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label.py
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import numpy as np
import os
from PIL import Image, ImageDraw
from tqdm import tqdm
import cfg
def point_inside_of_quad(px, py, quad_xy_list, p_min, p_max):
if (p_min[0] <= px <= p_max[0]) and (p_min[1] <= py <= p_max[1]):
xy_list = np.zeros((4, 2))
xy_list[:3, :] = quad_xy_list[1:4, :] - quad_xy_list[:3, :]
xy_list[3] = quad_xy_list[0, :] - quad_xy_list[3, :]
yx_list = np.zeros((4, 2))
yx_list[:, :] = quad_xy_list[:, -1:-3:-1]
a = xy_list * ([py, px] - yx_list)
b = a[:, 0] - a[:, 1]
if np.amin(b) >= 0 or np.amax(b) <= 0:
return True
else:
return False
else:
return False
def point_inside_of_nth_quad(px, py, xy_list, shrink_1, long_edge):
nth = -1
vs = [[[0, 0, 3, 3, 0], [1, 1, 2, 2, 1]],
[[0, 0, 1, 1, 0], [2, 2, 3, 3, 2]]]
for ith in range(2):
quad_xy_list = np.concatenate((
np.reshape(xy_list[vs[long_edge][ith][0]], (1, 2)),
np.reshape(shrink_1[vs[long_edge][ith][1]], (1, 2)),
np.reshape(shrink_1[vs[long_edge][ith][2]], (1, 2)),
np.reshape(xy_list[vs[long_edge][ith][3]], (1, 2))), axis=0)
p_min = np.amin(quad_xy_list, axis=0)
p_max = np.amax(quad_xy_list, axis=0)
if point_inside_of_quad(px, py, quad_xy_list, p_min, p_max):
if nth == -1:
nth = ith
else:
nth = -1
break
return nth
def shrink(xy_list, ratio=cfg.shrink_ratio):
if ratio == 0.0:
return xy_list, xy_list
diff_1to3 = xy_list[:3, :] - xy_list[1:4, :]
diff_4 = xy_list[3:4, :] - xy_list[0:1, :]
diff = np.concatenate((diff_1to3, diff_4), axis=0)
dis = np.sqrt(np.sum(np.square(diff), axis=-1))
# determine which are long or short edges
long_edge = int(np.argmax(np.sum(np.reshape(dis, (2, 2)), axis=0)))
short_edge = 1 - long_edge
# cal r length array
r = [np.minimum(dis[i], dis[(i + 1) % 4]) for i in range(4)]
# cal theta array
diff_abs = np.abs(diff)
diff_abs[:, 0] += cfg.epsilon
theta = np.arctan(diff_abs[:, 1] / diff_abs[:, 0])
# shrink two long edges
temp_new_xy_list = np.copy(xy_list)
shrink_edge(xy_list, temp_new_xy_list, long_edge, r, theta, ratio)
shrink_edge(xy_list, temp_new_xy_list, long_edge + 2, r, theta, ratio)
# shrink two short edges
new_xy_list = np.copy(temp_new_xy_list)
shrink_edge(temp_new_xy_list, new_xy_list, short_edge, r, theta, ratio)
shrink_edge(temp_new_xy_list, new_xy_list, short_edge + 2, r, theta, ratio)
return temp_new_xy_list, new_xy_list, long_edge
def shrink_edge(xy_list, new_xy_list, edge, r, theta, ratio=cfg.shrink_ratio):
if ratio == 0.0:
return
start_point = edge
end_point = (edge + 1) % 4
long_start_sign_x = np.sign(
xy_list[end_point, 0] - xy_list[start_point, 0])
new_xy_list[start_point, 0] = \
xy_list[start_point, 0] + \
long_start_sign_x * ratio * r[start_point] * np.cos(theta[start_point])
long_start_sign_y = np.sign(
xy_list[end_point, 1] - xy_list[start_point, 1])
new_xy_list[start_point, 1] = \
xy_list[start_point, 1] + \
long_start_sign_y * ratio * r[start_point] * np.sin(theta[start_point])
# long edge one, end point
long_end_sign_x = -1 * long_start_sign_x
