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import gradio as gr
import cv2
import matplotlib
import numpy as np
import os
from PIL import Image
import spaces
import torch
import tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download
from models.PDFNet import build_model
import torch
import cv2
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import argparse
from args import get_args_parser
from torchvision.transforms.functional import normalize
import huggingface_hub
from DAM_V2.depth_anything_v2.dpt import DepthAnythingV2
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
#download {
height: 62px;
}
"""
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device('cpu')
parser = argparse.ArgumentParser('PDFNet Testing script', parents=[get_args_parser()])
args = parser.parse_args(args=[])
model,model_name = build_model(args)
model_path = hf_hub_download(repo_id="Tennineee/PDFNet",filename="PDFNet_Best.pth", repo_type="model")
model.load_state_dict(torch.load(model_path,map_location='cpu'),strict=False)
model = model.to(device).eval()
DAMV2_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
}
encoder = 'vitb' # or 'vits', 'vitb', 'vitl'
DAMV2 = DepthAnythingV2(**DAMV2_configs[encoder])
filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model")
state_dict = torch.load(filepath, map_location="cpu")
DAMV2.load_state_dict(state_dict)
DAMV2 = DAMV2.to(device).eval()
title = "# PDFNet"
description = """Official demo for **PDFNet**, here use DAMV2-small to generate depth map.
Please refer to our [paper](https://arxiv.org/abs/2503.06100) and [github](https://github.com/Tennine2077/PDFNet) for more details."""
class GOSNormalize(object):
def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
self.mean = mean
self.std = std
def __call__(self,image):
image = normalize(image,self.mean,self.std)
return image
transforms = GOSNormalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
def predict(image):
H,W = image.shape[:2]
depth = DAMV2.infer_image(image)
image = torch.nn.functional.interpolate(torch.from_numpy(image).permute(2,0,1)[None,...],size=[1024,1024],mode='bilinear',align_corners=True)[0]
depth = torch.nn.functional.interpolate(torch.from_numpy(depth)[None,None,...],size=[1024,1024],mode='bilinear',align_corners=True)
image = torch.divide(image,255.0)
depth = torch.divide(depth,255.0)
image = transforms(image).unsqueeze(0)
DIS_map = model.inference(image.to(device),depth.to(device))[0][0][0].cpu()
DIS_map = cv2.resize(np.array(DIS_map), (W,H))
# return cv2.resize(np.array(depth[0][0]), (W,H))
return DIS_map
with gr.Blocks(css=css) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### Dichotomous Image Segmentation demo")
with gr.Row():
input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
dis_image_slider = ImageSlider(label="Pedict View", elem_id='img-display-output', position=0.5)
submit = gr.Button(value="Compute")
raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",)
def on_submit(image):
original_image = image.copy()
DIS_map = predict(image)
DIS_map = (DIS_map - DIS_map.min()) / (DIS_map.max() - DIS_map.min()) * 255.0
raw_DIS_map = Image.fromarray(DIS_map.astype('uint16'))
tmp_raw_DIS_map = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
raw_DIS_map.save(tmp_raw_DIS_map.name)
return [[original_image, DIS_map.astype(np.uint8)], tmp_raw_DIS_map.name]
submit.click(on_submit, inputs=[input_image], outputs=[dis_image_slider, raw_file])
example_files = os.listdir('assets/examples')
example_files.sort()
example_files = [os.path.join('assets/examples', filename) for filename in example_files]
examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[dis_image_slider, raw_file], fn=on_submit)
if __name__ == '__main__':
demo.queue().launch(share=True)
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