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Running
on
T4
Running
on
T4
| import os | |
| import shutil | |
| import gradio as gr | |
| from helper.examples.examples import DemoImages | |
| from src.htr_pipeline.gradio_backend import CustomTrack, SingletonModelLoader | |
| model_loader = SingletonModelLoader() | |
| custom_track = CustomTrack(model_loader) | |
| images_for_demo = DemoImages() | |
| with gr.Blocks() as stepwise_htr_tool_tab: | |
| with gr.Tabs(): | |
| with gr.Tab("1. Region Segmentation"): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| vis_data_folder_placeholder = gr.Markdown(visible=False) | |
| name_files_placeholder = gr.Markdown(visible=False) | |
| with gr.Row(): | |
| input_region_image = gr.Image( | |
| label="Image to Region segment", | |
| # type="numpy", | |
| tool="editor", | |
| ).style(height=350) | |
| with gr.Accordion("Region segment settings:", open=False): | |
| with gr.Row(): | |
| reg_pred_score_threshold_slider = gr.Slider( | |
| minimum=0.4, | |
| maximum=1, | |
| value=0.5, | |
| step=0.05, | |
| label="P-threshold", | |
| info="""Filter and determine the confidence score | |
| required for a prediction score to be considered""", | |
| ) | |
| reg_containments_threshold_slider = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| value=0.5, | |
| step=0.05, | |
| label="C-threshold", | |
| info="""The minimum required overlap or similarity | |
| for a detected region or object to be considered valid""", | |
| ) | |
| with gr.Row(): | |
| region_segment_model_dropdown = gr.Dropdown( | |
| choices=["Riksarkivet/RmtDet_region"], | |
| value="Riksarkivet/RmtDet_region", | |
| label="Region segment model", | |
| info="Will add more models later!", | |
| ) | |
| with gr.Row(): | |
| clear_button = gr.Button("Clear", variant="secondary", elem_id="clear_button") | |
| region_segment_button = gr.Button( | |
| "Segment Region", | |
| variant="primary", | |
| elem_id="region_segment_button", | |
| ) # .style(full_width=False) | |
| with gr.Row(): | |
| with gr.Accordion("Example images to use:", open=False) as example_accord: | |
| gr.Examples( | |
| examples=images_for_demo.examples_list, | |
| inputs=[name_files_placeholder, input_region_image], | |
| label="Example images", | |
| examples_per_page=5, | |
| ) | |
| with gr.Column(scale=3): | |
| output_region_image = gr.Image(label="Segmented regions", type="numpy").style(height=600) | |
| ############################################## | |
| with gr.Tab("2. Line Segmentation"): | |
| image_placeholder_lines = gr.Image( | |
| label="Segmented lines", | |
| # type="numpy", | |
| interactive="False", | |
| visible=True, | |
| ).style(height=600) | |
| with gr.Row(visible=False) as control_line_segment: | |
| with gr.Column(scale=2): | |
| with gr.Box(): | |
| regions_cropped_gallery = gr.Gallery( | |
| label="Segmented regions", | |
| show_label=False, | |
| elem_id="gallery", | |
| ).style( | |
| columns=[2], | |
| rows=[2], | |
| # object_fit="contain", | |
| height=400, | |
| preview=True, | |
| container=False, | |
| ) | |
| input_region_from_gallery = gr.Image( | |
| label="Region segmentation to line segment", interactive="False", visible=False | |
| ).style(height=400) | |
| with gr.Row(): | |
| with gr.Accordion("Line segment settings:", open=False): | |
| with gr.Row(): | |
| line_pred_score_threshold_slider = gr.Slider( | |
| minimum=0.3, | |
| maximum=1, | |
| value=0.4, | |
| step=0.05, | |
| label="Pred_score threshold", | |
| info="""Filter and determine the confidence score | |
| required for a prediction score to be considered""", | |
| ) | |
| line_containments_threshold_slider = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| value=0.5, | |
| step=0.05, | |
| label="Containments threshold", | |
| info="""The minimum required overlap or similarity | |
| for a detected region or object to be considered valid""", | |
| ) | |
| with gr.Row().style(equal_height=False): | |
| line_segment_model_dropdown = gr.Dropdown( | |
| choices=["Riksarkivet/RmtDet_lines"], | |
| value="Riksarkivet/RmtDet_lines", | |
| label="Line segment model", | |
| info="Will add more models later!", | |
| ) | |
| with gr.Row(): | |
| clear_line_segment_button = gr.Button( | |
| " ", | |
| variant="Secondary", | |
| # elem_id="center_button", | |
| ).style(full_width=True) | |
| line_segment_button = gr.Button( | |
| "Segment Lines", | |
| variant="primary", | |
| # elem_id="center_button", | |
| ).style(full_width=True) | |
| with gr.Column(scale=3): | |
| # gr.Markdown("""lorem ipsum""") | |
| output_line_from_region = gr.Image( | |
| label="Segmented lines", | |
| type="numpy", | |
| interactive="False", | |
| ).style(height=600) | |
| ############################################### | |
| with gr.Tab("3. Transcribe Text"): | |
| image_placeholder_htr = gr.Image( | |
| label="Transcribed lines", | |
| # type="numpy", | |
| interactive="False", | |
| visible=True, | |
| ).style(height=600) | |
| with gr.Row(visible=False) as control_htr: | |
| inputs_lines_to_transcribe = gr.Variable() | |
| with gr.Column(scale=2): | |
