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# python3.7
"""Utility functions for image editing."""
import numpy as np
import cv2
import torch
__all__ = ['to_numpy', 'linear_interpolate', 'make_transform',
'get_ind', 'mask2image']
def to_numpy(data):
"""Converts the input data to `numpy.ndarray`."""
if isinstance(data, (int, float)):
return np.array(data)
if isinstance(data, np.ndarray):
return data
if isinstance(data, torch.Tensor):
return data.detach().cpu().numpy()
raise TypeError(f'Not supported data type `{type(data)}` for '
f'converting to `numpy.ndarray`!')
def linear_interpolate(latent_code,
boundary,
layer_index=None,
start_distance=-10.0,
end_distance=10.0,
steps=21):
"""Interpolate between the latent code and boundary."""
assert (len(latent_code.shape) == 3 and len(boundary.shape) == 3 and
latent_code.shape[0] == 1 and boundary.shape[0] == 1 and
latent_code.shape[1] == boundary.shape[1])
linspace = np.linspace(start_distance, end_distance, steps)
linspace = linspace.reshape([-1, 1, 1]).astype(np.float32)
inter_code = linspace * boundary
is_manipulatable = np.zeros(inter_code.shape, dtype=bool)
is_manipulatable[:, layer_index, :] = True
mani_code = np.where(is_manipulatable, latent_code+inter_code, latent_code)
return mani_code
def make_transform(tx, ty, angle):
"""Transform the input feature maps with given
coordinates and rotation angle.
cos(theta) -sin(theta) tx
sin(theta) cos(theta) ty
0 0 1
"""
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = tx
m[1][0] = -s
m[1][1] = c
m[1][2] = ty
return m
def get_ind(seg_mask, label):
"""Get the index of the masked and unmasked region."""
mask = np.where(seg_mask == label,
np.ones_like(seg_mask),
np.zeros_like(seg_mask))
f_ind = np.where(mask == 1)
b_ind = np.where((1 - mask) == 1)
return f_ind, b_ind, mask
def mask2image(image, mask, r=3, g=255, b=118):
"""Show the mask on the given image."""
assert image.shape[0] == image.shape[1]
r_c = np.ones([256, 256, 1]) * r
g_c = np.ones([256, 256, 1]) * g
b_c = np.ones([256, 256, 1]) * b
img1 = np.concatenate([r_c, g_c, b_c], axis=2).astype(np.uint8)
mask = np.expand_dims(mask, axis=2).astype(np.uint8)
img1 = img1 * mask
image = cv2.addWeighted(image, 0.4, img1, 0.6, 0)
mask_i = np.tile(mask, [1, 1, 3]) * 255
return image, mask_i
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