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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+corrosion2.jpeg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,6 @@
 ---
 title: TimCCG
-emoji: 🚀
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.4
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.41.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+"""Corrosion Excel.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1XTrJK3G8Har_yZt83wasZX1LYD6DG1Sa
+"""
+
+#install dependencies
+!pip install roboflow
+!pip install gradio
+
+#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 == "cm²":
+                area *= conversion_factor  # Convert area from pixels to cm²
+            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):
+    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)
+
+        return img, corrosion_areas, prediction_json
+
+    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", "cm²"], label="Area Unit"),
+      gr.inputs.Textbox(label="Conversion Factor")
+    ],
+    outputs=[gr.outputs.Image(type="pil"), "json", "json"],
+    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=True, share=True)
+
+
+
+
+
+
+
+
+
+
+

requirements.txt

@@ -0,0 +1 @@
+roboflow