Image Upscale Ai (TensorFlow)

1. Image Upscaling Service using TensorFlow and Flask

from flask import Flask, jsonify, request
from flask_cors import CORS
import subprocess
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 base64
import io
from io import BytesIO
os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
        
app = Flask(__name__)
CORS(app)
        
SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
        
def preprocess_image(image_data):
    """ 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(image_data)
    # 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 save_image(image, filename):
    """
     Saves unscaled Tensor Images.
     Args:
       image: 3D image tensor. [height, width, channels]
       filename: Name of the file to save.
    """
        
    if not isinstance(image, Image.Image):
       image = tf.clip_by_value(image, 0, 255)
       image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
   #   image.save("%s.jpg" % filename)
   #   print("Saved as %s.jpg" % filename)
        
    return image
        
def plot_image(image, title=""):
    """ 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())
    plt.imshow(image)
    plt.axis("off")
    plt.title(title)
        
@app.route('/img', methods=['POST'])
def get_img():
    img = request.json['img']
    img_d = base64.b64decode(img)
    img_w = base64.urlsafe_b64encode(img_d)
        
    img_b = tf.io.decode_base64(img_w)
    hr_image = preprocess_image(img_b)
        
    # Plotting Original Resolution image
    # plot_image(tf.squeeze(hr_image), title="Original Image")
    # save_image(tf.squeeze(hr_image), filename="Original Image")
        
    model = hub.load(SAVED_MODEL_PATH)
        
    fake_image = model(hr_image)
    fake_image = tf.squeeze(fake_image)
        
    # Plotting Super Resolution Image
    plot_image(tf.squeeze(fake_image), title="Super Resolution")
    s_img = save_image(tf.squeeze(fake_image), filename="Super Resolution")
        
    buffered = BytesIO()
    s_img.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue())
        
    # print(img_str)
    return jsonify({'img': 'success', 'data' : img_str.decode('ascii')})
        
@app.route('/date', methods=['GET'])
def get_date():
    return jsonify({'date': '2020-01-01'})
        
if __name__ == '__main__':
    app.run()

This Python script creates a web service for upscaling images using TensorFlow and Flask. It sets up a Flask server that exposes two endpoints: /img for processing images and /date for retrieving the server's current date. The /img endpoint accepts a POST request containing a base64 encoded image. It then decodes the image, preprocesses it to make it ready for the upscaling model, and utilizes a pre-trained ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) model loaded from TensorFlow Hub to upscale the image. After upscaling, it converts the image back to base64 encoding and returns the resulting super-resolution image. The script utilizes libraries like TensorFlow Hub, PIL (Python Imaging Library), and Flask for image processing, handling HTTP requests, and serving the model predictions. This image upscaling service can be deployed to provide high-quality image upscaling capabilities in web applications.