pip3 install face_recognition

# part1
# 識別圖片中的人是誰
import face_recognition
known_image = face_recognition.load_image_file("lyf1.jpg")
unknown_image = face_recognition.load_image_file("lyf2.jpg")

lyf_encoding = face_recognition.face_encodings(known_image)[0]
unknown_encoding = face_recognition.face_encodings(unknown_image)[0]

results = face_recognition.compare_faces([lyf_encoding], unknown_encoding)	
# A list of True/False values indicating which known_face_encodings match the face encoding to check

print(type(results))
print(results)

if results[0] == True:
  print("yes")
else:
  print("no")

# part2
# 從圖片中找到人臉（定位人臉位置）

import face_recognition
import cv2

image = face_recognition.load_image_file("lyf1.jpg")

face_locations_useCNN = face_recognition.face_locations(image,model='cnn')
# model – Which face detection model to use. “hog” is less accurate but faster on CPUs.
# “cnn” is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is “hog”.

face_locations_noCNN=face_recognition.face_locations(image)
# A list of tuples of found face locations in css (top, right, bottom, left) order
# 因為返回值的順序是這樣子的，因此在后面的for循環(huán)里面賦值要注意按這個順序來

print("face_location_useCNN:")
print(face_locations_useCNN)
face_num1=len(face_locations_useCNN)
print(face_num1)    # The number of faces


print("face_location_noCNN:")
print(face_locations_noCNN)
face_num2=len(face_locations_noCNN)
print(face_num2)    # The number of faces
# 到這里為止，可以觀察兩種情況的坐標(biāo)和人臉數(shù)，一般來說，坐標(biāo)會不一樣，但是檢測出來的人臉數(shù)應(yīng)該是一樣的
# 也就是說face_num1?。健ace_num２；　face_locations_useCNN　和　face_locations_noCNN　不一樣


org = cv2.imread("lyf1.jpg")
img = cv2.imread("lyf1.jpg")
cv2.imshow("lyf1.jpg",img) # 原始圖片

# Go to get the data and draw the rectangle
# use CNN
for i in range(0,face_num1):
  top = face_locations_useCNN[i][0]
  right = face_locations_useCNN[i][1]
  bottom = face_locations_useCNN[i][2]
  left = face_locations_useCNN[i][3]

  start = (left, top)
  end = (right, bottom)

  color = (0,255,255)
  thickness = 2
  cv2.rectangle(img, start, end, color, thickness)  # opencv 里面畫矩形的函數(shù)

# Show the result
cv2.imshow("useCNN",img)


# for face_location in face_locations_noCNN:
#
#   # Print the location of each face in this image
#   top, right, bottom, left = face_location
# # 等價于下面的這種寫法

for i in range(0,face_num2):
  top = face_locations_noCNN[i][0]
  right = face_locations_noCNN[i][1]
  bottom = face_locations_noCNN[i][2]
  left = face_locations_noCNN[i][3]

  start = (left, top)
  end = (right, bottom)

  color = (0,255,255)
  thickness = 2
  cv2.rectangle(org, start, end, color, thickness)

cv2.imshow("no cnn ",org)

cv2.waitKey(0)
cv2.destroyAllWindows()

# part3
# 找到人臉并將其裁剪打印出來（使用cnn定位人臉）

from PIL import Image
import face_recognition

# Load the jpg file into a numpy array
image = face_recognition.load_image_file("lyf1.jpg")

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, left, bottom, right))

  face_image = image[top:bottom, left:right]
  pil_image = Image.fromarray(face_image)
  pil_image.show()

# part4　識別單張圖片中人臉的關(guān)鍵點

from PIL import Image, ImageDraw
import face_recognition

# Load the jpg file into a numpy array
image = face_recognition.load_image_file("lyf1.jpg")

# Find all facial features in all the faces in the image
face_landmarks_list = face_recognition.face_landmarks(image)
# print(face_landmarks_list)

print("I found {} face(s) in this photograph.".format(len(face_landmarks_list)))

# Create a PIL imagedraw object so we can draw on the picture
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)

for face_landmarks in face_landmarks_list:

  # Print the location of each facial feature in this image
  for facial_feature in face_landmarks.keys():
    print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))

  # Let's trace out each facial feature in the image with a line!
  for facial_feature in face_landmarks.keys():
    d.line(face_landmarks[facial_feature], width=5)

# Show the picture
pil_image.show()