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import subprocess | |
subprocess.run('sh setup.sh', shell=True) | |
print("Installed the dependencies!") | |
from typing import Tuple | |
import dnnlib | |
from PIL import Image | |
import numpy as np | |
import torch | |
import legacy | |
import paddlehub as hub | |
import cv2 | |
u2net = hub.Module(name='U2Net') | |
# gradio app imports | |
import gradio as gr | |
from torchvision.transforms import ToTensor, ToPILImage | |
image_to_tensor = ToTensor() | |
tensor_to_image = ToPILImage() | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
class_idx = None | |
truncation_psi = 0.1 | |
def create_model(network_pkl): | |
print('Loading networks from "%s"...' % network_pkl) | |
with dnnlib.util.open_url(network_pkl) as f: | |
G = legacy.load_network_pkl(f)['G_ema'] # type: ignore | |
G = G.eval().to(device) | |
netG_params = sum(p.numel() for p in G.parameters()) | |
print("Generator Params: {} M".format(netG_params/1e6)) | |
return G | |
def fcf_inpaint(G, org_img, erased_img, mask): | |
label = torch.zeros([1, G.c_dim], device=device) | |
if G.c_dim != 0: | |
if class_idx is None: | |
ValueError("class_idx can't be None.") | |
label[:, class_idx] = 1 | |
else: | |
if class_idx is not None: | |
print ('warn: --class=lbl ignored when running on an unconditional network') | |
pred_img = G(img=torch.cat([0.5 - mask, erased_img], dim=1), c=label, truncation_psi=truncation_psi, noise_mode='const') | |
comp_img = mask.to(device) * pred_img + (1 - mask).to(device) * org_img.to(device) | |
return comp_img | |
def show_images(img): | |
""" Display a batch of images inline. """ | |
return Image.fromarray(img) | |
def denorm(img): | |
img = np.asarray(img[0].cpu(), dtype=np.float32).transpose(1, 2, 0) | |
img = (img +1) * 127.5 | |
img = np.rint(img).clip(0, 255).astype(np.uint8) | |
return img | |
def pil_to_numpy(pil_img: Image) -> Tuple[torch.Tensor, torch.Tensor]: | |
img = np.array(pil_img) | |
return torch.from_numpy(img)[None].permute(0, 3, 1, 2).float() / 127.5 - 1 | |
def inpaint(input_img, mask, option): | |
width, height = input_img.size | |
if option == "Automatic": | |
result = u2net.Segmentation( | |
images=[cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)], | |
paths=None, | |
batch_size=1, | |
input_size=320, | |
output_dir='output', | |
visualization=True) | |
mask = Image.fromarray(result[0]['mask']) | |
else: | |
mask = mask.resize((width,height)) | |
input_img = input_img.resize(512, 512) | |
mask = mask.resize(512, 512) | |
mask = mask.convert('L') | |
mask = np.array(mask) / 255. | |
mask_tensor = torch.from_numpy(mask).to(torch.float32) | |
mask_tensor = mask_tensor.unsqueeze(0) | |
mask_tensor = mask_tensor.unsqueeze(0).to(device) | |
rgb = input_img.convert('RGB') | |
rgb = np.array(rgb) | |
rgb = rgb.transpose(2,0,1) | |
rgb = torch.from_numpy(rgb.astype(np.float32)).unsqueeze(0) | |
rgb = (rgb.to(torch.float32) / 127.5 - 1).to(device) | |
rgb_erased = rgb.clone() | |
rgb_erased = rgb_erased * (1 - mask_tensor) # erase rgb | |
rgb_erased = rgb_erased.to(torch.float32) | |
# model = create_model("models/places_512.pkl") | |
# comp_img = fcf_inpaint(G=model, org_img=rgb.to(torch.float32), erased_img=rgb_erased.to(torch.float32), mask=mask_tensor.to(torch.float32)) | |
rgb_erased = denorm(rgb_erased) | |
# comp_img = denorm(comp_img) | |
return show_images(rgb_erased), show_images(rgb_erased) | |
gradio_inputs = [gr.inputs.Image(type='pil', | |
tool=None, | |
label="Input Image"), | |
gr.inputs.Image(type='pil',source="canvas", label="Mask", invert_colors=True), | |
gr.inputs.Radio(choices=["Automatic", "Manual"], type="value", default="Manual", label="Masking Choice") | |
# gr.inputs.Image(type='pil', | |
# tool=None, | |
# label="Mask")] | |
] | |
# gradio_outputs = [gr.outputs.Image(label='Auto-Detected Mask (From drawn black pixels)')] | |
gradio_outputs = [gr.outputs.Image(label='Image with Hole'), | |
gr.outputs.Image(label='Inpainted Image')] | |
examples = [['test_512/person512.png', 'test_512/mask_auto.png', 'Automatic'], | |
['test_512/a_org.png', 'test_512/a_mask.png', 'Manual'], | |
['test_512/c_org.png', 'test_512/b_mask.png', 'Manual'], | |
['test_512/b_org.png', 'test_512/c_mask.png', 'Manual'], | |
['test_512/d_org.png', 'test_512/d_mask.png', 'Manual'], | |
['test_512/e_org.png', 'test_512/e_mask.png', 'Manual'], | |
['test_512/f_org.png', 'test_512/f_mask.png', 'Manual'], | |
['test_512/g_org.png', 'test_512/g_mask.png', 'Manual'], | |
['test_512/h_org.png', 'test_512/h_mask.png', 'Manual'], | |
['test_512/i_org.png', 'test_512/i_mask.png', 'Manual']] | |
title = "FcF-Inpainting" | |
description = "[Note: Queue time may take upto 20 seconds! The image and mask are resized to 512x512 before inpainting.] To use FcF-Inpainting: \n \ | |
(1) Upload an Image; \n \ | |
(2) Draw (Manual) a Mask on the White Canvas or Generate a mask using U2Net by selecting the Automatic option; \n \ | |
(3) Click on Submit and witness the MAGIC! 🪄 ✨ ✨" | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10741' target='_blank'> Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand</a> | <a href='https://github.com/SHI-Labs/FcF-Inpainting' target='_blank'>Github Repo</a></p>" | |
css = ".image-preview {height: 32rem; width: auto;} .output-image {height: 32rem; width: auto;} .panel-buttons { display: flex; flex-direction: row;}" | |
iface = gr.Interface(fn=inpaint, inputs=gradio_inputs, | |
outputs=gradio_outputs, | |
css=css, | |
layout="vertical", | |
examples_per_page=5, | |
thumbnail="fcf_gan.png", | |
allow_flagging="never", | |
examples=examples, title=title, | |
description=description, article=article) | |
iface.launch(enable_queue=True, | |
share=True, server_name="0.0.0.0") |