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xiaoyuxi
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eb0ce33864
88
app.py
88
app.py
@ -43,7 +43,9 @@ except ImportError as e:
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raise
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# Constants
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MAX_FRAMES = 80
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MAX_FRAMES_OFFLINE = 80
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MAX_FRAMES_ONLINE = 300
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COLORS = [(0, 0, 255), (0, 255, 255)] # BGR: Red for negative, Yellow for positive
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MARKERS = [1, 5] # Cross for negative, Star for positive
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MARKER_SIZE = 8
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@ -88,13 +90,16 @@ vggt4track_model = vggt4track_model.to("cuda")
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# Global model initialization
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print("🚀 Initializing local models...")
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tracker_model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline")
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tracker_model.eval()
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tracker_model_offline = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline")
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tracker_model_offline.eval()
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tracker_model_online = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Online")
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tracker_model_online.eval()
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predictor = get_sam_predictor()
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print("✅ Models loaded successfully!")
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gr.set_static_paths(paths=[Path.cwd().absolute()/"_viz"])
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@spaces.GPU
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def gpu_run_inference(predictor_arg, image, points, boxes):
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"""GPU-accelerated SAM inference"""
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if predictor_arg is None:
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@ -118,6 +123,7 @@ def gpu_run_inference(predictor_arg, image, points, boxes):
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return run_inference(predictor_arg, image, points, boxes)
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@spaces.GPU
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def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name, grid_size, vo_points, fps, mode="offline"):
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"""GPU-accelerated tracking"""
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import torchvision.transforms as T
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@ -127,8 +133,12 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
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print("Initializing tracker models inside GPU function...")
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out_dir = os.path.join(temp_dir, "results")
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os.makedirs(out_dir, exist_ok=True)
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tracker_model_arg, tracker_viser_arg = get_tracker_predictor(out_dir, vo_points=vo_points,
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tracker_model=tracker_model.cuda())
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if mode == "offline":
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tracker_model_arg, tracker_viser_arg = get_tracker_predictor(out_dir, vo_points=vo_points,
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tracker_model=tracker_model_offline.cuda())
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else:
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tracker_model_arg, tracker_viser_arg = get_tracker_predictor(out_dir, vo_points=vo_points,
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tracker_model=tracker_model_online.cuda())
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# Setup paths
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video_path = os.path.join(temp_dir, f"{video_name}.mp4")
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@ -146,7 +156,10 @@ def gpu_run_tracker(tracker_model_arg, tracker_viser_arg, temp_dir, video_name,
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if scale < 1:
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new_h, new_w = int(h * scale), int(w * scale)
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video_tensor = T.Resize((new_h, new_w))(video_tensor)
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video_tensor = video_tensor[::fps].float()[:MAX_FRAMES]
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if mode == "offline":
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video_tensor = video_tensor[::fps].float()[:MAX_FRAMES_OFFLINE]
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else:
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video_tensor = video_tensor[::fps].float()[:MAX_FRAMES_ONLINE]
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# Move to GPU
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video_tensor = video_tensor.cuda()
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@ -524,7 +537,7 @@ def reset_points(original_img: str, sel_pix):
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print(f"❌ Error in reset_points: {e}")
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return None, []
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def launch_viz(grid_size, vo_points, fps, original_image_state, mode="offline"):
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def launch_viz(grid_size, vo_points, fps, original_image_state, processing_mode):
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"""Launch visualization with user-specific temp directory"""
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if original_image_state is None:
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return None, None, None
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@ -536,7 +549,7 @@ def launch_viz(grid_size, vo_points, fps, original_image_state, mode="offline"):
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video_name = frame_data.get('video_name', 'video')
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print(f"🚀 Starting tracking for video: {video_name}")
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print(f"📊 Parameters: grid_size={grid_size}, vo_points={vo_points}, fps={fps}")
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print(f"📊 Parameters: grid_size={grid_size}, vo_points={vo_points}, fps={fps}, mode={processing_mode}")
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# Check for mask files
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mask_files = glob.glob(os.path.join(temp_dir, "*.png"))
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@ -550,11 +563,11 @@ def launch_viz(grid_size, vo_points, fps, original_image_state, mode="offline"):
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mask_path = mask_files[0] if mask_files else None
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# Run tracker
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print("🎯 Running tracker...")
