NYUDv2数据集预处理——获得语义分割的图像和标签并着色

NYUDv2数据集

• NYUDv2数据集包含RGB、RGB-D图像，共有849类。
• 本文只使用NYUDv2数据集中的RGB图像，且只包含40类。
• 只需下载Labeled dataset (~2.8 GB)

本文内容

• 获得NYUDv2数据集中的RGB原始图像、灰度分割图像
• 对灰度分割图像进行着色
• 将原始图像和分割图像划分为训练集和验证集

完整代码和数据集已上传至百度网盘，链接如下：
链接：https://pan.baidu.com/s/1QTbKN9WKIKkf9BmTv_1tyA
提取码：a7ux

获得原始图像和分割图像

• 运行preprocess.py，输出原始图像和分割图像，并且能够输出划分训练集和验证集的txt文件

## preprocess.py

import os
import h5py
import numpy as np
from PIL import Image
from tqdm import tqdm
from scipy.io import loadmat

def write_txt(f, list_ids):
    f.write('\n'.join(list_ids))
    f.close()

def extract_data(root):
    """
    extract images and labels.
    :param root:
    :return:
    """
    print('Extracting images and labels from nyu_depth_v2_labeled.mat...')
    data = h5py.File(os.path.join(root, 'nyu_depth_v2_labeled.mat'))
    images = np.array(data['images'])
    print(f'images shape: {images.shape}')
    num_img = images.shape[0]
    print(f'image number: {num_img}')

    images_dir = os.path.join(root, 'images')
    if not os.path.isdir(images_dir):
        os.makedirs(images_dir)

    bar = tqdm(range(num_img))
    for i in bar:
        img = images[i]
        r = Image.fromarray(img[0]).convert('L')
        g = Image.fromarray(img[1]).convert('L')
        b = Image.fromarray(img[2]).convert('L')
        img = Image.merge('RGB', (r, g, b))
        img = img.transpose(Image.ROTATE_270)
        img.save(os.path.join(images_dir, str(i) + '.jpg'), optimize=True)

def split(root):
    print('Generating training and validation split from split.mat...')
    split_file = loadmat(os.path.join(root, 'splits.mat'))
    train_images = tuple([int(x) for x in split_file["trainNdxs"]])
    test_images = tuple([int(x) for x in split_file["testNdxs"]])
    print("%d training images" % len(train_images))
    print("%d test images" % len(test_images))

    train_ids = [str(i - 1) for i in train_images]
    test_ids = [str(i - 1) for i in test_images]

    train_list_file = open(os.path.join(root, 'train.txt'), 'a')
    write_txt(train_list_file, train_ids)

    test_list_file = open(os.path.join(root, 'val.txt'), 'a')
    write_txt(test_list_file, test_ids)


def labels_40(root):
    print('Extracting labels with 40 classes from labels40.mat...')
    data = loadmat(os.path.join(root, 'labels40.mat'))
    labels = np.array(data['labels40'])
    print(f'labels shape: {labels.shape}')

    path_converted = os.path.join(root, 'labels40')
    if not os.path.isdir(path_converted):
        os.makedirs(path_converted)

    bar = tqdm(range(labels.shape[2]))
    for i in bar:
        label = np.array(labels[:, :, i].transpose((1, 0)))
        label_img = Image.fromarray(np.uint8(label))
        label_img = label_img.transpose(Image.ROTATE_270)
        label_img.save(os.path.join(path_converted, str(i) + '.png'), optimize=True)


def main():
    root = os.path.dirname(__file__)
    extract_data(root)
    split(root)
    labels_40(root)


if __name__ == '__main__':
    main()

• 在生成的images文件夹中存放着原始图像，共1449张，如下图所示：
  

• 在生成的labels40文件夹中存放着40分类的分割图像，共1449张，如下图所示：
  

• 生成的train.txt和val.txt中存放训练集和验证集的划分，其中训练集795张，验证集654张

分割图像着色

• 在根目录下新建一个文件夹out_color40，然后运行main.py，即可对分割图像着色，如下图所示：

## main.py

import os
import cv2
import numpy as np

import rgb2gray2rgb
import label2color

def label_color_label(img_path, color_map):
    src_label = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)

    # resize
    # src_label = cv2.resize(img, (256, 256))
    '''

    label2color
    '''
    color_img = rgb2gray2rgb.gray2color(src_label, color_map)
    #save
    dir, name = os.path.split(img_path)
    save_path=os.path.join('./out_color40', name)
    cv2.imwrite(save_path, color_img)

    '''
    color2label
    '''
    gray_img = rgb2gray2rgb.color2gray(color_img, color_map)

    '''
        原始lable图像的灰度值
        lable2color2label图像的灰度值,
    
    '''
    pix_value = np.unique(src_label)
    print('src_label_value', pix_value)
    pix_value = np.unique(gray_img)
    print("color2label_value", pix_value)



if __name__ == '__main__':

    img_dir = './labels40/'

    n_label = 41 # the  number of label class

    color_map1 = label2color.label_colormap(n_label=n_label, rgb1_bgr0=0)

    '''
    random gene color map
    '''
    random_state = np.random.RandomState(seed=1234)
    color_custom_map = np.random.randint(low=0, high=255, size=(n_label, 3), dtype=np.uint8)

    for img in sorted(os.listdir(img_dir)):

        if not img.endswith((".png", ".jpg")):
            continue

        img_path = os.path.join(img_dir, img)
        label_color_label(img_path, color_map1)

划分训练集和验证集

• 运行splits_trainval.py，按照train.txt和val.txt的内容，将原始图像和分割图像划分为训练集和验证集

## splits_trainval.py

import shutil
import os


def objFileName():
    local_file_name_list = "val.txt"
    obj_name_list = []
    for i in open(local_file_name_list, 'r'):
        obj_name_list.append(i.replace('\n', ''))
    return obj_name_list


def copy_img():
    local_img_name = "out_color40"
    # 指定要复制的图片路径
    path = "dataset/Segmentation/val"
    # 指定存放图片的目录
    for i in objFileName():
        new_obj_name = i + '.png'
        # 对于分割数据，数据是jpg格式，标签是png格式
        print(new_obj_name)
        shutil.copy(local_img_name + '/' + new_obj_name, path + '/' + new_obj_name)


if __name__ == '__main__':
    copy_img()

• 需要注意，原始图像的格式是.jpg，分割图像的格式是.png，在运行代码时需要根据图片格式进行修改