70 lines
3.2 KiB
Python
70 lines
3.2 KiB
Python
import base64
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import json
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import os
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import os.path as osp
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import numpy as np
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import PIL.Image
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from labelme import utils
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'''
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制作自己的语义分割数据集需要注意以下几点：
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1、我使用的labelme版本是3.16.7，建议使用该版本的labelme，有些版本的labelme会发生错误，
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具体错误为：Too many dimensions: 3 > 2
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安装方式为命令行pip install labelme==3.16.7
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2、此处生成的标签图是8位彩色图，与视频中看起来的数据集格式不太一样。
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虽然看起来是彩图，但事实上只有8位，此时每个像素点的值就是这个像素点所属的种类。
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所以其实和视频中VOC数据集的格式一样。因此这样制作出来的数据集是可以正常使用的。也是正常的。
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'''
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if __name__ == '__main__':
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jpgs_path = "datasets/JPEGImages"
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pngs_path = "datasets/SegmentationClass"
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classes = ["_background_","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
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# classes = ["_background_","cat","dog"]
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count = os.listdir("./datasets/before/")
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for i in range(0, len(count)):
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path = os.path.join("./datasets/before", count[i])
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if os.path.isfile(path) and path.endswith('json'):
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data = json.load(open(path))
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if data['imageData']:
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imageData = data['imageData']
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else:
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imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
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with open(imagePath, 'rb') as f:
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imageData = f.read()
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imageData = base64.b64encode(imageData).decode('utf-8')
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img = utils.img_b64_to_arr(imageData)
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label_name_to_value = {'_background_': 0}
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for shape in data['shapes']:
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label_name = shape['label']
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if label_name in label_name_to_value:
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label_value = label_name_to_value[label_name]
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else:
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label_value = len(label_name_to_value)
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label_name_to_value[label_name] = label_value
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# label_values must be dense
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label_values, label_names = [], []
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for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
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label_values.append(lv)
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label_names.append(ln)
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assert label_values == list(range(len(label_values)))
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lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
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PIL.Image.fromarray(img).save(osp.join(jpgs_path, count[i].split(".")[0]+'.jpg'))
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new = np.zeros([np.shape(img)[0],np.shape(img)[1]])
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for name in label_names:
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index_json = label_names.index(name)
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index_all = classes.index(name)
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new = new + index_all*(np.array(lbl) == index_json)
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utils.lblsave(osp.join(pngs_path, count[i].split(".")[0]+'.png'), new)
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print('Saved ' + count[i].split(".")[0] + '.jpg and ' + count[i].split(".")[0] + '.png')
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