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app.py
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import numpy as np
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import torch
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import sys
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import os
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from fastai.vision.all import *
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import gradio as gr
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"
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"""Create from list of `fnames` in `path`s with `label_func`."""
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datablock = DataBlock(blocks=(ImageBlock(cls=PILImage), ImageBlock(cls=PILImageBW)),
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get_y=label_func,
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splitter=RandomSplitter(valid_pct, seed=seed),
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item_tfms=item_transforms,
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batch_tfms=batch_transforms)
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res = cls.from_dblock(datablock, filenames, path=path, **kwargs)
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return res
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def get_y_fn(x):
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y = str(x.absolute()).replace('.jpg', '_depth.png')
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y = Path(y)
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return y
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def create_data(data_path):
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fnames = get_files(data_path/'train', extensions='.jpg')
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data = ImageImageDataLoaders.from_label_func(
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data_path/'train', seed=42, bs=4, num_workers=0, filenames=fnames, label_func=get_y_fn)
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return data
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data = create_data(Path('src/data/processed'))
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learner = unet_learner(data, resnet34, metrics=rmse,
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wd=1e-2, n_out=3, loss_func=MSELossFlat(), path='src/')
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learner.load('model')
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def gen(input_img):
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return PILImageBW.create((learner.predict(input_img))[0]).convert('L')
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################### Gradio Web APP ################################
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title = "SavtaDepth WebApp"
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description = """
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<p>
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<center>
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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.
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<img src="https://huggingface.co/spaces/kingabzpro/savtadepth/resolve/main/examples/cover.png" alt="logo" width="250"/>
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</center>
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</p>
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"""
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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>"
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examples = [
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["examples/00008.jpg"],
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["examples/00045.jpg"],
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]
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favicon = "examples/favicon.ico"
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thumbnail = "examples/SavtaDepth.png"
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def main():
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iface = gr.Interface(
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gen,
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gr.inputs.Image(shape=(640, 480), type='numpy'),
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"image",
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title=title,
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flagging_options=["incorrect", "worst", "ambiguous"],
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allow_flagging="manual",
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flagging_callback=hf_writer,
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description=description,
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article=article,
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examples=examples,
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theme="peach",
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allow_screenshot=True
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)
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iface.launch(enable_queue=True)
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# enable_queue=True,auth=("admin", "pass1234")
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if __name__ == '__main__':
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main()
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from layers import BilinearUpSampling2D
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from tensorflow.keras.models import load_model
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from utils import load_images, predict
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import matplotlib.pyplot as plt
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import numpy as np
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import gradio as gr
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from huggingface_hub import from_pretrained_keras
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custom_objects = {'BilinearUpSampling2D': BilinearUpSampling2D,
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'depth_loss_function': None}
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print('Loading model...')
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model = from_pretrained_keras(
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"keras-io/monocular-depth-estimation", custom_objects=custom_objects, compile=False)
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print('Successfully loaded model...')
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examples = ['examples/00015_colors.png',
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'examples/00084_colors.png', 'examples/00033_colors.png']
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def infer(image):
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inputs = load_images([image])
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outputs = predict(model, inputs)
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plasma = plt.get_cmap('plasma')
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rescaled = outputs[0][:, :, 0]
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rescaled = rescaled - np.min(rescaled)
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rescaled = rescaled / np.max(rescaled)
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image_out = plasma(rescaled)[:, :, :3]
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return image_out
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iface = gr.Interface(
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fn=infer,
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title="Monocular Depth Estimation",
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description="Keras Implementation of Unet architecture with Densenet201 backbone for estimating the depth of image 📏",
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inputs=[gr.inputs.Image(label="image", type="numpy", shape=(640, 480))],
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outputs="image",
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article="Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. The ideal based on the keras example from <a href=\"https://keras.io/examples/vision/depth_estimation/\">Victor Basu</a>",
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examples=examples, cache_examples=True).launch(debug=True)
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