new_xy_list[end_point, 0] = \
xy_list[end_point, 0] + \
long_end_sign_x * ratio * r[end_point] * np.cos(theta[start_point])
long_end_sign_y = -1 * long_start_sign_y
new_xy_list[end_point, 1] = \
xy_list[end_point, 1] + \
long_end_sign_y * ratio * r[end_point] * np.sin(theta[start_point])
def process_label(data_dir=cfg.data_dir):
with open(os.path.join(data_dir, cfg.val_fname), 'r') as f_val:
f_list = f_val.readlines()
with open(os.path.join(data_dir, cfg.train_fname), 'r') as f_train:
f_list.extend(f_train.readlines())
for line, _ in zip(f_list, tqdm(range(len(f_list)))):
line_cols = str(line).strip().split(',')
img_name, width, height = \
line_cols[0].strip(), int(line_cols[1].strip()), \
int(line_cols[2].strip())
gt = np.zeros((height // cfg.pixel_size, width // cfg.pixel_size, 7))
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name)
xy_list_array = np.load(os.path.join(train_label_dir,
img_name[:-4] + '.npy'))
train_image_dir = os.path.join(data_dir, cfg.train_image_dir_name)
with Image.open(os.path.join(train_image_dir, img_name)) as im:
draw = ImageDraw.Draw(im)
for xy_list in xy_list_array:
_, shrink_xy_list, _ = shrink(xy_list, cfg.shrink_ratio)
shrink_1, _, long_edge = shrink(xy_list, cfg.shrink_side_ratio)
p_min = np.amin(shrink_xy_list, axis=0)
p_max = np.amax(shrink_xy_list, axis=0)
# floor of the float
ji_min = (p_min / cfg.pixel_size - 0.5).astype(int) - 1
# +1 for ceil of the float and +1 for include the end
ji_max = (p_max / cfg.pixel_size - 0.5).astype(int) + 3
imin = np.maximum(0, ji_min[1])
imax = np.minimum(height // cfg.pixel_size, ji_max[1])
jmin = np.maximum(0, ji_min[0])
jmax = np.minimum(width // cfg.pixel_size, ji_max[0])
for i in range(imin, imax):
for j in range(jmin, jmax):
px = (j + 0.5) * cfg.pixel_size
py = (i + 0.5) * cfg.pixel_size
if point_inside_of_quad(px, py,
shrink_xy_list, p_min, p_max):
gt[i, j, 0] = 1
line_width, line_color = 1, 'red'
ith = point_inside_of_nth_quad(px, py,
xy_list,
shrink_1,
long_edge)
vs = [[[3, 0], [1, 2]], [[0, 1], [2, 3]]]
if ith in range(2):
gt[i, j, 1] = 1
if ith == 0:
line_width, line_color = 2, 'yellow'
else:
line_width, line_color = 2, 'green'
gt[i, j, 2:3] = ith
gt[i, j, 3:5] = \
xy_list[vs[long_edge][ith][0]] - [px, py]
gt[i, j, 5:] = \
xy_list[vs[long_edge][ith][1]] - [px, py]
draw.line([(px - 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size),
(px + 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size),
(px + 0.5 * cfg.pixel_size,
py + 0.5 * cfg.pixel_size),
(px - 0.5 * cfg.pixel_size,
py + 0.5 * cfg.pixel_size),
(px - 0.5 * cfg.pixel_size,
py - 0.5 * cfg.pixel_size)],
width=line_width, fill=line_color)
act_image_dir = os.path.join(cfg.data_dir,
cfg.show_act_image_dir_name)
if cfg.draw_act_quad:
im.save(os.path.join(act_image_dir, img_name))
train_label_dir = os.path.join(data_dir, cfg.train_label_dir_name)
np.save(os.path.join(train_label_dir,
img_name[:-4] + '_gt.npy'), gt)
if __name__ == '__main__':
process_label()