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| #import library | |
| import gradio as gr | |
| from roboflow import Roboflow | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| import pandas as pd | |
| import os | |
| # Initialize Roboflow with your API key | |
| rf = Roboflow(api_key="kKDoCn3ABT9AKeFQDCB4") | |
| # Function to calculate the area of a polygon using the shoelace formula | |
| def calculate_polygon_area(points): | |
| n = len(points) | |
| area = 0.0 | |
| for i in range(n): | |
| x1, y1 = points[i] | |
| x2, y2 = points[(i + 1) % n] | |
| area += (x1 * y2 - x2 * y1) | |
| return abs(area) / 2.0 | |
| # Function to process Roboflow prediction JSON and calculate corrosion areas | |
| def calculate_corrosion_areas(json_data, unit="pixels", conversion_factor=1): | |
| corrosion_areas = [] | |
| for prediction in json_data["predictions"]: | |
| if prediction["class"] == "Corrosion": | |
| points = [(point["x"], point["y"]) for point in prediction["points"]] | |
| area = calculate_polygon_area(points) | |
| if unit == "cm??": | |
| area *= conversion_factor # Convert area from pixels to cm?? | |
| corrosion_areas.append(area) | |
| total_corrosion_area = sum(corrosion_areas) | |
| # Prepare output | |
| result = { | |
| "individual_areas": [f"{area} {unit}" for area in corrosion_areas], | |
| "total_area": f"{total_corrosion_area} {unit}", | |
| "recommendation": get_inspection_recommendation(total_corrosion_area) | |
| } | |
| return result | |
| # Function to provide inspection recommendation based on total corrosion area | |
| def get_inspection_recommendation(total_area): | |
| if total_area < 1000: | |
| return "No immediate inspection needed." | |
| elif total_area < 5000: | |
| return "Schedule an inspection in the next 6 months." | |
| else: | |
| return "Immediate inspection required." | |
| # Define a Gradio interface to input a URL, run inference, and calculate corrosion areas | |
| def url_infer_and_calculate(url, location, unit="pixels", conversion_factor=1, corrosion_type="", inspection_standards=[], ndt_methods=[], manual_recommendation="", supporting_data=""): | |
| try: | |
| # Run inference using the Roboflow script | |
| rf_project = rf.workspace().project("corrosion-instance-segmentation-sfcpc") | |
| model = rf_project.version(3).model | |
| prediction = model.predict(url) | |
| # Ensure the response is properly formatted as JSON | |
| prediction_json = prediction.json() | |
| # Calculate corrosion areas from the Roboflow prediction | |
| corrosion_areas = calculate_corrosion_areas(prediction_json, unit, float(conversion_factor)) | |
| # Download the image from the URL and convert it to a PIL Image | |
| response = requests.get(url) | |
| img = Image.open(BytesIO(response.content)) | |
| # Create a pandas DataFrame for reporting | |
| df = pd.DataFrame([{'Number': index+1, 'URL': url, 'Location': location, 'corrosion_areas': corrosion_areas, 'Recommendation': corrosion_areas['recommendation']} for index in range(len(corrosion_areas))]) | |
| # Write DataFrame to local CSV file with index included immediately after creating it. | |
| df.to_csv('Corrosion_Report.csv', index=False) | |
| # Write DataFrame to a string in CSV format | |
| csv_string = df.to_csv(index=False) | |
| return img, corrosion_areas, prediction_json, csv_string | |
| except Exception as e: | |
| return {"error": str(e)} | |
| # Create a Gradio interface for URL input, inference, and corrosion area calculation | |
| iface = gr.Interface( | |
| fn=url_infer_and_calculate, | |
| inputs=[ | |
| gr.inputs.Textbox(label="Enter the URL of an image"), | |
| gr.inputs.Textbox(label="Enter the Location"), | |
| gr.inputs.Dropdown(choices=["pixels", "cm"], label="Area Unit"), | |
| gr.inputs.Textbox(label="Conversion Factor"), | |
| gr.inputs.Textbox(label="Enter the Corrosion Type"), | |
| gr.inputs.Textbox(label="Inspection Standards"), | |
| gr.inputs.CheckboxGroup(choices=["UT thickness", "UT scan", "Phased Array UT", "Short range UT", "Long range UT", "MT", "PT", "Acfm", "Pulse eddy current", "magnetic flux leakage", "positive material identification (PMI)"], label="NDT Inspection Methods"), | |
| gr.inputs.Textbox(label="Enter Manual Recommendation"), | |
| gr.inputs.Textbox(lines=5, label="Enter Supporting Data URLs (separated by commas)") | |
| ], | |
| outputs=[ | |
| gr.outputs.Image(type="pil"), | |
| "json", # JSON output | |
| gr.outputs.Textbox(label="CSV Data", type="text"), # CSV data as a plain text | |
| gr.outputs.Textbox(label="Corrosion Data"), # Display CSV data as a table | |
| ], | |
| title="Tim CCG", | |
| description="Enter the URL of an image to perform rust detection and calculate corrosion areas.", | |
| ) | |
| # Launch the Gradio interface | |
| iface.launch(debug=False, share=False) | |