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import torch | |
import os | |
from fastai.vision.all import * | |
import gradio as gr | |
############### HF ########################### | |
HF_TOKEN = os.getenv('HF_TOKEN') | |
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "savtadepth-flags-V2") | |
############## DVC ################################ | |
PROD_MODEL_PATH = "src/models" | |
TRAIN_PATH = "src/data/processed/train/bathroom" | |
TEST_PATH = "src/data/processed/test/bathroom" | |
if 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') | |
def gen(input_img): | |
return PILImageBW.create((learner.predict(input_img))[0]).convert('L') | |
################### Gradio Web APP ################################ | |
title = "SavtaDepth WebApp" | |
description = """ | |
<p> | |
<center> | |
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. | |
<img src="https://huggingface.co/spaces/kingabzpro/savtadepth/resolve/main/examples/cover.png" alt="logo" width="250"/> | |
</center> | |
</p> | |
""" | |
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><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/savtadepth' alt='visitor badge'></center></p>" | |
examples = [ | |
["examples/00008.jpg"], | |
["examples/00045.jpg"], | |
] | |
favicon = "examples/favicon.ico" | |
thumbnail = "examples/SavtaDepth.png" | |
def main(): | |
iface = gr.Interface( | |
gen, | |
gr.inputs.Image(shape=(640,480),type='numpy'), | |
"image", | |
title = title, | |
flagging_options=["incorrect", "worst","ambiguous"], | |
allow_flagging = "manual", | |
flagging_callback=hf_writer, | |
description = description, | |
article = article, | |
examples = examples, | |
theme ="peach", | |
allow_screenshot=True | |
) | |
iface.launch(enable_queue=True) | |
# enable_queue=True,auth=("admin", "pass1234") | |
if __name__ == '__main__': | |
main() |