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| import pandas as pd | |
| from IPython.display import display, HTML | |
| import os | |
| import imgkit | |
| import pdfkit | |
| from pypdf import PdfMerger | |
| def classify_wall_damage(crack_width): | |
| if crack_width <= 0.1: | |
| return "Negligible" | |
| elif 0.1 <= crack_width <= 1: | |
| return "Very Slight" | |
| elif 1.1 <= crack_width <= 5: | |
| return "Slight" | |
| elif 5 <= crack_width <= 15: | |
| return "Moderate" | |
| elif 15 <= crack_width <= 25: | |
| return "Severe" | |
| elif crack_width > 25: | |
| return "Very Severe" | |
| else: | |
| return "Invalid input" | |
| from collections import Counter | |
| def generate_html_summary(crack_list): | |
| # Define the possible damage levels | |
| damage_levels = ["Negligible", "Very Slight", "Slight", "Moderate", "Severe", "Very Severe"] | |
| # Count the occurrences of each damage level | |
| string_counts = Counter(crack_list) | |
| # Build the HTML string | |
| html_summary = "<html>\n<body>\n" | |
| html_summary += "<h2>Summary of this batch</h2>\n" | |
| html_summary += "<p><strong>Number of Cracks Detected:</strong></p>\n" | |
| html_summary += "<ul>\n" | |
| # Append the damage level and count to the HTML string | |
| for level in damage_levels: | |
| count = string_counts.get(level, 0) | |
| html_summary += f"<li>{level} = {count}</li>\n" | |
| html_summary += "</ul>\n" | |
| html_summary += "</body>\n</html>" | |
| print(html_summary) | |
| return html_summary | |
| def merge_html_files(file1_path, file2_path, output_path): | |
| # Read contents of the first HTML file | |
| with open(file1_path, 'r', encoding='utf-8') as file1: | |
| content1 = file1.read() | |
| # Read contents of the second HTML file | |
| with open(file2_path, 'r', encoding='utf-8') as file2: | |
| content2 = file2.read() | |
| # Concatenate the contents | |
| merged_content = content1 + content2 | |
| # Write the merged content to the output file | |
| with open(output_path, 'w', encoding='utf-8') as output_file: | |
| output_file.write(merged_content) | |
| def count_instance(result, filenames, uuid, width_list, orientation_list, image_path, reference, remark, damage): | |
| """ | |
| Counts the instances in the result and generates a CSV with the counts. | |
| Parameters: | |
| result (list): List containing results for each instance. | |
| filenames (list): Corresponding filenames for each result. | |
| uuid (str): Unique ID for the output folder name. | |
| width_list (list): List containing width values for each instance. | |
| orientation_list (list): List containing orientation values for each instance. | |
| Returns: | |
| tuple: Path to the generated CSV and dataframe with counts. | |
| """ | |
| # Initializing the dataframe | |
| uuid= f'{uuid}0' | |
| print(uuid) | |
| print(damage) | |
| data = { | |
| 'Index': [], | |
| 'FileName': [], | |
| 'Orientation': [], | |
| 'Width (mm)': [], | |
| 'Instance': [], | |
| 'Damage Level': [] | |
| } | |
| df_ref = pd.DataFrame({'Reference': [f'<img src="{ref}" width="640" >' for ref in reference]}) | |
| df = pd.DataFrame(data) | |
| # Populate the dataframe with counts, width, and orientation | |
| for i, res in enumerate(result): | |
| instance_count = len(res) | |
| df.loc[i] = [i, os.path.basename(filenames[i]), orientation_list[i], width_list[i], instance_count, damage[i]] | |
| # Reorder columns | |
| df = df[['Index', 'FileName', 'Orientation', 'Width (mm)','Damage Level', 'Instance']] | |
| # Create a new dataframe (df2) with all columns from df | |
| df2 = df.copy() | |
| summary = generate_html_summary(damage) | |
| # Add another column for the image (modify as per your requirement) | |
| print("IMG PATHS") | |
| print(image_path) | |
| base_path = [os.path.basename(path) for path in image_path] | |
| df2['Image'] = base_path | |
| df2['Remarks'] = remark | |
| # convert your links to html tags | |
| def path_to_image_html(path): | |
| return '<img src="'+ path + '" width="320" >' | |
| print("This executed 1") | |
| pd.set_option('display.max_colwidth', None) | |
| image_cols = ['Image'] | |
| format_dict = {} | |
| for image_col in image_cols: | |
| format_dict[image_col] = path_to_image_html | |
| print("This executed 2") | |
| col_widths = [100, 50, 50, 50, 50, 120, 150] | |
| df2 = df2.drop(df.columns[0], axis=1) | |
| # Create the HTML file | |
| df_html = df2.to_html(f'output/{uuid}/df_batch.html', escape=False, formatters=format_dict, col_space=col_widths, justify='left') | |
| df_refs = df_ref.to_html(f'output/{uuid}/df_ref.html', escape=False, justify='left') | |
| print("This executed 3") | |
| # Save the modified dataframe to a CSV file | |
| from bs4 import BeautifulSoup | |
| # Load the HTML file | |
| with open(f'output/{uuid}/df_ref.html', 'r') as file: | |
| html_content = file.read() | |
| # Parse the HTML using BeautifulSoup | |
| soup = BeautifulSoup(html_content, 'html.parser') | |
| # Find the table in the HTML (assuming there is only one table) | |
| table = soup.find('table') | |
| # Append the new summary HTML after the table | |
| table.insert_after(BeautifulSoup(summary, 'html.parser')) | |
| # Save the modified HTML to a new file | |
| with open(f'output/{uuid}/df_ref_summary.html', 'w') as file: | |
| file.write(str(soup)) | |
| html_table = HTML(df2.to_html(escape=False)) | |
| display(html_table) | |
| print('This executed 4') | |
| file1 = f'output/{uuid}/df_ref_summary.html' | |
| file2 = f'output/{uuid}/df_batch.html' | |
| merge_html_files(file1, file2, f'output/{uuid}/out.html') | |
| opt = {"enable-local-file-access": ""} | |
| # new_parser = HtmlToDocx() | |
| # new_parser.parse_html_file(f'output/{uuid}/df_batch.html', f'output/{uuid}/report_batch') | |
| # new_parser.parse_html_file(f'output/{uuid}/df_ref_summary.html', f'output/{uuid}/report_ref') | |
| # convert(f"output/{uuid}/report_batch.docx", f"output/{uuid}/Mine.pdf") | |
| pdfkit.from_file(f'output/{uuid}/df_batch.html', f'output/{uuid}/report_batch.pdf', options=opt) | |
| pdfkit.from_file(f'output/{uuid}/df_ref_summary.html', f'output/{uuid}/report_ref.pdf', options=opt) | |
| print("This executed 5") | |
| pdfs = [f'output/{uuid}/report_ref.pdf', f'output/{uuid}/report_batch.pdf'] | |
| merger = PdfMerger() | |
| for pdf in pdfs: | |
| merger.append(pdf) | |
| merger.write(f'output/{uuid}/report.pdf') | |
| merger.close() | |
| paths = [f'output/{uuid}/df_batch.html', f'output/{uuid}/df_ref_summary.html', | |
| f'output/{uuid}/df_ref.html', f'output/{uuid}/out.html', | |
| f'output/{uuid}/report_batch.pdf', f'output/{uuid}/report_ref.pdf'] | |
| for path in paths: | |
| if os.path.exists(path): | |
| os.remove(path) | |
| return f'output/{uuid}/report.pdf', df | |