Universe

Shadow/Reflection_T Computer Vision Dataset

bySpyneTask:
Object Detection
License:CC BY 4.0110 views2 downloads
See all 300 imagesShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sample showing ReflectionShadow/Reflection_T Shadow sample showing Reflection, ShadowShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sample showing Reflection, ShadowShadow/Reflection_T Shadow sample showing ReflectionShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sample showing Reflection, ShadowShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sampleShadow/Reflection_T Shadow sample showing Reflection

About Shadow/Reflection_T Dataset

Here are a few use cases for this project:

  1. Advanced Photography Assistance: The model can be used by photography apps and software to assess the quality of lighting and reflections in a given shot, providing enhancement suggestions to the user.

  2. Intelligent Lighting Design: Architects and interior designers can use this model to conduct real time simulations of how light interacts with different colors and materials, thus optimizing the lighting design of a space or building.

  3. Augmented Reality Enhancements: AR app developers can use this model to create more realistic AR experiences by accurately adding or adjusting reflections and shadows of virtual objects.

  4. Surveillance and Security: The model can be implemented in security systems to check the authenticity of a scene, by identifying normal shadows and reflections and detecting any anomalies.

  5. Autopilot Car Systems: The model could be used to improve the precision of autonomous vehicle guidance systems, by helping better interpret road conditions using shadow and reflection observations.

Use Free Reflection and Shadow Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Reflection, Shadow"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ shadow-reflection_t_dataset,
  title = { Shadow/Reflection_T Dataset },
  type = { Open Source Dataset },
  author = { Spyne },
  howpublished = { \url{ https://universe.roboflow.com/spyne-odwyj/shadow-reflection_t } },
  url = { https://universe.roboflow.com/spyne-odwyj/shadow-reflection_t },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2022 },
  month = { jun },
  note = { visited on 2026-07-29 },
}

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