python face_recognition安装及各种应用
2022/6/11 1:21:41
本文主要是介绍python face_recognition安装及各种应用,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
1.安装
首先,必须提前安装cmake、numpy、dlib,其中,由于博主所用的python版本是3.6.4(为了防止不兼容,所以用之前的版本),只能安装19.7.0及之前版本的dlib,所以直接pip install dlib会报错,需要pip install dlib==19.7.0
安装完预备库之后就可以直接pip install face_recognition
2.应用
(1)提取人脸
import face_recognition from PIL import Image image = face_recognition.load_image_file("1.jpg") face_locations = face_recognition.face_locations(image) # top, right, bottom, left #以下展示提取的人脸 for face_location in face_locations: # Print the location of each face in this image top, right, bottom, left = face_location # You can access the actual face itself like this: face_image = image[top:bottom, left:right] pil_image = Image.fromarray(face_image) pil_image.show()
(2)查找面部特征轮廓线
import face_recognition from PIL import Image,ImageDraw image = face_recognition.load_image_file("1.jpg") face_landmarks_list = face_recognition.face_landmarks(image) #以下为展示轮廓线 pil_image = Image.fromarray(image) d = ImageDraw.Draw(pil_image) for face_landmarks in face_landmarks_list: facial_features = [ 'chin', 'left_eyebrow', 'right_eyebrow', 'nose_bridge', 'nose_tip', 'left_eye', 'right_eye', 'top_lip', 'bottom_lip' ] for facial_feature in facial_features: d.line(face_landmarks[facial_feature], width=5) del d pil_image.show()
(3)比较人脸
import face_recognition known_image = face_recognition.load_image_file("known_person.jpg") unknown_image = face_recognition.load_image_file("unknown.jpg") biden_encoding = face_recognition.face_encodings(known_image)[0] unknown_encoding = face_recognition.face_encodings(unknown_image)[0] results = face_recognition.compare_faces([biden_encoding], unknown_encoding)
(4)同时识别多张人脸
①使用pillow库 #使用pillow库 import face_recognition from PIL import Image, ImageDraw # Load a second sample picture and learn how to recognize it. first_image = face_recognition.load_image_file("3.jpg") first_face_encoding = face_recognition.face_encodings(first_image)[0] second_image = face_recognition.load_image_file("5.jpg") second_face_encoding = face_recognition.face_encodings(second_image)[0] # Create arrays of known face encodings and their names known_face_encodings = [ first_face_encoding, second_face_encoding ] known_face_names = [ "first", "second" ] # Load an image with an unknown face unknown_image = face_recognition.load_image_file("1.jpg") # Find all the faces and face encodings in the unknown image unknown_face_locations = face_recognition.face_locations(unknown_image) unknown_face_encodings = face_recognition.face_encodings(unknown_image, unknown_face_locations) pil_image = Image.fromarray(unknown_image) # Create a Pillow ImageDraw Draw instance to draw with draw = ImageDraw.Draw(pil_image) # Loop through each face found in the unknown image for (top, right, bottom, left), unknown_face_encoding in zip(unknown_face_locations, unknown_face_encodings): # See if the face is a match for the known face(s) matches = face_recognition.compare_faces(known_face_encodings, unknown_face_encoding, tolerance=0.5) 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] # Draw a box around the face using the Pillow module draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255)) # Draw a label with a name below the face text_width, text_height = draw.textsize(name) draw.rectangle(((left, bottom-text_height-10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255)) draw.text((left+6, bottom-text_height-3), name, fill=(255, 255, 255, 255)) # Remove the drawing library from memory as per the Pillow docs del draw # Display the resulting image pil_image.show() ②使用opencv库 #使用opencv库 import face_recognition import cv2 # 人物名称的集合 known_face_names = ["first","second"] face_locations = [] face_encodings = [] demo_names = [] process_this_demo = True # 本地图像一 first_image = face_recognition.load_image_file("1.jpg") first_encoding = face_recognition.face_encodings(first_image)[0] # 本地图像二 second_image = face_recognition.load_image_file("5.jpg") second_encoding = face_recognition.face_encodings(second_image)[0] known_face_encodings = [first_encoding,second_encoding] # demo path = "7.jpg" demo = cv2.imread(path) demo_image = face_recognition.load_image_file(path) demo_encodings = face_recognition.face_encodings(demo_image) rgb_demo = demo[:, :, ::-1] demo_face_locations = face_recognition.face_locations(rgb_demo) for demo_encoding in demo_encodings: # 默认为unknown matches = face_recognition.compare_faces(known_face_encodings, demo_encoding,tolerance=0.5) name = "unknown" if True in matches: first_match_index = matches.index(True) name = known_face_names[first_match_index] demo_names.append(name) # 将捕捉到的人脸显示出来 for (top, right, bottom, left), name in zip(demo_face_locations, demo_names): # Scale back up face locations since the demo we detected in was scaled to 1/4 size # 矩形框 cv2.rectangle(demo, (left, top), (right, bottom), (0, 0, 255), thickness=1) #加上标签 cv2.rectangle(demo, (left, bottom-15), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(demo, name, (left+5,bottom-3), font, 0.5, (255, 255, 255), 1 ) # Display cv2.imshow("CJK's practice", demo) cv2.waitKey(0) cv2.destroyAllWindows()
(5)摄像头实时辨别人脸
import face_recognition import cv2,time video_capture = cv2.VideoCapture(0) # 本地图像一 first_image = face_recognition.load_image_file("1.jpg") first_face_encoding = face_recognition.face_encodings(first_image)[0] # 本地图像二 second_image = face_recognition.load_image_file("3.jpg") second_face_encoding = face_recognition.face_encodings(second_image)[0] # 本地图片三 third_image = face_recognition.load_image_file("5.jpg") third_face_encoding = face_recognition.face_encodings(third_image)[0] # Create arrays of known face encodings and their names # 脸部特征数据的集合 known_face_encodings = [ first_face_encoding, second_face_encoding, third_face_encoding ] # 人物名称的集合 known_face_names = [ "first", "second", "third" ] face_locations = [] face_encodings = [] face_names = [] process_this_frame = True while True: # 读取摄像头画面 ret, frame = video_capture.read() # 改变摄像头图像的大小,图像小,所做的计算就少 small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) # opencv的图像是BGR格式的,而我们需要是的RGB格式的,因此需要进行一个转换。 rgb_small_frame = small_frame[:, :, ::-1] # Only process every other frame of video to save time if process_this_frame: # 根据encoding来判断是不是同一个人,是就输出true,不是为flase 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: # 默认为unknown matches = face_recognition.compare_faces(known_face_encodings, face_encoding) name = "Unknown" if True in matches: first_match_index = matches.index(True) name = known_face_names[first_match_index] face_names.append(name) process_this_frame = not process_this_frame # 将捕捉到的人脸显示出来 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 # 矩形框 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) #加上标签 cv2.rectangle(frame, (left, bottom-15), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left+5, bottom-3), font, 1.0, (255, 255, 255), 1) # Display cv2.imshow('monitor', frame) # 按Q退出 if cv2.waitKey(1) & 0xFF == ord('q'): break video_capture.release() cv2.destroyAllWindows()
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