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