目录

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• 正文
• 总结

dlib库的安装 本博客提供三种方法进行安装   T1方法：pip install dlib 此方法是需要在你安装cmake、Boost环境的计算机使用 。T2方法：conda install -c menpo dlib=18.18此方法适合那些已经安装好conda库的环境的计算机使用。T3方法：pip install dlib-19.8.1-cp36-cp36m-win_amd64.whl dlib库的whl文件――dlib-19.7.0-cp36-cp36m-win_amd64.rar dlib-19.3.1-cp35-cp35m-win_amd64.whl

​cv2库安装方法：  pip install opencv-python

# 五官class Organ():	def __init__(self, img, img_hsv, temp_img, temp_hsv, landmarks, name, ksize=None):		self.img = img		self.img_hsv = img_hsv		self.landmarks = landmarks		self.name = name		self.get_rect()		self.shape = (int(self.bottom-self.top), int(self.right-self.left))		self.size = self.shape[0] * self.shape[1] * 3		self.move = int(np.sqrt(self.size/3)/20)		self.ksize = self.get_ksize()		self.patch_img, self.patch_hsv = self.get_patch(self.img), self.get_patch(self.img_hsv)		self.set_temp(temp_img, temp_hsv)		self.patch_mask = self.get_mask_relative()	# 获取定位方框	def get_rect(self):		y, x = self.landmarks[:, 1], self.landmarks[:, 0]		self.top, self.bottom, self.left, self.right = np.min(y), np.max(y), np.min(x), np.max(x)	# 获得ksize,高斯模糊处理的参数	def get_ksize(self, rate=15):		size = max([int(np.sqrt(self.size/3)/rate), 1])		size = (size if size%2==1 else size+1)		return(size, size)	# 截取局部切片	def get_patch(self, img):		shape = img.shape		return img[np.max([self.top-self.move, 0]): np.min([self.bottom+self.move, shape[0]]), np.max([self.left-self.move, 0]): np.min([self.right+self.move, shape[1]])]	def set_temp(self, temp_img, temp_hsv):		self.img_temp, self.hsv_temp = temp_img, temp_hsv		self.patch_img_temp, self.patch_hsv_temp = self.get_patch(self.img_temp), self.get_patch(self.hsv_temp)	# 确认	def confirm(self):		self.img[:], self.img_hsv[:] = self.img_temp[:], self.hsv_temp[:]	# 更新	def update_temp(self):		self.img_temp[:], self.hsv_temp[:] = self.img[:], self.img_hsv[:]	# 勾画凸多边形	def _draw_convex_hull(self, img, points, color):		points = cv2.convexHull(points)		cv2.fillConvexPoly(img, points, color=color)	# 获得局部相对坐标遮盖	def get_mask_relative(self, ksize=None):		if ksize == None:			ksize = self.ksize		landmarks_re = self.landmarks.copy()		landmarks_re[:, 1] -= np.max([self.top-self.move, 0])		landmarks_re[:, 0] -= np.max([self.left-self.move, 0])		mask = np.zeros(self.patch_img.shape[:2], dtype=np.float64)		self._draw_convex_hull(mask, landmarks_re, color=1)		mask = np.array([mask, mask, mask]).transpose((1, 2, 0))		mask = (cv2.GaussianBlur(mask, ksize, 0) > 0) * 1.0		return cv2.GaussianBlur(mask, ksize, 0)[:]	# 获得全局绝对坐标遮盖	def get_mask_abs(self, ksize=None):		if ksize == None:			ksize = self.ksize		mask = np.zeros(self.img.shape, dtype=np.float64)		patch = self.get_patch(mask)		patch[:] = self.patch_mask[:]		return mask

