Update app.py

app.py

@@ -1,19 +1,15 @@
 import numpy as np
 import cv2 as cv
 import gradio as gr
-import base64
-from PIL import Image
-import io
 
-def match_features(target_img, comparison_gallery):
-    # Initialize list to store results and match counts
+def match_features(target_img, comp_img1, comp_img2, comp_img3, comp_img4, comp_img5, comp_img6, comp_img7, comp_img8):
+    # Initialize list to store results
     result_images = []
     match_counts = []
     
-    # Skip processing if no target image
-    if target_img is None:
-        return [], []
-        
+    # List of comparison images
+    comparison_imgs = [comp_img1, comp_img2, comp_img3, comp_img4, comp_img5, comp_img6, comp_img7, comp_img8]
+    
     # Convert target image to grayscale OpenCV format
     target_cv = np.array(target_img.convert("L"))
     
@@ -28,8 +24,14 @@ def match_features(target_img, comparison_gallery):
     flann = cv.FlannBasedMatcher(index_params, search_params)
     
     # Process each comparison image
-    for img in comparison_gallery:
+    for img in comparison_imgs:
+        # Create a default blank image with "No image" text
+        blank_img = np.zeros((400, 800, 3), dtype=np.uint8)
+        cv.putText(blank_img, "No image provided", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+        
         if img is None:
+            result_images.append(blank_img)
+            match_counts.append(0)
             continue
             
         # Convert to grayscale OpenCV format
@@ -40,6 +42,9 @@ def match_features(target_img, comparison_gallery):
         
         # Skip if no features detected
         if des_img is None or des_target is None:
+            cv.putText(blank_img, "No features detected", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+            result_images.append(blank_img)
+            match_counts.append(0)
             continue
         
         try:
@@ -91,102 +96,85 @@ def match_features(target_img, comparison_gallery):
             cv.rectangle(result_img, (x-5, y-text_height-5), (x+text_width+5, y+5), (0,0,0), -1)
             cv.putText(result_img, text, (x, y), font, font_scale, font_color, thickness)
             
-            # Convert to PIL image for gallery
-            pil_img = Image.fromarray(result_img)
-            result_images.append((pil_img, f"Matches: {match_count}"))
-            
+            result_images.append(result_img)
         except Exception as e:
-            print(f"Error processing image: {str(e)}")
+            # Handle any errors
+            error_img = np.zeros((400, 800, 3), dtype=np.uint8)
+            cv.putText(error_img, f"Error: {str(e)}", (50, 200), cv.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 1)
+            result_images.append(error_img)
+            match_counts.append(0)
+    
+    # Ensure we have 8 results
+    while len(result_images) < 8:
+        blank_img = np.zeros((400, 800, 3), dtype=np.uint8)
+        cv.putText(blank_img, "No image provided", (250, 200), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
+        result_images.append(blank_img)
+        match_counts.append(0)
+    
+    # Return all match results and counts
+    return result_images + [match_counts]
+
+# Create Gradio interface with 1 target and up to 8 comparison images
+with gr.Blocks(title="Image Feature Matching Comparison") as iface:
+    gr.Markdown("# Image Feature Matching with SIFT+FLANN")
+    gr.Markdown("""
+    Upload a target image and up to 8 comparison images to find feature matches using 
+    SIFT (Scale-Invariant Feature Transform) with FLANN (Fast Library for Approximate Nearest Neighbors).
+    The number of matches will be displayed for each comparison.
+    """)
     
-    # Sort results by match counts (descending)
-    sorted_results = sorted(zip(result_images, match_counts), key=lambda x: x[1], reverse=True)
+    with gr.Row():
+        target_input = gr.Image(type="pil", label="Target Image")
     
-    # Unzip the sorted results
-    if sorted_results:
-        sorted_images, sorted_counts = zip(*sorted_results)
-    else:
-        sorted_images, sorted_counts = [], []
+    with gr.Row():
+        comp_img1 = gr.Image(type="pil", label="Comparison Image 1")
+        comp_img2 = gr.Image(type="pil", label="Comparison Image 2")
     
