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import os |
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import lmdb |
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import cv2 |
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from tqdm import tqdm |
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import numpy as np |
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import io |
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from PIL import Image |
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""" a modified version of CRNN torch repository https://github.com/bgshih/crnn/blob/master/tool/create_dataset.py """ |
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def get_datalist(data_dir, data_path, max_len): |
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""" |
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获取训练和验证的数据list |
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:param data_dir: 数据集根目录 |
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:param data_path: 训练的dataset文件列表，每个文件内以如下格式存储 ‘path/to/img\tlabel’ |
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:return: |
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""" |
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train_data = [] |
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if isinstance(data_path, list): |
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for p in data_path: |
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train_data.extend(get_datalist(data_dir, p, max_len)) |
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else: |
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with open(data_path, 'r', encoding='utf-8') as f: |
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for line in tqdm(f.readlines(), |
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desc=f'load data from {data_path}'): |
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line = (line.strip('\n').replace('.jpg ', '.jpg\t').replace( |
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'.png ', '.png\t').split('\t')) |
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if len(line) > 1: |
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img_path = os.path.join(data_dir, line[0].strip(' ')) |
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label = line[1] |
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if len(label) > max_len: |
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continue |
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if os.path.exists( |
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img_path) and os.path.getsize(img_path) > 0: |
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train_data.append([str(img_path), label]) |
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return train_data |
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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.frombuffer(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(data_list, 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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for imagePath, label in tqdm(data_list, |
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desc=f'make dataset, save to {outputPath}'): |
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with open(imagePath, 'rb') as f: |
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imageBin = f.read() |
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buf = io.BytesIO(imageBin) |
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w, h = Image.open(buf).size |
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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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continue |
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imageKey = 'image-%09d'.encode() % cnt |
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labelKey = 'label-%09d'.encode() % cnt |
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whKey = 'wh-%09d'.encode() % cnt |
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cache[imageKey] = imageBin |
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cache[labelKey] = label.encode() |
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cache[whKey] = (str(w) + '_' + str(h)).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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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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data_dir = './Union14M-L/' |
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label_file_list = [ |
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'./Union14M-L/train_annos/filter_jsonl_mmocr0.x/filter_train_challenging.jsonl.txt', |
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'./Union14M-L/train_annos/filter_jsonl_mmocr0.x/filter_train_easy.jsonl.txt', |
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'./Union14M-L/train_annos/filter_jsonl_mmocr0.x/filter_train_hard.jsonl.txt', |
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'./Union14M-L/train_annos/filter_jsonl_mmocr0.x/filter_train_medium.jsonl.txt', |
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'./Union14M-L/train_annos/filter_jsonl_mmocr0.x/filter_train_normal.jsonl.txt' |
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] |
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save_path_root = './Union14M-L-LMDB-Filtered/' |
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for data_list in label_file_list: |
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save_path = save_path_root + data_list.split('/')[-1].split( |
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'.')[0] + '/' |
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os.makedirs(save_path, exist_ok=True) |
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print(save_path) |
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train_data_list = get_datalist(data_dir, data_list, 800) |
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createDataset(train_data_list, save_path) |
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