Update app.py

app.py

@@ -3,83 +3,87 @@ import cv2 as cv
 import gradio as gr
 
 def match_features(img1, img2):
-    try:
-        # Convert Gradio image inputs (PIL) to OpenCV format (numpy array)
-        img1 = np.array(img1.convert("L"))
-        img2 = np.array(img2.convert("L"))
+    # Convert Gradio image inputs (PIL) to OpenCV format (numpy array)
+    img1 = np.array(img1.convert("L"))
+    img2 = np.array(img2.convert("L"))
 
-        # Initialize variables to store match counts  
-        sift_flann_matches_count = 0  
+    # SIFT with FLANN
+    sift = cv.SIFT_create()
+    kp1, des1 = sift.detectAndCompute(img1, None)
+    kp2, des2 = sift.detectAndCompute(img2, None)
+    
+    # Initialize FLANN
+    FLANN_INDEX_KDTREE = 1
+    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
+    search_params = dict(checks=50)
+    flann = cv.FlannBasedMatcher(index_params, search_params)
+    
+    # Find matches
+    matches = flann.knnMatch(des1, des2, k=2)
+    
+    # Apply ratio test
+    matchesMask = [[0, 0] for i in range(len(matches))]
+    good_matches = []
+    for i, (m, n) in enumerate(matches):
+        if m.distance < 0.7 * n.distance:
+            matchesMask[i] = [1, 0]
+            good_matches.append(m)
+    
+    # Calculate match count
+    sift_flann_matches_count = len(good_matches)
+    
+    # Draw matches
+    draw_params = dict(
+        matchColor=(0, 255, 0),
+        singlePointColor=(255, 0, 0),
+        matchesMask=matchesMask,
+        flags=cv.DrawMatchesFlags_DEFAULT
+    )
+    img3_sift_flann = cv.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **draw_params)
+    
+    # Convert image to RGB format for display in Gradio
+    img3_sift_flann = cv.cvtColor(img3_sift_flann, cv.COLOR_BGR2RGB)
+    
+    # Add text with match count to the image
+    font = cv.FONT_HERSHEY_SIMPLEX
+    font_scale = 1
+    font_color = (255, 255, 255)  # White color
+    thickness = 2
+    
+    # Add count text
+    h, w = img3_sift_flann.shape[:2]
+    text = f"SIFT-FLANN Matches: {sift_flann_matches_count}"
+    
+    # Get text size
+    (text_width, text_height), _ = cv.getTextSize(text, font, font_scale, thickness)
+    
+    # Position text at bottom center
+    x = (w - text_width) // 2
+    y = h - 20  # 20 pixels from bottom
+    
+    # Add black background for better visibility
+    cv.rectangle(img3_sift_flann, (x-5, y-text_height-5), (x+text_width+5, y+5), (0,0,0), -1)
+    
+    # Add text
+    cv.putText(img3_sift_flann, text, (x, y), font, font_scale, font_color, thickness)
+    
+    return img3_sift_flann
 
-        # SIFT with FLANN
-        FLANN_INDEX_KDTREE = 1
-        index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
-        search_params = dict(checks=50)
-        flann = cv.FlannBasedMatcher(index_params, search_params)
-        kp1, des1 = cv.SIFT_create().detectAndCompute(img1, None)
-        kp2, des2 = cv.SIFT_create().detectAndCompute(img2, None)
-        
-        matches = flann.knnMatch(des1, des2, k=2)
-        matchesMask = [[0, 0] for i in range(len(matches))]
-        good_matches = []
-        for i, (m, n) in enumerate(matches):
-            if m.distance < 0.7 * n.distance:
-                matchesMask[i] = [1, 0]
-                good_matches.append(m)
-        
-        sift_flann_matches_count = len(good_matches)
-        
-        # Draw matches
-        draw_params = dict(matchColor=(0, 255, 0),
-                          singlePointColor=(255, 0, 0),
-                          matchesMask=matchesMask,
-                          flags=cv.DrawMatchesFlags_DEFAULT)
-        img3_sift_flann = cv.drawMatchesKnn(img1, kp1, img2, kp2, matches, None, **draw_params)
-
-        # Convert image to RGB format for display in Gradio
-        img3_sift_flann = cv.cvtColor(img3_sift_flann, cv.COLOR_BGR2RGB)
-
-        # Add text with match count
-        font = cv.FONT_HERSHEY_SIMPLEX
-        font_scale = 1
-        font_color = (255, 255, 255)  # White color
-        thickness = 2
-
-        # Add count text to the image
-        h, w = img3_sift_flann.shape[:2]
-        text = f"SIFT-FLANN Matches: {sift_flann_matches_count}"
-        # Get text size
-        (text_width, text_height), _ = cv.getTextSize(text, font, font_scale, thickness)
-        # Position text at bottom center
-        x = (w - text_width) // 2
-        y = h - 20  # 20 pixels from bottom
-        # Add black background for better visibility
-        cv.rectangle(img3_sift_flann, (x-5, y-text_height-5), (x+text_width+5, y+5), (0, 0, 0), -1)
-        # Add text
-        cv.putText(img3_sift_flann, text, (x, y), font, font_scale, font_color, thickness)
-
-        return img3_sift_flann
-
-    except Exception as e:
-        # Return the error message if something goes wrong
-        return None, f"Error occurred: {str(e)}"
-
-# Gradio interface with no example images
+# Gradio interface without examples
 iface = gr.Interface(
     fn=match_features,
     inputs=[
         gr.Image(type="pil", label="Image 1"),
         gr.Image(type="pil", label="Image 2")
     ],
-    outputs=[
-        gr.Image(label="SIFT (FLANN) Matches"),
-        gr.Textbox(label="Error Log", placeholder="Error details will appear here", interactive=False)  # Error message box
-    ],
-    title="SIFT-FLANN Image Feature Matching",
+    outputs=gr.Image(label="SIFT (FLANN) Matches"),
+    title="Image Feature Matching with SIFT+FLANN",
     description="""
-    Upload two images of the same subject taken from different angles to find the best feature matches using the SIFT with FLANN algorithm.
-    """,
-    cache_examples=False  # No examples
+    Upload two images of the same subject taken from different angles to find 
+    feature matches using SIFT (Scale-Invariant Feature Transform) with 
+    FLANN (Fast Library for Approximate Nearest Neighbors).
+    """
 )
 
+# Launch the interface
 iface.launch()