| import json |
| from tqdm import tqdm |
| import os |
| import warnings |
|
|
| import numpy as np |
| from PIL import Image |
| from sklearn.cluster import KMeans |
|
|
| from seghist.utils.image_utils import ImageToolkits |
|
|
| warnings.filterwarnings('ignore', category=np.RankWarning) |
|
|
| def get_image_size(file_path): |
| with Image.open(file_path) as img: |
| return img.size |
|
|
| def clean_redundant_points(poly): |
| ''' |
| clean redundant points when len(poly) % 2 != 0 |
| ''' |
| kmeans = KMeans(((len(poly)+1) // 2), n_init=3).fit(poly[:, 1:]) |
| cluster_counts = np.bincount(kmeans.labels_) |
| for i in range(len(cluster_counts)): |
| if cluster_counts[i] == 1: |
| for idx, l in enumerate(kmeans.labels_): |
| if l==i: |
| return np.concatenate([poly[:idx], poly[idx+1:]], axis=0) |
|
|
| def main(root, |
| label_list, |
| data_prefix_list, |
| output_list, |
| separate_entry=True): |
| metainfo = {"dataset_type": "TextDetDataset", "task_name": "textdet", |
| "category": [{"id": 0, "name": "single_entry_text", |
| "id": 1, "name": "double_entry_text"}]} |
| for label, data_prefix, output in zip(label_list, data_prefix_list, output_list): |
| label = os.path.join(root, label) |
| datas = dict(metainfo=metainfo, data_list=[]) |
| with open(label) as f: |
| ann_file = json.load(f) |
| for img_path, instances in tqdm(ann_file.items()): |
| data = dict(img_path=img_path, instances=[]) |
| data['width'], data['height'] = get_image_size(os.path.join(root, data_prefix, img_path)) |
| for idx, inst in enumerate(instances): |
| |
| if len(inst['points']) % 4 != 0: |
| poly = np.array(inst['points']).reshape(-1, 2) |
| poly = clean_redundant_points(poly) |
| instances[idx]['points'] = poly.reshape(-1).tolist() |
| '''data["instances"].append(dict( |
| ignore=False, |
| text=inst['transcription'], |
| bbox_label=0, |
| polygon=inst['points'] |
| ))''' |
| if separate_entry: |
| hi = ImageToolkits([np.array(d['points']).reshape(-1, 2) for d in instances], |
| np.array((data['height'], data['width'])), |
| img_path, |
| texts=[d['transcription'] for d in instances]) |
| hi.process() |
| data['instances'] = hi.output_json() |
| else: |
| for idx, inst in enumerate(instances): |
| data["instances"].append(dict( |
| ignore=False, |
| text=inst['transcription'], |
| bbox_label=0, |
| polygon=inst['points'] |
| )) |
| datas['data_list'].append(data) |
| with open(os.path.join(root, output), mode='w') as f: |
| json.dump(datas, f) |
|
|
| root = './data/historical_document/IACC2022_CHDAC/official_dataset' |
|
|
| label_list = ['final/test/label_test.json', |
| 'final/train/label_train.json', |
| 'preliminary/train/label_train.json'] |
| data_prefix_list = ['final/test/image', |
| 'final/train/image', |
| 'preliminary/train/image'] |
| output_list = ['final/test/ocr_test.json', |
| 'final/train/ocr_train.json', |
| 'preliminary/train/ocr_train.json'] |
| |
| main(root, label_list, data_prefix_list, output_list) |
|
|
