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"""
@Date: 2021/07/27
@description:
"""
import numpy as np
import cv2
import functools
from utils.conversion import pixel2lonlat, lonlat2pixel, uv2lonlat, lonlat2uv, pixel2uv
@functools.lru_cache()
def prepare_stretch(w, h):
lon = pixel2lonlat(np.array(range(w)), w=w, axis=0)
lat = pixel2lonlat(np.array(range(h)), h=h, axis=1)
sin_lon = np.sin(lon)
cos_lon = np.cos(lon)
tan_lat = np.tan(lat)
return sin_lon, cos_lon, tan_lat
def pano_stretch_image(pano_img, kx, ky, kz):
"""
Note that this is the inverse mapping, which refers to Equation 3 in HorizonNet paper (the coordinate system in
the paper is different from here, xz needs to be swapped)
:param pano_img: a panorama image, shape must be [h,w,c]
:param kx: stretching along left-right direction
:param ky: stretching along up-down direction
:param kz: stretching along front-back direction
:return:
"""
w = pano_img.shape[1]
h = pano_img.shape[0]
sin_lon, cos_lon, tan_lat = prepare_stretch(w, h)
n_lon = np.arctan2(sin_lon * kz / kx, cos_lon)
n_lat = np.arctan(tan_lat[..., None] * np.sin(n_lon) / sin_lon * kx / ky)
n_pu = lonlat2pixel(n_lon, w=w, axis=0, need_round=False)
n_pv = lonlat2pixel(n_lat, h=h, axis=1, need_round=False)
pixel_map = np.empty((h, w, 2), dtype=np.float32)
pixel_map[..., 0] = n_pu
pixel_map[..., 1] = n_pv
map1 = pixel_map[..., 0]
map2 = pixel_map[..., 1]
# using wrap mode because it is continues at left or right of panorama
new_img = cv2.remap(pano_img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_WRAP)
return new_img
def pano_stretch_conner(corners, kx, ky, kz):
"""
:param corners:
:param kx: stretching along left-right direction
:param ky: stretching along up-down direction
:param kz: stretching along front-back direction
:return:
"""
lonlat = uv2lonlat(corners)
sin_lon = np.sin(lonlat[..., 0:1])
cos_lon = np.cos(lonlat[..., 0:1])
tan_lat = np.tan(lonlat[..., 1:2])
n_lon = np.arctan2(sin_lon * kx / kz, cos_lon)
a = np.bitwise_or(corners[..., 0] == 0.5, corners[..., 0] == 1)
b = np.bitwise_not(a)
w = np.zeros_like(n_lon)
w[b] = np.sin(n_lon[b]) / sin_lon[b]
w[a] = kx / kz
n_lat = np.arctan(tan_lat * w / kx * ky)
lst = [n_lon, n_lat]
lonlat = np.concatenate(lst, axis=-1)
new_corners = lonlat2uv(lonlat)
return new_corners
def pano_stretch(pano_img, corners, kx, ky, kz):
"""
:param pano_img: a panorama image, shape must be [h,w,c]
:param corners:
:param kx: stretching along left-right direction
:param ky: stretching along up-down direction
:param kz: stretching along front-back direction
:return:
"""
new_img = pano_stretch_image(pano_img, kx, ky, kz)
new_corners = pano_stretch_conner(corners, kx, ky, kz)
return new_img, new_corners
class PanoDataAugmentation:
def __init__(self, aug):
self.aug = aug
self.parameters = {}
def need_aug(self, name):
return name in self.aug and self.aug[name]
def execute_space_aug(self, corners, image):
if image is None:
return image
if self.aug is None:
return corners, image
w = image.shape[1]
h = image.shape[0]
if self.need_aug('STRETCH'):
kx = np.random.uniform(1, 2)
kx = 1 / kx if np.random.randint(2) == 0 else kx
# we found that the ky transform may cause IoU to drop (HorizonNet also only x and z transform)
# ky = np.random.uniform(1, 2)
# ky = 1 / ky if np.random.randint(2) == 0 else ky
ky = 1
kz = np.random.uniform(1, 2)
kz = 1 / kz if np.random.randint(2) == 0 else kz
image, corners = pano_stretch(image, corners, kx, ky, kz)
self.parameters['STRETCH'] = {'kx': kx, 'ky': ky, 'kz': kz}
else:
self.parameters['STRETCH'] = None
if self.need_aug('ROTATE'):
d_pu = np.random.randint(w)
image = np.roll(image, d_pu, axis=1)
corners[..., 0] = (corners[..., 0] + pixel2uv(np.array([d_pu]), w, h)) % pixel2uv(np.array([w]), w, h)
self.parameters['ROTATE'] = d_pu
else:
self.parameters['ROTATE'] = None
if self.need_aug('FLIP') and np.random.randint(2) == 0:
image = np.flip(image, axis=1).copy()
corners[..., 0] = pixel2uv(np.array([w]), w, h) - corners[..., 0]
corners = corners[::-1]
self.parameters['FLIP'] = True
else:
self.parameters['FLIP'] = None
return corners, image
def execute_visual_aug(self, image):
if self.need_aug('GAMMA'):
p = np.random.uniform(1, 2)
if np.random.randint(2) == 0:
p = 1 / p
image = image ** p
self.parameters['GAMMA'] = p
