Face recognition

python face identification

Install dlib and face_recognition on a Raspberry Pi

sudo apt-get update

sudo apt-get upgrade

sudo apt-get install build-essential cmake gfortran git wget curl graphicsmagick libgraphicsmagick1-dev libatlas-dev libavcodec-dev libavformat-dev libboost-all-dev libgtk2.0-dev libjpeg-dev liblapack-dev libswscale-dev pkg-config python3-dev python3-numpy python3-pip zip

sudo apt-get clean

臉部特徵定點辨識下載 shape_predictor_68_face_landmarks.dat

# import the necessary packagesfrom imutils import face_utilsimport dlibimport cv2import urllib.request
# initialize dlib's face detector (HOG-based) and then create# the facial landmark predictorurllib.request.urlretrieve("http://mgt.tmu.edu.tw/~TMU_MGT/files/archive/491_11d37e36.jpg", "face.jpg")p = "shape_predictor_68_face_landmarks.dat"detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(p)
# load the input image and convert it to grayscaleimage = cv2.imread("face.jpg")gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale imagerects = detector(gray, 0)
# loop over the face detectionsfor (i, rect) in enumerate(rects): # determine the facial landmarks for the face region, then # convert the facial landmark (x, y)-coordinates to a NumPy # array shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape)
# loop over the (x, y)-coordinates for the facial landmarks # and draw them on the image for (x, y) in shape: cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
# show the output image with the face detections + facial landmarkscv2.imshow("Output", image)cv2.waitKey(0)

臉部化妝

https://beta.deepnote.org/launch?template=face_recognition Jupyter User Interface

import face_recognitionfrom matplotlib.pyplot import imshowimport matplotlib.pyplot as plt
import urllib.request
urllib.request.urlretrieve("https://images.unsplash.com/photo-1534559529872-1a83a6cbc03d?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1924&q=80", "face.jpg")image = face_recognition.load_image_file("face.jpg")f, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(15,15))
ax1.imshow(image)
imageface_locations = face_recognition.face_locations(image)imageface_locations
face_landmarks_list = face_recognition.face_landmarks(image)face_landmarks_list
from PIL import Image, ImageDraw# import face_recognition
# Load the jpg file into a numpy array# image = face_recognition.load_image_file("face.jpg")
# Find all facial features in all the faces in the imageface_landmarks_list = face_recognition.face_landmarks(image)
pil_image = Image.fromarray(image)for face_landmarks in face_landmarks_list: d = ImageDraw.Draw(pil_image, 'RGBA')
# Make the eyebrows into a nightmare d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128)) d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128)) d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=5) d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=5)
# Gloss the lips d.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128)) d.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128)) d.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=8) d.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=8)
# Sparkle the eyes d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30)) d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
# Apply some eyeliner d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=6) d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=6)
ax2.imshow(pil_image)

攝影頭臉部辨識下載 image.zip

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.
# Get a reference to webcam #0 (the default one)video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.image1 = face_recognition.load_image_file("image0.png")obama_face_encoding = face_recognition.face_encodings(image1)[0]
# Load a second sample picture and learn how to recognize it.image2 = face_recognition.load_image_file("image10.png")biden_face_encoding = face_recognition.face_encodings(image2)[0]
# Load a second sample picture and learn how to recognize it.image3 = face_recognition.load_image_file("image11.png")Jack_encoding = face_recognition.face_encodings(image3)[0]
# Create arrays of known face encodings and their namesknown_face_encodings = [ obama_face_encoding, biden_face_encoding, Jack_encoding]
known_face_names = [ "Professor JJ", "Dean Kuo", "Jack Li"]
# Initialize some variablesface_locations = []face_encodings = []face_names = []process_this_frame = True
while 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), 1)
# 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, 0.6, (255, 255, 255), 1) # print(name) # 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()

攝影頭臉部辨識 --語音回答

import face_recognitionimport cv2import numpy as npimport timeimport tempfilefrom gtts import gTTSfrom pygame import mixerfrom time import sleep

# 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)
# Load a sample picture and learn how to recognize it.image1 = face_recognition.load_image_file("image0.png")obama_face_encoding = face_recognition.face_encodings(image1)[0]
# Load a second sample picture and learn how to recognize it.image2 = face_recognition.load_image_file("image10.png")biden_face_encoding = face_recognition.face_encodings(image2)[0]
# Load a second sample picture and learn how to recognize it.image3 = face_recognition.load_image_file("image11.png")Jack_encoding = face_recognition.face_encodings(image3)[0]
# Create arrays of known face encodings and their namesknown_face_encodings = [ obama_face_encoding, biden_face_encoding, Jack_encoding]
known_face_names = [ "Professor JJ", "Dean Kuo", "Jack Li", "unknown"]
# Initialize some variablesface_locations = []face_encodings = []face_names = []process_this_frame = True
# modify by JJdef speak(sentence): with tempfile.NamedTemporaryFile(delete=True) as fp: print(sentence) tts = gTTS(text=sentence, lang='zh-TW') tts.save('{}.mp3'.format(fp.name)) mixer.init() mixer.music.load('{}.mp3'.format(fp.name)) mixer.music.play() while mixer.music.get_busy(): sleep(1) print ("done")
names_id_list = ["unknown","unknown","unknown","unknown","unknown","unknown","unknown","unknown","unknown","unknown"]def id_name(username): names_id_list.append(username) names_id_list.pop(0) name_id="unknown" max=9 #print(names_id_list) name_id = "unknown" for x in known_face_names: if names_id_list.count(x) > max : max = names_id_list.count(x) name_id = x return name_id
sayhellotime = 30lasttime = time.time()-sayhellotimehello_time = [ ["Professor JJ",lasttime,"簡文山教授你好"], ["Dean Kuo",lasttime,"郭乃文院長你好"], ["Jack Li",lasttime,"李友專院長你好"], ["unknown",lasttime,""]]def say_hello(username): i=0 for x in hello_time : if (time.time()-hello_time[i][1] > sayhellotime) and (username ==hello_time[i][0]): hello_time[i][1] = time.time() print(hello_time[i][0]," ",hello_time[i][1]) speak(hello_time[i][2]) i=i+1
while 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] name=id_name(name) # modify by JJ if name != "unknown": # modify by JJ say_hello(name) # modify by JJ 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), 1)
# 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, 0.6, (255, 255, 255), 1) # print(name) # 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()

