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import gradio as gr |
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import pandas as pd |
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import numpy as np |
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from typing import List |
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from gradio_image_annotation import image_annotator |
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from gradio_image_annotation.image_annotator import AnnotatedImageData |
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from tools.file_conversion import is_pdf, convert_review_json_to_pandas_df |
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from tools.helper_functions import get_file_path_end, output_folder |
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from tools.file_redaction import redact_page_with_pymupdf |
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import json |
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import os |
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import pymupdf |
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from fitz import Document |
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from PIL import ImageDraw, Image |
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from collections import defaultdict |
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def decrease_page(number:int): |
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''' |
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Decrease page number for review redactions page. |
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''' |
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if number > 1: |
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return number - 1, number - 1 |
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else: |
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return 1, 1 |
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def increase_page(number:int, image_annotator_object:AnnotatedImageData): |
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''' |
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Increase page number for review redactions page. |
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''' |
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if not image_annotator_object: |
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return 1, 1 |
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max_pages = len(image_annotator_object) |
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if number < max_pages: |
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return number + 1, number + 1 |
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else: |
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return max_pages, max_pages |
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def update_zoom(current_zoom_level:int, annotate_current_page:int, decrease:bool=True): |
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if decrease == False: |
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if current_zoom_level >= 70: |
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current_zoom_level -= 10 |
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else: |
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if current_zoom_level < 110: |
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current_zoom_level += 10 |
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return current_zoom_level, annotate_current_page |
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def remove_duplicate_images_with_blank_boxes(data: List[dict]) -> List[dict]: |
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''' |
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Remove items from the annotator object where the same page exists twice. |
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''' |
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image_groups = defaultdict(list) |
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for item in data: |
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image_groups[item['image']].append(item) |
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result = [] |
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for image, items in image_groups.items(): |
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non_empty_boxes = [item for item in items if item.get('boxes')] |
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if non_empty_boxes: |
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result.append(non_empty_boxes[0]) |
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else: |
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result.append(items[0]) |
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return result |
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def get_recogniser_dataframe_out(image_annotator_object, recogniser_dataframe_gr): |
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recogniser_entities_list = ["Redaction"] |
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recogniser_entities_drop = gr.Dropdown(value="", choices=[""], allow_custom_value=True, interactive=True) |
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recogniser_dataframe_out = recogniser_dataframe_gr |
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try: |
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review_dataframe = convert_review_json_to_pandas_df(image_annotator_object)[["page", "label"]] |
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recogniser_entities = review_dataframe["label"].unique().tolist() |
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recogniser_entities.append("ALL") |
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recogniser_entities_for_drop = sorted(recogniser_entities) |
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recogniser_dataframe_out = gr.Dataframe(review_dataframe) |
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recogniser_entities_drop = gr.Dropdown(value=recogniser_entities_for_drop[0], choices=recogniser_entities_for_drop, allow_custom_value=True, interactive=True) |
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recogniser_entities_list = [entity for entity in recogniser_entities_for_drop if entity != 'Redaction' and entity != 'ALL'] |
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recogniser_entities_list.insert(0, 'Redaction') |
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except Exception as e: |
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print("Could not extract recogniser information:", e) |
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recogniser_dataframe_out = recogniser_dataframe_gr |
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recogniser_entities_drop = gr.Dropdown(value="", choices=[""], allow_custom_value=True, interactive=True) |
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recogniser_entities_list = ["Redaction"] |
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return recogniser_dataframe_out, recogniser_dataframe_out, recogniser_entities_drop, recogniser_entities_list |
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def update_annotator(image_annotator_object:AnnotatedImageData, page_num:int, recogniser_entities_drop=gr.Dropdown(value="ALL", allow_custom_value=True), recogniser_dataframe_gr=gr.Dataframe(pd.DataFrame(data={"page":[], "label":[]})), zoom:int=100): |
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''' |
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Update a gradio_image_annotation object with new annotation data |
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''' |
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recogniser_entities_list = ["Redaction"] |
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recogniser_dataframe_out = pd.DataFrame() |
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if recogniser_dataframe_gr.empty: |
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recogniser_dataframe_gr, recogniser_dataframe_out, recogniser_entities_drop, recogniser_entities_list = get_recogniser_dataframe_out(image_annotator_object, recogniser_dataframe_gr) |
