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 get_dataset(filenames, epochs, colorMap, batch_size, output_shape):
shapes = load_shapes(ds.epochs(filenames, epochs), colorMap)
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, base_path):
lm = load_masks(dataset, base_path)
stream = ds.epochs(dataset.image_ids, epochs)
stream = ds.stream(lambda x: (dataset.load_image(x), lm(x)), stream)
stream = ds.stream(ds.apply_to_x(ds.resize(input_shape)), stream)
batch = ds.stream_batch(stream,
size=batch_size,
fun=lambda x, y: (np.array(x), fix_y(y)))
return batch
def get_dataset(filenames, relations, epochs, colorMap, batch_size,
input_shape, output_shape):
shapes = load_shapes(ds.epochs(filenames, epochs), relations, colorMap)
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: np.array(
[to_categorical(xv, output_shape) for xv in x]).sum(axis=0)),
shapes)
shapes = ds.stream_batch(
shapes,
lambda x, y: [np.array(x), generate_y(np.array(y))], batch_size)
return shapes
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(filenames, relations, epochs, colorMap, batch_size,
input_shape, output_shape):
shapes = load_shapes(ds.epochs(filenames, epochs), relations, colorMap)
shapes = ds.stream(ds.apply_to_x(lambda x: cv2.resize(x, input_shape[:2])),
shapes)
# def fu(x):
# ba = list(np.array(
# [to_categorical(xv, output_shape) for xv in x]
# ).sum(axis=0))
# import ipdb; ipdb.set_trace()
# return ba
shapes = ds.stream(
ds.apply_to_y(lambda x: np.array(
[to_categorical(xv, output_shape) for xv in x]).sum(axis=0)),
shapes)
shapes = ds.stream(ds.apply_to_y(generate_neg), shapes)
shapes = ds.stream_batch(
shapes, lambda x, y: [np.array(x), np.array(y)], batch_size)
return shapes