Semantic segmentation mask to json

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This article introduces two methods of semantic segmentation mask to json:

方法1：It is a reference to semantic segmentation mask to json (improved version) , based on converting the image to 二值图and using it cv2.findContoursto identify contour points, and saving the contour points as json
方法2: Use the open source tools in github to transfer directly, it supports

1 mask2json - code implementation

1.1 Obtain the gray value corresponding to each category through mask

import os
import cv2
from tqdm import tqdm
 
mask_path = 'mask'
 
data_files = os.listdir(mask_path)
 
# color_list = []
# for data_file in tqdm(data_files):
#     img_file_path = os.path.join(data_path,data_file)
#     img = cv2.imread(img_file_path)
#     for x in range(img.shape[0]):
#         for y in range(img.shape[1]):
#             color = img[x,y]
#             color = list(color)
#             if color not in color_list:
#                 color_list.append(color)
#
#
# print(color_list)
 
gray_list = []
 
for data_file in tqdm(data_files):
    img_file_path = os.path.join(mask_path,data_file)
    img = cv2.imread(img_file_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    for x in range(img.shape[0]):
        for y in range(img.shape[1]):
            value = gray[x,y]
            if value not in gray_list:
                gray_list.append(value)
 
print(gray_list)

1.2 mask to json

By extracting the pixel value of each category of the grayscale image to obtain the contour information and convert it into json

import cv2
import os
import json
from PIL import Image
import io
import base64
 
# class_dict = {
    
    
#     "sky": 10,
#     "building": 0,
#     "column pole": 1,
#     "road": 2,
#     "sidewalk": 3,
#     "tree": 4,
#     "sign symbol": 5,
#     "fence": 6,
#     "car": 7,
#     "pedestrian": 8,
#     "bicyclist": 9
 
# }
 
 
color_list = [128, 0, 0], [0, 0, 244], [255, 96, 0], [240, 0, 0], [255, 212, 0], [0, 212, 255], [0, 100, 255], [74, 255,
                                                                                                                182]
 
gray_list = [15, 73, 85, 27, 154, 201, 135, 213]
 
# def rgb_to_gray_value(RGB):
#     R = RGB[0]
#     G = RGB[1]
#     B = RGB[2]
#     Gray = (R * 299 + G * 587 + B * 114) / 1000
#     return round(Gray)
#
#
# def bgr_2_rgb(color):
#     color[0], color[2] = color[2], color[0]
#     return color
 
 
# class_dict = {
    
    
#     "A1 尾胶面破损": rgb_to_gray_value(bgr_2_rgb(color_list[0])),
#     "B1 尾胶少胶": rgb_to_gray_value(bgr_2_rgb(color_list[1])),
#     "C1 尾胶裂": rgb_to_gray_value(bgr_2_rgb(color_list[2])),
#     "E1 骨架破损": rgb_to_gray_value(bgr_2_rgb(color_list[3])),
#     "F1 骨架裂纹": rgb_to_gray_value(bgr_2_rgb(color_list[4])),
#     "G1 尾胶溢胶": rgb_to_gray_value(bgr_2_rgb(color_list[5])),
#     "H1 磁芯": rgb_to_gray_value(bgr_2_rgb(color_list[6])),
# }
 
class_dict = {
    
    
    "A1 尾胶面破损": gray_list[0],
    "B1 尾胶少胶": gray_list[1],
    "C1 尾胶裂": gray_list[2],
    "E1 骨架破损": gray_list[3],
    "F1 骨架裂纹": gray_list[4],
    "G1 尾胶溢胶": gray_list[5],
    "H1 磁芯": gray_list[6]
}
 
 
def img_tobyte(img_pil):
    # 类型转换 重要代码
    # img_pil = Image.fromarray(roi)
    ENCODING = 'utf-8'
    img_byte = io.BytesIO()
    img_pil.save(img_byte, format='PNG')
    binary_str2 = img_byte.getvalue()
    imageData = base64.b64encode(binary_str2)
    base64_string = imageData.decode(ENCODING)
    return base64_string
 
