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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"""
@classmethod
@delegates(DataLoaders.from_dblock)
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></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() |