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| import os | |
| import shutil | |
| from difflib import Differ | |
| import evaluate | |
| import gradio as gr | |
| from helper.examples.examples import DemoImages | |
| from helper.utils import TrafficDataHandler | |
| from src.htr_pipeline.gradio_backend import CustomTrack, SingletonModelLoader | |
| model_loader = SingletonModelLoader() | |
| custom_track = CustomTrack(model_loader) | |
| images_for_demo = DemoImages() | |
| cer_metric = evaluate.load("cer") | |
| with gr.Blocks() as stepwise_htr_tool_tab: | |
| with gr.Tabs(): | |
| with gr.Tab("1. Region segmentation"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| vis_data_folder_placeholder = gr.Markdown(visible=False) | |
| name_files_placeholder = gr.Markdown(visible=False) | |
| with gr.Group(): | |
| input_region_image = gr.Image( | |
| label="Image to region segment", | |
| # type="numpy", | |
| tool="editor", | |
| height=500, | |
| ) | |
| with gr.Accordion("Settings", open=False): | |
| with gr.Group(): | |
| reg_pred_score_threshold_slider = gr.Slider( | |
| minimum=0.4, | |
| maximum=1, | |
| value=0.5, | |
| step=0.05, | |
| label="P-threshold", | |
| info="""Filter the confidence score 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""", | |
| ) | |
| region_segment_model_dropdown = gr.Dropdown( | |
| choices=["Riksarkivet/rtm_region"], | |
| value="Riksarkivet/rtm_region", | |
| label="Region segmentation model", | |
| info="More models will be added", | |
| ) | |
| with gr.Row(): | |
| clear_button = gr.Button("Clear", variant="secondary", elem_id="clear_button") | |
| region_segment_button = gr.Button( | |
| "Run", | |
| variant="primary", | |
| elem_id="region_segment_button", | |
| ) | |
| region_segment_button_var = gr.State(value="region_segment_button") | |
| with gr.Column(scale=2): | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| 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.Group(): | |
| 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("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 the confidence score 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/rtmdet_lines"], | |
| value="Riksarkivet/rtmdet_lines", | |
| label="Line segment model", | |
| info="More models will be added", | |
| ) | |
| with gr.Row(): | |
| clear_line_segment_button = gr.Button( | |
| " ", | |
| variant="Secondary", | |
| # elem_id="center_button", | |
| scale=1, | |
| ) | |
| line_segment_button = gr.Button( | |
| "Run", | |
| 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. Text recognition"): | |
| 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): | |
| with gr.Group(): | |
| image_inputs_lines_to_transcribe = gr.Image( | |
| label="Transcribed lines", type="numpy", interactive="False", visible=False, height=470 | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("Settings", open=False): | |
| transcriber_model = gr.Dropdown( | |
| choices=["Riksarkivet/satrn_htr", "microsoft/trocr-base-handwritten"], | |
| value="Riksarkivet/satrn_htr", | |
| label="Text recognition model", | |
| info="More models will be added", | |
| ) | |
| gr.Slider( | |
| value=0.6, | |
| minimum=0.5, | |
| maximum=1, | |
| label="HTR threshold", | |
| info="Prediction score threshold for transcribed lines", | |
| scale=1, | |
| ) | |
| with gr.Row(): | |
| clear_transcribe_button = gr.Button(" ", variant="Secondary", visible=True, scale=1) | |
| transcribe_button = gr.Button("Run", variant="primary", visible=True, scale=1) | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| transcribed_text = gr.Textbox( | |
| label="Transcribed text", | |
| info="Transcribed text is being streamed back from the Text recognition model", | |
| lines=26, | |
| value="", | |
| show_copy_button=True, | |
| ) | |
| ##################################### | |
| 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.Group(): | |
| 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=150, | |
| preview=True, | |
| container=False, | |
| ) | |
| with gr.Row(): | |
| dataframe_text_index = gr.Textbox( | |
| label="Text from DataFrame selection", | |
| placeholder="Select row from the DataFrame.", | |
| interactive=False, | |
| ) | |
| with gr.Row(): | |
| gt_text_index = gr.Textbox( | |
| label="Ground Truth", | |
| placeholder="Provide the ground truth, if available.", | |
| interactive=True, | |
| ) | |
| with gr.Row(): | |
| diff_token_output = gr.HighlightedText( | |
| label="Text diff", | |
| combine_adjacent=True, | |
| show_legend=True, | |
| color_map={"+": "red", "-": "green"}, | |
| ) | |
| with gr.Row(equal_height=False): | |
| cer_output = gr.Textbox(label="CER:") | |
| calc_cer_button = gr.Button("Calculate CER", variant="primary", visible=True) | |
| 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 | |
| def diff_texts(text1, text2): | |
| d = Differ() | |
| return [(token[2:], token[0] if token[0] != " " else None) for token in d.compare(text1, text2)] | |
| def compute_cer(dataframe_text_index, gt_text_index): | |
| if gt_text_index is not None and gt_text_index.strip() != "": | |
| return cer_metric.compute(predictions=[dataframe_text_index], references=[gt_text_index]) | |
| else: | |
| return "Ground truth not provided" | |
| calc_cer_button.click(compute_cer, inputs=[dataframe_text_index, gt_text_index], outputs=cer_output) | |
| calc_cer_button.click(diff_texts, inputs=[dataframe_text_index, gt_text_index], outputs=[diff_token_output]) | |
| 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=[inputs_lines_to_transcribe], | |
| outputs=[ | |
| transcribed_text, | |
| 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, | |
| 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, | |
| ], | |
| ) | |
| SECRET_KEY = os.environ.get("AM_I_IN_A_DOCKER_CONTAINER", False) | |
| if SECRET_KEY: | |
| region_segment_button.click(fn=TrafficDataHandler.store_metric_data, inputs=region_segment_button_var) | |