人脸关键点区域添加矩形黑框mask

人脸区域添加矩形黑框

• 依赖：Dlib library
• Dlib简介：Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference.
  • http://dlib.net/face_landmark_detection_ex.cpp.html
  • http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
• 基本思路：先使用dlib检测出人脸关键点，根据关键点的位置，确定框的中心点以及宽高；
• 主要mask区域有：左眼，右眼，鼻子，嘴部，脸颊

#coding=utf-8

import os
import sys
import cv2
import dlib
import numpy as np

# change directory to this module path
try:
    this_file = __file__
except NameError:
    this_file = sys.argv[0]
this_file = os.path.abspath(this_file)

#print this_file

if os.path.dirname(this_file):
    os.chdir(os.path.dirname(this_file))
    
script_dir = os.getcwd()
#print script_dir
   
def get_rect_mask(shape,index_area=0,h = None,w = None,w_ratio = None,h_ratio = None):
    """
    shape : image of landmarks/parts
    index_area:
        0:refer to left eye
        1:refer to right eye
        2:refer to nose;
        3:refer to mouth or face; 
    """
    if index_area==0:#36-41 six points
    
        if w_ratio is None or h_ratio is None:
            w_ratio = np.random.choice([2.0, 2.05, 2.10,2.15],1,p =[0.2,0.4,0.2,0.2])[0]
            h_ratio = np.random.choice([2.3, 2.35, 2.25,2.38],1,p =[0.2, 0.4, 0.25, 0.15])[0] 
            
        x_center = int(shape.part(36).x + shape.part(37).x + shape.part(39).x)/3
        y_center = int(shape.part(37).y+shape.part(41).y)/2
        width = int(w_ratio*(shape.part(39).x-shape.part(36).x)/2)
        height = int(h_ratio*(shape.part(41).y-shape.part(37).y)/2)
        
        x0 = max(x_center - width,0)
        y0 = max(y_center - height,0)
        
        x1 = min(x_center + width,w)
        y1 = max(y_center - height,0)
        
        x2 = min(x_center + width,w)
        y2 = min(y_center + height,h)
        
        x3 = max(x_center - width,0)
        y3 = min(y_center + height,h)
        
        pts = np.asarray([[x0,y0],[x1,y1],[x2,y2],[x3,y3]])
                
        return pts
        
    elif index_area==1:#42-47 six points
    
        if w_ratio is None or h_ratio is None:
            w_ratio = np.random.choice([2.0, 2.05, 2.10,2.15],1,p =[0.2,0.4,0.2,0.2])[0]
            h_ratio = np.random.choice([2.3, 2.35, 2.25, 2.38],1,p =[0.2, 0.4, 0.25, 0.15])[0] 
    
        x_center = int(shape.part(42).x + shape.part(45).x + shape.part(44).x)/3
        y_center = int(shape.part(43).y+shape.part(47).y)/2
        
        width = int(w_ratio*(shape.part(45).x-shape.part(42).x)/2)
        height = int(h_ratio*(shape.part(47).y-shape.part(43).y)/2)
        
        x0 = max(x_center - width,0)
        y0 = max(y_center - height,0)
        
        x1 = min(x_center + width,w)
        y1 = max(y_center - height,0)
        
        x2 = min(x_center + width,w)
        y2 = min(y_center + height,h)
        
        x3 = max(x_center - width,0)
        y3 = min(y_center + height,h)
        
        pts = np.asarray([[x0,y0],[x1,y1],[x2,y2],[x3,y3]])
        
        return pts
        
    elif index_area==2:#27-35 nine points
        if w_ratio is None or h_ratio is None:
            w_ratio = np.random.choice([1.3,1.4,1.45],1,p=[0.3,0.4,0.3])[0]
            h_ratio = np.random.choice([0.8,0.9,1.0],1,p=[0.5,0.4,0.1])[0] 
            
        x_center = int(shape.part(31).x+shape.part(35).x+shape.part(27).x)/3
        width = int(w_ratio*(shape.part(35).x-shape.part(31).x)/2)
        
        y_center = int(shape.part(27).y + shape.part(29).y + shape.part(33).y)/3
        
        height = int(h_ratio*(shape.part(33).y - shape.part(27).y+10)/2)
        
        x0 = x_center
        y0 = max(y_center - height,0)
        
        x1 = max(x_center - width,0)
        y1 = min(y_center + height,h)
        
        x2 = min(x_center + width,w)
        y2 = min(y_center + height,h)
        
        pts = np.asarray([[x0,y0],[x1,y1],[x2,y2]])
        
        return pts
        
    elif index_area==3: #1.2,1.5
        
        if w_ratio is None or h_ratio is None:
            w_ratio = np.random.choice([0.95, 1.15, 1.2, 1.25],1,p=[0.25,0.2,0.25,0.3])[0]
            h_ratio = np.random.choice([0.85,0.95,1.05,1.1],1,p=[0.3,0.25,0.35,0.1])[0] 
            
