[FIXED] How to display images from tf.image.crop_and_resize

Issue

I have to apply tf.image.crop_and_resize on my images and want to generate 5 boxes from each image. I have written the below code which works fine

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np

# Load the pre-trained Xception model to be used as the base encoder.
xception = keras.applications.Xception(
    include_top=False, weights="imagenet", pooling="avg"
)
# Set the trainability of the base encoder.
for layer in xception.layers:
  layer.trainable = False
# Receive the images as inputs.
inputs = layers.Input(shape=(299, 299, 3), name="image_input")

input ='/content/1.png'

input = tf.keras.preprocessing.image.load_img(input,target_size=(299,299,3))
image = tf.expand_dims(np.asarray(input)/255, axis=0)
BATCH_SIZE = 1
NUM_BOXES = 5
IMAGE_HEIGHT = 256
IMAGE_WIDTH = 256
CHANNELS = 3
CROP_SIZE = (24, 24)


boxes = tf.random.uniform(shape=(NUM_BOXES, 4))
box_indices = tf.random.uniform(shape=(NUM_BOXES,), minval=0, maxval=BATCH_SIZE, dtype=tf.int32)


output = tf.image.crop_and_resize(image, boxes, box_indices, CROP_SIZE)
xception_input = tf.keras.applications.xception.preprocess_input(output)

The above code works fine however when I want to display these boxes I run below code

for i in range(5):

  # define subplot
  plt.subplot(330 + 1 + i)

  # generate batch of images
  batch = xception_input.next()

  # convert to unsigned integers for viewing
  image = batch[0].astype('uint8')

  image = np.reshape(24,24,3)

  # plot raw pixel data
  plt.imshow(image)

#show the figure
plt.show()

But it generates this error AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 'next'.

Solution

You have to use [i] instead of .next()

And there is also problem with converting it to uint8 (but it doesn't need to reshape)

for i in range(5):

  plt.subplot(331 + i)

  tensor = xception_input[i]
  #print(tensor)

  tensor = tensor*255
  image = np.array(tensor, dtype=np.uint8)
  #print(image)

  plt.imshow(image)

or use for to get items

for i, tensor in enumerate(xception_input):
  #print(tensor)

  plt.subplot(331 + i)

  tensor = tensor*255
  image = np.array(tensor, dtype=np.uint8)
  #print(image)

  plt.imshow(image)

I don't know what your code should do but this gives me empty images because tensor has values like -0.9 and it convert it all to 0

Answered By - furas