将自己的数据制作成cityscape格式

GitHub上很多语义分割的项目都使用了cityscape数据集，在训练自己的数据时，不想改太多代码，需要将自己的标签数据转换成cityscape格式.

1. cityscape标签数据格式

1. cityscape标签数据是单通道的灰度图像，第n类目标的像素灰度值为n;
2. cityscape有自己的标注软件， cityscape使用笔记，我没有使用；

2. 生成自己的数据

1. 我是使用labelme打的语义分割标签；
2. labelme安装、使用、标签指定颜色
3. 在制定颜色时，将其中一个通道®的灰度值设置为类值n，
   lable.py
   
4. json文件夹里label.png的R通道就是需要的gtFine标签图像；
   标签图像、.json文件和生成的json文件夹都在2020文件夹里
   

# gtFine_labelIds.py

'''
将color.png =>  gtFine_labelIds.png
单通道，灰度值0-n， n为类别
labelme json文件处理生成的label.png R通道的像素灰度值对应类别n
直接取R通道的图像作为gtFine
'''

import os
import cv2

json_path = './2020-6-11_11_5'
for root, dirs, files in os.walk(json_path):
    for file in files:
        if file.strip() == "label.png":
            file_path = os.path.join(root, file)

            src_image_path = os.path.join(root, 'img.png')

            image = cv2.imread(file_path)
            src = cv2.imread(src_image_path)

            file_name = os.path.split(root)[-1]
            file_name = file_name.split('_')[0] + '.png'

            # label处理  直接取R通道的图像就是gtFine
            B, G, R = cv2.split(image)
            # R = R.astype('uint8')
            # print(thresh)

            path = os.path.join(os.path.abspath('.'), 'gtFine')
            if not os.path.exists(path):
                os.makedirs(path)
            # print(path)
            labelId_image = os.path.join(path, file_name.split('.')[0] + '_gtFine_labelIds.' + file_name.split('.')[-1])
            print(labelId_image)
            cv2.imwrite(labelId_image, R)

            src_image_path = os.path.join(os.path.abspath('.'), 'leftImg8bit')
            if not os.path.exists(src_image_path):
                os.makedirs(src_image_path)
            src_image = os.path.join(src_image_path, file_name)
            cv2.imwrite(src_image, src)

            # 显示
            R = R * 100
            cv2.imshow('label', R)
            cv2.waitKey(1)
            print(file_name)

5. 生成train、val、test文件夹

# train_val.py

'''
将数据分为train val
'''

import os
import random
import shutil

total_list = []
train_list = []
val_list = []
test_list = []


image_path = './leftImg8bit'
label_path = './gtFine'

# 清空
for dir in ['train', 'val', 'test']:
    image_dir = os.path.join(image_path, dir)
    label_dir = os.path.join(label_path, dir)
    if os.path.exists(image_dir):
        shutil.rmtree(image_dir)
    os.makedirs(image_dir)
    if os.path.exists(label_dir):
        shutil.rmtree(label_dir)
    os.makedirs(label_dir)


for root, dirs, files in os.walk(image_path):
    for file in files:
        if file.endswith('png'):
            total_list.append(file)

total_size = len(total_list)
train_size = int(total_size * 0.7)
val_size = int(total_size * 0.2)

train_list = random.sample(total_list, train_size)
remain_list = list(set(total_list) - set(train_list))
val_list = random.sample(remain_list, val_size)
test_list = list(set(remain_list) - set(val_list))

print(len(total_list))
print(len(train_list))
print(len(val_list))
print(len(test_list))


image_path = './leftImg8bit'
label_path = './gtFine'

# 清空
for dir in ['train', 'val', 'test']:
    image_dir = os.path.join(image_path, dir)
    label_dir = os.path.join(label_path, dir)
    if os.path.exists(image_dir):
        shutil.rmtree(image_dir)
        os.makedirs(image_dir)
    if os.path.exists(label_dir):
        shutil.rmtree(label_dir)
        os.makedirs(label_dir)


for file in total_list:
    image_path_0 = os.path.join(image_path, file)
    label_file = file.split('.')[0] + '_gtFine_labelIds' + '.png'
    label_path_0 = os.path.join(label_path, label_file)
    if file in train_list:
        image_path_1 = os.path.join(image_path, 'train', file)
        shutil.move(image_path_0, image_path_1)

        label_path_1 = os.path.join(label_path, 'train', label_file)
        shutil.move(label_path_0, label_path_1)

    elif file in val_list:
        image_path_1 = os.path.join(image_path, 'val', file)
        shutil.move(image_path_0, image_path_1)

        label_path_1 = os.path.join(label_path, 'val', label_file)
        shutil.move(label_path_0, label_path_1)

    elif file in test_list:
        image_path_1 = os.path.join(image_path, 'test', file)
        shutil.move(image_path_0, image_path_1)

        label_path_1 = os.path.join(label_path, 'test', label_file)
        shutil.move(label_path_0, label_path_1)

6. 还可能需要crop

# crop.py
'''
将1280×720
=》1280×704
'''
import cv2
import os
import shutil

image_path = './leftImg8bit'
label_path = './gtFine'

print("processing")
for root, dirs, files in os.walk(image_path):
    for file in files:
        if file.endswith('.png'):
            image_name = os.path.join(root, file)
            image = cv2.imread(image_name, -1)
            crop_image = image[:704, :, :]
            os.remove(image_name)
            cv2.imwrite(image_name, crop_image)
            
            # cv2.imshow("crop_image", crop_image)
            # cv2.waitKey(0)

for root, dirs, files in os.walk(label_path):
    for file in files:
        if file.endswith('.png'):
            image_name = os.path.join(root, file)
            image = cv2.imread(image_name, -1)
            crop_image = image[:704, :]
            os.remove(image_name)
            cv2.imwrite(image_name, crop_image)
            print("processing")
            # cv2.imshow("crop_image", crop_image)
            # cv2.waitKey(0)