added app.py

app.py

@@ -0,0 +1,96 @@
+import gradio as gr
+import cv2
+from huggingface_hub import hf_hub_download
+import yolov9
+
+def detect_objects_on_video(video_path, model_path, interval, image_size, conf_threshold, iou_threshold):
+    cap = cv2.VideoCapture(video_path)
+    count = 0
+    detections = []
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        count += 1
+        if count % interval == 0:
+            # Perform object detection on the frame
+            model = yolov9.load(model_path)
+            model.conf = conf_threshold
+            model.iou = iou_threshold
+            results = model(frame, size=image_size)
+            
+            # Optionally, show detection bounding boxes on image
+            output = results.render()
+            detections.append(output[0])
+
+    cap.release()
+    cv2.destroyAllWindows()
+    return detections
+
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                video_path = gr.Video(type="file", label="Video")
+                model_path = gr.Dropdown(
+                    label="Model",
+                    choices=[
+                        "best.pt",
+                    ],
+                    value="./best.pt",
+                )
+                interval = gr.Number(label="Screenshot Interval (seconds)", default=30, step=1)
+                image_size = gr.Slider(
+                    label="Image Size",
+                    minimum=320,
+                    maximum=1280,
+                    step=32,
+                    value=640,
+                )
+                conf_threshold = gr.Slider(
+                    label="Confidence Threshold",
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.4,
+                )
+                iou_threshold = gr.Slider(
+                    label="IoU Threshold",
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.5,
+                )
+                yolov9_infer = gr.Button(value="Detect Objects")
+
+            with gr.Column():
+                output_images = gr.Image(type="numpy", label="Output Images")
+
+        yolov9_infer.click(
+            fn=detect_objects_on_video,
+            inputs=[
+                video_path,
+                model_path,
+                interval,
+                image_size,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_images],
+        )
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    YOLOv9: Detect Objects in Video
+    </h1>
+    """
+    )
+    with gr.Row():
+        with gr.Column():
+            app()
+
+gradio_app.launch(debug=True)