Add model and demo

Procfile

@@ -0,0 +1 @@
+  web: sh setup.sh && streamlit run app.py

app.py

@@ -0,0 +1,91 @@
+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)

model-final.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6268c0b73c7bc3fefd3918d113fb74976f9780f4737bf6e4c088811a1a6872ec
+size 3867929

predict.py

@@ -0,0 +1,79 @@
+import sys
+sys.path.insert(0, './WordLM')
+
+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
+
+model=torch.load("model-final.pth")
+
+def colorize(img_path,print_img=True):
+    img=cv2.imread(img_path)
+    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
+
+# out=colorize("download.png")
+# print(out)

requirements.txt

@@ -0,0 +1,8 @@
+-f https://download.pytorch.org/whl/torch_stable.html
+torch==1.7.1+cpu
+torchvision==0.9.1+cpu
+numpy==1.18.5
+opencv-python-headless==4.4.0.46
+matplotlib==3.4.2
+scikit-image==0.18.1
+streamlit==0.81.1