给人脸戴上口罩，Python实战项目来了

Posted 2022-05-20 Python学习与数据挖掘

大家好，人生苦短，我用Python。今天给大家分享一个Python 实战案例：为人脸照片添加口罩，喜欢本文记得收藏、点赞、关注。

废话不多说，我们先展示最终的效果。

【注】完整版代码、资料，技术沟通，文末获取。

效果展示

数据集展示

数据集来源：使用了开源数据集FaceMask_CelebA

github地址：https://github.com/sevenHsu/FaceMask_CelebA.git

部分人脸数据集：

口罩样本数据集：

为人脸照片添加口罩代码

这部分有个库face_recognition需要安装，如果之前没有用过的小伙伴可能得费点功夫。

Face Recognition 库主要封装了dlib这一 C++ 图形库，通过 Python 语言将它封装为一个非常简单就可以实现人脸识别的 API 库，屏蔽了人脸识别的算法细节，大大降低了人脸识别功能的开发难度。

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author  : 2014Vee
import os
import numpy as np
from PIL import Image, ImageFile

__version__ = '0.3.0'

IMAGE_DIR = os.path.dirname('E:/play/FaceMask_CelebA-master/facemask_image/')
WHITE_IMAGE_PATH = os.path.join(IMAGE_DIR, 'front_14.png')
BLUE_IMAGE_PATH = os.path.join(IMAGE_DIR, 'front_14.png')
SAVE_PATH = os.path.dirname('E:/play/FaceMask_CelebA-master/save/synthesis/')
SAVE_PATH2 = os.path.dirname('E:/play/FaceMask_CelebA-master/save/masks/')


class FaceMasker:
    KEY_FACIAL_FEATURES = ('nose_bridge', 'chin')

    def __init__(self, face_path, mask_path, white_mask_path, save_path, save_path2, model='hog'):
        self.face_path = face_path
        self.mask_path = mask_path
        self.save_path = save_path
        self.save_path2 = save_path2
        self.white_mask_path = white_mask_path
        self.model = model
        self._face_img: ImageFile = None
        self._black_face_img = None
        self._mask_img: ImageFile = None
        self._white_mask_img = None

    def mask(self):
        import face_recognition

        face_image_np = face_recognition.load_image_file(self.face_path)
        face_locations = face_recognition.face_locations(face_image_np, model=self.model)
        face_landmarks = face_recognition.face_landmarks(face_image_np, face_locations)
        self._face_img = Image.fromarray(face_image_np)
        self._mask_img = Image.open(self.mask_path)
        self._white_mask_img = Image.open(self.white_mask_path)
        self._black_face_img = Image.new('RGB', self._face_img.size, 0)

        found_face = False
        for face_landmark in face_landmarks:
            # check whether facial features meet requirement
            skip = False
            for facial_feature in self.KEY_FACIAL_FEATURES:
                if facial_feature not in face_landmark:
                    skip = True
                    break
            if skip:
                continue

            # mask face
            found_face = True
            self._mask_face(face_landmark)

        if found_face:
            # save
            self._save()
        else:
            print('Found no face.')

    def _mask_face(self, face_landmark: dict):
        nose_bridge = face_landmark['nose_bridge']
        nose_point = nose_bridge[len(nose_bridge) * 1 // 4]
        nose_v = np.array(nose_point)

        chin = face_landmark['chin']
        chin_len = len(chin)
        chin_bottom_point = chin[chin_len // 2]
        chin_bottom_v = np.array(chin_bottom_point)
        chin_left_point = chin[chin_len // 8]
        chin_right_point = chin[chin_len * 7 // 8]

        # split mask and resize
        width = self._mask_img.width
        height = self._mask_img.height
        width_ratio = 1.2
        new_height = int(np.linalg.norm(nose_v - chin_bottom_v))

        # left
        mask_left_img = self._mask_img.crop((0, 0, width // 2, height))
        mask_left_width = self.get_distance_from_point_to_line(chin_left_point, nose_point, chin_bottom_point)
        mask_left_width = int(mask_left_width * width_ratio)
        mask_left_img = mask_left_img.resize((mask_left_width, new_height))

        # right
        mask_right_img = self._mask_img.crop((width // 2, 0, width, height))
        mask_right_width = self.get_distance_from_point_to_line(chin_right_point, nose_point, chin_bottom_point)
        mask_right_width = int(mask_right_width * width_ratio)
        mask_right_img = mask_right_img.resize((mask_right_width, new_height))

        # merge mask
        size = (mask_left_img.width + mask_right_img.width, new_height)
        mask_img = Image.new('RGBA', size)
        mask_img.paste(mask_left_img, (0, 0), mask_left_img)
        mask_img.paste(mask_right_img, (mask_left_img.width, 0), mask_right_img)

        # rotate mask
        angle = np.arctan2(chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])
        rotated_mask_img = mask_img.rotate(angle, expand=True)

        # calculate mask location
        center_x = (nose_point[0] + chin_bottom_point[0]) // 2
        center_y = (nose_point[1] + chin_bottom_point[1]) // 2

        offset = mask_img.width // 2 - mask_left_img.width
        radian = angle * np.pi / 180
        box_x = center_x + int(offset * np.cos(radian)) - rotated_mask_img.width // 2
        box_y = center_y + int(offset * np.sin(radian)) - rotated_mask_img.height // 2

        # add mask
        self._face_img.paste(mask_img, (box_x, box_y), mask_img)

        # split mask and resize
        width = self._white_mask_img.width
        height = self._white_mask_img.height
        width_ratio = 1.2
        new_height = int(np.linalg.norm(nose_v - chin_bottom_v))

