Spaces:
Running
on
Zero
Running
on
Zero
webm webcam output?
Browse files- app.py +5 -10
- pyproject.toml +2 -2
- requirements.txt +1 -1
- uv.lock +0 -0
app.py
CHANGED
@@ -76,6 +76,7 @@ def predict_depth(input_image):
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@rr.thread_local_stream("rerun_example_ml_depth_pro")
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def run_rerun(path_to_video):
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stream = rr.binary_stream()
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blueprint = rrb.Blueprint(
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rrb.Vertical(
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@@ -93,15 +94,16 @@ def run_rerun(path_to_video):
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rr.send_blueprint(blueprint)
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yield stream.read()
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video_asset = rr.AssetVideo(path=path_to_video)
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rr.log("world/video", video_asset, static=True)
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# Send automatically determined video frame timestamps.
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frame_timestamps_ns = video_asset.read_frame_timestamps_ns()
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# load the video using opencv
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cap = cv2.VideoCapture(path_to_video)
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for i in range(len(frame_timestamps_ns)):
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ret, frame = cap.read()
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if not ret:
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@@ -161,19 +163,12 @@ def run_rerun(path_to_video):
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yield stream.read()
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# Example images
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example_images = [
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"examples/lemur.jpg",
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"examples/cat.jpg",
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"examples/silly-cat.png",
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]
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with gr.Blocks() as interface:
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gr.Markdown(
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"""
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# DepthPro Rerun Demo
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[DepthPro](https://huggingface.co/apple/DepthPro) is a fast metric depth prediction model. Simply upload
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High resolution videos will be automatically resized to 256x256 pixels, to speed up the inference and visualize multiple frames.
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"""
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@rr.thread_local_stream("rerun_example_ml_depth_pro")
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def run_rerun(path_to_video):
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stream = rr.binary_stream()
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+
print("video path:", path_to_video)
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blueprint = rrb.Blueprint(
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rrb.Vertical(
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rr.send_blueprint(blueprint)
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yield stream.read()
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+
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video_asset = rr.AssetVideo(path=path_to_video)
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rr.log("world/video", video_asset, static=True)
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# Send automatically determined video frame timestamps.
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frame_timestamps_ns = video_asset.read_frame_timestamps_ns()
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cap = cv2.VideoCapture(path_to_video)
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num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
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print(f"Number of frames in the video: {num_frames}")
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for i in range(len(frame_timestamps_ns)):
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ret, frame = cap.read()
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if not ret:
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yield stream.read()
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with gr.Blocks() as interface:
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gr.Markdown(
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"""
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# DepthPro Rerun Demo
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+
[DepthPro](https://huggingface.co/apple/DepthPro) is a fast metric depth prediction model. Simply upload a video to visualize the depth predictions in real-time.
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High resolution videos will be automatically resized to 256x256 pixels, to speed up the inference and visualize multiple frames.
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"""
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pyproject.toml
CHANGED
@@ -3,14 +3,14 @@ name = "rerun-apple-depth-pro"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.
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dependencies = [
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"attrs>=24.2.0",
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"depth-pro",
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"gradio>=4.44.1",
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"gradio-rerun>=0.0.8",
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"rerun-sdk==0.19.0",
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"spaces>=0.30.
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]
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[tool.uv]
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.8"
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dependencies = [
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"attrs>=24.2.0",
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"depth-pro",
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"gradio>=4.44.1",
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"gradio-rerun>=0.0.8",
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"rerun-sdk==0.19.0",
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"spaces>=0.30.4",
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]
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[tool.uv]
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requirements.txt
CHANGED
@@ -80,7 +80,7 @@ semantic-version==2.10.0
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shellingham==1.5.4
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six==1.16.0
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sniffio==1.3.1
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spaces==0.30.
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stack-data==0.6.3
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starlette==0.38.6
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sympy==1.13.3
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shellingham==1.5.4
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six==1.16.0
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sniffio==1.3.1
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spaces==0.30.4
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stack-data==0.6.3
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starlette==0.38.6
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sympy==1.13.3
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uv.lock
CHANGED
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