# 美白	def whitening(self, rate=0.15, confirm=True):		if confirm:			self.confirm()			self.patch_hsv[:, :, -1] = np.minimum(self.patch_hsv[:, :, -1]+self.patch_hsv[:, :, -1]*self.patch_mask[:, :, -1]*rate, 255).astype("uint8")			self.img[:]=cv2.cvtColor(self.img_hsv, cv2.COLOR_HSV2BGR)[:]			self.update_temp()		else:			self.patch_hsv_temp[:] = cv2.cvtColor(self.patch_img_temp, cv2.COLOR_BGR2HSV)[:]			self.patch_hsv_temp[:, :, -1] = np.minimum(self.patch_hsv_temp[:, :, -1]+self.patch_hsv_temp[:, :, -1]*self.patch_mask[:, :, -1]*rate, 255).astype("uint8")			self.patch_img_temp[:] = cv2.cvtColor(self.patch_hsv_temp, cv2.COLOR_HSV2BGR)[:]	# 提升鲜艳度	def brightening(self, rate=0.3, confirm=True):		patch_mask = self.get_mask_relative((1, 1))		if confirm:			self.confirm()			patch_new = self.patch_hsv[:, :, 1]*patch_mask[:, :, 1]*rate			patch_new = cv2.GaussianBlur(patch_new, (3, 3), 0)			self.patch_hsv[:, :, 1] = np.minimum(self.patch_hsv[:, :, 1]+patch_new, 255).astype("uint8")			self.img[:]=cv2.cvtColor(self.img_hsv, cv2.COLOR_HSV2BGR)[:]			self.update_temp()		else:			self.patch_hsv_temp[:] = cv2.cvtColor(self.patch_img_temp, cv2.COLOR_BGR2HSV)[:]			patch_new = self.patch_hsv_temp[:, :, 1]*patch_mask[:, :, 1]*rate			patch_new = cv2.GaussianBlur(patch_new, (3, 3), 0)			self.patch_hsv_temp[:, :, 1] = np.minimum(self.patch_hsv[:, :, 1]+patch_new, 255).astype("uint8")			self.patch_img_temp[:] = cv2.cvtColor(self.patch_hsv_temp, cv2.COLOR_HSV2BGR)[:]	# 磨平	def smooth(self, rate=0.6, ksize=None, confirm=True):		if ksize == None:			ksize=self.get_ksize(80)		index = self.patch_mask > 0		if confirm:			self.confirm()			patch_new = cv2.GaussianBlur(cv2.bilateralFilter(self.patch_img, 3, *ksize), ksize, 0)			self.patch_img[index] = np.minimum(rate*patch_new[index]+(1-rate)*self.patch_img[index], 255).astype("uint8")			self.img_hsv[:] = cv2.cvtColor(self.img, cv2.COLOR_BGR2HSV)[:]			self.update_temp()		else:			patch_new = cv2.GaussianBlur(cv2.bilateralFilter(self.patch_img_temp, 3, *ksize), ksize, 0)			self.patch_img_temp[index] = np.minimum(rate*patch_new[index]+(1-rate)*self.patch_img_temp[index], 255).astype("uint8")			self.patch_hsv_temp[:] = cv2.cvtColor(self.patch_img_temp, cv2.COLOR_BGR2HSV)[:]	# 锐化	def sharpen(self, rate=0.3, confirm=True):		patch_mask = self.get_mask_relative((3, 3))		kernel = np.zeros((9, 9), np.float32)		kernel[4, 4] = 2.0		boxFilter = np.ones((9, 9), np.float32) / 81.0		kernel = kernel - boxFilter		index = patch_mask > 0		if confirm:			self.confirm()			sharp = cv2.filter2D(self.patch_img, -1, kernel)			self.patch_img[index] = np.minimum(((1-rate)*self.patch_img)[index]+sharp[index]*rate, 255).astype("uint8")			self.update_temp()		else:			sharp = cv2.filter2D(self.patch_img_temp, -1, kernel)			self.patch_img_temp[:] = np.minimum(self.patch_img_temp+self.patch_mask*sharp*rate, 255).astype("uint8")			self.patch_hsv_temp[:] = cv2.cvtColor(self.patch_img_temp, cv2.COLOR_BGR2HSV)[:]             # 额头class ForeHead(Organ):	def __init__(self, img, img_hsv, temp_img, temp_hsv, landmarks, mask_organs, name, ksize=None):		self.mask_organs = mask_organs		super(ForeHead, self).