嗜睡檢測 source_file

(facerec) C:\Users\jj\.spyder-py3\drowsiness-detection> python detect_drowsiness.py --shape-predictor shape_predictor_68_face_landmarks.dat --alarm 1

# USAGE# python detect_drowsiness.py --shape-predictor shape_predictor_68_face_landmarks.dat# python detect_drowsiness.py --shape-predictor shape_predictor_68_face_landmarks.dat --alarm alarm.wav
# import the necessary packagesfrom scipy.spatial import distance as distfrom imutils.video import VideoStreamfrom imutils import face_utilsfrom threading import Thread#import numpy as npimport argparseimport imutilsimport timeimport dlibimport cv2#import pygameimport tempfilefrom gtts import gTTSfrom pygame import mixerfrom time import sleep
# modify by JJdef speak(sentence): with tempfile.NamedTemporaryFile(delete=True) as fp: print(sentence) tts = gTTS(text=sentence, lang='zh-TW') tts.save('{}.mp3'.format(fp.name)) mixer.init() mixer.music.load('{}.mp3'.format(fp.name)) mixer.music.play() while mixer.music.get_busy(): sleep(1) print ("done")
def sound_alarm(path): # play an alarm sound #playsound.playsound(path) speak("主人你累了嗎")
def eye_aspect_ratio(eye): # compute the euclidean distances between the two sets of # vertical eye landmarks (x, y)-coordinates A = dist.euclidean(eye[1], eye[5]) B = dist.euclidean(eye[2], eye[4])
# compute the euclidean distance between the horizontal # eye landmark (x, y)-coordinates C = dist.euclidean(eye[0], eye[3])
# compute the eye aspect ratio ear = (A + B) / (2.0 * C)
# return the eye aspect ratio return ear # construct the argument parse and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-p", "--shape-predictor", required=True, help="path to facial landmark predictor")ap.add_argument("-a", "--alarm", type=str, default="", help="path alarm .WAV file")ap.add_argument("-w", "--webcam", type=int, default=0, help="index of webcam on system")args = vars(ap.parse_args()) # define two constants, one for the eye aspect ratio to indicate# blink and then a second constant for the number of consecutive# frames the eye must be below the threshold for to set off the# alarmEYE_AR_THRESH = 0.3EYE_AR_CONSEC_FRAMES = 48
# initialize the frame counter as well as a boolean used to# indicate if the alarm is going offCOUNTER = 0ALARM_ON = False
# initialize dlib's face detector (HOG-based) and then create# the facial landmark predictorprint("[INFO] loading facial landmark predictor...")detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(args["shape_predictor"])
# grab the indexes of the facial landmarks for the left and# right eye, respectively(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"](rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
# start the video stream threadprint("[INFO] starting video stream thread...")vs = VideoStream(src=args["webcam"]).start()time.sleep(1.0)
# loop over frames from the video streamwhile True: # grab the frame from the threaded video file stream, resize # it, and convert it to grayscale # channels) frame = vs.read() frame = imutils.resize(frame, width=450) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame rects = detector(gray, 0)
# loop over the face detections for rect in rects: # determine the facial landmarks for the face region, then # convert the facial landmark (x, y)-coordinates to a NumPy # array shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape)
# extract the left and right eye coordinates, then use the # coordinates to compute the eye aspect ratio for both eyes leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye)
# average the eye aspect ratio together for both eyes ear = (leftEAR + rightEAR) / 2.0
# compute the convex hull for the left and right eye, then # visualize each of the eyes leftEyeHull = cv2.convexHull(leftEye) rightEyeHull = cv2.convexHull(rightEye) cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1) cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
# check to see if the eye aspect ratio is below the blink # threshold, and if so, increment the blink frame counter if ear < EYE_AR_THRESH: COUNTER += 1
# if the eyes were closed for a sufficient number of # then sound the alarm if COUNTER >= EYE_AR_CONSEC_FRAMES: # if the alarm is not on, turn it on if not ALARM_ON: ALARM_ON = True
# check to see if an alarm file was supplied, # and if so, start a thread to have the alarm # sound played in the background if args["alarm"] != "": t = Thread(target=sound_alarm, args=(args["alarm"],)) t.deamon = True t.start()
# draw an alarm on the frame cv2.putText(frame, "DROWSINESS ALERT!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# otherwise, the eye aspect ratio is not below the blink # threshold, so reset the counter and alarm else: COUNTER = 0 ALARM_ON = False
# draw the computed eye aspect ratio on the frame to help # with debugging and setting the correct eye aspect ratio # thresholds and frame counters cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # show the frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break
# do a bit of cleanupcv2.destroyAllWindows()vs.stop()