        # left
        mask_left_img = self._white_mask_img.crop((0, 0, width // 2, height))
        mask_left_width = self.get_distance_from_point_to_line(chin_left_point, nose_point, chin_bottom_point)
        mask_left_width = int(mask_left_width * width_ratio)
        mask_left_img = mask_left_img.resize((mask_left_width, new_height))

        # right
        mask_right_img = self._white_mask_img.crop((width // 2, 0, width, height))
        mask_right_width = self.get_distance_from_point_to_line(chin_right_point, nose_point, chin_bottom_point)
        mask_right_width = int(mask_right_width * width_ratio)
        mask_right_img = mask_right_img.resize((mask_right_width, new_height))

        # merge mask
        size = (mask_left_img.width + mask_right_img.width, new_height)
        mask_img = Image.new('RGBA', size)
        mask_img.paste(mask_left_img, (0, 0), mask_left_img)
        mask_img.paste(mask_right_img, (mask_left_img.width, 0), mask_right_img)

        # rotate mask
        angle = np.arctan2(chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])
        rotated_mask_img = mask_img.rotate(angle, expand=True)

        # calculate mask location
        center_x = (nose_point[0] + chin_bottom_point[0]) // 2
        center_y = (nose_point[1] + chin_bottom_point[1]) // 2

        offset = mask_img.width // 2 - mask_left_img.width
        radian = angle * np.pi / 180
        box_x = center_x + int(offset * np.cos(radian)) - rotated_mask_img.width // 2
        box_y = center_y + int(offset * np.sin(radian)) - rotated_mask_img.height // 2

        # add mask
        self._black_face_img.paste(mask_img, (box_x, box_y), mask_img)

    def _save(self):
        path_splits = os.path.splitext(self.face_path)
        # new_face_path = self.save_path + '/' + os.path.basename(self.face_path) + '-with-mask' + path_splits[1]
        # new_face_path2 = self.save_path2 + '/' + os.path.basename(self.face_path) + '-binary' + path_splits[1]
        new_face_path = self.save_path + '/' + os.path.basename(self.face_path) + '-with-mask' + path_splits[1]
        new_face_path2 = self.save_path2 + '/'  + os.path.basename(self.face_path) + '-binary' + path_splits[1]
        self._face_img.save(new_face_path)
        self._black_face_img.save(new_face_path2)

    #         print(f'Save to new_face_path')

    @staticmethod
    def get_distance_from_point_to_line(point, line_point1, line_point2):
        distance = np.abs((line_point2[1] - line_point1[1]) * point[0] +
                          (line_point1[0] - line_point2[0]) * point[1] +
                          (line_point2[0] - line_point1[0]) * line_point1[1] +
                          (line_point1[1] - line_point2[1]) * line_point1[0]) / \\
                   np.sqrt((line_point2[1] - line_point1[1]) * (line_point2[1] - line_point1[1]) +
                           (line_point1[0] - line_point2[0]) * (line_point1[0] - line_point2[0]))
        return int(distance)

    # FaceMasker("/home/aistudio/data/人脸.png", WHITE_IMAGE_PATH, True, 'hog').mask()


from pathlib import Path

images = Path("E:/play/FaceMask_CelebA-master/bbox_align_celeba").glob("*")
cnt = 0
for image in images:
    if cnt < 1:
        cnt += 1
        continue
    FaceMasker(image, BLUE_IMAGE_PATH, WHITE_IMAGE_PATH, SAVE_PATH, SAVE_PATH2, 'hog').mask()
    cnt += 1
    print(f"正在处理第cnt张图片，还有99 - cnt张图片")

掩膜生成代码

这部分其实就是对使用的口罩样本的二值化，因为后续要相关模型会用到

import os
from PIL import Image

# 源目录
# MyPath = 'E:/play/FaceMask_CelebA-master/facemask_image/'
MyPath = 'E:/play/FaceMask_CelebA-master/save/masks/'
# 输出目录
OutPath = 'E:/play/FaceMask_CelebA-master/save/Binarization/'


def processImage(filesoure, destsoure, name, imgtype):
    '''
    filesoure是存放待转换图片的目录
    destsoure是存在输出转换后图片的目录
    name是文件名
    imgtype是文件类型
    '''
    imgtype = 'bmp' if imgtype == '.bmp' else 'png'
    # 打开图片
    im = Image.open(filesoure + name)
    # =============================================================================
    #     #缩放比例
    #     rate =max(im.size[0]/640.0 if im.size[0] > 60 else 0, im.size[1]/1136.0 if im.size[1] > 1136 else 0)
    #     if rate:
    #         im.thumbnail((im.size[0]/rate, im.size[1]/rate))
    # =============================================================================

    img = im.convert("RGBA")
    pixdata = img.load()
    # 二值化
    for y in range(img.size[1]):
        for x in range(img.size[0]):
            if pixdata[x, y][0] < 90:
                pixdata[x, y] = (0, 0, 0, 255)

    for y in range(img.size[1]):
        for x in range(img.size[0]):
            if pixdata[x, y][1] < 136:
                pixdata[x, y] = (0, 0, 0, 255)

    for y in range(img.size[1]):
        for x in range(img.size[0]):
            if pixdata[x, y][2] > 0:
                pixdata[x, y] = (255, 255, 255, 255)
    img.save(destsoure + name, imgtype)


def run():
    # 切换到源目录，遍历源目录下所有图片
    os.chdir(MyPath)
    for i in os.listdir(os.getcwd()):
        # 检查后缀
        postfix = os.path.splitext(i)[1]
        name = os.path.splitext(i)[0]
        name2 = name.split('.')
        if name2[1] == 'jpg-binary' or name2[1] == 'png-binary':
            processImage(MyPath, OutPath, i, postfix)


if __name__ == '__main__':    run()

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