else:
self.parameters['GAMMA'] = None
# The following visual augmentation methods are only implemented but not tested
if self.need_aug('HUE') or self.need_aug('SATURATION'):
image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
if self.need_aug('HUE') and np.random.randint(2) == 0:
p = np.random.uniform(-0.1, 0.1)
image[..., 0] = np.mod(image[..., 0] + p * 180, 180)
self.parameters['HUE'] = p
else:
self.parameters['HUE'] = None
if self.need_aug('SATURATION') and np.random.randint(2) == 0:
p = np.random.uniform(0.5, 1.5)
image[..., 1] = np.clip(image[..., 1] * p, 0, 1)
self.parameters['SATURATION'] = p
else:
self.parameters['SATURATION'] = None
image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
if self.need_aug('CONTRAST') and np.random.randint(2) == 0:
p = np.random.uniform(0.9, 1.1)
mean = image.mean(axis=0).mean(axis=0)
image = (image - mean) * p + mean
image = np.clip(image, 0, 1)
self.parameters['CONTRAST'] = p
else:
self.parameters['CONTRAST'] = None
return image
def execute_aug(self, corners, image):
corners, image = self.execute_space_aug(corners, image)
if image is not None:
image = self.execute_visual_aug(image)
return corners, image
if __name__ == '__main__1':
from tqdm import trange
from visualization.floorplan import draw_floorplan
from dataset.communal.read import read_image, read_label
from utils.time_watch import TimeWatch
from utils.conversion import uv2xyz
from utils.boundary import corners2boundary
np.random.seed(123)
pano_img_path = "../../src/dataset/mp3d/image/TbHJrupSAjP_f320ae084f3a447da3e8ab11dd5f9320.png"
label_path = "../../src/dataset/mp3d/label/TbHJrupSAjP_f320ae084f3a447da3e8ab11dd5f9320.json"
pano_img = read_image(pano_img_path)
label = read_label(label_path)
corners = label['corners']
ratio = label['ratio']
pano_aug = PanoDataAugmentation(aug={
'STRETCH': True,
'ROTATE': True,
'FLIP': True,
'GAMMA': True,
# 'HUE': True,
# 'SATURATION': True,
# 'CONTRAST': True
})
# draw_floorplan(corners, show=True, marker_color=0.5, center_color=0.8, plan_y=1.6, show_radius=8)
# draw_boundaries(pano_img, corners_list=[corners], show=True, length=1024, ratio=ratio)
w = TimeWatch("test")
for i in trange(50000):
new_corners, new_pano_img = pano_aug.execute_aug(corners.copy(), pano_img.copy())
# draw_floorplan(uv2xyz(new_corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
# show_radius=8)
# draw_boundaries(new_pano_img, corners_list=[new_corners], show=True, length=1024, ratio=ratio)
if __name__ == '__main__':
from utils.boundary import corners2boundary
from visualization.floorplan import draw_floorplan
from utils.boundary import visibility_corners
corners = np.array([[0.7664539, 0.7416811],
[0.06641078, 0.6521386],
[0.30997428, 0.57855356],
[0.383300784, 0.58726823],
[0.383300775, 0.8005296],
[0.5062902, 0.74822706]])
corners = visibility_corners(corners)
print(corners)
# draw_floorplan(uv2xyz(corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
# show_radius=8)
visible_floor_boundary = corners2boundary(corners, length=256, visible=True)
# visible_depth = xyz2depth(uv2xyz(visible_floor_boundary, 1), 1)
print(len(visible_floor_boundary))
if __name__ == '__main__0':
from visualization.floorplan import draw_floorplan
from dataset.communal.read import read_image, read_label
from utils.time_watch import TimeWatch
from utils.conversion import uv2xyz
# np.random.seed(1234)
pano_img_path = "../../src/dataset/mp3d/image/VVfe2KiqLaN_35b41dcbfcf84f96878f6ca28c70e5af.png"
label_path = "../../src/dataset/mp3d/label/VVfe2KiqLaN_35b41dcbfcf84f96878f6ca28c70e5af.json"
pano_img = read_image(pano_img_path)
label = read_label(label_path)
corners = label['corners']
ratio = label['ratio']
# draw_floorplan(corners, show=True, marker_color=0.5, center_color=0.8, plan_y=1.6, show_radius=8)
w = TimeWatch()
for i in range(5):
kx = np.random.uniform(1, 2)
kx = 1 / kx if np.random.randint(2) == 0 else kx
ky = np.random.uniform(1, 2)
ky = 1 / ky if np.random.randint(2) == 0 else ky
kz = np.random.uniform(1, 2)
kz = 1 / kz if np.random.randint(2) == 0 else kz
new_corners = pano_stretch_conner(corners.copy(), kx, ky, kz)
draw_floorplan(uv2xyz(new_corners, plan_y=1.6)[..., ::2], show=True, marker_color=0.5, center_color=0.8,
show_radius=8)
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