def _build_test_dataset(filepaths,
input_shape=(256, 256, 3),
norm=255,
single_channel=False):
"""
Load a set of images into memory from file and mask the centers to
use as a test set.
:filepaths: list of strings pointing to image files
:input_shape: dimensions of input images
:norm: normalizing value for images
Returns
img_arr, mask
"""
img_arr = np.stack([
_load_img(f,
norm=norm,
num_channels=input_shape[2],
resize=input_shape[:2]) for f in filepaths
])
mask = _make_test_mask(*input_shape)
mask = np.stack([mask for _ in range(img_arr.shape[0])])
return img_arr, mask
def test_load_single_channel_img(test_single_channel_png_path):
img_arr = _load_img(test_single_channel_png_path,
resize=(32, 32),
num_channels=1)
assert isinstance(img_arr, np.ndarray)
assert len(img_arr.shape) == 3
assert img_arr.shape[2] == 1
def _make_sprite(imfiles, norm=1, num_channels=3, resize=(50,50)):
"""
Input a 4D tensor, output a sprite image.
assumes your pictures are all the
same size and square
"""
num_sprites = len(imfiles)
gridsize = np.int(np.ceil(np.sqrt(num_sprites)))
sprite_arr = np.stack([
_load_img(f, norm, num_channels, resize)
for f in imfiles
])#.astype(np.uint8)
output = np.zeros((resize[0]*gridsize, resize[1]*gridsize, 3),
dtype=np.uint8)
for i in range(num_sprites):
col = i // gridsize
row = i % gridsize
output[resize[0]*col:resize[0]*(col+1),
resize[1]*row:resize[1]*(row+1),:] = sprite_arr[i,:,:,:3]
img = Image.fromarray(output)
img = img.resize((resize[0]*gridsize, resize[1]*gridsize))
return img
def _load_img(self, f):
"""
Wrapper for patchwork._util._load_img
:f: string; path to file
"""
return _load_img(f,
norm=self._norm,
num_channels=self._num_channels,
resize=self._imshape)
def _multiple_augplot(filepaths, aug_params=True, num_resamps=5,
norm=255, num_channels=3, resize=None):
"""
"""
num_files = len(filepaths)
img = _load_img(filepaths[0], norm=norm, num_channels=num_channels, resize=resize)
aug_func = augment_function(img.shape[:2], aug_params)
plt.figure()
for i in range(num_files):
img = _load_img(filepaths[i], norm=norm, num_channels=num_channels, resize=resize)
plt.subplot(num_files+1, num_resamps+1, 1+i*(num_resamps+1))
plt.imshow(img)
plt.axis(False)
if i == 0: plt.title("original")
for j in range(num_resamps):
plt.subplot(num_files+1, num_resamps+1, j+2+i*(num_resamps+1))
plt.imshow(aug_func(img).numpy())
plt.axis(False)
def _single_augplot(filepath, aug_params=True, norm=255, num_channels=3, resize=None):
"""
Input a path to an image and an augmentation function; sample
15 augmentations and display using matplotlib.
"""
img = _load_img(filepath, norm=norm, num_channels=num_channels, resize=resize)
aug_func = augment_function(img.shape[:2], aug_params)
plt.figure()
plt.subplot(4,4,1)
plt.imshow(img)
plt.axis(False)
plt.title("original")
for i in range(2,17):
plt.subplot(4,4,i)
plt.imshow(aug_func(img).numpy())
plt.axis(False)
def test_load_and_stack_single_channel_img(test_single_channel_png_path):
img_arr = _load_img(test_single_channel_png_path, num_channels=3)
assert isinstance(img_arr, np.ndarray)
assert len(img_arr.shape) == 3
assert img_arr.shape[2] == 3
def test_load_img_on_png_with_resize(test_png_path):
img_arr = _load_img(test_png_path, resize=(71, 71))
assert isinstance(img_arr, np.ndarray)
assert len(img_arr.shape) == 3
assert img_arr.shape == (71, 71, 3)
def test_load_img_on_jpg(test_jpg_path):
img_arr = _load_img(test_jpg_path)
assert isinstance(img_arr, np.ndarray)
assert len(img_arr.shape) == 3
assert img_arr.shape[2] == 3
def test_load_img_on_geotif(test_geotif_path):
img_arr = _load_img(test_geotif_path, num_channels=4, norm=1)
assert isinstance(img_arr, np.ndarray)
assert len(img_arr.shape) == 3
assert img_arr.shape[2] == 4
