import Image
import face_recognition
image = face_recognition.load_image_file('F:/Python27/Scripts/all.jpg')
face_locations = face_recognition.face_locations(image)#face_locations =face_recognition.#face_locations(image,number_of_times_to_upsample=0,model='cnn')
print('i found {} face(s) in this photograph.'.format(len(face_locations)))
for face_location in face_locations:top,right,bottom,left = face_locationprint('A face is located at pixel location Top:{},Left:{},Bottom:{},Right:{}'.format(top,right,bottom,left))face_image = image[top:bottom,left:right]pil_image=Image.fromarray(face_image)pil_image.show()

from PIL import Image, ImageDraw
face_recognition
import face_recognitionimage = face_recognition.load_image_file("F:/Python27/Scripts/known_people/obama.jpg")#查找圖像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)for face_landmarks in face_landmarks_list:pil_image = Image.fromarray(image)d = ImageDraw.Draw(pil_image, 'RGBA')#讓眉毛變成了一場噩夢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)#光澤的嘴唇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)#閃耀眼睛d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))#涂一些眼線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)pil_image.show()

import face_recognition
import cv2# 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.
obama_image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/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("F:/Python27/Scripts/known_people/mike.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","mike"
]# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = Truewhile True:# Grab a single frame of videoret, frame = video_capture.read()# Resize frame of video to 1/4 size for faster face recognition processingsmall_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 timeif process_this_frame:# Find all the faces and face encodings in the current frame of videoface_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]face_names.append(name)process_this_frame = not process_this_frame# Display the resultsfor (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 sizetop *= 4right *= 4bottom *= 4left *= 4# Draw a box around the facecv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)# Draw a label with a name below the facecv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)font = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)# Display the resulting imagecv2.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()