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| import os | |
| os.system("pip install git+https://github.com/ai-forever/ReadingPipeline.git") | |
| import cv2 | |
| import json | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from ocrpipeline.predictor import PipelinePredictor | |
| from ocrpipeline.linefinder import get_structured_text | |
| def get_config_and_download_weights(repo_id, device='cpu'): | |
| # download weights and configs | |
| pipeline_config_path = hf_hub_download(repo_id, "pipeline_config.json") | |
| ocr_model_path = hf_hub_download(repo_id, "ocr/ocr_model.onnx") | |
| kenlm_path = hf_hub_download(repo_id, "ocr/kenlm_corpus.arpa") | |
| ocr_config_path = hf_hub_download(repo_id, "ocr/ocr_config.json") | |
| segm_model_path = hf_hub_download(repo_id, "segm/segm_model.onnx") | |
| segm_config_path = hf_hub_download(repo_id, "segm/segm_config.json") | |
| # change paths to downloaded weights and configs in main pipeline_config | |
| with open(pipeline_config_path, 'r') as f: | |
| pipeline_config = json.load(f) | |
| pipeline_config['main_process']['SegmPrediction']['model_path'] = segm_model_path | |
| pipeline_config['main_process']['SegmPrediction']['config_path'] = segm_config_path | |
| pipeline_config['main_process']['SegmPrediction']['num_threads'] = 2 | |
| pipeline_config['main_process']['SegmPrediction']['device'] = device | |
| pipeline_config['main_process']['SegmPrediction']['runtime'] = "OpenVino" | |
| pipeline_config['main_process']['OCRPrediction']['model_path'] = ocr_model_path | |
| pipeline_config['main_process']['OCRPrediction']['lm_path'] = kenlm_path | |
| pipeline_config['main_process']['OCRPrediction']['config_path'] = ocr_config_path | |
| pipeline_config['main_process']['OCRPrediction']['num_threads'] = 2 | |
| pipeline_config['main_process']['OCRPrediction']['device'] = device | |
| pipeline_config['main_process']['OCRPrediction']['runtime'] = "OpenVino" | |
| # save pipeline_config | |
| with open(pipeline_config_path, 'w') as f: | |
| json.dump(pipeline_config, f) | |
| return pipeline_config_path | |
| def predict(image_path): | |
| image = cv2.imread(image_path) | |
| rotated_image, pred_data = PREDICTOR(image) | |
| structured_text = get_structured_text(pred_data, ['shrinked_text']) | |
| result_text = [' '.join(line_text) for page_text in structured_text | |
| for line_text in page_text] | |
| return '\n'.join(result_text) | |
| PIPELINE_CONFIG_PATH = get_config_and_download_weights("sberbank-ai/ReadingPipeline-notebooks") | |
| PREDICTOR = PipelinePredictor(pipeline_config_path=PIPELINE_CONFIG_PATH) | |
| gr.Interface( | |
| predict, | |
| inputs=gr.Image(label="Upload an image of handwritten school notebook", type="filepath"), | |
| outputs=gr.Textbox(label="Text on the image"), | |
| title="School notebook recognition", | |
| ).launch() |