Advanced Face Recognition Using Python: Data Capture and Encoding
Explore the world of face recognition with Python through data collection, encoding, and image processing. Learn to integrate OpenCV and face recognition libraries to detect and encode faces, creating a powerful tool for identity verification and tracking. This comprehensive guide covers image manipulation, data storage, and face matching algorithms, offering a deep dive into the practical applications of facial recognition technology.
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Presentation Transcript
2021 0817
Face_recognition faceR01.py faceR02.py faceR03.py webcam
Face_recognition faceR04.py array.bin faceR05.py faceR06.py webcam
Face_recognition data datac
# faceR04.py import cv2 import face_recognition import os import numpy as np path = 'datac' pic = 1 images = [] classNames = [] encodeListKnown=[] myList = os.listdir(path) print("myList=", myList) print(" data ") for cl in myList: curImg = cv2.imdecode(np.fromfile(file=f {path}/{cl} , dtype=np.uint8), cv2.IMREAD_COLOR) images.append(curImg) classNames.append(os.path.splitext(cl)[0]) # ( ) classNames print(" encodeListKnown ") for img in images: print(" "+str(pic)+" "+ classNames[pic-1]) pic +=1 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) encode = face_recognition.face_encodings(img, model="cnn")[0] encodeListKnown.append(encode)
# faceR04.py print(" ( ) classNamesFile.txt !!") with open("classNamesFile.txt", "w") as f: for i in range(len(images)): f.write(classNames[i]+'\n') print(" ( ) array.bin !!") with open("array.bin", "wb") as f: for i in range(len(encodeListKnown)): encodeListKnown[i].tofile(f) # print(encodeListKnown) # print('classNames = ', classNames) # print('# of imgs = ', len(images)) # print('people in encodeListKnown = ', len(encodeListKnown)) print(' ')
# faceR05.py import cv2 import face_recognition import numpy as np from PIL import Image, ImageDraw, ImageFont # classNamesFile.txt ( ) with open("classNamesFile.txt", "r") as f: classNames = f.read().splitlines() print('classNames = ', classNames) with open("array.bin", "rb") as f: # array.bin encodeListKnown = np.fromfile(f) encodeListKnown = encodeListKnown.reshape(-1, 128) print(' ', len(encodeListKnown),' ) image = cv2.imread("pin31.JPG") facesCurFrame = face_recognition.face_locations(image) # encodesCurFrame = face_recognition.face_encodings(image, facesCurFrame)
# faceR05.py for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): matches = face_recognition.compare_faces(encodeListKnown, encodeFace) faceDis = face_recognition.face_distance(encodeListKnown, encodeFace) matchIndex = np.argmin(faceDis) # faceDis Index if matches[matchIndex]: # match name = classNames[matchIndex].upper() # index print("faceDis = ", faceDis) print("matchIndex = ", matchIndex) print(name) y1, x2, y2, x1 = faceLoc cv2.rectangle(image, (x1, y1), (x2, y2), (0, 0, 255), 2) cv2.rectangle(image, (x1, y2 + 25), (x2, y2), (0, 0, 255), cv2.FILLED) font = ImageFont.truetype("simsun.ttc", 20) # img_pil = Image.fromarray(image) # numpy array PIL draw = ImageDraw.Draw(img_pil) # # draw.text((x1 + 6, y2 + 2), name, font=font, fill=(255, 255, 255,1)) image = np.array(img_pil) cv2.imshow('MATCH', image) cv2.waitKey(0)
faceR06.py webcam
# faceR06.py import cv2 import face_recognition import numpy as np from PIL import Image, ImageDraw, ImageFont import time import codecs keyb = 0 # classNamesFile.txt ( ) with open("classNamesFile.txt", "r") as f: classNames = f.read().splitlines() print('classNames = ', classNames) with open("array.bin", "rb") as f: # array.bin encodeListKnown = np.fromfile(f) encodeListKnown = encodeListKnown.reshape(-1, 128) print(' ', len(encodeListKnown),' ) cap = cv2.VideoCapture(0)
while True: prev_time = time.time() success, frame = cap.read() frame = cv2.resize(frame,(800, 450)) facesCurFrame = face_recognition.face_locations(frame, model="cnn") encodesCurFrame = face_recognition.face_encodings(frame, facesCurFrame) for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): matches = face_recognition.compare_faces(encodeListKnown, encodeFace) faceDis = face_recognition.face_distance(encodeListKnown, encodeFace) matchIndex = np.argmin(faceDis) if matches[matchIndex]: name = classNames[matchIndex].upper() print("matchIndex = ", matchIndex, name , "faceDis = ", faceDis) y1, x2, y2, x1 = faceLoc cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2) cv2.rectangle(frame, (x1, y2 + 25), (x2, y2), (0, 0, 255), cv2.FILLED) font = ImageFont.truetype("simsun.ttc", 20) # img_pil = Image.fromarray(image) # PIL draw = ImageDraw.Draw(img_pil) # # draw.text((x1 + 6, y2 + 2), name, font=font, fill=(255, 255, 255,1)) frame = np.array(img_pil)
with codecs.open('report.txt', 'r+, encoding='utf-8') as f: myDataList = f.readlines() nameList = [] for line in myDataList: entry = line.split(',') nameList.append(entry[0]) now = datetime.now() dtString = now.strftime('%H:%M:%S') if (name not in nameList) or (keyb == ord('s')): f.writelines(f'\n{name},{dtString}') cv2.putText(frame, 'fps: ' + str(int(1 / (time.time() - prev_time))), (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA) cv2.imshow('Webcam match', frame) keyb = cv2.waitKey(1) & 0xFF if keyb == 27: break
while True: prev_time = time.time() success, frame = cap.read() frame = cv2.resize(frame,(800, 450)) imgS = cv2.resize(frame, (0, 0), None, 0.25, 0.25) facesCurFrame = face_recognition.face_locations(imgS, model="cnn") encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame) for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame): matches = face_recognition.compare_faces(encodeListKnown, encodeFace) faceDis = face_recognition.face_distance(encodeListKnown, encodeFace) matchIndex = np.argmin(faceDis) if matches[matchIndex]: ssss name = classNames[matchIndex].upper() print("matchIndex = ", matchIndex, name , "faceDis = ", faceDis) y1, x2, y2, x1 = faceLoc y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4 cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2) cv2.rectangle(frame, (x1, y2 + 25), (x2, y2), (0, 0, 255), cv2.FILLED)
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