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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Content/content_3.png filter=lfs diff=lfs merge=lfs -text
+Styles/style_3.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,85 @@
+'''
+Neural Style Transfer using TensorFlow's Pretrained Style Transfer Model 
+https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2 
+
+'''
+
+
+import gradio as gr
+import tensorflow as tf
+import tensorflow_hub as hub
+from PIL import Image
+import numpy as np
+import cv2
+import os
+
+
+
+model = hub.load("https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2")
+
+
+# source: https://stackoverflow.com/questions/4993082/how-can-i-sharpen-an-image-in-opencv 
+def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0): 
+    """Return a sharpened version of the image, using an unsharp mask."""
+    blurred = cv2.GaussianBlur(image, kernel_size, sigma)
+    sharpened = float(amount + 1) * image - float(amount) * blurred
+    sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
+    sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
+    sharpened = sharpened.round().astype(np.uint8)
+    if threshold > 0:
+        low_contrast_mask = np.absolute(image - blurred) < threshold
+        np.copyto(sharpened, image, where=low_contrast_mask)
+    return sharpened
+
+                     
+def style_transfer(content_img,style_image, style_weight = 1, content_weight = 1, style_blur=False):
+    content_img = unsharp_mask(content_img,amount=1)
+    content_img = tf.image.resize(tf.convert_to_tensor(content_img,tf.float32)[tf.newaxis,...] / 255.,(512,512),preserve_aspect_ratio=True)
+    style_img = tf.convert_to_tensor(style_image,tf.float32)[tf.newaxis,...] / 255.
+    if style_blur:
+        style_img=  tf.nn.avg_pool(style_img, [3,3], [1,1], "VALID")
+    style_img = tf.image.adjust_contrast(style_img, style_weight)        
+    content_img = tf.image.adjust_contrast(content_img,content_weight)     
+    content_img = tf.image.adjust_saturation(content_img, 2)        
+    content_img = tf.image.adjust_contrast(content_img,1.5)        
+    stylized_img = model(content_img, style_img)[0]
+    
+    return Image.fromarray(np.uint8(stylized_img[0]*255))
+
+
+
+
+title = "PixelFusion🧬"
+description = "Gradio Demo for Artistic Neural Style Transfer. To use it, simply upload a content image and a style image. [Learn More](https://www.tensorflow.org/tutorials/generative/style_transfer)."
+article = "</br><p style='text-align: center'><a href='https://github.com/0xsynapse' target='_blank'>GitHub</a></p> "
+
+
+content_input = gr.inputs.Image(label="Upload an image to which you want the style to be applied.",)
+style_input = gr.inputs.Image( label="Upload Style Image ",shape= (256,256), )
+style_slider = gr.inputs.Slider(0,2,label="Adjust Style Density" ,default=1,)
+content_slider = gr.inputs.Slider(1,5,label="Content Sharpness" ,default=1,)
+# style_checkbox = gr.Checkbox(value=False,label="Tune Style(experimental)")
+
+
+examples  = [
+                ["Content/content_1.jpg","Styles/style_1.jpg",1.20,1.70,"style_checkbox"], 
+                ["Content/content_2.jpg","Styles/style_2.jpg",0.91,2.54,"style_checkbox"],
+                ["Content/content_3.png","Styles/style_3.jpg",1.02,2.47,"style_checkbox"]
+            ]
+interface = gr.Interface(fn=style_transfer,
+                         inputs=[content_input,
+                                style_input,
+                                style_slider ,
+                                content_slider,
+                                # style_checkbox
+                                ],
+                         outputs=gr.outputs.Image(),
+                         title=title,
+                         description=description,
+                         article=article,
+                         examples=examples,
+                         enable_queue=True
+                         )
+    
+    
+interface.launch()

requirements.txt

@@ -0,0 +1,6 @@
+tensorflow==2.12.0
+tensorflow_hub==0.12.0
+gradio==3.1.4
+opencv-python==4.6.0.66
+protobuf==3.20.0
+