人脸识别准备 -- 基于raspberry pi 3b + movidius

#! /usr/bin/env python3import syssys.path.insert(0, "../../ncapi2_shim")import mvnc_simple_api as mvncimport numpyimport cv2import sysimport osfrom picamera.array import PiRGBArrayfrom picamera import PiCameraimport time# initialize the camera and grab a reference to the raw camera capturecamera = PiCamera()camera.resolution = (280, 280)camera.framerate = 32rawCapture = PiRGBArray(camera, size=(280, 280))frame_name=''EXAMPLES_BASE_DIR='../../'IMAGES_DIR = './'VALIDATED_IMAGES_DIR = IMAGES_DIR + 'validated_images/'validated_image_filename = VALIDATED_IMAGES_DIR + 'my1.png'GRAPH_FILENAME = "facenet_celeb_ncs.graph"# name of the opencv windowCV_WINDOW_NAME = "FaceNet"# the same face will return 0.0# different faces return higher numbers# this is NOT between 0.0 and 1.0FACE_MATCH_THRESHOLD = 1.2# Run an inference on the passed image# image_to_classify is the image on which an inference will be performed# ???upon successful return this image will be overlayed with boxes# ???and labels identifying the found objects within the image.# ssd_mobilenet_graph is the Graph object from the NCAPI which will# ???be used to peform the inference.def run_inference(image_to_classify, facenet_graph): ???# get a resized version of the image that is the dimensions ???# SSD Mobile net expects ???resized_image = preprocess_image(image_to_classify) ???# *************************************************************** ???# Send the image to the NCS ???# *************************************************************** ???facenet_graph.LoadTensor(resized_image.astype(numpy.float16), None) ???# *************************************************************** ???# Get the result from the NCS ???# *************************************************************** ???output, userobj = facenet_graph.GetResult() ???return output# overlays the boxes and labels onto the display image.# display_image is the image on which to overlay to# image info is a text string to overlay onto the image.# matching is a Boolean specifying if the image was a match.# returns Nonedef overlay_on_image(display_image, image_info, matching): ???rect_width = 10 ???offset = int(rect_width/2) ???if (image_info != None): ???????cv2.putText(display_image, image_info, (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) ???if (matching): ???????# match, green rectangle ???????cv2.rectangle(display_image, (0+offset, 0+offset), ?????????????????????(display_image.shape[1]-offset-1, display_image.shape[0]-offset-1), ?????????????????????(0, 255, 0), 10) ???else: ???????# not a match, red rectangle ???????cv2.rectangle(display_image, (0+offset, 0+offset), ?????????????????????(display_image.shape[1]-offset-1, display_image.shape[0]-offset-1), ?????????????????????(0, 0, 255), 10)# whiten an imagedef whiten_image(source_image): ???source_mean = numpy.mean(source_image) ???source_standard_deviation = numpy.std(source_image) ???std_adjusted = numpy.maximum(source_standard_deviation, 1.0 / numpy.sqrt(source_image.size)) ???whitened_image = numpy.multiply(numpy.subtract(source_image, source_mean), 1 / std_adjusted) ???return whitened_image# create a preprocessed image from the source image that matches the# network expectations and return itdef preprocess_image(src): ???# scale the image ???NETWORK_WIDTH = 160 ???NETWORK_HEIGHT = 160 ???preprocessed_image = cv2.resize(src, (NETWORK_WIDTH, NETWORK_HEIGHT)) ???#convert to RGB ???preprocessed_image = cv2.cvtColor(preprocessed_image, cv2.COLOR_BGR2RGB) ???#whiten ???preprocessed_image = whiten_image(preprocessed_image) ???# return the preprocessed image ???return preprocessed_image# determine if two images are of matching faces based on the# the network output for both images.def face_match(face1_output, face2_output): ???if (len(face1_output) != len(face2_output)): ???????print('length mismatch in face_match') ???????return False ???total_diff = 0 ???for output_index in range(0, len(face1_output)): ???????this_diff = numpy.square(face1_output[output_index] - face2_output[output_index]) ???????total_diff += this_diff ???print('Total Difference is: ' + str(total_diff)) ???if (total_diff < FACE_MATCH_THRESHOLD): ???????# the total difference between the two is under the threshold so ???????# the faces match. ???????return True ???# differences between faces was over the threshold above so ???# they didn't match. ???return False# handles key presses# raw_key is the return value from cv2.waitkey# returns False if program should end, or True if should continuedef handle_keys(raw_key): ???ascii_code = raw_key & 0xFF ???if ((ascii_code == ord('q')) or (ascii_code == ord('Q'))): ???????return False ???return True# start the opencv webcam streaming and pass each frame# from the camera to the facenet network for an inference# Continue looping until the result of the camera frame inference# matches the valid face output and then return.# valid_output is inference result for the valid image# validated image filename is the name of the valid image file# graph is the ncsdk Graph object initialized with the facenet graph file# ??which we will run the inference on.# returns Nonedef run_camera(valid_output, validated_image_filename, graph): ???frame_count = 0 ???cv2.namedWindow(CV_WINDOW_NAME) ???found_match = False ???for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True): ???????# grab the raw NumPy array representing the image, then initialize the timestamp ???????# and occupied/unoccupied text ???????vid_image = frame.array ???????test_output = run_inference(vid_image, graph) ???????if (face_match(valid_output, test_output)): ???????????????print('PASS! ?File ' + frame_name + ' matches ' + validated_image_filename) ???????????????found_match = True ???????else: ???????????found_match = False ???????????print('FAIL! ?File ' + frame_name + ' does not match ' + validated_image_filename) ???????overlay_on_image(vid_image, frame_name, found_match) ???????# check if the window is visible, this means the user hasn't closed ???????# the window via the X button ???????prop_val = cv2.getWindowProperty(CV_WINDOW_NAME, cv2.WND_PROP_ASPECT_RATIO) ???????if (prop_val < 0.0): ???????????print('window closed') ???????????break ???????# display the results and wait for user to hit a key ???????cv2.imshow(CV_WINDOW_NAME, vid_image) ???????raw_key = cv2.waitKey(1) ???????if (raw_key != -1): ???????????if (handle_keys(raw_key) == False): ???????????????print('user pressed Q') ???????????????break ???????# show the frame ???????#cv2.imshow("Frame", image) ???????key = cv2.waitKey(1) & 0xFF ???????# clear the stream in preparation for the next frame ???????rawCapture.truncate(0) ???????# if the `q` key was pressed, break from the loop ???????if key == ord("q"): ???????????break# This function is called from the entry point to do# all the work of the programdef main(): ???# Get a list of ALL the sticks that are plugged in ???# we need at least one ???devices = mvnc.EnumerateDevices() ???if len(devices) == 0: ???????print('No NCS devices found') ???????quit() ???# Pick the first stick to run the network ???device = mvnc.Device(devices[0]) ???# Open the NCS ???device.OpenDevice() ???# The graph file that was created with the ncsdk compiler ???graph_file_name = GRAPH_FILENAME ???# read in the graph file to memory buffer ???with open(graph_file_name, mode='rb') as f: ???????graph_in_memory = f.read() ???# create the NCAPI graph instance from the memory buffer containing the graph file. ???graph = device.AllocateGraph(graph_in_memory) ???validated_image = cv2.imread(validated_image_filename) ???valid_output = run_inference(validated_image, graph) ???run_camera(valid_output, validated_image_filename, graph) ???# Clean up the graph and the device ???graph.DeallocateGraph() ???device.CloseDevice()# main entry point for program. we'll call main() to do what needs to be done.if __name__ == "__main__": ???sys.exit(main())