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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) | |