app.py

import gradio as gr
import os
import cv2 
import shutil
import sys
from subprocess import call
import torch
import numpy as np
from skimage import color
import torchvision.transforms as transforms
from PIL import Image
import torch
import uuid
 
#os.system("pip install dlib")
os.system('bash setup.sh')

def lab2rgb(L, AB):
    """Convert an Lab tensor image to a RGB numpy output
    Parameters:
        L  (1-channel tensor array): L channel images (range: [-1, 1], torch tensor array)
        AB (2-channel tensor array):  ab channel images (range: [-1, 1], torch tensor array)
    Returns:
        rgb (RGB numpy image): rgb output images  (range: [0, 255], numpy array)
    """
    AB2 = AB * 110.0
    L2 = (L + 1.0) * 50.0
    Lab = torch.cat([L2, AB2], dim=1)
    Lab = Lab[0].data.cpu().float().numpy()
    Lab = np.transpose(Lab.astype(np.float64), (1, 2, 0))
    rgb = color.lab2rgb(Lab) * 255
    return rgb

def get_transform(model_name,params=None, grayscale=False, method=Image.BICUBIC):
    #params
    preprocess = 'resize'
    load_size = 256
    crop_size = 256
    transform_list = []
    if grayscale:
        transform_list.append(transforms.Grayscale(1))
    if model_name == "Pix2Pix Unet 256":
        osize = [load_size, load_size]
        transform_list.append(transforms.Resize(osize, method))
    # if 'crop' in preprocess:
    #     if params is None:
    #         transform_list.append(transforms.RandomCrop(crop_size))

    return transforms.Compose(transform_list)

def inferRestoration(img, model_name):
    #if model_name == "Pix2Pix":
    model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'pix2pixRestoration_unet256')
    transform_list = [
                transforms.ToTensor(),
                transforms.Resize([256,256], Image.BICUBIC),
                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                ]
    transform = transforms.Compose(transform_list)
    img = transform(img)
    img = torch.unsqueeze(img, 0)
    result = model(img)
    result = result[0].detach()
    result = (result +1)/2.0
    
    result = transforms.ToPILImage()(result)
    return result

def inferColorization(img):
    model_name = "Deoldify"
    model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'DeOldifyColorization')
    transform_list = [
              transforms.ToTensor(),
              transforms.Normalize((0.5,), (0.5,))
                ]
    transform = transforms.Compose(transform_list)
    #a = transforms.ToTensor()(a)
    img = img.convert('L')
    img = transform(img)
    img = torch.unsqueeze(img, 0)
    result = model(img)
    
    result = result[0].detach()
    result = (result +1)/2.0
    
    #img = transforms.Grayscale(3)(img)
    #img = transforms.ToTensor()(img)
    #img = torch.unsqueeze(img, 0)
    #result = model(img)
    #result = torch.clip(result, min=0, max=1)
    image_pil = transforms.ToPILImage()(result)
    return image_pil
    
    transform_seq = get_transform(model_name)
    img = transform_seq(img)
    # if model_name == "Pix2Pix Unet 256":
    #     img.resize((256,256))
    img = np.array(img)
    lab = color.rgb2lab(img).astype(np.float32)
    lab_t = transforms.ToTensor()(lab)
    A = lab_t[[0], ...] / 50.0 - 1.0
    B = lab_t[[1, 2], ...] / 110.0
    #data = {'A': A, 'B': B, 'A_paths': "", 'B_paths': ""}
    L = torch.unsqueeze(A, 0)
    #print(L.shape)
    ab = model(L)
    Lab = lab2rgb(L, ab).astype(np.uint8)
    image_pil = Image.fromarray(Lab)
    #image_pil.save('test.png')
    #print(Lab.shape)
    return image_pil
    
def colorizaition(image,model_name):
    image = Image.fromarray(image)
    result = inferColorization(image,model_name)
    return result


def run_cmd(command):
    try:
        call(command, shell=True)
    except KeyboardInterrupt:
        print("Process interrupted")
        sys.exit(1)

def run(image):
    uid = uuid.uuid4()
    
    if os.path.isdir(f"Temp{uid}"):
        shutil.rmtree(f"Temp{uid}")
    
    os.makedirs(f"Temp{uid}")
    os.makedirs(f"Temp{uid}/input")
    print(type(image))
    cv2.imwrite(f"Temp{uid}/input/input_img.png", image)

    command = ("python run.py --input_folder "
            + f"Temp{uid}/input"
            + " --output_folder "
            + f"Temp{uid}"
            + " --GPU "
            + "-1"
            + " --with_scratch")
    run_cmd(command)

    result_restoration = Image.open(f"Temp{uid}/final_output/input_img.png")
    shutil.rmtree(f"Temp{uid}")
    
    result_colorization = inferColorization(result_restoration)

    return result_colorization
def load_im(url):
    return url
 
    
with gr.Blocks() as app:
    im = gr.Image(label="Input Image")
    with gr.Row():
        im_u = gr.Textbox()
        lim_btn=gr.Button("Load")
    im_btn=gr.Button(label="Restore")
    out_im = gr.Image(label="Restored Image")

    #lim_btn(load_im,im_u,im)
    im_btn.click(run,[im,im_u],out_im)
app.queue(concurrency_count=100).launch(show_api=False)