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| import numpy as np | |
| import torch | |
| import sys | |
| import os | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| ############## DVC ################################ | |
| PROD_MODEL_PATH = "src/models" | |
| TRAIN_PATH = "src/data/processed/train/bathroom" | |
| TEST_PATH = ""src/data/processed/test/bathroom"" | |
| if "DYNO" in os.environ and os.path.isdir(".dvc"): | |
| print("Running DVC") | |
| os.system("dvc config cache.type copy") | |
| os.system("dvc config core.no_scm true") | |
| if os.system(f"dvc pull {PROD_MODEL_PATH} {TRAIN_PATH } {TEST_PATH }") != 0: | |
| exit("dvc pull failed") | |
| os.system("rm -r .dvc .apt/usr/lib/dvc") | |
| ############## Inference ############################## | |
| class ImageImageDataLoaders(DataLoaders): | |
| """Basic wrapper around several `DataLoader`s with factory methods for Image to Image problems""" | |
| def from_label_func(cls, path, filenames, label_func, valid_pct=0.2, seed=None, item_transforms=None, | |
| batch_transforms=None, **kwargs): | |
| """Create from list of `fnames` in `path`s with `label_func`.""" | |
| datablock = DataBlock(blocks=(ImageBlock(cls=PILImage), ImageBlock(cls=PILImageBW)), | |
| get_y=label_func, | |
| splitter=RandomSplitter(valid_pct, seed=seed), | |
| item_tfms=item_transforms, | |
| batch_tfms=batch_transforms) | |
| res = cls.from_dblock(datablock, filenames, path=path, **kwargs) | |
| return res | |
| def get_y_fn(x): | |
| y = str(x.absolute()).replace('.jpg', '_depth.png') | |
| y = Path(y) | |
| return y | |
| def create_data(data_path): | |
| fnames = get_files(data_path/'train', extensions='.jpg') | |
| data = ImageImageDataLoaders.from_label_func(data_path/'train', seed=42, bs=4, num_workers=0, filenames=fnames, label_func=get_y_fn) | |
| return data | |
| data = create_data(Path('src/data/processed')) | |
| learner = unet_learner(data,resnet34, metrics=rmse, wd=1e-2, n_out=3, loss_func=MSELossFlat(), path='src/') | |
| learner.load('model') | |
| ################### Gradio Web APP ################################ | |
| 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): | |
| return PILImageBW.create((learner.predict(input_img))[0]).convert('L') | |
| def main(): | |
| iface = gr.Interface(sepia, gr.inputs.Image(shape=(640,480),type='numpy'), "image", title = title, description = description, article = article, examples = examples,theme ="peach",thumbnail=thumbnail) | |
| iface.launch(favicon_path=favicon,server_name="0.0.0.0",server_port=8080) | |
| # enable_queue=True,auth=("admin", "pass1234") | |
| if __name__ == '__main__': | |
| main() |