imgmanipulation.py

import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

def load_image(path_to_img):
  max_dim = 512
  img = Image.open(path_to_img)
  long = max(img.size)
  scale = max_dim/long
  img = img.resize((round(img.size[0]*scale), round(img.size[1]*scale)), Image.ANTIALIAS)
  img = tf.keras.preprocessing.image.img_to_array(img)
  img = np.expand_dims(img, axis=0)
  return img

def display(img, title=None):
  out = np.squeeze(img, axis=0)
  out = out.astype('uint8')
  plt.imshow(out)
  if title is not None:
    plt.title(title)
  plt.imshow(out)

def load_and_process_img(path_to_img):
  img = load_image(path_to_img)
  img = tf.keras.applications.vgg19.preprocess_input(img)
  return img

#VGG networks are trained on image with each channel normalized by mean = [103.939, 116.779, 123.68]and with channels BGR.
def deprocess_img(processed_img):
  x = processed_img.copy()
  if len(x.shape) == 4:
    x = np.squeeze(x, 0)
  assert len(x.shape) == 3
  
  x[:, :, 0] += 103.939
  x[:, :, 1] += 116.779
  x[:, :, 2] += 123.68
  x = x[:, :, ::-1]

  x = np.clip(x, 0, 255).astype('uint8')
  return x