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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)
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