In this chapter, we will be working with the Facades dataset, which is available at the following link:

http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz.

This dataset contains facade labels and ground truth facade images. A facade is generally the front side of a building, and facade labels are architectural labels of a facade image. We will learn more about facades after we download the dataset. Perform the following commands to download and extract the dataset:

1. Download the dataset by executing the following commands:

# Before downloading the dataset navigate to data directory
cd data

# Download the dataset
wget http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz

2. After downloading the dataset, extract the dataset using the following command:

tar -xvzf facades.tar.gz

The file structure of the downloaded dataset is as follows:

The dataset is divided into training, testing, and validation datasets. Let's work on extracting the images.

Perform the following steps to load the dataset:

1. Start by creating a list of .h5 files containing facade labels and another list of .h5 containing facade images as follows:

data_dir_path = os.path.join(data_dir, data_type)

# Get all .h5 files containing training images
facade_photos_h5 = [f for f in os.listdir(os.path.join(data_dir_path, 'images')) if '.h5' in f]
facade_labels_h5 = [f for f in os.listdir(os.path.join(data_dir_path, 'facades')) if '.h5' in f]

2. Next, iterate (loop) over the lists to load each image sequentially:

final_facade_photos = None
final_facade_labels = None

for index in range(len(facade_photos_h5)):

All code, following this step, will be inside the preceding for loop. 

3. Next, load the h5 files containing images and retrieve the Numpy NDArrays of the actual images:

facade_photos_path = data_dir_path + '/images/' +   
                     facade_photos_h5[index]
facade_labels_path = data_dir_path + '/facades/' +  
                     facade_labels_h5[index]

facade_photos = h5py.File(facade_photos_path, 'r')
facade_labels = h5py.File(facade_labels_path, 'r')

4. Next, resize the images to the desired image size as follows:

# Resize and normalize images
num_photos = facade_photos['data'].shape[0]
num_labels = facade_labels['data'].shape[0]

all_facades_photos = np.array(facade_photos['data'], dtype=np.float32)
all_facades_photos = all_facades_photos.reshape((num_photos, img_width, img_height, 1)) / 255.0

all_facades_labels = np.array(facade_labels['data'], dtype=np.float32)
all_facades_labels = all_facades_labels.reshape((num_labels, img_width, img_height, 1)) / 255.0

5. Next, add the resized images to the final NDArrays:

if final_facade_photos is not None and final_facade_labels is not None:
final_facade_photos = np.concatenate([final_facade_photos, 
                                   all_facades_photos], axis=0)
final_facade_labels = np.concatenate([final_facade_labels, all_facades_labels], axis=0)
    else:
        final_facade_photos = all_facades_photos
        final_facade_labels = all_facades_labels

The entire code to load and resize images looks as follows:

def load_dataset(data_dir, data_type, img_width, img_height):
    data_dir_path = os.path.join(data_dir, data_type)

    # Get all .h5 files containing training images
    facade_photos_h5 = [f for f in os.listdir(os.path.join(data_dir_path, 'images')) if '.h5' in f]
    facade_labels_h5 = [f for f in os.listdir(os.path.join(data_dir_path, 'facades')) if '.h5' in f]

    final_facade_photos = None
    final_facade_labels = None

    for index in range(len(facade_photos_h5)):
        facade_photos_path = data_dir_path + '/images/' + facade_photos_h5[index]
        facade_labels_path = data_dir_path + '/facades/' + facade_labels_h5[index]

        facade_photos = h5py.File(facade_photos_path, 'r')
        facade_labels = h5py.File(facade_labels_path, 'r')

        # Resize and normalize images
        num_photos = facade_photos['data'].shape[0]
        num_labels = facade_labels['data'].shape[0]

        all_facades_photos = np.array(facade_photos['data'], dtype=np.float32)
        all_facades_photos = all_facades_photos.reshape((num_photos, img_width, img_height, 1)) / 255.0

        all_facades_labels = np.array(facade_labels['data'], dtype=np.float32)
        all_facades_labels = all_facades_labels.reshape((num_labels, img_width, img_height, 1)) / 255.0

        if final_facade_photos is not None and final_facade_labels is not None:
            final_facade_photos = np.concatenate([final_facade_photos, all_facades_photos], axis=0)
            final_facade_labels = np.concatenate([final_facade_labels, all_facades_labels], axis=0)
        else:
            final_facade_photos = all_facades_photos
            final_facade_labels = all_facades_labels

    return final_facade_photos, final_facade_labels

The previous function will load images from the .h5 files inside the training, testing, and validation directories.