人工智能之基于face_recognition的人脸检测与识别(代码片段)

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_location
    print(\'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_recognition


image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/obama.jpg")

#查找图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\')

    #让眉毛变成了一场噩梦
    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)

    #光泽的嘴唇
    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)

    #闪耀眼睛
    d.polygon(face_landmarks[\'left_eye\'], fill=(255, 255, 255, 30))
    d.polygon(face_landmarks[\'right_eye\'], fill=(255, 255, 255, 30))

    #涂一些眼线
    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 = 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]

            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), 2)

        # 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, 1.0, (255, 255, 255), 1)

    # 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 webcam
video_capture.release()
cv2.destroyAllWindows()