app.py

from typing import Tuple
import dnnlib
from PIL import Image
import numpy as np
import torch
import legacy
import cv2
import paddlehub as hub

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))

    mask = mask.convert('L')
    mask = np.array(mask) / 255.
    mask = cv2.resize(mask,
            (512, 512), interpolation=cv2.INTER_NEAREST)
    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 = cv2.resize(rgb,
            (512, 512), interpolation=cv2.INTER_AREA)
    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")