 ##########################Mask2json 主函数#####################################
def func(file: str) -> dict:
    if os.path.basename(file) == "0016E5_07959.png":
        print('t')
    png = cv2.imread(file)
    gray = cv2.cvtColor(png, cv2.COLOR_BGR2GRAY)
    img_file_path = os.path.join(img_path, os.path.basename(file).split('.')[0] + '.jpg')
    img = Image.open(img_file_path)
    imgData = img_tobyte(img)
    dic = {
    
    "version": "5.1.1", "flags": {
    
    }, "shapes": list(), "imagePath": os.path.basename(file), "imageData": imgData,
           "imageHeight": png.shape[0], "imageWidth": png.shape[1]}
    #
    # cv2.imshow("mask", gray)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    for k, v in class_dict.items():
 
        # _, binary = cv2.threshold(gray, v + 1 , 255, cv2.THRESH_TOZERO_INV)
        # _, binary = cv2.threshold(binary, v , 255, cv2.THRESH_BINARY_INV)
        binary = gray.copy()
        binary[binary != v] = 0
        binary[binary == v] = 255
 
        # _, binary = cv2.threshold(gray, i+1, 255, cv2.THRESH_BINARY_INV)
        # _, binary = cv2.threshold(binary, i, 255, cv2.THRESH_TOZERO_INV)
        # _, binary = cv2.threshold(binary, 125, 255, cv2.THRESH_BINARY_INV)
 
        # if os.path.basename(file) == "16729150388540_class3.png":
        # print('t')
        # cv2.imshow('bin', binary)
        # cv2.waitKey(0)
        # cv2.destroyAllWindows()
        # 只检测外轮廓并存储所有的轮廓点
        contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
        for contour in contours:
 
            # img = cv2.imread(img_file_path)
            # cv2.drawContours(img, contour, -1, (0, 0, 255), 3)
            # cv2.imshow("img", img)
            # cv2.waitKey(0)
            # cv2.destroyWindow("img")
            temp = list()
            if len(contour) < 4:
                continue
            for point in contour:
                # if (point[0][0] <  edge_th and point[0][1] < edge_th) or (point[0][0] < edge_th and point[0][1] > png.shape[0] -edge_th) \
                #         or (point[0][0] > png.shape[1] - edge_th and point[0][1] < edge_th) or (point[0][0] > png.shape[1] - edge_th and point[0][1] > png.shape[0] -edge_th):
                #     continue
                # if len(temp) > 1 and temp[-2][0] * temp[-2][1] * int(point[0][0]) * int(point[0][1]) != 0 and (
                #         int(point[0][0]) - temp[-2][0]) * (
                #         temp[-1][1] - temp[-2][1]) == (int(point[0][1]) - temp[-2][1]) * (temp[-1][0] - temp[-1][0]):
                #     temp[-1][0] = int(point[0][0])
                #     temp[-1][1] = int(point[0][1])
                # else:
                temp.append([float(point[0][0]), float(point[0][1])])
            dic["shapes"].append({
    
    "label": k, "points": temp, "group_id": None,
                                  "shape_type": "polygon", "flags": {
    
    }})
 
    return dic
 
 
if __name__ == "__main__":
    # print(rgb_to_gray_value(bgr_2_rgb(color_list[0])))
    # print(class_dict)
 
    # edge_th = 2
    img_path = 'image'
    mask_path = 'mask'
    save_path = 'json'
    os.makedirs(save_path, exist_ok=True)
 
    mask_files = os.listdir(mask_path)
    for mask_file in mask_files:
        mask_file_path = os.path.join(mask_path, mask_file)
        save_file = mask_file.split('.')[0] + '.json'
        save_file_path = os.path.join(save_path, save_file)
        with open(save_file_path, mode='w', encoding='utf-8') as f:
            json.dump(func(mask_file_path), f)

2 mask2json - use the tool to turn

github address: https://github.com/guchengxi1994/mask2json

insert image description here
The tool supports multiple format conversions:

2.1 Support data enhancement

(1)image crop supported.(single and multiple crops,rectangle and polygon support. See here)
(2).image resize supported (auto labeled). See here and the test script is here
(3) image distortion supported.see here or test-script for details.

2.2 Support multiple format conversion

xml2yolo
json2xml
其他格式等等。。。