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import PIL
import torch
import torch.nn as nn
import cv2
from skimage.color import lab2rgb, rgb2lab, rgb2gray
from skimage import io
import matplotlib.pyplot as plt
import numpy as np
class ColorizationNet(nn.Module):
def __init__(self, input_size=128):
super(ColorizationNet, self).__init__()
MIDLEVEL_FEATURE_SIZE = 128
resnet=models.resnet18(pretrained=True)
resnet.conv1.weight=nn.Parameter(resnet.conv1.weight.sum(dim=1).unsqueeze(1))
self.midlevel_resnet =nn.Sequential(*list(resnet.children())[0:6])
self.upsample = nn.Sequential(
nn.Conv2d(MIDLEVEL_FEATURE_SIZE, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Upsample(scale_factor=2),
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32, 2, kernel_size=3, stride=1, padding=1),
nn.Upsample(scale_factor=2)
)
def forward(self, input):
# Pass input through ResNet-gray to extract features
midlevel_features = self.midlevel_resnet(input)
# Upsample to get colors
output = self.upsample(midlevel_features)
return output
def show_output(grayscale_input, ab_input):
'''Show/save rgb image from grayscale and ab channels
Input save_path in the form {'grayscale': '/path/', 'colorized': '/path/'}'''
color_image = torch.cat((grayscale_input, ab_input), 0).detach().numpy() # combine channels
color_image = color_image.transpose((1, 2, 0)) # rescale for matplotlib
color_image[:, :, 0:1] = color_image[:, :, 0:1] * 100
color_image[:, :, 1:3] = color_image[:, :, 1:3] * 255 - 128
color_image = lab2rgb(color_image.astype(np.float64))
grayscale_input = grayscale_input.squeeze().numpy()
# plt.imshow(grayscale_input)
# plt.imshow(color_image)
return color_image
def colorize(img,print_img=True):
# img=cv2.imread(img)
img=cv2.resize(img,(224,224))
grayscale_input= torch.Tensor(rgb2gray(img))
ab_input=model(grayscale_input.unsqueeze(0).unsqueeze(0)).squeeze(0)
predicted=show_output(grayscale_input.unsqueeze(0), ab_input)
if print_img:
plt.imshow(predicted)
return predicted
# device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
# torch.load with map_location=torch.device('cpu')
model=torch.load("model-final.pth",map_location ='cpu')
import streamlit as st
st.title("Image Colorizer")
file=st.file_uploader("Please upload the B/W image",type=["jpg","jpeg","png"])
print(file)
if file is None:
st.text("Please Upload an image")
else:
file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
opencv_image = cv2.imdecode(file_bytes, 1)
im=colorize(opencv_image)
st.image(im)
st.text("Colorized!!")
# st.image(file)
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