def load_data(width_height=192,
num_points=1,
input_directory='data/inputs/',
mask_directory='data/masks/'):
def simplify_masks(masks):
# Merge mask classes
BACKGROUND = np.array([0, 0, 170])
WALL = np.array([0, 0, 255])
DOOR = np.array([0, 170, 255])
WINDOW = np.array([0, 85, 255])
new_list = []
for picture in masks:
# new_picture = deepcopy(picture)
width, height, _ = picture.shape
new_picture = np.zeros((width, height, 4))
for x in range(width):
for y in range(height):
rgb = picture[x][y]
if np.allclose(rgb, WALL):
new_picture[x][y][1] = 1
elif np.allclose(rgb, DOOR):
new_picture[x][y][2] = 1
elif np.allclose(rgb, WINDOW):
new_rgb = WINDOW
new_picture[x][y][3] = 1
else:
new_picture[x][y][0] = 1
new_list.append(new_picture)
return new_list
print('Preparing input dataset...')
inputs = chicken.get_images(input_directory)
inputs = inputs + chicken.fliplr(inputs)
inputs = chicken.resize_and_smart_crop_square(inputs, width_height)
inputs = inputs[:num_points]
inputs = np.array(inputs)
print('Preparing mask dataset...')
masks = chicken.get_images(mask_directory)
masks = masks + chicken.fliplr(masks)
masks = chicken.resize_and_smart_crop_square(masks, width_height)
masks = masks[:num_points]
masks = simplify_masks(masks)
masks = np.array(masks)
print('Data loaded')
return inputs, masks
def load_data(self):
print('Loading Data...')
images = chicken.get_images('data64/')
data2d = chicken.grayscale_to_2d(images)
data2d = np.reshape(data2d, (-1, 64, 64, 1))
data2d = np.array(data2d, dtype=np.float32)
data2d = data2d / 255
print('shape:', data2d.shape)
# print('sample image:', data2d[0])
print('Data Loaded.')
return data2d
def check_largest_dimensions(input_directory='data/masks/'):
inputs = chicken.get_images(input_directory)
largest_w = 0
largest_h = 0
for image in inputs:
shape = image.shape
width = shape[0]
height = shape[1]
largest_w = max(width, largest_w)
largest_h = max(height, largest_h)
print(largest_w, largest_h)