Add trainings stuff

Plots/CreatePlot_showImages.py

@@ -0,0 +1,84 @@
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+
+import sys
+sys.path.append("..")
+from Autoencoder import Autoencoder
+from Training import prepare_data, getRGB
+
+def main():
+
+    labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
+    imagenet_labels = np.array(open(labels_path).read().splitlines())
+
+    data_dir = '/home/timwitte/Downloads/'
+    write_dir = '../imagenet'
+
+    # Construct a tf.data.Dataset
+    download_config = tfds.download.DownloadConfig(
+                        extract_dir=os.path.join(write_dir, 'extracted'),
+                        manual_dir=data_dir
+                    )
+    download_and_prepare_kwargs = {
+        'download_dir': os.path.join(write_dir, 'downloaded'),
+        'download_config': download_config,
+    }
+
+    train_dataset, test_dataset= tfds.load('imagenet2012', 
+                data_dir=os.path.join(write_dir, 'data'),         
+                split=['train', 'validation'], 
+                shuffle_files=True, 
+                download=True,
+                as_supervised=True,
+                download_and_prepare_kwargs=download_and_prepare_kwargs)
+    
+    test_dataset = test_dataset.take(32).apply(prepare_data)
+
+    autoencoder = Autoencoder()
+
+    autoencoder.build((1, 256, 256, 1)) # need a batch size
+    autoencoder.load_weights("../saved_models/trainied_weights_epoch_12")
+    autoencoder.summary()
+    
+    autoencoder.encoder.summary()
+    autoencoder.decoder.summary()
+
+    for img_L, img_AB_orginal in test_dataset.take(1):
+
+        img_AB_reconstructed = autoencoder(img_L)
+
+        img_rgb_orginal = getRGB(img_L, img_AB_orginal)
+        img_rgb_reconstructed = getRGB(img_L, img_AB_reconstructed)
+        
+        NUM_IMGS = 5
+        fig, axs = plt.subplots(NUM_IMGS, 3)
+
+        axs[0, 0].set_title("Input", fontsize=30)
+        axs[0, 1].set_title("Output", fontsize=30)
+        axs[0, 2].set_title("Ground Truth", fontsize=30)
+
+        for i in range(NUM_IMGS):
+            
+            axs[i, 0].imshow(img_L[i], cmap="gray")
+            axs[i, 0].set_axis_off()
+
+            axs[i, 1].imshow(img_rgb_reconstructed[i])
+            axs[i, 1].set_axis_off()
+
+            axs[i, 2].imshow(img_rgb_orginal[i])
+            axs[i, 2].set_axis_off()
+        
+        plt.tight_layout()
+
+        fig.set_size_inches(15, 25)
+        fig.savefig("ColoredImages.png")
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("KeyboardInterrupt received")