app.py

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")
st.write('\n')
st.write('Find more info at: https://github.com/Pranav082001/Neural-Image-Colorizer or at https://medium.com/@pranav.kushare2001/colorize-your-black-and-white-photos-using-ai-4652a34e967.')

# Sidebar
st.sidebar.title("Upload Image")
file=st.sidebar.file_uploader("Please upload a Black and White image",type=["jpg","jpeg","png"])

if st.sidebar.button("Colorize image"):
    with st.spinner('Colorizing...'):
      file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
      opencv_image = cv2.imdecode(file_bytes, 1)
      im=colorize(opencv_image)
    st.text("Original")
    st.image(file)
    st.text("Colorized!!")
    st.image(im)