Update app.py

app.py

@@ -1,20 +1,23 @@
 import cv2
 import numpy as np
-from PIL import Image, ImageDraw
+from PIL import Image, ImageDraw, ImageFont
 import gradio as gr
 
-def detect_cracks(image: Image.Image) -> Image.Image:
+def detect_cracks(image: Image.Image):
     try:
-        # Convert PIL image to an OpenCV image (BGR format)
-        cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        # Convert the PIL image to an OpenCV (RGB) image
+        rgb_image = np.array(image)
+        # Also create a copy for annotation
+        annotated = image.copy()
+        draw = ImageDraw.Draw(annotated)
         
-        # Convert to grayscale for processing
-        gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
+        # Convert to grayscale
+        gray = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2GRAY)
         
-        # Apply Gaussian blur to reduce noise and enhance edges
+        # Apply Gaussian blur to reduce noise
         blurred = cv2.GaussianBlur(gray, (5, 5), 0)
         
-        # Use adaptive thresholding to highlight potential crack areas
+        # Adaptive thresholding to highlight crack-like features
         thresh = cv2.adaptiveThreshold(
             blurred, 255,
             cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
@@ -22,41 +25,70 @@ def detect_cracks(image: Image.Image) -> Image.Image:
             11, 2
         )
         
-        # Apply morphological closing to bridge gaps in detected lines
+        # Use morphological closing to fill gaps in potential cracks
         kernel = np.ones((3, 3), np.uint8)
         morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
         
-        # Detect edges with Canny edge detector
+        # Edge detection
         edges = cv2.Canny(morph, 50, 150)
         
-        # Find contours based on the detected edges
+        # Find contours from edges
         contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
         
-        # Convert original image to PIL for drawing
-        annotated = image.copy()
-        draw = ImageDraw.Draw(annotated)
+        detections = []  # to hold detection details
         
-        # Draw bounding boxes around contours that are large enough to be meaningful cracks
         for cnt in contours:
-            # Filter out noise with a minimum arc length threshold (adjustable)
+            # Filter out small contours (noise)
             if cv2.arcLength(cnt, True) > 100:
                 x, y, w, h = cv2.boundingRect(cnt)
-                draw.rectangle([x, y, x + w, y + h], outline="red", width=2)
+                
+                # Extract ROI from the original image (for material classification)
+                roi = rgb_image[y:y+h, x:x+w]
+                if roi.size == 0:
+                    continue
+                # Convert ROI to grayscale and compute mean intensity
+                roi_gray = cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY)
+                mean_intensity = np.mean(roi_gray)
+                
+                # Simple heuristic: classify material based on brightness
+                # (These thresholds are arbitrary and should be tuned based on real data.)
+                if mean_intensity < 80:
+                    material = "Concrete"
+                elif mean_intensity < 150:
+                    material = "Tile"
+                else:
+                    material = "Wood"
+                
+                label = f"Crack ({material})"
+                detections.append(f"Detected crack at ({x}, {y}, {w}, {h}) on {material} (mean intensity: {mean_intensity:.1f})")
+                
+                # Draw rectangle and label on the annotated image
+                draw.rectangle([x, y, x+w, y+h], outline="red", width=2)
+                # Draw the label above the rectangle
+                draw.text((x, y-10), label, fill="red")
+        
+        # Create a text summary of detections
+        if detections:
+            summary = "\n".join(detections)
+        else:
+            summary = "No significant cracks detected."
+        
+        return annotated, summary
         
-        return annotated
     except Exception as e:
-        print("Error during crack detection:", e)
-        return image  # Fallback: return the original image if any error occurs
+        print("Error during detection:", e)
+        return image, f"Error: {e}"
 
-# Create a Gradio interface for the Space
+# Create a Gradio interface with two outputs: image and text
 iface = gr.Interface(
     fn=detect_cracks,
-    inputs=gr.Image(type="pil", label="Upload a Floor/Wall Image"),
-    outputs=gr.Image(label="Detected Cracks"),
-    title="Home Inspection: Crack Detection",
+    inputs=gr.Image(type="pil", label="Upload an Image (Floor/Wall)"),
+    outputs=[gr.Image(label="Annotated Image"), gr.Textbox(label="Detection Summary")],
+    title="Home Inspection: Granular Crack & Material Detector",
     description=(
-        "Upload an image of a floor or wall to detect cracks and other defects. "
-        "This demo uses traditional computer vision techniques to highlight potential issues."
+        "Upload an image of a floor or wall to detect cracks and infer the underlying material "
+        "(Concrete, Tile, or Wood) using classical computer vision techniques. "
+        "This demo returns both an annotated image and a textual summary."
     )
 )