现在的美颜特效有多可怕(现在的美颜特效有多可怕视频)

小编：圣子更新时间：2022-08-23

导语

现在每一次出门，女友就喜欢拍照！BUT 嫌弃我给拍的照片角度不对，采光不好.......

总之一大堆理由，啥时候让我拍照的水平能有美颜相机三分之一的效果就好！

果然都是锻炼出来的，至少现在我能看出来朋友圈哪些小姐姐批没批过照片。

逃不掉

逃不掉啊，为了摆脱这种局面——

立马给女友写了一款简易版本的美颜相机给她偷偷地用！这样子就不担心被锤了。机智如我.jpg

正文

环境安装：

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

人脸五官，坐标、进行高斯模糊处理等等。

# 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

主要美颜效果进行的处理如下：

# 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]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)

效果如下：