Update app.py

app.py

@@ -5,7 +5,6 @@ import cv2
 from PIL import Image
 import numpy as np
 
-
 import warnings
 import torch
 warnings.filterwarnings("ignore")
@@ -30,16 +29,16 @@ def run_grounding(input_image, grounding_caption, box_threshold, text_threshold)
         if input_image.ndim == 3:
             input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB)
         input_image = Image.fromarray(input_image)
-    
+
     init_image = input_image.convert("RGB")
-    
+
     # Process input using transformers
     inputs = processor(images=init_image, text=grounding_caption, return_tensors="pt").to(device)
-    
+
     # Run inference
     with torch.no_grad():
         outputs = model(**inputs)
-    
+
     # Post-process results
     results = processor.post_process_grounded_object_detection(
         outputs,
@@ -48,43 +47,54 @@ def run_grounding(input_image, grounding_caption, box_threshold, text_threshold)
         text_threshold=text_threshold,
         target_sizes=[init_image.size[::-1]]
     )
-    
+
     result = results[0]
-    
+
     # Convert image for supervision visualization
     image_np = np.array(init_image)
-    
+
     # Create detections for supervision
     boxes = []
     labels = []
     confidences = []
     class_ids = []
-    
+
     for i, (box, score, label) in enumerate(zip(result["boxes"], result["scores"], result["labels"])):
-        # Convert box to xyxy format
+        # box is xyxy format [xmin, ymin, xmax, ymax]
         xyxy = box.tolist()
         boxes.append(xyxy)
         labels.append(label)
         confidences.append(float(score))
         class_ids.append(i)  # Use index as class_id (integer)
-    
-    # Create Detections object for supervision
+
+    # Build the text summary in the requested format
+    if boxes:
+        lines = []
+        for label, xyxy, conf in zip(labels, boxes, confidences):
+            x1, y1, x2, y2 = [int(round(v)) for v in xyxy]
+            # Format: class top_left_x, top_left_y, bot_x, bot_y
+            lines.append(f"{label} {x1}, {y1}, {x2}, {y2}")
+        detection_text = "\n".join(lines)
+    else:
+        detection_text = "No detections."
+
+    # Create Detections object for supervision & annotate
     if boxes:
         detections = sv.Detections(
             xyxy=np.array(boxes),
             confidence=np.array(confidences),
-            class_id=np.array(class_ids, dtype=np.int32),  # Ensure it's an integer array
+            class_id=np.array(class_ids, dtype=np.int32),
         )
-        
+
         text_scale = sv.calculate_optimal_text_scale(resolution_wh=init_image.size)
         line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=init_image.size)
-    
+
         # Create annotators
         box_annotator = sv.BoxAnnotator(
             thickness=2,
             color=sv.ColorPalette.DEFAULT,
         )
-        
+
         label_annotator = sv.LabelAnnotator(
             color=sv.ColorPalette.DEFAULT,
             text_color=sv.Color.WHITE,
@@ -92,40 +102,41 @@ def run_grounding(input_image, grounding_caption, box_threshold, text_threshold)
             text_thickness=line_thickness,
             text_padding=3
         )
-        
+
         # Create formatted labels for each detection
         formatted_labels = [
-            f"{label}: {conf:.2f}" 
+            f"{label}: {conf:.2f}"
             for label, conf in zip(labels, confidences)
         ]
-        
+
         # Apply annotations to the image
         annotated_image = box_annotator.annotate(scene=image_np, detections=detections)
         annotated_image = label_annotator.annotate(
-            scene=annotated_image, 
-            detections=detections, 
+            scene=annotated_image,
+            detections=detections,
             labels=formatted_labels
         )
     else:
         annotated_image = image_np
-    
+
     # Convert back to PIL Image
     image_with_box = Image.fromarray(annotated_image)
-    
-    return image_with_box
+
+    # Return both the annotated image and the detection text
+    return image_with_box, detection_text
 
 if __name__ == "__main__":
-    
+
     parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True)
     parser.add_argument("--debug", action="store_true", help="using debug mode")
     parser.add_argument("--share", action="store_true", help="share the app")
     args = parser.parse_args()
-    
+
     css = """
   #mkd {
-    height: 500px; 
-    overflow: auto; 
-    border: 1px solid #ccc; 
+    height: 500px;
+    overflow: auto;
+    border: 1px solid #ccc;
   }
 """
     with gr.Blocks(css=css) as demo:
@@ -135,16 +146,19 @@ if __name__ == "__main__":
         with gr.Row():
             with gr.Column():
                 input_image = gr.Image(label="Input Image", type="pil")
-                grounding_caption = gr.Textbox(label="Detection Prompt(VERY important: text queries need to be lowercased + end with a dot, example: a cat. a remote control.)", value="a person. a car.")
+                grounding_caption = gr.Textbox(
+                    label="Detection Prompt (lowercase + each ends with a dot)",
+                    value="a person. a car."
+                )
                 run_button = gr.Button("Run")
-                
+
                 with gr.Accordion("Advanced options", open=False):
                     box_threshold = gr.Slider(
-                        minimum=0.0, maximum=1.0, value=0.3, step=0.001, 
+                        minimum=0.0, maximum=1.0, value=0.3, step=0.001,
                         label="Box Threshold"
                     )
                     text_threshold = gr.Slider(
-                        minimum=0.0, maximum=1.0, value=0.25, step=0.001, 
+                        minimum=0.0, maximum=1.0, value=0.25, step=0.001,
                         label="Text Threshold"
                     )
 
@@ -153,22 +167,28 @@ if __name__ == "__main__":
                     label="Detection Result",
                     type="pil"
                 )
+                det_text = gr.Textbox(
+                    label="Detections (class top_left_x, top_left_y, bot_x, bot_y)",
+                    lines=12,
+                    interactive=False,
+                    show_copy_button=True
+                )
 
         run_button.click(
-            fn=run_grounding, 
-            inputs=[input_image, grounding_caption, box_threshold, text_threshold], 
-            outputs=[gallery]
+            fn=run_grounding,
+            inputs=[input_image, grounding_caption, box_threshold, text_threshold],
+            outputs=[gallery, det_text]
         )
-        
+
         gr.Examples(
             examples=[
                 ["000000039769.jpg", "a cat. a remote control.", 0.3, 0.25],
                 ["KakaoTalk_20250430_163200504.jpg", "cup. screen. hand.", 0.3, 0.25]
-                ],
+            ],
             inputs=[input_image, grounding_caption, box_threshold, text_threshold],
-            outputs=[gallery],
+            outputs=[gallery, det_text],
             fn=run_grounding,
             cache_examples=True,
         )
-    
-    demo.launch(share=args.share, debug=args.debug, show_error=True)
+
+    demo.launch(share=args.share, debug=args.debug, show_error=True)