Update app.py

app.py

@@ -87,9 +87,12 @@ def process_image_detection(image, target_label, surprise_rating):
     device = "cuda" if torch.cuda.is_available() else "cpu"
 
     # Get original image DPI and size
-    original_dpi = image.info.get('dpi', (72, 72))  # Default to 72 DPI if not specified
+    original_dpi = image.info.get('dpi', (72, 72))
     original_size = image.size
 
+    # Calculate relative font size based on image dimensions
+    base_fontsize = min(original_size) / 40  # Adjust this divisor to change overall font size
+
     owlv2_processor = Owlv2Processor.from_pretrained("google/owlv2-large-patch14")
     owlv2_model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-large-patch14").to(device)
 
@@ -105,12 +108,10 @@ def process_image_detection(image, target_label, surprise_rating):
     target_sizes = torch.tensor([image.size[::-1]]).to(device)
     results = owlv2_processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]
 
-    # Create figure with the exact pixel size of the original image
-    dpi = 100  # Base DPI for calculation
+    dpi = 300  # Increased DPI for better text rendering
     figsize = (original_size[0] / dpi, original_size[1] / dpi)
     fig = plt.figure(figsize=figsize, dpi=dpi)
 
-    # Remove margins and spacing
     ax = plt.Axes(fig, [0., 0., 1., 1.])
     fig.add_axes(ax)
 
@@ -142,47 +143,55 @@ def process_image_detection(image, target_label, surprise_rating):
             mask = masks[0].numpy() if isinstance(masks[0], torch.Tensor) else masks[0]
             show_mask(mask, ax=ax)
 
+            # Draw rectangle with increased line width
             rect = patches.Rectangle(
                 (box[0], box[1]),
                 box[2] - box[0],
                 box[3] - box[1],
-                linewidth=2,
+                linewidth=max(2, min(original_size) / 500),  # Scale line width with image size
                 edgecolor='red',
                 facecolor='none'
             )
             ax.add_patch(rect)
 
+            # Add confidence score with improved visibility
             plt.text(
-                box[0], box[1] - 5,
+                box[0], box[1] - base_fontsize,
                 f'{max_score:.2f}',
-                color='red'
+                color='red',
+                fontsize=base_fontsize,
+                fontweight='bold',
+                bbox=dict(facecolor='white', alpha=0.7, edgecolor='none', pad=2)
             )
 
+            # Add label and rating with improved visibility
             plt.text(
-                box[2] + 5, box[1],
+                box[2] + base_fontsize / 2, box[1],
                 f'Unexpected (Rating: {surprise_rating}/5)\n{target_label}',
                 color='red',
-                fontsize=10,
+                fontsize=base_fontsize,
+                fontweight='bold',
+                bbox=dict(facecolor='white', alpha=0.7, edgecolor='none', pad=2),
                 verticalalignment='bottom'
             )
 
     plt.axis('off')
 
-    # Save with original resolution and DPI
+    # Save with high DPI
     buf = io.BytesIO()
     plt.savefig(buf,
                 format='png',
                 dpi=dpi,
                 bbox_inches='tight',
-                pad_inches=0)
+                pad_inches=0,
+                metadata={'dpi': original_dpi})
     buf.seek(0)
     plt.close()
 
-    # Open the buffer and create a new image with original properties
+    # Process final image
     output_image = Image.open(buf)
     output_image = output_image.resize(original_size, Image.Resampling.LANCZOS)
 
-    # Create a new buffer with the properly sized image
     final_buf = io.BytesIO()
     output_image.save(final_buf, format='PNG', dpi=original_dpi)
     final_buf.seek(0)