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", 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", ) 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", height=600) ############################################## with gr.Tab("2. Line Segmentation"): image_placeholder_lines = gr.Image( label="Segmented lines", # type="numpy", interactive="False", visible=True, 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", elem_id="gallery", columns=[2], rows=[2], # object_fit="contain", height=450, preview=True, container=False, ) input_region_from_gallery = gr.Image( label="Region segmentation to line segment", interactive="False", visible=False, 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(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", scale=1, ) line_segment_button = gr.Button( "Segment Lines", variant="primary", # elem_id="center_button", scale=1, ) with gr.Column(scale=3): # gr.Markdown("""lorem ipsum""") output_line_from_region = gr.Image( label="Segmented lines", type="numpy", interactive="False", height=600 ) ############################################### with gr.Tab("3. Transcribe Text"): image_placeholder_htr = gr.Image( label="Transcribed lines", # type="numpy", interactive="False", visible=True, 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, 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, scale=1) transcribe_button = gr.Button("Transcribe Lines", variant="primary", visible=True, scale=1) with gr.Column(scale=3): with gr.Row(): transcribed_text_df = gr.Dataframe( headers=["Transcribed text"], max_rows=14, col_count=(1, "fixed"), wrap=True, interactive=False, overflow_row_behaviour="paginate", height=600, ) ##################################### with gr.Tab("4. Explore Results"): image_placeholder_explore_results = gr.Image( label="Cropped transcribed lines", # type="numpy", interactive="False", visible=True, height=600, ) with gr.Row(visible=False, equal_height=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", elem_id="gallery_lines", columns=[3], rows=[3], # object_fit="contain", height=300, preview=True, container=False, ) dataframe_text_index = gr.Textbox( label="Text from DataFrame selection", info="Click on a dataframe cell to view the corresponding transcribed text line crop. You can also sort the dataframe to easily locate specific entries.", lines=2, interactive=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=14, col_count=(2, "fixed"), wrap=True, interactive=False, overflow_row_behaviour="paginate", 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, dataframe_text_index], ) 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, # Hide control_results_transcribe, image_placeholder_explore_results, ], ) 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, ], )