commit

image_data_loader.py

@@ -0,0 +1,111 @@
+import os
+import tensorflow as tf
+import glob
+from sklearn.preprocessing import LabelEncoder
+def process_files(absolute_path):
+    image = []
+    label = []
+
+    for instance in absolute_path:
+        image.append(instance)
+        temp = instance.split('\\')[-2]
+        label.append(temp)
+
+    return image, label
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+def read_image(image, label):
+    image = tf.io.read_file(image)
+    image = tf.image.decode_jpeg(image)
+    image = tf.image.resize(image, size=(256, 256))
+    return image, tf.cast(label, tf.float32)
+
+
+
+if __name__ == "__main__":
+
+    AUTOTUNE=tf.data.AUTOTUNE
+    BUFFERSIZE= 800
+
+    # origin_url = 'https://www.kaggle.com/datasets/imsparsh/flowers-dataset/download?datasetVersionNumber=2'
+    # path = tf.keras.utils.get_file('flower_dataset', origin= origin_url, untar=True)
+    # step 1: Retrive the path of the images
+    root_dir = r'C:\Users\Yash PC\PycharmProjects\tftuts\data\train'
+
+    files = []
+
+    # for instance in os.listdir(root_dir):
+    #     for flower in os.listdir((os.path.join(root_dir, instance))):
+    #         files.append(os.path.join(root_dir, instance, flower))
+
+    files = glob.glob(os.path.join(root_dir, '*', '*.jpg'), recursive= True)
+    print(len(files))
+
+
+
+    image_path, label_path = process_files(files)
+
+    print(image_path[:2])
+    print(label_path[:2])
+
+    #Label encoding
+    encoder = LabelEncoder()
+    labels = encoder.fit_transform(label_path)
+    print(labels[:5])
+
+    # validation set
+
+    dataset = tf.data.Dataset.from_tensor_slices((image_path, labels))
+    train_size = int(dataset.cardinality().numpy()*0.8)
+    temp = dataset.shuffle(BUFFERSIZE)
+    train = temp.take(train_size)
+    test = temp.skip(train_size)
+
+
+    val_size = int(train.cardinality().numpy() * 0.2)
+    temp = train.shuffle(BUFFERSIZE)
+    val = temp.take(val_size)
+    train = temp.skip(val_size)
+
+    # print(train.cardinality(), test.cardinality(),
+    #       val.cardinality())
+
+    train = train.map(read_image, num_parallel_calls=AUTOTUNE)
+    test = test.map(read_image, num_parallel_calls=AUTOTUNE)
+    val = val.map(read_image, num_parallel_calls=AUTOTUNE)
+
+    train = train.cache().shuffle(BUFFERSIZE).batch(32).prefetch(AUTOTUNE)
+    test = test.cache().shuffle(BUFFERSIZE).batch(32).prefetch(AUTOTUNE)
+    val = val.cache().shuffle(BUFFERSIZE).batch(32).prefetch(AUTOTUNE)
+
+
+    #
+    """'train = train.map(lambda image, label: (tf.divide(image, 255.), label))
+    test = test.map(lambda image, label: (tf.divide(image, 255.), label))
+    val = val.map(lambda image, label: (tf.divide(image, 255.), label))"""
+
+
+    # Data Augmentation
+
+    augmentation = tf.keras.Sequential([
+        tf.keras.layers.RandomRotation(factor=0.2),
+        tf.keras.layers.RandomContrast(factor=0.2),
+    ])
+
+    rescale = tf.keras.layers.Rescaling(1/ 255.)
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(256, 256, 3)),
+        rescale,
+        tf.keras.layers.Conv2D(16, 3, activation='relu'),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(5)
+    ])
+
+    print(model.summary())
+
+
+
+
+
+

tf_dataset.py

@@ -0,0 +1,24 @@
+import os
+import tensorflow as tf
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+if __name__ == "__main__":
+
+    root_dir = r'C:\Users\Yash PC\PycharmProjects\tftuts\data\train'
+
+    train = tf.keras.utils.image_dataset_from_directory(
+        root_dir,
+        seed=1234,
+        validation_split=0.2,
+        subset='training'
+    )
+
+    val = tf.keras.utils.image_dataset_from_directory(
+        root_dir,
+        seed=1234,
+        validation_split=0.2,
+        subset='validation'
+    )
+
+    for img, label in train.take(1):
+        print(img.shape, label.shape)