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elif recogniser_dataframe_gr.iloc[0,0] == "": |
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recogniser_dataframe_gr, recogniser_dataframe_out, recogniser_entities_drop, recogniser_entities_list = get_recogniser_dataframe_out(image_annotator_object, recogniser_dataframe_gr) |
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else: |
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review_dataframe = update_entities_df(recogniser_entities_drop, recogniser_dataframe_gr) |
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recogniser_dataframe_out = gr.Dataframe(review_dataframe) |
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recogniser_entities_list = recogniser_dataframe_gr["label"].unique().tolist() |
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print("recogniser_entities_list all options:", recogniser_entities_list) |
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recogniser_entities_list = sorted(recogniser_entities_list) |
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recogniser_entities_list = [entity for entity in recogniser_entities_list if entity != 'Redaction'] |
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recogniser_entities_list.insert(0, 'Redaction') |
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print("recogniser_entities_list:", recogniser_entities_list) |
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zoom_str = str(zoom) + '%' |
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recogniser_colour_list = [(0, 0, 0) for _ in range(len(recogniser_entities_list))] |
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if not image_annotator_object: |
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page_num_reported = 1 |
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out_image_annotator = image_annotator( |
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image_annotator_object[page_num_reported - 1], |
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boxes_alpha=0.1, |
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box_thickness=1, |
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label_list=recogniser_entities_list, |
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label_colors=recogniser_colour_list, |
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show_label=False, |
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height=zoom_str, |
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width=zoom_str, |
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box_min_size=1, |
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box_selected_thickness=2, |
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handle_size=4, |
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sources=None, |
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show_clear_button=False, |
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show_share_button=False, |
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show_remove_button=False, |
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handles_cursor=True, |
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interactive=True |
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) |
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number_reported = gr.Number(label = "Page (press enter to change)", value=page_num_reported, precision=0) |
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return out_image_annotator, number_reported, number_reported, page_num_reported, recogniser_entities_drop, recogniser_dataframe_out, recogniser_dataframe_gr |
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if page_num is None: |
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page_num = 0 |
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if page_num > 0: |
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page_num_reported = page_num |
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elif page_num == 0: page_num_reported = 1 |
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else: |
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page_num = 0 |
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page_num_reported = 1 |
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page_max_reported = len(image_annotator_object) |
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if page_num_reported > page_max_reported: |
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page_num_reported = page_max_reported |
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image_annotator_object = remove_duplicate_images_with_blank_boxes(image_annotator_object) |
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out_image_annotator = image_annotator( |
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value = image_annotator_object[page_num_reported - 1], |
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boxes_alpha=0.1, |
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box_thickness=1, |
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label_list=recogniser_entities_list, |
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label_colors=recogniser_colour_list, |
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show_label=False, |
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height=zoom_str, |
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width=zoom_str, |
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box_min_size=1, |
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box_selected_thickness=2, |
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handle_size=4, |
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sources=None, |
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show_clear_button=False, |
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show_share_button=False, |
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show_remove_button=False, |
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handles_cursor=True, |
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interactive=True |
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) |
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number_reported = gr.Number(label = "Page (press enter to change)", value=page_num_reported, precision=0) |
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return out_image_annotator, number_reported, number_reported, page_num_reported, recogniser_entities_drop, recogniser_dataframe_out, recogniser_dataframe_gr |
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def modify_existing_page_redactions(image_annotated:AnnotatedImageData, current_page:int, previous_page:int, all_image_annotations:List[AnnotatedImageData], recogniser_entities_drop=gr.Dropdown(value="ALL", allow_custom_value=True),recogniser_dataframe=gr.Dataframe(pd.DataFrame(data={"page":[], "label":[]})), clear_all:bool=False): |
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''' |
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Overwrite current image annotations with modifications |
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''' |
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if not current_page: |
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current_page = 1 |
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image_annotated['image'] = all_image_annotations[previous_page - 1]["image"] |
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if clear_all == False: |
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all_image_annotations[previous_page - 1] = image_annotated |
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else: |
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all_image_annotations[previous_page - 1]["boxes"] = [] |
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try: |
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review_dataframe = convert_review_json_to_pandas_df(all_image_annotations)[["page", "label"]] |
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recogniser_entities = review_dataframe["label"].unique().tolist() |
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recogniser_entities.append("ALL") |
