Jetson nano | 实现人脸识别

人脸识别代码库

https://github.com/ageitgey/face_recognition

安装参考：https://blog.youkuaiyun.com/ABC__xiaoming/article/details/116611523

报错

linano@jetson-nano$:python3 mpy-Copyl.py
[ WARN:0 ] qlobal /home/nvidia/host/build opencv/nv opencv/modules/videoio/src/cap qstreamer. Cpp (1757) handleMessage OpenCV | GStreamer warninq: Embedded video playback halted; module v412src0 reported: Internal data stream error
[WARN: 0] qlobal /home/nvidia/host/build opencv/nv opencv/modules/videoio/src/cap gst.reamer.cpp (886) open OpenCV | GStreamer warning: unable to start pipeline
[ WARN:0 ] global /home/nvidia/host/build opencv/nv opencv/modules/videoio/src/cap qatreamer.cpp (480) isPipelinePlaying Opencv | Gstreamer warning: Gstreamer: pipeline have not been created

原因：
我是csi摄像头。cv2.VideoCapture(0)通常用于usb。

解决：
利用JetCam：JetCam是用于NVIDIA Jetson的易于使用的Python摄像头界面。

https://github.com/NVIDIA-AI-IOT/jetcam

安装
git clone https://github.com/NVIDIA-AI-IOT/jetcam
cd jetcam
sudo python3 setup.py install

使用JetCam+opencv 调用CSI，成功则成功安装

from jetcam.csi_camera import CSICamera
import cv2

camera0 = CSICamera(capture_device=0, width=224, height=224)
image0 = camera0.read()
print(image0.shape)
print(camera0.value.shape)
while 1:
    image0 = camera0.read()
    cv2.imshow("CSI Camera0", image0)
    kk = cv2.waitKey(1)
    if kk == ord('q'):  # 按下 q 键，退出
        break

找到face_recognition/examples文件夹里的 facerec_from_webcam_faster.py 文件，仿照该文件新建一个facerec_from_CSI_faster.py

import face_recognition
import cv2
import numpy as np
from jetcam.csi_camera import CSICamera

# 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.

# Get a reference to webcam #0 (the default one)
# video_capture = cv2.VideoCapture(0)
video_capture = CSICamera(capture_device=0, width=1280, height=720)

# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
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 names
known_face_encodings = [
    obama_face_encoding,
    biden_face_encoding
]
known_face_names = [
    "Barack Obama",
    "Joe Biden"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    # ret, frame = video_capture.read()
    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]
    rgb_small_frame = small_frame

    # 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 webcam
video_capture.release()
cv2.destroyAllWindows()

运行

python3  facerec_from_CSI_faster.py

如果想检测自己的脸，更改如下，添加进去即可