Update app.py

app.py

@@ -1,74 +1,68 @@
 import gradio as gr
-import cv2
-import tempfile
-from ultralytics import YOLOv10
+from ultralytics import YOLOv10 
+import supervision as sv
 import spaces
+from huggingface_hub import hf_hub_download
+
+
+def download_models(model_id):
+    hf_hub_download("kadirnar/Yolov10", filename=f"{model_id}", local_dir=f"./")
+    return f"./{model_id}"
+    
+box_annotator = sv.BoxAnnotator()
+category_dict = {
+    0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
+    6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
+    11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
+    16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
+    22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
+    27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
+    32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
+    36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
+    40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
+    46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
+    51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
+    56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
+    61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
+    67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
+    72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
+    77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
+}
+
+
+@spaces.GPU(duration=200)
+def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold):
+    model_path = download_models(model_id)
+    model = YOLOv10(model_path)
+    results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    
+    labels = [
+        f"{category_dict[class_id]} {confidence:.2f}"
+        for class_id, confidence in zip(detections.class_id, detections.confidence)
+    ]
+    annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
 
-
-@spaces.GPU
-def yolov10_inference(image, video, model_id, image_size, conf_threshold):
-    # model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
-    model = YOLOv10.from_pretrained('weights/yolov10s.pt')
-    if image:
-        results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
-        annotated_image = results[0].plot()
-        return annotated_image[:, :, ::-1], None
-    else:
-        video_path = tempfile.mktemp(suffix=".webm")
-        with open(video_path, "wb") as f:
-            with open(video, "rb") as g:
-                f.write(g.read())
-
-        cap = cv2.VideoCapture(video_path)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-
-        output_video_path = tempfile.mktemp(suffix=".webm")
-        out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))
-
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-
-            results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
-            annotated_frame = results[0].plot()
-            out.write(annotated_frame)
-
-        cap.release()
-        out.release()
-
-        return None, output_video_path
-
-
-def yolov10_inference_for_examples(image, model_path, image_size, conf_threshold):
-    annotated_image, _ = yolov10_inference(image, None, model_path, image_size, conf_threshold)
     return annotated_image
 
-
 def app():
     with gr.Blocks():
         with gr.Row():
             with gr.Column():
-                image = gr.Image(type="pil", label="Image", visible=True)
-                video = gr.Video(label="Video", visible=False)
-                input_type = gr.Radio(
-                    choices=["Image", "Video"],
-                    value="Image",
-                    label="Input Type",
-                )
+                # image = gr.Image(type="pil", label="Image")
+                video = gr.Video(type="Video", visible=False)
+                
                 model_id = gr.Dropdown(
                     label="Model",
                     choices=[
-                        "yolov10n",
-                        "yolov10s",
-                        "yolov10m",
-                        "yolov10b",
-                        "yolov10l",
-                        "yolov10x",
+                        "yolov10n.pt",
+                        "yolov10s.pt",
+                        "yolov10m.pt",
+                        "yolov10b.pt",
+                        "yolov10l.pt",
+                        "yolov10x.pt",
                     ],
-                    value="yolov10m",
+                    value="yolov10m.pt",
                 )
                 image_size = gr.Slider(
                     label="Image Size",
@@ -79,68 +73,70 @@ def app():
                 )
                 conf_threshold = gr.Slider(
                     label="Confidence Threshold",
-                    minimum=0.0,
+                    minimum=0.1,
                     maximum=1.0,
-                    step=0.05,
+                    step=0.1,
                     value=0.25,
                 )
+                iou_threshold = gr.Slider(
+                    label="IoU Threshold",
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.45,
+                )
                 yolov10_infer = gr.Button(value="Detect Objects")
 
             with gr.Column():
-                output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
+                output_image = gr.Image(type="pil", label="Annotated Image")
                 output_video = gr.Video(label="Annotated Video", visible=False)
 
-        def update_visibility(input_type):
-            image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
-            video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
-            output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
-            output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
-
-            return image, video, output_image, output_video
-
-        input_type.change(
-            fn=update_visibility,
-            inputs=[input_type],
-            outputs=[image, video, output_image, output_video],
-        )
-
-        def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
-            if input_type == "Image":
-                return yolov10_inference(image, None, model_id, image_size, conf_threshold)
-            else:
-                return yolov10_inference(None, video, model_id, image_size, conf_threshold)
-
-
         yolov10_infer.click(
-            fn=run_inference,
-            inputs=[image, video, model_id, image_size, conf_threshold, input_type],
-            outputs=[output_image, output_video],
+            fn=yolov10_inference,
+            inputs=[
+                image,
+                model_id,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_image],
         )
 
         gr.Examples(
             examples=[
                 [
-                    "ultralytics/assets/bus.jpg",
-                    "yolov10s",
+                    "dog.jpeg",
+                    "yolov10x.pt",
                     640,
                     0.25,
+                    0.45,
                 ],
                 [
-                    "ultralytics/assets/zidane.jpg",
-                    "yolov10s",
+                    "huggingface.jpg",
+                    "yolov10m.pt",
                     640,
                     0.25,
+                    0.45,
+                ],
+                [
+                    "zidane.jpg",
+                    "yolov10b.pt",
+                    640,
+                    0.25,
+                    0.45,
                 ],
             ],
-            fn=yolov10_inference_for_examples,
+            fn=yolov10_inference,
             inputs=[
                 image,
                 model_id,
                 image_size,
                 conf_threshold,
+                iou_threshold,
             ],
             outputs=[output_image],
-            cache_examples='lazy',
+            cache_examples=True,
         )
 
 gradio_app = gr.Blocks()
@@ -154,11 +150,12 @@ with gradio_app:
     gr.HTML(
         """
         <h3 style='text-align: center'>
-        <a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
+        Follow me for more!
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
         </h3>
         """)
     with gr.Row():
         with gr.Column():
             app()
-if __name__ == '__main__':
-    gradio_app.launch()
+
+gradio_app.launch(debug=True)