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Update app.py
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app.py
CHANGED
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import gradio as gr
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import
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import
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import
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""
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""
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osize = [load_size, load_size]
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transform_list.append(transforms.Resize(osize, method))
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# if 'crop' in preprocess:
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# if params is None:
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# transform_list.append(transforms.RandomCrop(crop_size))
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return transforms.Compose(transform_list)
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def inferRestoration(img, model_name):
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#if model_name == "Pix2Pix":
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model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'pix2pixRestoration_unet256')
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transform_list = [
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transforms.ToTensor(),
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transforms.Resize([256,256], Image.BICUBIC),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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]
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transform = transforms.Compose(transform_list)
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img = transform(img)
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img = torch.unsqueeze(img, 0)
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result = model(img)
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result = result[0].detach()
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result = (result +1)/2.0
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result = transforms.ToPILImage()(result)
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return result
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def inferColorization(img):
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model_name = "Deoldify"
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model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'DeOldifyColorization')
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transform_list = [
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,))
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]
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transform = transforms.Compose(transform_list)
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#a = transforms.ToTensor()(a)
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img = img.convert('L')
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img = transform(img)
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img = torch.unsqueeze(img, 0)
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result = model(img)
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result = result[0].detach()
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result = (result +1)/2.0
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#img = transforms.Grayscale(3)(img)
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#img = transforms.ToTensor()(img)
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#img = torch.unsqueeze(img, 0)
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#result = model(img)
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#result = torch.clip(result, min=0, max=1)
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image_pil = transforms.ToPILImage()(result)
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return image_pil
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transform_seq = get_transform(model_name)
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img = transform_seq(img)
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# if model_name == "Pix2Pix Unet 256":
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# img.resize((256,256))
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img = np.array(img)
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lab = color.rgb2lab(img).astype(np.float32)
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lab_t = transforms.ToTensor()(lab)
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A = lab_t[[0], ...] / 50.0 - 1.0
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B = lab_t[[1, 2], ...] / 110.0
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#data = {'A': A, 'B': B, 'A_paths': "", 'B_paths': ""}
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L = torch.unsqueeze(A, 0)
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#print(L.shape)
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ab = model(L)
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Lab = lab2rgb(L, ab).astype(np.uint8)
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image_pil = Image.fromarray(Lab)
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#image_pil.save('test.png')
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#print(Lab.shape)
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return image_pil
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def colorizaition(image,model_name):
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image = Image.fromarray(image)
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result = inferColorization(image,model_name)
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return result
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def run_cmd(command):
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try:
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call(command, shell=True)
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except KeyboardInterrupt:
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print("Process interrupted")
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sys.exit(1)
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def run(image):
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uid = uuid.uuid4()
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if os.path.isdir(f"Temp{uid}"):
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shutil.rmtree(f"Temp{uid}")
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os.makedirs(f"Temp{uid}")
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os.makedirs(f"Temp{uid}/input")
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print(type(image))
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cv2.imwrite(f"Temp{uid}/input/input_img.png", image)
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command = ("python run.py --input_folder "
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+ f"Temp{uid}/input"
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+ " --output_folder "
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+ f"Temp{uid}"
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+ " --GPU "
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+ "-1"
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+ " --with_scratch")
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run_cmd(command)
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result_restoration = Image.open(f"Temp{uid}/final_output/input_img.png")
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shutil.rmtree(f"Temp{uid}")
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result_colorization = inferColorization(result_restoration)
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return result_colorization
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def load_im(url):
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return url
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with gr.Blocks() as app:
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im = gr.Image(label="Input Image")
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with gr.Row():
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import gradio as gr
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from PIL import Image
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import requests
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import random
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r = requests.get(f'https://huggingface.co/spaces/xp3857/bin/raw/main/css.css')
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css = r.text
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name2 = "xp3857/Image_Restoration_Colorization"
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spaces=[
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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gr.Interface.load(f"spaces/{name2}"),
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]
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def colorize(input):
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if input !=None:
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rn = random.randint(0, 19)
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space=spaces[rn]
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result=space(input)
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out1 = gr.Pil.update(value=result,visible=True)
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out2 = gr.Accordion.update(label="Original Image",open=False)
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else:
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out1 = None
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out2 = None
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pass
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return out1, out2
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with gr.Blocks(css=css) as myface:
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with gr.Row():
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gr.Column()
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with gr.Column():
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with gr.Accordion(label="Input Image",open=True) as og:
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in_win=gr.Pil(label="Input", type="filepath", interactive=True)
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out_win=gr.Pil(label="Output",visible=False)
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gr.Column()
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in_win.change(rem_bg,in_win,[out_win,og])
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myface.queue(concurrency_count=120)
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myface.launch()
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