Update app.py

app.py

@@ -87,6 +87,8 @@ def process_image(job_id, image_path, object_type):
 
         jobs[job_id]['progress'] = 20
         img_height, img_width = image.shape[:2]
+        # Compute dynamic thickness based on image size.
+        thickness = max(2, int(min(img_width, img_height) / 300))
         detection_info = []
 
         if object_type == "box":
@@ -158,7 +160,8 @@ def process_image(job_id, image_path, object_type):
                 x1, y1, x2, y2 = pred["box"]
                 label = pred["class"]
                 confidence = pred["confidence"]
-                cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                # Use dynamic thickness here
+                cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), thickness)
                 if conversion_factor is not None:
                     box_width_pixels = x2 - x1
                     box_height_pixels = y2 - y1
@@ -198,7 +201,8 @@ def process_image(job_id, image_path, object_type):
                         ymax = int(row['ymax'])
                         conf = row['confidence']
                         label = row['name']
-                        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
+                        # Use dynamic thickness here as well
+                        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), thickness)
                         text = f"{label} ({conf:.2f})"
                         (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
                         cv2.rectangle(image, (xmin, ymin - text_height - baseline - 5), (xmin + text_width, ymin - 5), (255, 0, 0), -1)