faceRect + landmark

import os
import json
import numpy as np

dir_json = 'D:/Data/MyAnnoData/landmarks/20200915/json/'
dir_txt = 'D:/Data/MyAnnoData/landmarks/20200915/'
remove_txt = "images/"

if not os.path.exists(dir_txt):
    os.makedirs(dir_txt)
list_json = os.listdir(dir_json)

ftxt = open(dir_txt+"label.txt",'w+')

for cnt,json_name in enumerate(list_json):
    #print('cnt=%d,name=%s'%(cnt,json_name))
    path_json = dir_json + json_name
    path_txt = dir_txt + json_name.replace('.json','.txt')
    # print(path_json, path_txt)
    path_img = dir_txt + json_name.replace('.json', '.jpg')#图片路径
    remove_path = remove_txt + json_name.replace('.json', '.jpg')#图片路径
    ftxt.writelines("# "+remove_path+"
")

    with open(path_json, 'r') as path_json:
        jsonx = json.load(path_json)  # json文件信息读入
        rect_add_landmark = ""
        cntPerson = int(len(jsonx['shapes']) / 6)
        saveInfo = ''

        flag = 0
        landmarkInfo = ""
        facemaskInfo = ""

        for shape in jsonx['shapes']:  # 读取shapes目录
            xy = np.array(shape['points'])  # 读取points信息，五官和人脸框
            typeItem = str(shape['shape_type']) #读取shape_type

            if(typeItem == "point"):#点
                flag+=1
                if(flag%6==0):
                    flag=1
                label1 = str(shape['label'])  # 读取label信息
                if (label1==str(flag)):
                    for i in range(len(xy)):
                        for j in range(len(xy[i])):
                            landmarkInfo += str(xy[i][j]) + ','
                else:
                    print("label not match...")
                    print(path_img)
            elif (typeItem == "rectangle"):
                if(flag%5!=0):
                    print("landmark and face error")
                    print(path_img)

                label2 = str(shape['label'])  # 读取label信息
                if(label2=="face"):
                    for i in range(len(xy)):
                        for j in range(len(xy[i])):
                            facemaskInfo += str(xy[i][j]) + ','
                elif(label2=="mask"):
                    for i in range(len(xy)):
                        for j in range(len(xy[i])):
                            facemaskInfo += str(xy[i][j]) + ','

    facemaskList = facemaskInfo.split(",")
    landmarkList = landmarkInfo.split(",")

    #print(facemaskList)
    cutPoint = 0.0
    for i in range(cntPerson):
    #先存储人脸信息
        x = float(facemaskList[i*4])
        y = float(facemaskList[i*4+1])
        w = float(facemaskList[i*4+2]) - float(facemaskList[i*4])
        h = float(facemaskList[i*4+3]) - float(facemaskList[i*4+1])
        ftxt.writelines(str(x) + " ")
        ftxt.writelines(str(y)+ " ")
        ftxt.writelines(str(w)+ " ")
        ftxt.writelines(str(h)+ " ")


        point1x = str(landmarkList[i * 10])
        point1y = str(landmarkList[i * 10 + 1])
        ftxt.writelines(str(point1x)+ " ")
        ftxt.writelines(str(point1y) + " ")
        ftxt.writelines(str(cutPoint) + " ")

        point2x = str(landmarkList[i * 10 + 2])
        point2y = str(landmarkList[i * 10 + 3])
        ftxt.writelines(str(point2x) + " ")
        ftxt.writelines(str(point2y) + " ")
        ftxt.writelines(str(cutPoint) + " ")

        point3x = str(landmarkList[i * 10 + 4])
        point3y = str(landmarkList[i * 10 + 5])
        ftxt.writelines(str(point3x) + " ")
        ftxt.writelines(str(point3y) + " ")
        ftxt.writelines(str(cutPoint) + " ")

        point4x = str(landmarkList[i * 10 + 6])
        point4y = str(landmarkList[i * 10 + 7])
        ftxt.writelines(str(point4x) + " ")
        ftxt.writelines(str(point4y) + " ")
        ftxt.writelines(str(cutPoint) + " ")

        point5x = str(landmarkList[i * 10 + 8])
        point5y = str(landmarkList[i * 10 + 9])
        ftxt.writelines(str(point5x) + " ")
        ftxt.writelines(str(point5y) + " ")
        ftxt.writelines(str(cutPoint) + " ")
        ftxt.writelines(str(cutPoint))
        ftxt.writelines("
")

ftxt.close()
print("end...")




//显示

import os
import cv2

if __name__ == '__main__':
    #txt_path = r"D:/Detection/Pytorch_Retinaface-master/data/widerface/train/labeltest.txt"
    txt_path = r"F:/FaceLandmarks/widerface/train/labelwider.txt"
    imgs_path = []
    words = []
    f = open(txt_path, 'r')
    lines = f.readlines()
    isFirst = True
    labels = []
    for line in lines:
        line = line.rstrip()
        if line.startswith('#'):
            if isFirst is True:
                isFirst = False
            else:
                labels_copy = labels.copy()
                words.append(labels_copy)
                labels.clear()
            path = line[2:]
            path = txt_path.replace('labelwider.txt', 'imageswider/') + path
            #path = txt_path.replace('/label.txt', '/') + path
            imgs_path.append(path)
        else:
            line = line.split(' ')
            label = [float(x) for x in line]
            labels.append(label)

    words.append(labels)

    print(len(words))
    index = 0
    for index, file in enumerate(imgs_path):
        labels = words[index]
        img_raw = cv2.imread(file)
        for label in labels:
            x1 = int(label[0])
            y1 = int(label[1])
            x2 = int(label[0] + label[2])
            y2 = int(label[1] + label[3])
            if label[4] < 0:
                continue
            # if label[2] * label[3] > 11 * 12:
            #         face_num += 1
            else:
                cv2.rectangle(img_raw, (x1, y1), (x2, y2), (0, 255, 0), 3)

                # landmarks
            cv2.circle(img_raw,(int(label[4]),int(label[5])),3,(0,0,255),3)
            cv2.circle(img_raw, (int(label[7]), int(label[8])), 3, (0, 0, 255), 3)
            cv2.circle(img_raw, (int(label[10]), int(label[11])), 3, (0, 0, 255), 3)
            cv2.circle(img_raw, (int(label[13]), int(label[14])), 3, (0, 0, 255), 3)
            cv2.circle(img_raw, (int(label[16]), int(label[17])), 3, (0, 0, 255), 3)
#        if img_raw.shape[0] > 1000:
#            fy = 1000 / img_raw.shape[0]
#            img_raw = cv2.resize(img_raw, (0, 0), fx=fy, fy=fy, interpolation=cv2.INTER_NEAREST)
        cv2.imshow("aaa", img_raw)
        cv2.waitKey()