Duplicate from tensorflow/esrgan-tf2

Co-authored-by: AK <[email protected]>

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README.md

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+---
+title: Esrgan Tf2
+emoji: ⚡
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false
+duplicated_from: tensorflow/esrgan-tf2
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import os
+import time
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+import gradio as gr
+
+# Declaring Constants
+SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
+
+def resize(width,img):
+  basewidth = width
+  img = Image.open(img)
+  wpercent = (basewidth/float(img.size[0]))
+  hsize = int((float(img.size[1])*float(wpercent)))
+  img = img.resize((basewidth,hsize), Image.ANTIALIAS)
+  img.save('somepic.jpg')
+  return 'somepic.jpg'
+  
+def preprocess_image(image_path):
+  """ Loads image from path and preprocesses to make it model ready
+      Args:
+        image_path: Path to the image file
+  """
+  hr_image = tf.image.decode_image(tf.io.read_file(image_path))
+  # If PNG, remove the alpha channel. The model only supports
+  # images with 3 color channels.
+  if hr_image.shape[-1] == 4:
+    hr_image = hr_image[...,:-1]
+  hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
+  hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
+  hr_image = tf.cast(hr_image, tf.float32)
+  return tf.expand_dims(hr_image, 0)
+
+  
+def plot_image(image):
+  """
+    Plots images from image tensors.
+    Args:
+      image: 3D image tensor. [height, width, channels].
+      title: Title to display in the plot.
+  """
+  image = np.asarray(image)
+  image = tf.clip_by_value(image, 0, 255)
+  image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
+  return image
+  
+model = hub.load(SAVED_MODEL_PATH)
+def inference(img):
+  resize_image = resize(256,img)
+  hr_image = preprocess_image(resize_image)
+  fake_image = model(hr_image)
+  fake_image = tf.squeeze(fake_image)
+  pil_image = plot_image(tf.squeeze(fake_image))
+  return pil_image
+  
+title="esrgan-tf2"
+description="Enhanced Super Resolution GAN for image super resolution. Produces x4 Super Resolution Image from images of {Height, Width} >=64. Works best on Bicubically downsampled images. (*This is because, the model is originally trained on Bicubically Downsampled DIV2K Dataset*)"
+article = "<p style='text-align: center'><a href='https://tfhub.dev/captain-pool/esrgan-tf2/1' target='_blank'>Tensorflow Hub</a></p>"
+examples=[['input.png']]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),"image",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)
+  

requirements.txt

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+Pillow
+numpy
+tensorflow
+tensorflow_hub
+matplotlib