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import sys
from typing import Dict
sys.path.insert(0, 'gradio-modified')
import gradio as gr
import numpy as np
import torch.nn as nn
from PIL import Image
import torch
if torch.cuda.is_available():
t = torch.cuda.get_device_properties(0).total_memory
r = torch.cuda.memory_reserved(0)
a = torch.cuda.memory_allocated(0)
f = t-a # free inside reserved
if f < 2**32:
device = 'cpu'
else:
device = 'cuda'
else:
device = 'cpu'
torch._C._jit_set_bailout_depth(0)
print('Use device:', device)
net = torch.jit.load(f'weights/pkp-v1.{device}.jit.pt')
class BaseColor(nn.Module):
def __init__(self):
super(BaseColor, self).__init__()
self.l_cent = 50.
self.l_norm = 100.
self.ab_norm = 110.
def normalize_l(self, in_l):
return (in_l-self.l_cent)/self.l_norm
def unnormalize_l(self, in_l):
return in_l*self.l_norm + self.l_cent
def normalize_ab(self, in_ab):
return in_ab/self.ab_norm
def unnormalize_ab(self, in_ab):
return in_ab*self.ab_norm
class ECCVGenerator(BaseColor):
def __init__(self, norm_layer=nn.BatchNorm2d):
super(ECCVGenerator, self).__init__()
model1=[nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=True),]
model1+=[nn.ReLU(True),]
model1+=[nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=True),]
model1+=[nn.ReLU(True),]
model1+=[norm_layer(64),]
model2=[nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=True),]
model2+=[nn.ReLU(True),]
model2+=[nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1, bias=True),]
model2+=[nn.ReLU(True),]
model2+=[norm_layer(128),]
model3=[nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[norm_layer(256),]
model4=[nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[norm_layer(512),]
model5=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[norm_layer(512),]
model6=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[norm_layer(512),]
model7=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[norm_layer(512),]
model8=[nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 313, kernel_size=1, stride=1, padding=0, bias=True),]
self.model1 = nn.Sequential(*model1)
self.model2 = nn.Sequential(*model2)
self.model3 = nn.Sequential(*model3)
self.model4 = nn.Sequential(*model4)
self.model5 = nn.Sequential(*model5)
self.model6 = nn.Sequential(*model6)
self.model7 = nn.Sequential(*model7)
self.model8 = nn.Sequential(*model8)
self.softmax = nn.Softmax(dim=1)
self.model_out = nn.Conv2d(313, 2, kernel_size=1, padding=0, dilation=1, stride=1, bias=False)
self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, input_l):
conv1_2 = self.model1(self.normalize_l(input_l))
conv2_2 = self.model2(conv1_2)
conv3_3 = self.model3(conv2_2)
conv4_3 = self.model4(conv3_3)
conv5_3 = self.model5(conv4_3)
conv6_3 = self.model6(conv5_3)
conv7_3 = self.model7(conv6_3)
conv8_3 = self.model8(conv7_3)
out_reg = self.model_out(self.softmax(conv8_3))
return self.unnormalize_ab(self.upsample4(out_reg))
# model_net = torch.load(f'weights/colorizer.pt')
model = ECCVGenerator()
model_net.load_state_dict(torch.load(f'weights/colorizer.pt'))
def resize_original(img: Image.Image):
if img is None:
return img
if isinstance(img, dict):
img = img["image"]
guide_img = img.convert('L')
w, h = guide_img.size
scale = 256 / min(guide_img.size)
guide_img = guide_img.resize([int(round(s*scale)) for s in guide_img.size], Image.Resampling.LANCZOS)
guide = np.asarray(guide_img)
h, w = guide.shape[-2:]
rows = int(np.ceil(h/64))*64
cols = int(np.ceil(w/64))*64
ph_1 = (rows-h) // 2
ph_2 = rows-h - (rows-h) // 2
pw_1 = (cols-w) // 2
pw_2 = cols-w - (cols-w) // 2
