用python画口罩,使用python识别戴口罩的人
本文主要介绍了计算机编程语言为人脸照片添加口罩实战，文中通过示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
目录
效果展示为人脸照片添加口罩代码掩膜生成代码
效果展示
数据集展示
数据集来源：使用了开源数据集FaceMask_CelebA
开源代码库地址：https://github。com/seven hsu/面膜_ celeba。饭桶
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
这部分有个库人脸识别需要安装，如果之前没有用过的小伙伴可能得费点功夫。
人脸识别库主要封装了dlib这一C图形库，通过计算机编程语言语言将它封装为一个非常简单就可以实现人脸识别的应用程序接口库，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
# -*-编码： utf-8 -*-
# @作者： 2014Vee
导入操作系统
将数组作为铭牌导入
从太平航运导入图像，图像文件
__版本__=0.3.0
IMAGE _ DIR=OS。路径。dirname( e :/play/face mask _ CelebA-master/face mask _ IMAGE/)
WHITE _ IMAGE _ PATH=OS。路径。join(IMAGE _ DIR， front_14.png )
蓝色图像路径=操作系统。路径。join(IMAGE _ DIR， front_14.png )
保存路径=操作系统。路径。dirname( e :/play/face mask _ CelebA-master/SAVE/synthesis/)
保存路径2=操作系统。路径。dirname( e :/play/face mask _ CelebA-master/SAVE/masks/)
class FaceMasker:
KEY _ face _ FEATURES=(鼻梁,下巴)
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=保存路径
self.save_path2=save_path2
自我。白色掩码路径=白色掩码路径
自我模型=模型
自我. face_img: ImageFile=无
自我. black_face_img=None
自我. mask_img: ImageFile=无
自我. white_mask_img=无
定义掩码（自身):
导入人脸识别
face _ image _ NP=face _ recognition。load _ image _ file(自身。face _ path)
面部位置=面部识别。面部位置(面部图像)
p, 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(fSave 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 osfrom 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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