def gen_warp_params(w,
flip=False,
rotation_range=[-2, 2],
scale_range=[-0.5, 0.5],
tx_range=[-0.05, 0.05],
ty_range=[-0.05, 0.05],
rnd_state=None):
if rnd_state is None:
rnd_state = np.random
rw = None
if w < 64:
rw = w
w = 64
rotation = rnd_state.uniform(rotation_range[0], rotation_range[1])
scale = rnd_state.uniform(1 + scale_range[0], 1 + scale_range[1])
tx = rnd_state.uniform(tx_range[0], tx_range[1])
ty = rnd_state.uniform(ty_range[0], ty_range[1])
p_flip = flip and rnd_state.randint(10) < 4
#random warp by grid
cell_size = [w // (2**i) for i in range(1, 4)][rnd_state.randint(3)]
cell_count = w // cell_size + 1
grid_points = np.linspace(0, w, cell_count)
mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
mapy = mapx.T
mapx[1:-1, 1:-1] = mapx[1:-1, 1:-1] + randomex.random_normal(
size=(cell_count - 2, cell_count - 2)) * (cell_size * 0.24)
mapy[1:-1, 1:-1] = mapy[1:-1, 1:-1] + randomex.random_normal(
size=(cell_count - 2, cell_count - 2)) * (cell_size * 0.24)
half_cell_size = cell_size // 2
mapx = cv2.resize(mapx, (w + cell_size, ) *
2)[half_cell_size:-half_cell_size,
half_cell_size:-half_cell_size].astype(np.float32)
mapy = cv2.resize(mapy, (w + cell_size, ) *
2)[half_cell_size:-half_cell_size,
half_cell_size:-half_cell_size].astype(np.float32)
#random transform
random_transform_mat = cv2.getRotationMatrix2D((w // 2, w // 2), rotation,
scale)
random_transform_mat[:, 2] += (tx * w, ty * w)
params = dict()
params['mapx'] = mapx
params['mapy'] = mapy
params['rmat'] = random_transform_mat
u_mat = random_transform_mat.copy()
u_mat[:, 2] /= w
params['umat'] = u_mat
params['w'] = w
params['rw'] = rw
params['flip'] = p_flip
return params
def gen_warp_params(source,
flip,
rotation_range=[-10, 10],
scale_range=[-0.5, 0.5],
tx_range=[-0.05, 0.05],
ty_range=[-0.05, 0.05],
rnd_seed=None):
h, w, c = source.shape
if (h != w):
raise ValueError('gen_warp_params accepts only square images.')
if rnd_seed != None:
rnd_state = np.random.RandomState(rnd_seed)
else:
rnd_state = np.random
rotation = rnd_state.uniform(rotation_range[0], rotation_range[1])
scale = rnd_state.uniform(1 + scale_range[0], 1 + scale_range[1])
tx = rnd_state.uniform(tx_range[0], tx_range[1])
ty = rnd_state.uniform(ty_range[0], ty_range[1])
p_flip = flip and rnd_state.randint(10) < 4
#random warp by grid
cell_size = [w // (2**i) for i in range(1, 4)][rnd_state.randint(3)]
cell_count = w // cell_size + 1
grid_points = np.linspace(0, w, cell_count)
mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
mapy = mapx.T
mapx[1:-1, 1:-1] = mapx[1:-1, 1:-1] + randomex.random_normal(
size=(cell_count - 2, cell_count - 2)) * (cell_size * 0.24)
mapy[1:-1, 1:-1] = mapy[1:-1, 1:-1] + randomex.random_normal(
size=(cell_count - 2, cell_count - 2)) * (cell_size * 0.24)
half_cell_size = cell_size // 2
mapx = cv2.resize(mapx, (w + cell_size, ) *
2)[half_cell_size:-half_cell_size - 1,
half_cell_size:-half_cell_size - 1].astype(np.float32)
mapy = cv2.resize(mapy, (w + cell_size, ) *
2)[half_cell_size:-half_cell_size - 1,
half_cell_size:-half_cell_size - 1].astype(np.float32)
#random transform
random_transform_mat = cv2.getRotationMatrix2D((w // 2, w // 2), rotation,
scale)
random_transform_mat[:, 2] += (tx * w, ty * w)
params = dict()
params['mapx'] = mapx
params['mapy'] = mapy
params['rmat'] = random_transform_mat
params['w'] = w
params['flip'] = p_flip
return params
