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utils.py
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utils.py
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import numpy as np
from menpo.shape import PointCloud
jaw_indices = np.arange(0, 17)
lbrow_indices = np.arange(17, 22)
rbrow_indices = np.arange(22, 27)
upper_nose_indices = np.arange(27, 31)
lower_nose_indices = np.arange(31, 36)
leye_indices = np.arange(36, 42)
reye_indices = np.arange(42, 48)
outer_mouth_indices = np.arange(48, 60)
inner_mouth_indices = np.arange(60, 68)
parts_68 = (jaw_indices, lbrow_indices, rbrow_indices, upper_nose_indices,
lower_nose_indices, leye_indices, reye_indices,
outer_mouth_indices, inner_mouth_indices)
mirrored_parts_68 = np.hstack([
jaw_indices[::-1], rbrow_indices[::-1], lbrow_indices[::-1],
upper_nose_indices, lower_nose_indices[::-1],
np.roll(reye_indices[::-1], 4), np.roll(leye_indices[::-1], 4),
np.roll(outer_mouth_indices[::-1], 7),
np.roll(inner_mouth_indices[::-1], 5)
])
def mirror_landmarks_68(lms, image_size):
return PointCloud(abs(np.array([0, image_size[1]]) - lms.as_vector(
).reshape(-1, 2))[mirrored_parts_68])
def mirror_image(im):
im = im.copy()
im.pixels = im.pixels[..., ::-1].copy()
for group in im.landmarks:
lms = im.landmarks[group].lms
if lms.points.shape[0] == 68:
im.landmarks[group] = mirror_landmarks_68(lms, im.shape)
return im
def mirror_image_bb(im):
im = im.copy()
im.pixels = im.pixels[..., ::-1]
im.landmarks['bounding_box'] = PointCloud(abs(np.array([0, im.shape[
1]]) - im.landmarks['bounding_box'].lms.points))
return im
def line(image, x0, y0, x1, y1, color):
steep = False
if x0 < 0 or x0 >= 400 or x1 < 0 or x1 >= 400 or y0 < 0 or y0 >= 400 or y1 < 0 or y1 >= 400:
return
if abs(x0 - x1) < abs(y0 - y1):
x0, y0 = y0, x0
x1, y1 = y1, x1
steep = True
if x0 > x1:
x0, x1 = x1, x0
y0, y1 = y1, y0
for x in range(int(x0), int(x1) + 1):
t = (x - x0) / float(x1 - x0)
y = y0 * (1 - t) + y1 * t
if steep:
image[x, int(y)] = color
else:
image[int(y), x] = color
def draw_landmarks(img, lms):
try:
img = img.copy()
for i, part in enumerate(parts_68[1:]):
circular = []
if i in (4, 5, 6, 7):
circular = [part[0]]
for p1, p2 in zip(part, list(part[1:]) + circular):
p1, p2 = lms[p1], lms[p2]
line(img, p2[1], p2[0], p1[1], p1[0], 1)
except:
pass
return img
def batch_draw_landmarks(imgs, lms):
return np.array([draw_landmarks(img, l) for img, l in zip(imgs, lms)])
def get_central_crop(images, box=(6, 6)):
_, w, h, _ = images.get_shape().as_list()
half_box = (box[0] / 2., box[1] / 2.)
a = slice(int((w // 2) - half_box[0]), int((w // 2) + half_box[0]))
b = slice(int((h // 2) - half_box[1]), int((h // 2) + half_box[1]))
return images[:, a, b, :]
def build_sampling_grid(patch_shape):
patch_shape = np.array(patch_shape)
patch_half_shape = np.require(np.round(patch_shape / 2), dtype=int)
start = -patch_half_shape
end = patch_half_shape
sampling_grid = np.mgrid[start[0]:end[0], start[1]:end[1]]
return sampling_grid.swapaxes(0, 2).swapaxes(0, 1)
default_sampling_grid = build_sampling_grid((30, 30))
def extract_patches(pixels, centres, sampling_grid=default_sampling_grid):
""" Extracts patches from an image.
Args:
pixels: a numpy array of dimensions [width, height, channels]
centres: a numpy array of dimensions [num_patches, 2]
sampling_grid: (patch_width, patch_height, 2)
Returns:
a numpy array [num_patches, width, height, channels]
"""
pixels = pixels.transpose(2, 0, 1)
max_x = pixels.shape[-2] - 1
max_y = pixels.shape[-1] - 1
patch_grid = (sampling_grid[None, :, :, :] + centres[:, None, None, :]
).astype('int32')
X = patch_grid[:, :, :, 0].clip(0, max_x)
Y = patch_grid[:, :, :, 1].clip(0, max_y)
return pixels[:, X, Y].transpose(1, 2, 3, 0)