'imagecolorization'

image_colorization.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from tensorflow.keras.utils import img_to_array, load_img
+from skimage.transform import resize
+from skimage.io import imsave, imshow
+from skimage.color import rgb2lab, lab2rgb
+
+def main_colorization():
+    
+    st.header("Image Colorization")
+    model = tf.keras.models.load_model("models/image-colorization.h5")
+
+    image_file = st.file_uploader(
+        "Upload image for testing", type=['jpeg', 'png', 'jpg', 'webp'])
+   
+    
+    if st.button("Process"):
+        image = Image.open(image_file)
+        h,w = 256,256
+                          
+        img1_color=[]
+
+        img1 = img_to_array(image)
+        img1 = resize(img1 ,(256,256))
+        img1_color.append(img1)
+        
+        img1_color = np.array(img1_color, dtype=float)
+        img1_color = rgb2lab(1.0/255*img1_color)[:,:,:,0]
+        img1_color = img1_color.reshape(img1_color.shape+(1,))
+        output1 = model.predict(img1_color)
+        output1 = output1*128
+
+        result = np.zeros((256, 256, 3))
+        result[:,:,0] = img1_color[0][:,:,0]
+        result[:,:,1:] = output1[0]
+        col1, col2 = st.columns([1,1])
+        image = image.resize((h,w))
+        
+        with col1:
+            st.text("Original Image")
+            st.image(image)
+        with col2 :
+            st.text("Colourful Image")
+            st.image(resize(lab2rgb(result),(256,256)))
+        
+    st.write("Image Colorization Notebook [link](https://github.com/bozkurtmert0/deep-learning-projects/blob/main/Image_Colorization.ipynb)")
+
+if __name__ == '__main__':
+    main_colorization()	   

models/image-colorization.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5970a02141fa41203f01c4e9df0b1c250779facf6be59513fbf93c0d7fecc1a0
+size 17807024

streamlit_app.py

@@ -5,6 +5,7 @@ from face_detec import main_face
 from movie_rec import main_movie
 from disaster_twet import main_twet
 from catvsdog import main_catvsdog
+from image_colorization import main_colorization
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 # -------------------------------------------------------------------------------------------------------------
@@ -17,7 +18,7 @@ def main():
     st.sidebar.write("")
     st.sidebar.subheader("Select an option")
     activities = [
-        "Cats vs Dogs", "Disaster Tweet Classification", "Movie Recommender", "Face Detection"]
+        "Cats vs Dogs", "Disaster Tweet Classification", "Movie Recommender", "Face Detection","Image Colorization"]
     choice = st.sidebar.selectbox("", activities)
 
 # ------------Cats Vs Dogs ----------------------------------------------------------------
@@ -34,6 +35,10 @@ def main():
 # -------------------------------------------------------------------------------
     if choice == "Face Detection": 
         main_face()
+#-----------------------------------------------------------------------
+    if choice == "Image Colorization":
+        main_colorization()
+
 
 if __name__ == '__main__':
     main()