Update app.py

app.py

@@ -55,32 +55,30 @@ for model in models:
       model["cfg"].MODEL.DEVICE = "cpu"
 
 
-def inference(image_url, image, min_score, model_name):
-    if image_url:
-        r = requests.get(image_url)
-        if r:
-            im = np.frombuffer(r.content, dtype="uint8")
-            im = cv2.imdecode(im, cv2.IMREAD_COLOR_BGR2RGB)
-    else:
-        # Model expect BGR!
-        im = image[:,:,::-1]
-
+def inference(image, min_score, model_name):
+    results = []
+    
     model_id = model_name_to_id[model_name]
-
     models[model_id]["cfg"].MODEL.ROI_HEADS.SCORE_THRESH_TEST = min_score
     predictor = DefaultPredictor(models[model_id]["cfg"])
 
-    outputs = predictor(im)
+    for img in image:
+        # Convert image from BGR to RGB if necessary
+        im = img[:,:,::-1]
 
-    v = Visualizer(im, models[model_id]["metadata"], scale=1.2)
-    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-        
-    # Convertir la imagen de salida de BGR a RGB
-    result_image = out.get_image()  # Esto sigue estando en BGR
-    result_image_rgb = result_image[:, :, ::-1]  # Convertir BGR a RGB
+        # Make the prediction
+        outputs = predictor(im)
 
-    return result_image_rgb
+        v = Visualizer(im, models[model_id]["metadata"], scale=1.2)
+        out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+        
+        # Convert the result from BGR to RGB
+        result_image = out.get_image()
+        result_image_rgb = result_image[:, :, ::-1]  # Convert BGR to RGB
 
+        results.append(result_image_rgb)  # Add the processed image to the list
+    
+    return results  # Return all the results
 
 title = "# DBMDZ Detectron2 Model Demo"
 description = """
@@ -95,21 +93,18 @@ with gr.Blocks() as demo:
     gr.Markdown(title)
     gr.Markdown(description)
     
-    with gr.Tab("From URL"):
-        url_input = gr.Textbox(label="Image URL", placeholder="https://api.digitale-sammlungen.de/iiif/image/v2/bsb10483966_00008/full/500,/0/default.jpg")
-    
     with gr.Tab("From Image"):
-        image_input = gr.Image(type="numpy", label="Input Image")
+        image_input = gr.Image(type="numpy", label="Input Images", elem_id="input_image", multiple=True)
 
     min_score = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Minimum score")
 
     model_name = gr.Radio(choices=[model["name"] for model in models], value=models[0]["name"], label="Select Detectron2 model")
 
-    output_image = gr.Image(type="pil", label="Output")
+    output_gallery = gr.Gallery(label="Output Images", elem_id="output_images").style(grid=2)
 
     inference_button = gr.Button("Submit")
     
-    inference_button.click(fn=inference, inputs=[url_input, image_input, min_score, model_name], outputs=output_image)
+    inference_button.click(fn=inference, inputs=[image_input, min_score, model_name], outputs=output_gallery)
 
     gr.Markdown(footer)