Update app.py

app.py

@@ -3,24 +3,28 @@ import cv2
 import numpy as np
 from skimage.metrics import structural_similarity as ssim
 
-def preprocess_image(image):
+def preprocess_image(image, blur_value):
     # Convert to grayscale
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     # Apply Gaussian blur to reduce noise
-    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    blurred = cv2.GaussianBlur(gray, (blur_value, blur_value), 0)
     return blurred
 
-def compare_images(image1, image2):
+def compare_images(image1, image2, blur_value, technique):
     # Preprocess images
-    gray1 = preprocess_image(image1)
-    gray2 = preprocess_image(image2)
+    gray1 = preprocess_image(image1, blur_value)
+    gray2 = preprocess_image(image2, blur_value)
     
     # Compute SSIM between the two images
     score, diff = ssim(gray1, gray2, full=True)
     diff = (diff * 255).astype("uint8")
     
-    # Apply adaptive thresholding for better object isolation
-    _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY_INV)
+    if technique == "Adaptive Threshold":
+        _, thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY_INV)
+    elif technique == "Otsu's Threshold":
+        _, thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
+    else:  # Default to simple binary threshold
+        _, thresh = cv2.threshold(diff, 50, 255, cv2.THRESH_BINARY)
     
     # Find contours of differences
     contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
@@ -35,17 +39,25 @@ def compare_images(image1, image2):
     # Apply the mask to highlight the object added in the second image
     highlighted = cv2.bitwise_and(image2, mask)
     
-    return highlighted
+    # Show the raw difference in magenta
+    diff_colored = cv2.merge([np.zeros_like(diff), diff, diff])
+    
+    return highlighted, diff_colored
 
 demo = gr.Interface(
     fn=compare_images,
     inputs=[
         gr.Image(type="numpy", label="Image Without Object"),
-        gr.Image(type="numpy", label="Image With Object")
+        gr.Image(type="numpy", label="Image With Object"),
+        gr.Slider(minimum=1, maximum=15, step=2, value=5, label="Gaussian Blur"),
+        gr.Dropdown(["Adaptive Threshold", "Otsu's Threshold", "Simple Binary"], label="Thresholding Technique", value="Adaptive Threshold")
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Highlighted Differences"),
+        gr.Image(type="numpy", label="Raw Difference (Magenta)")
     ],
-    outputs=gr.Image(type="numpy", label="Highlighted Differences"),
     title="Object Difference Highlighter",
-    description="Upload two images: one without an object and one with an object. The app will highlight only the newly added object."
+    description="Upload two images: one without an object and one with an object. The app will highlight only the newly added object and show the real differences in magenta."
 )
 
 demo.launch()