| image_inputs_lines_to_transcribe = gr.Image( | |
| label="Transcribed lines", | |
| type="numpy", | |
| interactive="False", | |
| visible=False, | |
| ).style(height=470) | |
| with gr.Row(): | |
| with gr.Accordion("Transcribe settings:", open=False): | |
| transcriber_model = gr.Dropdown( | |
| choices=["Riksarkivet/SATRN_transcriber", "microsoft/trocr-base-handwritten"], | |
| value="Riksarkivet/SATRN_transcriber", | |
| label="Transcriber model", | |
| info="Will add more models later!", | |
| ) | |
| with gr.Row(): | |
| clear_transcribe_button = gr.Button(" ", variant="Secondary", visible=True).style( | |
| full_width=True | |
| ) | |
| transcribe_button = gr.Button("Transcribe lines", variant="primary", visible=True).style( | |
| full_width=True | |
| ) | |
| donwload_txt_button = gr.Button("Download text", variant="secondary", visible=False).style( | |
| full_width=True | |
| ) | |
| with gr.Row(): | |
| txt_file_downlod = gr.File(label="Download text", visible=False) | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| transcribed_text_df = gr.Dataframe( | |
| headers=["Transcribed text"], | |
| max_rows=15, | |
| col_count=(1, "fixed"), | |
| wrap=True, | |
| interactive=False, | |
| overflow_row_behaviour="paginate", | |
| ).style(height=600) | |
| ##################################### | |
| with gr.Tab("4. Explore Results"): | |
| image_placeholder_explore_results = gr.Image( | |
| label="Cropped transcribed lines", | |
| # type="numpy", | |
| interactive="False", | |
| visible=True, | |
| ).style(height=600) | |
| with gr.Row(visible=False) as control_results_transcribe: | |
| with gr.Column(scale=1, visible=True): | |
| with gr.Box(): | |
| temp_gallery_input = gr.Variable() | |
| gallery_inputs_lines_to_transcribe = gr.Gallery( | |
| label="Cropped transcribed lines", | |
| show_label=True, | |
| elem_id="gallery_lines", | |
| ).style( | |
| columns=[3], | |
| rows=[3], | |
| # object_fit="contain", | |
| # height="600", | |
| preview=True, | |
| container=False, | |
| ) | |
| with gr.Column(scale=1, visible=True): | |
| mapping_dict = gr.Variable() | |
| transcribed_text_df_finish = gr.Dataframe( | |
| headers=["Transcribed text", "Pred score"], | |
| max_rows=15, | |
| col_count=(2, "fixed"), | |
| wrap=True, | |
| interactive=False, | |
| overflow_row_behaviour="paginate", | |
| ).style(height=600) | |
| # custom track | |
| region_segment_button.click( | |
| custom_track.region_segment, | |
| inputs=[input_region_image, reg_pred_score_threshold_slider, reg_containments_threshold_slider], | |
| outputs=[output_region_image, regions_cropped_gallery, image_placeholder_lines, control_line_segment], | |
| ) | |
| regions_cropped_gallery.select( | |
| custom_track.get_select_index_image, regions_cropped_gallery, input_region_from_gallery | |
| ) | |
| transcribed_text_df_finish.select( | |
| fn=custom_track.get_select_index_df, | |
| inputs=[transcribed_text_df_finish, mapping_dict], | |
| outputs=gallery_inputs_lines_to_transcribe, | |
| ) | |
| line_segment_button.click( | |
| custom_track.line_segment, | |
| inputs=[input_region_from_gallery, line_pred_score_threshold_slider, line_containments_threshold_slider], | |
| outputs=[ | |
| output_line_from_region, | |
| image_inputs_lines_to_transcribe, | |
| inputs_lines_to_transcribe, | |
| gallery_inputs_lines_to_transcribe, | |
| temp_gallery_input, | |
| # Hide | |
| transcribe_button, | |
| image_inputs_lines_to_transcribe, | |
| image_placeholder_htr, | |
| control_htr, | |
| ], | |
| ) | |
| transcribe_button.click( | |
| custom_track.transcribe_text, | |
| inputs=[transcribed_text_df, inputs_lines_to_transcribe], | |
| outputs=[ | |
| transcribed_text_df, | |
| transcribed_text_df_finish, | |
| mapping_dict, | |
| txt_file_downlod, | |
| control_results_transcribe, | |
| image_placeholder_explore_results, | |
| ], | |
| ) | |
| donwload_txt_button.click( | |
| custom_track.download_df_to_txt, | |
| inputs=transcribed_text_df, | |
| outputs=[txt_file_downlod, txt_file_downlod], | |
| ) | |
| # def remove_temp_vis(): | |
| # if os.path.exists("./vis_data"): | |
| # os.remove("././vis_data") | |
| # return None | |
| clear_button.click( | |
| lambda: ( | |
| (shutil.rmtree("./vis_data") if os.path.exists("./vis_data") else None, None)[1], | |
| None, | |
| None, | |
| None, | |
| gr.update(visible=False), | |
| None, | |
| None, | |
| None, | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=True), | |
| None, | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=True), | |
| gr.update(visible=True), | |
| ), | |
| inputs=[], | |
| outputs=[ | |
| vis_data_folder_placeholder, | |
| input_region_image, | |
| regions_cropped_gallery, | |
| input_region_from_gallery, | |
| control_line_segment, | |
| output_line_from_region, | |
| inputs_lines_to_transcribe, | |
| transcribed_text_df, | |
| control_htr, | |
| inputs_lines_to_transcribe, | |
| image_placeholder_htr, | |
| output_region_image, | |
| image_inputs_lines_to_transcribe, | |
| control_results_transcribe, | |
| image_placeholder_explore_results, | |
| image_placeholder_lines, | |
| ], | |
| ) | |