深度学习-人脸识别环境搭建与demo演示（windows+face_recognition+dlib+pycharm）_深度学习 人脸识别 demo github

搭建windows的python环境

推荐直接安装anaconda 5.2（自带python3.6.8）
安装地址：https://repo.anaconda.com/archive/
安装后，最下角输入anaconda，找到对应shell


安装cmake
https://cmake.org/download/
直接安装即可，完毕后，打开shell，输入cmake -version（我的是3.15）

安装dlib
pip install dlib
注：安装过程中，可能会出现超时的问题，重试即可

安装face_recognition
pip install face_recognition

下载pycharm

http://www.jetbrains.com/pycharm/download/#section=windows

下载face_recognition源码

源码地址：https://github.com/ageitgey/face_recognition
git clone https://github.com/ageitgey/face_recognition.git

demo演示

打开pycharm，点击 file-open，选择face_recognition下载目录。

下面我们开始运行demo看看效果。
点击examples/find_faces_in_picture.py，在文件中点右键，选择run xxxx.python

点击examples/identify_and_draw_boxes_on_faces.py，点击右键运行，效果如下：

源码初步分析

人脸识别分为三步：

• location，人脸定位
• encoding，人脸编码
• identity，人脸识别

人脸定位参考find_faces_in_picture.py

from PIL import Image
import face_recognition

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

# Find all the faces in the image using the default HOG-based model.
# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
# See also: find_faces_in_picture_cnn.py

# 人脸定位的核心代码。
face_locations = face_recognition.face_locations(image)

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

for face_location in face_locations:

    # Print the location of each face in this image
    top, right, bottom, left = face_location
    print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))

    # 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()

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face_locations为所有人脸的位置信息，结构如下：

人脸编码和识别参考identify_and_draw_boxes_on_faces.py

import face_recognition
from PIL import Image, ImageDraw

# This is an example of running face recognition on a single image
# and drawing a box around each person that was identified.

# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("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("biden.jpg")

# 这里是人脸编码的核心代码
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",
    "Joe Biden"
]

# Load an image with an unknown face
unknown_image = face_recognition.load_image_file("two_people.jpg")

# Find all the faces and face encodings in the unknown image
face_locations = face_recognition.face_locations(unknown_image)
print(face_locations)
face_encodings = face_recognition.face_encodings(unknown_image, face_locations)

# Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
# See http://pillow.readthedocs.io/ for more about PIL/Pillow
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), face_encoding in zip(face_locations, face_encodings):
    # See if the face is a match for the known face(s)
    # 这里是人脸距离对比的核心代码
    distances = face_recognition.face_distance(known_face_encodings, face_encoding)
    print(distances)
    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
    print(matches)

    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 - 5), 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()

# You can also save a copy of the new image to disk if you want by uncommenting this line
# pil_image.save("image_with_boxes.jpg")

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face_encodings的结构如下：

distances的结构如下：