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wearmask.py
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wearmask.py
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import os
import sys
import argparse
import numpy as np
import cv2
import tqdm
import face_recognition
import math
import dlib
from PIL import Image, ImageFile
__version__ = '0.3.0'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
IMAGE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'masks')
# IMAGE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'images')
DEFAULT_IMAGE_PATH = os.path.join(IMAGE_DIR, 'default-mask.png')
BLACK_IMAGE_PATH = os.path.join(IMAGE_DIR, 'black-mask.png')
BLUE_IMAGE_PATH = os.path.join(IMAGE_DIR, 'blue-mask.png')
RED_IMAGE_PATH = os.path.join(IMAGE_DIR, 'red-mask.png')
SKIN_IMAGE_PATH = os.path.join(IMAGE_DIR, 'skin-mask.png')
CROPE_SIZE = 128
def rect_to_bbox(rect):
"""获得人脸矩形的坐标信息"""
# print(rect)
x = rect[3]
y = rect[0]
w = rect[1] - x
h = rect[2] - y
return (x, y, w, h)
predictor = dlib.shape_predictor("./models/shape_predictor_68_face_landmarks.dat")
def face_alignment(faces):
# 预测关键点
faces_aligned = []
for face in faces:
face_gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
rec = dlib.rectangle(0, 0, face.shape[0], face.shape[1])
shape = predictor(face_gray, rec)
#shape = predictor(np.uint8(face), rec)
# left eye, right eye, nose, left mouth, right mouth
order = [36, 45, 30, 48, 54]
for j in order:
x = shape.part(j).x
y = shape.part(j).y
# 计算两眼的中心坐标
eye_center = ((shape.part(36).x + shape.part(45).x) * 1. / 2, (shape.part(36).y + shape.part(45).y) * 1. / 2)
dx = (shape.part(45).x - shape.part(36).x)
dy = (shape.part(45).y - shape.part(36).y)
# 计算角度
angle = math.atan2(dy, dx) * 180. / math.pi
# 计算仿射矩阵
RotateMatrix = cv2.getRotationMatrix2D(eye_center, angle, scale=1)
# 进行仿射变换，即旋转
RotImg = cv2.warpAffine(face, RotateMatrix, (face.shape[0], face.shape[1]))
faces_aligned.append(RotImg)
return faces_aligned
def cli(pic_path ,save_pic_path):
parser = argparse.ArgumentParser(description='Wear a face mask in the given picture.')
# parser.add_argument('pic_path', default='/Users/wuhao/lab/wear-a-mask/spider/new_lfw/Aaron_Tippin/Aaron_Tippin_0001.jpg',help='Picture path.')
# parser.add_argument('--show', action='store_true', help='Whether show picture with mask or not.')
parser.add_argument('--model', default='hog', choices=['hog', 'cnn'], help='Which face detection model to use.')
group = parser.add_mutually_exclusive_group()
group.add_argument('--black', action='store_true', help='Wear black mask')
group.add_argument('--blue', action='store_true', help='Wear blue mask')
group.add_argument('--red', action='store_true', help='Wear red mask')
args = parser.parse_args()
if not os.path.exists(pic_path):
print(f'Picture {pic_path} not exists.')
sys.exit(1)
mask_path = SKIN_IMAGE_PATH
unmasked_paths = FaceMasker(pic_path, mask_path, True, 'cnn',save_pic_path).mask()
return unmasked_paths
class FaceMasker:
KEY_FACIAL_FEATURES = ('nose_bridge', 'chin')
def __init__(self, face_path, mask_path, show=False, model='cnn',save_path = ''):
self.face_path = face_path
self.mask_path = mask_path
self.save_path = save_path
self.show = show
self.model = model
self._face_img: ImageFile = None
self._mask_img: ImageFile = None
def mask(self):
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)
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)
found_face = True
self._mask_face(face_landmark)
unmasked_paths = []
if found_face:
# align
src_faces = []
src_face_num = 0
with_mask_face = np.asarray(self._face_img)
for (i, rect) in enumerate(face_locations):
src_face_num = src_face_num + 1
(x, y, w, h) = rect_to_bbox(rect)
detect_face = with_mask_face[y:y + h, x:x + w]
src_faces.append(detect_face)
# 人脸对齐操作并保存
faces_aligned = face_alignment(src_faces)
face_num = 0
for faces in faces_aligned:
face_num = face_num + 1
faces = cv2.cvtColor(faces, cv2.COLOR_RGBA2BGR)
size = (int(CROPE_SIZE), int(CROPE_SIZE))
faces_after_resize = cv2.resize(faces, size, interpolation=cv2.INTER_AREA)
cv2.imwrite(self.save_path, faces_after_resize)
# if self.show:
# self._face_img.show()
# save
# self._save()
else:
#在这里记录没有裁的图片
print('Found no face.' + self.save_path)
unmasked_paths.append(self.save_path)
return unmasked_paths
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)
if mask_left_width > 0 and new_height > 0:
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)
if mask_right_width > 0 and new_height> 0:
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)
def _save(self):
path_splits = os.path.splitext(self.face_path)
new_face_path = path_splits[0] + '-with-mask' + path_splits[1]
self._face_img.save(new_face_path)
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)
if __name__ == '__main__':
dataset_path ='./CASIA-WebFace'
save_dataset_path = './webface_masked'
#dataset_path = './lfw'
#save_dataset_path = './lfw_masked'
unmasked_paths=[]
for root, dirs, files in os.walk(dataset_path, topdown=False):
for dir in tqdm.tqdm(dirs):
fs = os.listdir(root + '/' + dir)
for name in fs:
new_root = root.replace(dataset_path, save_dataset_path)
new_root = new_root + '/' + dir
if not os.path.exists(new_root):
os.makedirs(new_root)
# deal
imgpath = os.path.join(root,dir, name)
save_imgpath = os.path.join(new_root,name)
if os.path.exists(save_imgpath):
pass
else:
unmasked_paths = cli(imgpath,save_imgpath)
if unmasked_paths != []:
fiePath = './record/record_unmasked_faces.txt'
with open(fiePath, 'w', encoding='utf-8') as f:
for v in unmasked_paths:
f.write(v + '\n')
f.write('\n')