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recogniser_entities = sorted(recogniser_entities) |
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recogniser_dataframe_out = gr.Dataframe(review_dataframe) |
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recogniser_entities_drop = gr.Dropdown(value=recogniser_entities_drop, choices=recogniser_entities, allow_custom_value=True, interactive=True) |
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except Exception as e: |
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print("Could not extract recogniser information:", e) |
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recogniser_dataframe_out = recogniser_dataframe |
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return all_image_annotations, current_page, current_page, recogniser_entities_drop, recogniser_dataframe_out |
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def apply_redactions(image_annotated:AnnotatedImageData, file_paths:List[str], doc:Document, all_image_annotations:List[AnnotatedImageData], current_page:int, review_file_state, save_pdf:bool=True, progress=gr.Progress(track_tqdm=True)): |
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''' |
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Apply modified redactions to a pymupdf and export review files |
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''' |
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output_files = [] |
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output_log_files = [] |
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image_annotated['image'] = all_image_annotations[current_page - 1]["image"] |
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all_image_annotations[current_page - 1] = image_annotated |
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if not image_annotated: |
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print("No image annotations found") |
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return doc, all_image_annotations |
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if isinstance(file_paths, str): |
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file_paths = [file_paths] |
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for file_path in file_paths: |
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file_base = get_file_path_end(file_path) |
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file_extension = os.path.splitext(file_path)[1].lower() |
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if save_pdf == True: |
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if (is_pdf(file_path) == False) & (file_extension not in '.csv'): |
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image = Image.open(file_paths[-1]) |
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draw = ImageDraw.Draw(image) |
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for img_annotation_box in image_annotated['boxes']: |
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coords = [img_annotation_box["xmin"], |
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img_annotation_box["ymin"], |
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img_annotation_box["xmax"], |
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img_annotation_box["ymax"]] |
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fill = img_annotation_box["color"] |
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draw.rectangle(coords, fill=fill) |
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image.save(output_folder + file_base + "_redacted.png") |
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doc = [image] |
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elif file_extension in '.csv': |
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print("This is a csv") |
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pdf_doc = [] |
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elif is_pdf(file_path) == True: |
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pdf_doc = pymupdf.open(file_path) |
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number_of_pages = pdf_doc.page_count |
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print("Saving pages to file.") |
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for i in progress.tqdm(range(0, number_of_pages), desc="Saving redactions to file", unit = "pages"): |
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image_loc = all_image_annotations[i]['image'] |
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if isinstance(image_loc, np.ndarray): |
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image = Image.fromarray(image_loc.astype('uint8')) |
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elif isinstance(image_loc, Image.Image): |
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image = image_loc |
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elif isinstance(image_loc, str): |
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image = Image.open(image_loc) |
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pymupdf_page = pdf_doc.load_page(i) |
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pymupdf_page = redact_page_with_pymupdf(pymupdf_page, all_image_annotations[i], image) |
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else: |
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print("File type not recognised.") |
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if pdf_doc: |
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out_pdf_file_path = output_folder + file_base + "_redacted.pdf" |
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pdf_doc.save(out_pdf_file_path) |
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output_files.append(out_pdf_file_path) |
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try: |
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print("Saving annotations to JSON") |
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out_annotation_file_path = output_folder + file_base + '_review_file.json' |
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with open(out_annotation_file_path, 'w') as f: |
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json.dump(all_image_annotations, f) |
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output_log_files.append(out_annotation_file_path) |
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print("Saving annotations to CSV review file") |
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review_df = convert_review_json_to_pandas_df(all_image_annotations, review_file_state) |
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out_review_file_file_path = output_folder + file_base + '_review_file.csv' |
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review_df.to_csv(out_review_file_file_path, index=None) |
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output_files.append(out_review_file_file_path) |
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except Exception as e: |
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print("Could not save annotations to json or csv file:", e) |
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return doc, all_image_annotations, output_files, output_log_files |
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def get_boxes_json(annotations:AnnotatedImageData): |
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return annotations["boxes"] |
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def update_entities_df(choice:str, df:pd.DataFrame): |
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if choice=="ALL": |
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return df |
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else: |
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return df.loc[df["label"]==choice,:] |
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def df_select_callback(df: pd.DataFrame, evt: gr.SelectData): |
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row_value_page = evt.row_value[0] |
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return row_value_page |
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