Update app.py

app.py

@@ -44,18 +44,6 @@ colors = np.random.uniform(0, 255, size=(len(model.names), 3))
 total_inference_time = 0
 inference_count = 0
 
-def preprocess_image(image):
-    """Prepares image for YOLOv5 detection while maintaining aspect ratio."""
-    h, w, _ = image.shape
-    scale = 640 / max(h, w)
-    new_w, new_h = int(w * scale), int(h * scale)
-
-    resized_image = cv2.resize(image, (new_w, new_h))
-    padded_image = np.full((640, 640, 3), 114, dtype=np.uint8)  # Gray padding
-    padded_image[:new_h, :new_w] = resized_image
-
-    return cv2.cvtColor(padded_image, cv2.COLOR_RGB2BGR)  # Convert to BGR for OpenCV
-
 def detect_objects(image):
     global total_inference_time, inference_count
 
@@ -64,11 +52,14 @@ def detect_objects(image):
 
     start_time = time.time()
 
-    # Preprocess image
-    image = preprocess_image(image)
+    # Convert image to BGR format for OpenCV
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # Get image dimensions
+    h, w, _ = image.shape
 
     with torch.inference_mode():  # Faster than torch.no_grad()
-        results = model(image, size=640)
+        results = model(image_bgr)
 
     inference_time = time.time() - start_time
     total_inference_time += inference_time
@@ -85,17 +76,17 @@ def detect_objects(image):
         color = colors[class_id].tolist()
 
         # Keep bounding boxes within image bounds
-        x1, y1, x2, y2 = max(0, x1), max(0, y1), min(640, x2), min(640, y2)
+        x1, y1, x2, y2 = max(0, x1), max(0, y1), min(w, x2), min(h, y2)
 
         # Draw bounding box
         cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 3, lineType=cv2.LINE_AA)
 
         label = f"{model.names[class_id]} {conf:.2f}"
         font_scale, font_thickness = 0.9, 2
-        (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)
 
         # Label background
-        cv2.rectangle(output_image, (x1, y1 - h - 10), (x1 + w + 10, y1), color, -1)
+        cv2.rectangle(output_image, (x1, y1 - th - 10), (x1 + tw + 10, y1), color, -1)
         cv2.putText(output_image, label, (x1 + 5, y1 - 5),
                     cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), font_thickness, lineType=cv2.LINE_AA)
 
@@ -116,10 +107,10 @@ def detect_objects(image):
 example_images = ["spring_street_after.jpg", "pexels-hikaique-109919.jpg"]
 os.makedirs("examples", exist_ok=True)
 
-with gr.Blocks(title="Optimized YOLOv5 Object Detection") as demo:
+with gr.Blocks(title="YOLOv5 Object Detection (High Quality, High FPS)") as demo:
     gr.Markdown("""
-    # Optimized YOLOv5 Object Detection  
-    Detects objects using YOLOv5 with enhanced visualization and FPS tracking.
+    # YOLOv5 Object Detection - High Quality & High FPS  
+    Detects objects with full-resolution output and ultra-fast performance.
     """)
 
     with gr.Row():