__init__(img, img_hsv, temp_img, temp_hsv, landmarks, name, ksize)	# 获得局部相对坐标mask	def get_mask_relative(self, ksize=None):		if ksize == None:			ksize = self.ksize		landmarks_re = self.landmarks.copy()		landmarks_re[:, 1] -= np.max([self.top-self.move, 0])		landmarks_re[:, 0] -= np.max([self.left-self.move, 0])		mask = np.zeros(self.patch_img.shape[:2], dtype=np.float64)		self._draw_convex_hull(mask, landmarks_re, color=1)		mask = np.array([mask, mask, mask]).transpose((1, 2, 0))		mask = (cv2.GaussianBlur(mask, ksize, 0) > 0) * 1.0		patch_organs = self.get_patch(self.mask_organs)		mask= cv2.GaussianBlur(mask, ksize, 0)[:]		mask[patch_organs>0] = (1-patch_organs[patch_organs>0])		return mask  # 脸类class Face(Organ):	def __init__(self, img, img_hsv, temp_img, temp_hsv, landmarks, index):		self.index = index		# 五官：下巴、嘴、鼻子、左右眼、左右耳		self.organs_name = ["jaw", "mouth", "nose", "left_eye", "right_eye", "left_brow", "right_brow"]		# 五官标记点		self.organs_point = [list(range(0, 17)), list(range(48, 61)), 							 list(range(27, 35)), list(range(42, 48)), 							 list(range(36, 42)), list(range(22, 27)),							 list(range(17, 22))]		self.organs = {name: Organ(img, img_hsv, temp_img, temp_hsv, landmarks[points], name) for name, points in zip(self.organs_name, self.organs_point)}		# 额头		mask_nose = self.organs["nose"].get_mask_abs()		mask_organs = (self.organs["mouth"].get_mask_abs()+mask_nose+self.organs["left_eye"].get_mask_abs()+self.organs["right_eye"].get_mask_abs()+self.organs["left_brow"].get_mask_abs()+self.organs["right_brow"].get_mask_abs())		forehead_landmark = self.get_forehead_landmark(img, landmarks, mask_organs, mask_nose)		self.organs["forehead"] = ForeHead(img, img_hsv, temp_img, temp_hsv, forehead_landmark, mask_organs, "forehead")		mask_organs += self.organs["forehead"].get_mask_abs()		# 人脸的完整标记点		self.FACE_POINTS = np.concatenate([landmarks, forehead_landmark])		super(Face, self).__init__(img, img_hsv, temp_img, temp_hsv, self.FACE_POINTS, "face")		mask_face = self.get_mask_abs() - mask_organs		self.patch_mask = self.get_patch(mask_face)	# 计算额头坐标	def get_forehead_landmark(self, img, face_landmark, mask_organs, mask_nose):		radius = (np.linalg.norm(face_landmark[0]-face_landmark[16])/2).astype("int32")		center_abs = tuple(((face_landmark[0]+face_landmark[16])/2).astype("int32"))		angle = np.degrees(np.arctan((lambda l:l[1]/l[0])(face_landmark[16]-face_landmark[0]))).astype("int32")		mask = np.zeros(mask_organs.shape[:2], dtype=np.float64)		cv2.ellipse(mask, center_abs, (radius, radius), angle, 180, 360, 1, -1)		# 剔除与五官重合部分		mask[mask_organs[:, :, 0]>0]=0		# 根据鼻子的肤色判断真正的额头面积		index_bool = []		for ch in range(3):			mean, std = np.mean(img[:, :, ch][mask_nose[:, :, ch]>0]), np.std(img[:, :, ch][mask_nose[:, :, ch]>0])			up, down = mean+0.5*std, mean-0.5*std			index_bool.append((img[:, :, ch]<down)|(img[:, :, ch]>up))		index_zero = ((mask>0)&index_bool[0]&index_bool[1]&index_bool[2])		mask[index_zero] = 0		index_abs = np.array(np.where(mask>0)[::-1]).transpose()		landmark = cv2.convexHull(index_abs).squeeze()		return landmark  # 化妆器class Makeup():	def __init__(self, predictor_path="./predictor/shape_predictor_68_face_landmarks.dat"):		self.photo_path = []		self.predictor_path = predictor_path		self.faces = {}		# 人脸检测与特征提取		self.detector = dlib.get_frontal_face_detector()		self.predictor = dlib.shape_predictor(self.predictor_path)	# 人脸定位和特征提取	# img为numpy数组	# 返回值为人脸特征(x, y)坐标的矩阵	def get_faces(self, img, img_hsv, temp_img, temp_hsv, name, n=1):		rects = self.detector(img, 1)		if len(rects) < 1:			print("[Warning]:No face detected...")			return None		return {name: [Face(img, img_hsv, temp_img, temp_hsv, np.array([[p.x, p.y] for p in self.predictor(img, rect).parts()]), i) for i, rect in enumerate(rects)]}	# 读取图片	def read_img(self, fname, scale=1):		img = cv2.imdecode(np.fromfile(fname, dtype=np.uint8), -1)		if not type(img):			print("[ERROR]:Fail to Read %s" % fname)			return None		return img	def read_and_mark(self, fname):		img = self.read_img(fname)		img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)		temp_img, temp_hsv = img.copy(), img_hsv.copy()		return img, temp_img, self.get_faces(img, img_hsv, temp_img, temp_hsv, fname)