FCN Image Segmentation | Data Preparation

Reference: How to label mask for image segmentation model training

1. Details of using the marking tool

Use VIA annotation tool

1. remove sample image
   

2. add own picture
   

3. Set attributes according to different tasks. It is not necessary to
   purely segment an object. If you classify multiple objects, please inquire separately.
   

4. Draw contour
   
   Note: You can press esc when you want to exit during drawing, press enter when you finish drawing, and click to select the contour to adjust the position of the node. For details, please refer to Help-Gettong Started.
   

5. Save
   -ok

2. Convert to mask

Run the following code:

import os
import json
import numpy as np
import skimage.draw
import cv2

IMAGE_FOLDER = "./train/"
MASK_FOLOER = "./mask/"
PATH_ANNOTATION_JSON = 'box.json'

# 加载VIA导出的json文件
annotations = json.load(open(PATH_ANNOTATION_JSON, 'r'))
imgs = annotations["_via_img_metadata"]

for imgId in imgs:
    filename = imgs[imgId]['filename']
    regions = imgs[imgId]['regions']
    if len(regions) <= 0:
        continue

    # 取出第一个标注的类别，本例只标注了一个物件
    polygons = regions[0]['shape_attributes']

    # 图片路径
    image_path = os.path.join(IMAGE_FOLDER, filename)
    # 读出图片，目的是获取到宽高信息
    image = cv2.imread(image_path)  # image = skimage.io.imread(image_path)
    height, width = image.shape[:2]

    # 创建空的mask
    maskImage = np.zeros((height,width), dtype=np.uint8)
    countOfPoints = len(polygons['all_points_x'])
    points = [None] * countOfPoints
    for i in range(countOfPoints):
        x = int(polygons['all_points_x'][i])
        y = int(polygons['all_points_y'][i])
        points[i] = (x, y)

    contours = np.array(points)

    # 遍历图片所有坐标
    for i in range(width):
        for j in range(height):
            if cv2.pointPolygonTest(contours, (i, j), False) > 0:
                maskImage[j,i] = 1

    savePath = MASK_FOLOER + filename
    # 保存mask
    cv2.imwrite(savePath, maskImage)