Spaces:
Build error
Build error
File size: 4,772 Bytes
f7ebd0a a65f78b f7ebd0a a65f78b f7ebd0a a65f78b f7ebd0a a65f78b f7ebd0a a65f78b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
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
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,Restoration_mode, Colorizaition_mode):
Restoration_mode == "BOPBTL"
if os.path.isdir("Temp"):
shutil.rmtree("Temp")
os.makedirs("Temp")
os.makedirs("Temp/input")
print(type(image))
cv2.imwrite("Temp/input/input_img.png", image)
command = ("python run.py --input_folder "
+ "Temp/input"
+ " --output_folder "
+ "Temp"
+ " --GPU "
+ "-1"
+ " --with_scratch")
run_cmd(command)
result_restoration = Image.open("Temp/final_output/input_img.png")
shutil.rmtree("Temp")
result_colorization = inferColorization(result_restoration,Colorizaition_mode)
return result_colorization
iface = gr.Interface(run,
[gr.inputs.Image(),gr.inputs.Radio(["BOPBTL", "Pix2Pix"]),gr.inputs.Radio(["Deoldify"])],
outputs="image").launch(debug=True,share=False) |