88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
""" a modified version of CRNN torch repository https://github.com/bgshih/crnn/blob/master/tool/create_dataset.py """
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import fire
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import os
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import lmdb
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import cv2
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import numpy as np
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def checkImageIsValid(imageBin):
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if imageBin is None:
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return False
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imageBuf = np.fromstring(imageBin, dtype=np.uint8)
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img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE)
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imgH, imgW = img.shape[0], img.shape[1]
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if imgH * imgW == 0:
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return False
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return True
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def writeCache(env, cache):
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with env.begin(write=True) as txn:
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for k, v in cache.items():
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txn.put(k, v)
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def createDataset(inputPath, gtFile, outputPath, checkValid=True):
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"""
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Create LMDB dataset for training and evaluation.
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ARGS:
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inputPath : input folder path where starts imagePath
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outputPath : LMDB output path
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gtFile : list of image path and label
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checkValid : if true, check the validity of every image
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"""
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os.makedirs(outputPath, exist_ok=True)
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env = lmdb.open(outputPath, map_size=1099511627776)
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cache = {}
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cnt = 1
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with open(gtFile, 'r', encoding='utf-8') as data:
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datalist = data.readlines()
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nSamples = len(datalist)
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for i in range(nSamples):
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imagePath, label = datalist[i].strip('\n').split('\t')
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imagePath = os.path.join(inputPath, imagePath)
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# # only use alphanumeric data
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# if re.search('[^a-zA-Z0-9]', label):
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# continue
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if not os.path.exists(imagePath):
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print('%s does not exist' % imagePath)
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continue
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with open(imagePath, 'rb') as f:
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imageBin = f.read()
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if checkValid:
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try:
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if not checkImageIsValid(imageBin):
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print('%s is not a valid image' % imagePath)
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continue
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except:
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print('error occured', i)
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with open(outputPath + '/error_image_log.txt', 'a') as log:
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log.write('%s-th image data occured error\n' % str(i))
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continue
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imageKey = 'image-%09d'.encode() % cnt
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labelKey = 'label-%09d'.encode() % cnt
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cache[imageKey] = imageBin
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cache[labelKey] = label.encode()
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if cnt % 1000 == 0:
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writeCache(env, cache)
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cache = {}
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print('Written %d / %d' % (cnt, nSamples))
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cnt += 1
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nSamples = cnt-1
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cache['num-samples'.encode()] = str(nSamples).encode()
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writeCache(env, cache)
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print('Created dataset with %d samples' % nSamples)
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if __name__ == '__main__':
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fire.Fire(createDataset)
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