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| import numpy as np | |
| import gradio as gr | |
| title = "SavtaDepth WebApp" | |
| description = "Savta Depth is a collaborative Open Source Data Science project for monocular depth estimation - Turn 2d photos into 3d photos. To test the model and code please check out the link bellow." | |
| article = "<p style='text-align: center'><a href='https://dagshub.com/OperationSavta/SavtaDepth' target='_blank'>SavtaDepth Project from OperationSavta</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1XU4DgQ217_hUMU1dllppeQNw3pTRlHy1?usp=sharing' target='_blank'>Google Colab Demo</a></p></center></p>" | |
| examples = [ | |
| ["examples/00008.jpg"], | |
| ["examples/00045.jpg"], | |
| ] | |
| favicon = "examples/favicon.ico" | |
| thumbnail = "examples/SavtaDepth.png" | |
| def sepia(input_img): | |
| sepia_filter = np.array( | |
| [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]] | |
| ) | |
| sepia_img = input_img.dot(sepia_filter.T) | |
| sepia_img /= sepia_img.max() | |
| return sepia_img | |
| def main(): | |
| iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image", title = title, description = description, article = article, examples = examples,theme ="peach",thumbnail=thumbnail) | |
| iface.launch(favicon_path=favicon,auth=("admin", "dagshubsota123"),server_name="0.0.0.0") | |
| # enable_queue=True,auth=("admin", "pass1234") | |
| if __name__ == '__main__': | |
| main() |