        x_center = int(shape.part(48).x + shape.part(54).x)/2
        width = int(w_ratio*(shape.part(54).x - shape.part(48).x)/2)
        
        y_center = int(shape.part(58).y + shape.part(50).y)/2
        
        height = int(h_ratio*(shape.part(58).y - shape.part(50).y)/2)
        
        x0 = max(x_center - width,0)
        y0 = max(y_center - height,0)
        
        x1 = min(x_center + width,w)
        y1 = max(y_center - height,0)
        
        x2 = min(x_center + width,w)
        y2 = min(y_center + height,h)
        
        x3 = max(x_center - width,0)
        y3 = min(y_center + height,h)
        
        pts = np.asarray([[x0,y0],[x1,y1],[x2,y2],[x3,y3]])
        
        return pts
        
    elif index_area==4:
        
        if w_ratio is None or h_ratio is None:
            w_ratio = np.random.choice([1.2,1.35,1.25,1.3,1.32],1,p=[0.2,0.2,0.2,0.3,0.1])[0]
            h_ratio = np.random.choice([0.6,0.66,0.56,0.48,0.5],1,p=[0.2,0.2,0.3,0.2,0.1])[0]
            
        Flags = np.random.choice([0,1],1)
        #已关键点确定mask的中心点坐标 (x_center, y_center_)
        if Flags==0:
            x_point = np.random.choice([3,4,5,6],2,p=[0.3,0.3,0.2,0.2])
        else:
            x_point = np.random.choice([10,11,12,13],2,p=[0.25,0.3,0.25,0.2])
            
        x_center = int((shape.part(x_point[0]).x+shape.part(x_point[1]).x)/2)
        y_point = np.random.choice([28,29,30,33],2,p=[0.2,0.3,0.3,0.2])
        
        y_center = int((shape.part(y_point[0]).y + shape.part(y_point[1]).y)/2)
        #中心点的左右宽度
        width = int(w_ratio*(shape.part(29).x - shape.part(2).x)/2)
        
        #中心点的上下高度
        height = int(h_ratio*(shape.part(8).y - shape.part(20).y))
        
        
        x0 = max(x_center - width,int(shape.part(2).x))
        y0 = max(y_center - height,5)
        
        x1 = min(x_center + width,int(shape.part(16).x))
        y1 = max(y_center - height,5)
        
        x2 = min(x_center + width,int(shape.part(16).x))
        y2 = min(y_center + height,h-5)
        
        x3 = max(x_center - width,int(shape.part(2).x))
        y3 = min(y_center + height,h-5)
        
        pts = np.asarray([[x0,y0],[x1,y1],[x2,y2],[x3,y3]])
        
        return pts
        
    else:
        raise ValueError("parameter index_area value is invalid")
    
def add_mask(dir_path):
    img_list = os.listdir(dir_path)
    
    save_dir = "./occ_mask"
    
    for item in img_list:
        full_path = os.path.join(dir_path,item)
        
        img = cv2.imread(full_path)
        
        height,width = img.shape[:2]
        print(height,width)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         
        #使用默认人脸识别的模型
        detector = dlib.get_frontal_face_detector()
        # 获取人脸关键点预训练模型
        # 模型获取地址：http://dlib.net/files/
        predictor = dlib.shape_predictor("./config/shape_predictor_68_face_landmarks.dat")      
        # Ask the detector to find the bounding boxes of each face. The 1 in the
        # second argument indicates that we should upsample the image 1 time. This
        # will make everything bigger and allow us to detect more faces.
        dets = detector(gray, 1)
        #print("Number of faces detected: {}".format(len(dets)))

        for k, d in enumerate(dets):
            print("Left: {} Top: {} Right: {} Bottom: {}".format(
                d.left(), d.top(), d.right(), d.bottom()))
            # Get the landmarks/parts for the face in box d.
            shape = predictor(img, d)
            #print shape.part(0).x, shape.part(0).y
            
            pts_list = []
            #随机选择黑框的个数
            nums_mask = np.random.choice([1,2,3],1,p=[0.35,0.5,0.15])[0]
            #随机选择关键点的大致位置
            index_list = np.random.randint(0,4,nums_mask)
            #print set(index_list)
            #index_list = [4]
            for index in set(index_list):
                #print index
                pts_list.append(get_rect_mask(shape,index,height,width))
            #image = cv2.fillConvexPoly(img,pts,(0,0,0))
            image = cv2.fillPoly(img,pts_list,(0,0,0))
            
            save_name = os.path.join(save_dir,item)
            cv2.imwrite(save_name,image)
if __name__=="__main__":
    
    dir_path = sys.argv[1]
    add_mask(dir_path)
        

• 结果展示：

• Dlib关键点的对应关系