding = face_recognition.face_encodings(biden_image)[0]# Create arrays of known face encodings and their namesknown_face_encodings = [    obama_face_encoding,    biden_face_encoding]known_face_names = [    "me",    "Joe Biden"]# Initialize some variablesface_locations = []face_encodings = []face_names = []process_this_frame = Truewhile True:    # Grab a single frame of video    ret, frame = video_capture.read()    # Resize frame of video to 1/4 size for faster face recognition processing    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)    rgb_small_frame = small_frame[:, :, ::-1]    # Only process every other frame of video to save time    if process_this_frame:        # Find all the faces and face encodings in the current frame of video        face_locations = face_recognition.face_locations(rgb_small_frame)        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)        face_names = []        for face_encoding in face_encodings:            # See if the face is a match for the known face(s)            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)            name = "Unknown"            # # If a match was found in known_face_encodings, just use the first one.            # if True in matches:            #     first_match_index = matches.index(True)            #     name = known_face_names[first_match_index]            # Or instead, use the known face with the smallest distance to the new face            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)            best_match_index = np.argmin(face_distances)            if matches[best_match_index]:                name = known_face_names[best_match_index]            face_names.append(name)    process_this_frame = not process_this_frame    # Display the results    for (top, right, bottom, left), name in zip(face_locations, face_names):        # Scale back up face locations since the frame we detected in was scaled to 1/4 size        top *= 4        right *= 4        bottom *= 4        left *= 4        # Draw a box around the face        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)        # Draw a label with a name below the face        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)        font = cv2.FONT_HERSHEY_DUPLEX        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)    # Display the resulting image    cv2.imshow('Video', frame)    # Hit 'q' on the keyboard to quit!    if cv2.waitKey(1) & 0xFF == ord('q'):        break# Release handle to the webcamvideo_capture.release()cv2.destroyAllWindows()