-    return sorted_images, list(sorted_counts)
-
-def format_match_counts(counts):
-    if not counts:
-        return "No matches found"
+    with gr.Row():
+        comp_img3 = gr.Image(type="pil", label="Comparison Image 3")
+        comp_img4 = gr.Image(type="pil", label="Comparison Image 4")
     
-    formatted = ["### Match Counts (sorted by highest match)"]
-    for i, count in enumerate(counts):
-        formatted.append(f"Image {i+1}: **{count}** matches")
+    with gr.Row():
+        comp_img5 = gr.Image(type="pil", label="Comparison Image 5")
+        comp_img6 = gr.Image(type="pil", label="Comparison Image 6")
     
-    return "\n".join(formatted)
-
-# Create Gradio interface with gallery components
-with gr.Blocks(title="Image Feature Matching with Gallery") as iface:
-    gr.Markdown("# Image Feature Matching with SIFT+FLANN")
-    gr.Markdown("""
-    Upload a target image and multiple comparison images to find feature matches using 
-    SIFT (Scale-Invariant Feature Transform) with FLANN (Fast Library for Approximate Nearest Neighbors).
+    with gr.Row():
+        comp_img7 = gr.Image(type="pil", label="Comparison Image 7")
+        comp_img8 = gr.Image(type="pil", label="Comparison Image 8")
     
-    The results will be sorted by the number of matches (highest first), and each result will display the match count.
-    """)
+    compare_btn = gr.Button("Compare Images")
     
     with gr.Row():
-        with gr.Column(scale=1):
-            target_input = gr.Image(type="pil", label="Target Image")
-            gr.Markdown("### Upload a single target image")
-            
-        with gr.Column(scale=2):
-            comparison_gallery = gr.Gallery(
-                label="Comparison Images", 
-                object_fit="contain",
-                columns=3,
-                rows=2,
-                height="auto",
-                allow_preview=False,
-                show_download_button=False
-            )
-            upload_button = gr.UploadButton("Click to Upload Images", file_types=["image"], file_count="multiple")
-            gr.Markdown("### Upload as many comparison images as you want")
-            
-    compare_btn = gr.Button("Compare Images", variant="primary")
+        result1 = gr.Image(label="Result 1")
+        result2 = gr.Image(label="Result 2")
     
     with gr.Row():
-        with gr.Column(scale=2):
-            results_gallery = gr.Gallery(
-                label="Results (Sorted by Match Count)",
-                object_fit="contain",
-                columns=2, 
-                rows=2,
-                height="auto",
-                allow_preview=True,
-                show_download_button=True,
-                show_label=True
-            )
-        with gr.Column(scale=1):
-            match_counts_output = gr.Markdown(label="Match Counts")
-    
-    # Handle uploading multiple images to the gallery
-    upload_button.upload(
-        lambda files: [file for file in files],
-        upload_button,
-        comparison_gallery
-    )
+        result3 = gr.Image(label="Result 3")
+        result4 = gr.Image(label="Result 4")
+    
+    with gr.Row():
+        result5 = gr.Image(label="Result 5")
+        result6 = gr.Image(label="Result 6")
+    
+    with gr.Row():
+        result7 = gr.Image(label="Result 7")
+        result8 = gr.Image(label="Result 8")
+    
+    match_counts_output = gr.JSON(label="Match Counts")
     
-    # Handle comparison button click
     compare_btn.click(
         fn=match_features,
-        inputs=[target_input, comparison_gallery],
-        outputs=[results_gallery, match_counts_output]
+        inputs=[
+            target_input,
+            comp_img1, comp_img2, comp_img3, comp_img4,
+            comp_img5, comp_img6, comp_img7, comp_img8
+        ],
+        outputs=[
+            result1, result2, result3, result4,
+            result5, result6, result7, result8,
+            match_counts_output
+        ]
     )
-    
-    gr.Markdown("""
-    ### Instructions
-    
-    1. Upload a target image on the left side
-    2. Upload multiple comparison images using the upload button (you can select multiple files at once)
-    3. Click "Compare Images" to find feature matches
-    4. Results will be sorted by the number of matches (highest first)
-    5. Each result image shows the matches between target and comparison image
-    """)
 
 # Launch the interface
 iface.launch()