PYTHON

image processing for GC

from time import time
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import layers
from tensorflow.keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from skimage import io
import os
import glob

# path to your dataset

data_dir = '/content/drive/MyDrive/Manifold_cnn_v2/'
oring_cls = ["Complete","Missing"]

batch_size = 32
img_height = 400
img_width = 400

AUTOTUNE = tf.data.AUTOTUNE

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir+"training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir+"test",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)

#plot the first 10 images with their labels
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[labels[i]])
    plt.axis("off")