def prepare(dataset, epochs, batch_size, input_shape, output_shape):
stream = ds.epochs(dataset.image_ids, epochs)
stream = ds.stream(
lambda x: (dataset.load_image(x), dataset.load_output(x)),
stream)
stream = ds.stream(ds.apply_to_x(ds.resize(input_shape)), stream)
stream = ds.stream(ds.apply_to_y(resize_all(output_shape)), stream)
stream = ds.bufferize(stream, size=20)
batch = ds.stream_batch(stream, size=batch_size, fun=ds.pack_elements)
batch = ds.stream(ds.apply_to_y(ds.apply_to_xn(
lambda x: ds.image2mask(x).reshape(x.shape + (1,)))), batch)
return batch
def get_dataset(dataset, epochs, input_shape, output_shape, colored=True):
shapes = ds.epochs(dataset, epochs)
shapes = ds.stream(
ds.apply_to_x(
ds.apply_to_xn(lambda x: ds.colorize(
cv2.imread(x[0]), x[1] if colored else [255, 0, 0]))), shapes)
# shapes = ds.stream(ds.apply_to_x(check), shapes)
shapes = ds.stream(ds.apply_to_x(sum), shapes)
shapes = ds.stream(ds.apply_to_x(lambda x: cv2.resize(x, input_shape[:2])),
shapes)
shapes = ds.stream(
ds.apply_to_y(lambda x: to_categorical(x, output_shape)), shapes)
shapes = ds.stream_batch(
shapes, lambda x, y: [np.array(x), np.array(y)], batch_size)
return shapes
def prepare(dataset, epochs, batch_size, input_shape):
stream = ds.epochs(dataset.image_ids, epochs)
stream = ds.stream(
lambda x: (dataset.load_image(x), dataset.load_output(x)), stream)
stream = ds.stream(ds.apply_to_xn(ds.resize(input_shape[:2])), stream)
# stream = ds.stream(ds.apply_to_x(check), stream)
stream = ds.bufferize(stream, size=20)
batch = ds.stream_batch(stream, size=batch_size)
batch = ds.stream(
ds.apply_to_y(lambda x: ds.mask2image(x).reshape(x.shape + (1, ))),
batch)
return batch
def prepare(dataset, epochs, batch_size, input_shape, output_shape, load_image,
resizer):
stream = ds.epochs(dataset.image_ids, epochs)
stream = ds.stream(
lambda x: (load_image(x), resizer(dataset.load_output(x))), stream)
def transpose(x, y):
y = np.array(y)
return np.array(x), y.reshape((1, ) + y.shape)
batch = ds.stream_batch(stream, size=batch_size, fun=transpose)
batch = ds.stream(
ds.apply_to_y(
ds.apply_to_xn(lambda x: ds.image2mask(x).reshape(x.shape +
(1, )))), batch)
return batch
def prepare(dataset, input_shape, output_shape):
stream = ds.epochs(dataset.image_ids, epochs=1)
stream = ds.stream(
lambda x: (dataset._img_filenames[x], (x, dataset.load_output(x))),
stream)
# stream = ds.stream(ds.apply_to_x(ds.resize(input_shape)), stream)
stream = ds.stream(ds.apply_to_y(resize_all(dataset, output_shape)),
stream)
stream = ds.bufferize(stream, size=10)
batch = ds.stream_batch(stream, size=10, fun=ds.pack_elements)
# batch = ds.stream(ds.apply_to_y(check), batch)
batch = ds.stream(
ds.apply_to_y(ds.apply_to_xn(lambda x: ds.image2mask(x))), batch)
return batch