guide = np.pad(guide, ((ph_1, ph_2), (pw_1, pw_2)), mode='constant', constant_values=255)
guide_img = Image.fromarray(guide)
return gr.Image.update(value=guide_img.convert('RGBA')), guide_img.convert('RGBA')
def colorize(img: Dict[str, Image.Image], guide_img: Image.Image, seed: int, hint_mode: str):
if not isinstance(img, dict):
return gr.update(visible=True)
if hint_mode == "Roughly Hint":
hint_mode_int = 0
elif hint_mode == "Precisely Hint":
hint_mode_int = 0
guide_img = guide_img.convert('L')
hint_img = img["mask"].convert('RGBA') # I modified gradio to enable it upload colorful mask
guide = torch.from_numpy(np.asarray(guide_img))[None,None].float().to(device) / 255.0 * 2 - 1
hint = torch.from_numpy(np.asarray(hint_img)).permute(2,0,1)[None].float().to(device) / 255.0 * 2 - 1
hint_alpha = (hint[:,-1:] > 0.99).float()
hint = hint[:,:3] * hint_alpha - 2 * (1 - hint_alpha)
np.random.seed(int(seed))
b, c, h, w = hint.shape
h //= 8
w //= 8
noises = [torch.from_numpy(np.random.randn(b, c, h, w)).float().to(device) for _ in range(16+1)]
with torch.inference_mode():
sample = net(noises, guide, hint, hint_mode_int)
out = sample[0].cpu().numpy().transpose([1,2,0])
out = np.uint8(((out + 1) / 2 * 255).clip(0,255))
return Image.fromarray(out).convert('RGB')
def colorize2(img: Image.Image, model_option: str):
if not isinstance(img, dict):
return gr.update(visible=True)
if hint_mode == "Model 1":
model_int = 0
elif hint_mode == "Model 2":
model_int = 0
with torch.inference_mode():
out2 = model(input)
out = sample[0].cpu().numpy().transpose([1,2,0])
out = np.uint8(((out + 1) / 2 * 255).clip(0,255))
return Image.fromarray(out).convert('RGB')
with gr.Blocks() as demo:
gr.Markdown('''<center><h1>Image Colorization With Hint</h1></center>
<h2>Colorize your images/sketches with hint points.</h2>
<br />
''')
with gr.Row():
with gr.Column():
inp = gr.Image(
source="upload",
tool="sketch", # tool="color-sketch", # color-sketch upload image mixed with the original
type="pil",
label="Sketch",
interactive=True,
elem_id="sketch-canvas"
)
inp_store = gr.Image(
type="pil",
interactive=False
)
inp_store.visible = False
with gr.Column():
seed = gr.Slider(1, 2**32, step=1, label="Seed", interactive=True, randomize=True)
hint_mode = gr.Radio(["Roughly Hint", "Precisely Hint"], value="Roughly Hint", label="Hint Mode")
btn = gr.Button("Run")
with gr.Column():
output = gr.Image(type="pil", label="Output", interactive=False)
with gr.Row():
with gr.Column():
inp2 = gr.Image(
source="upload",
type="pil",
label="Sketch",
interactive=True
)
inp_store2 = gr.Image(
type="pil",
interactive=False
)
inp_store2.visible = False
with gr.Column():
# seed = gr.Slider(1, 2**32, step=1, label="Seed", interactive=True, randomize=True)
model_option = gr.Radio(["Model 1", "Model 2"], value="Model 1", label="Model 2")
btn2 = gr.Button("Run Colorization")
with gr.Column():
output2 = gr.Image(type="pil", label="Output2", interactive=False)
gr.Markdown('''
Upon uploading an image, kindly give color hints at specific points, and then run the model. Average inference time is about 52 seconds.<br />
''')
gr.Markdown('''Authors: <a href=\"https://www.linkedin.com/in/chakshu-dhannawat/">Chakshu Dhannawat</a>, <a href=\"https://www.linkedin.com/in/navlika-singh-963120204/">Navlika Singh</a>,<a href=\"https://www.linkedin.com/in/akshat-jain-103550201/"> Akshat Jain</a>''')
inp.upload(
resize_original,
inp,
[inp, inp_store],
)
btn.click(
colorize,
[inp, inp_store, seed, hint_mode],
output
)
btn2.click(
colorize2,
[inp, model_option],
output2
)
if __name__ == "__main__":
demo.launch()
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