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print(f"🎯 Running tracker in {processing_mode} mode...")
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out_dir = os.path.join(temp_dir, "results")
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os.makedirs(out_dir, exist_ok=True)
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gpu_run_tracker(None, None, temp_dir, video_name, grid_size, vo_points, fps, mode=mode)
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gpu_run_tracker(None, None, temp_dir, video_name, grid_size, vo_points, fps, mode=processing_mode)
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# Process results
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npz_path = os.path.join(out_dir, "result.npz")
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@ -607,6 +620,7 @@ def clear_all_with_download():
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gr.update(value=50),
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gr.update(value=756),
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gr.update(value=3),
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gr.update(value="offline"), # processing_mode
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None, # tracking_video_download
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None) # HTML download component
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@ -639,6 +653,13 @@ def get_video_settings(video_name):
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return video_settings.get(video_name, (50, 756, 3))
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def update_status_indicator(processing_mode):
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"""Update status indicator based on processing mode"""
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if processing_mode == "offline":
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return "**Status:** 🟢 Local Processing Mode (Offline)"
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else:
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return "**Status:** 🔵 Cloud Processing Mode (Online)"
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# Create the Gradio interface
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print("🎨 Creating Gradio interface...")
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@ -844,7 +865,7 @@ with gr.Blocks(
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""")
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# Status indicator
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gr.Markdown("**Status:** 🟢 Local Processing Mode")
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status_indicator = gr.Markdown("**Status:** 🟢 Local Processing Mode (Offline)")
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# Main content area - video upload left, 3D visualization right
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with gr.Row():
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@ -943,18 +964,29 @@ with gr.Blocks(
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with gr.Row():
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gr.Markdown("### ⚙️ Tracking Parameters")
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with gr.Row():
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grid_size = gr.Slider(
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minimum=10, maximum=100, step=10, value=50,
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label="Grid Size", info="Tracking detail level"
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)
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vo_points = gr.Slider(
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minimum=100, maximum=2000, step=50, value=756,
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label="VO Points", info="Motion accuracy"
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)
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fps = gr.Slider(
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minimum=1, maximum=20, step=1, value=3,
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label="FPS", info="Processing speed"
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)
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# 添加模式选择器
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with gr.Column(scale=1):
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processing_mode = gr.Radio(
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choices=["offline", "online"],
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value="offline",
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label="Processing Mode",
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info="Offline: default mode | Online: Sliding Window Mode"
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)
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with gr.Column(scale=1):
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grid_size = gr.Slider(
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minimum=10, maximum=100, step=10, value=50,
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label="Grid Size", info="Tracking detail level"
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)
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with gr.Column(scale=1):
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vo_points = gr.Slider(
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minimum=100, maximum=2000, step=50, value=756,
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label="VO Points", info="Motion accuracy"
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)
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with gr.Column(scale=1):
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fps = gr.Slider(
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minimum=1, maximum=20, step=1, value=3,
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label="FPS", info="Processing speed"
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)
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# Advanced Point Selection with SAM - Collapsed by default
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with gr.Row():
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@ -1080,6 +1112,12 @@ with gr.Blocks(
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outputs=[original_image_state, interactive_frame, selected_points, grid_size, vo_points, fps]
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)
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processing_mode.change(
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fn=update_status_indicator,
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inputs=[processing_mode],
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outputs=[status_indicator]
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)
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interactive_frame.select(
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fn=select_point,
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inputs=[original_image_state, selected_points, point_type],
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@ -1094,12 +1132,12 @@ with gr.Blocks(
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clear_all_btn.click(
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fn=clear_all_with_download,
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outputs=[video_input, interactive_frame, selected_points, grid_size, vo_points, fps, tracking_video_download, html_download]
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outputs=[video_input, interactive_frame, selected_points, grid_size, vo_points, fps, processing_mode, tracking_video_download, html_download]
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)
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launch_btn.click(
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fn=launch_viz,
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inputs=[grid_size, vo_points, fps, original_image_state],
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inputs=[grid_size, vo_points, fps, original_image_state, processing_mode],
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outputs=[viz_html, tracking_video_download, html_download]
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)
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