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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():
# placeholder_line_button = gr.Button(
# "",
# variant="secondary",
# scale=1,
# )
gr.Markdown(" ")
line_segment_button = gr.Button(
"Run",
variant="primary",
# elem_id="center_button",
scale=1,
)
with gr.Column(scale=3):
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", "Riksarkivet/trocr-base-handwritten-swe"],
value="Riksarkivet/trocr-base-handwritten-swe",
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():
copy_textarea = gr.Button("Copy text", 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,
elem_id="textarea_stepwise_3",
)
#####################################
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="Character Error Rate")
gr.Markdown("")
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", "Prediction 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 round(cer_metric.compute(predictions=[dataframe_text_index], references=[gt_text_index]), 4)
else:
return "Ground truth not provided"
calc_cer_button.click(
compute_cer,
inputs=[dataframe_text_index, gt_text_index],
outputs=cer_output,
api_name=False,
)
calc_cer_button.click(
diff_texts,
inputs=[dataframe_text_index, gt_text_index],
outputs=[diff_token_output],
api_name=False,
)
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],
api_name=False,
)
regions_cropped_gallery.select(
custom_track.get_select_index_image,
regions_cropped_gallery,
input_region_from_gallery,
api_name=False,
)
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],
api_name=False,
)
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,
],
api_name=False,
)
copy_textarea.click(
fn=None,
_js="""document.querySelector("#textarea_stepwise_3 > label > button").click()""",
api_name=False,
)
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,
],
api_name=False,
)
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,
],
api_name=False,
)
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)
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