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layout.py
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layout.py
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"""
@Date: 2021/10/06
@description: Use the approach proposed by DuLa-Net
"""
import cv2
import numpy as np
import math
import matplotlib.pyplot as plt
import sys
import os.path as osp
sys.path.append(osp.abspath(osp.join(__file__, '../../../')))
from visualization.floorplan import draw_floorplan
def calc_angle(v1: np.array, v2: np.array):
norm = np.linalg.norm(v1) * np.linalg.norm(v2)
theta = np.arccos(np.dot(v1, v2) / norm)
return theta
def merge_near(lst, diag, min):
group = [[min, ]]
for i in range(1, len(lst)):
if lst[i][1] == 0 and lst[i][0] - np.mean(group[-1]) < diag * 0.02:
group[-1].append(lst[i][0])
else:
group.append([lst[i][0], ])
if len(group) == 1:
group = [lst[0][0], lst[-1][0]]
else:
group = [int(np.mean(x)) for x in group]
return group
def fit_layout(floor_xz, need_cube=False, show=False, block_eps=5):
show_radius = np.linalg.norm(floor_xz, axis=-1).max()
side_l = 512
floorplan = draw_floorplan(xz=floor_xz, show_radius=show_radius, show=show, scale=1, side_l=side_l).astype(np.uint8)
center = np.array([side_l / 2, side_l / 2])
polys = cv2.findContours(floorplan, 1, 2)
if isinstance(polys, tuple):
if len(polys) == 3:
# opencv 3
polys = list(polys[1])
else:
polys = list(polys[0])
polys.sort(key=lambda x: cv2.contourArea(x), reverse=True)
poly = polys[0]
sub_x, sub_y, w, h = cv2.boundingRect(poly)
floorplan_sub = floorplan[sub_y:sub_y + h, sub_x:sub_x + w]
sub_center = center - np.array([sub_x, sub_y])
polys = cv2.findContours(floorplan_sub, 1, 2)
if isinstance(polys, tuple):
if len(polys) == 3:
polys = polys[1]
else:
polys = polys[0]
poly = polys[0]
epsilon = 0.005 * cv2.arcLength(poly, True)
poly = cv2.approxPolyDP(poly, epsilon, True)
x_lst = [[poly[:, 0, 0].min(), 0], ]
y_lst = [[poly[:, 0, 1].min(), 0], ]
ans = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[1]))
for i in range(len(poly)):
p1 = poly[i][0]
p2 = poly[(i + 1) % len(poly)][0]
# We added occlusion detection
cp1 = p1 - sub_center
cp2 = p2 - sub_center
p12 = p2 - p1
l1 = np.linalg.norm(cp1)
l2 = np.linalg.norm(cp2)
l3 = np.linalg.norm(p12)
# We added occlusion detection
is_block1 = np.rad2deg(calc_angle(cp1, cp2)) < block_eps
is_block2 = np.rad2deg(calc_angle(cp2, p12)) < block_eps*2
is_block3 = np.rad2deg(calc_angle(cp2, -p12)) < block_eps*2
is_block = is_block1 and (is_block2 or is_block3)
if (p2[0] - p1[0]) == 0:
slope = 10
else:
slope = abs((p2[1] - p1[1]) / (p2[0] - p1[0]))
if is_block:
s = p1[1] if l1 < l2 else p2[1]
y_lst.append([s, 1])
s = p1[0] if l1 < l2 else p2[0]
x_lst.append([s, 1])
left = p1[0] if p1[0] < p2[0] else p2[0]
right = p1[0] if p1[0] > p2[0] else p2[0]
top = p1[1] if p1[1] < p2[1] else p2[1]
bottom = p1[1] if p1[1] > p2[1] else p2[1]
sample = floorplan_sub[top:bottom, left:right]
score = 0 if sample.size == 0 else sample.mean()
if score >= 0.3:
ans[top:bottom, left:right] = 1
else:
if slope <= 1:
s = int((p1[1] + p2[1]) / 2)
y_lst.append([s, 0])
elif slope > 1:
s = int((p1[0] + p2[0]) / 2)
x_lst.append([s, 0])
debug_show = False
if debug_show:
plt.figure(dpi=300)
plt.axis('off')
a = cv2.drawMarker(floorplan_sub.copy()*0.5, tuple(sub_center.astype(int)), [1], markerType=0, markerSize=10, thickness=2)
plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1))
plt.savefig('src/1.png', bbox_inches='tight', transparent=True, pad_inches=0)
plt.show()
plt.figure(dpi=300)
plt.axis('off')
a = cv2.drawMarker(ans.copy()*0.5, tuple(sub_center.astype(int)), [1], markerType=0, markerSize=10, thickness=2)
plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1))
# plt.show()
plt.savefig('src/2.png', bbox_inches='tight', transparent=True, pad_inches=0)
plt.show()
x_lst.append([poly[:, 0, 0].max(), 0])
y_lst.append([poly[:, 0, 1].max(), 0])
x_lst.sort(key=lambda x: x[0])
y_lst.sort(key=lambda x: x[0])
diag = math.sqrt(math.pow(floorplan_sub.shape[1], 2) + math.pow(floorplan_sub.shape[0], 2))
x_lst = merge_near(x_lst, diag, poly[:, 0, 0].min())
y_lst = merge_near(y_lst, diag, poly[:, 0, 1].min())
if need_cube and len(x_lst) > 2:
x_lst = [x_lst[0], x_lst[-1]]
if need_cube and len(y_lst) > 2:
y_lst = [y_lst[0], y_lst[-1]]
for i in range(len(x_lst) - 1):
for j in range(len(y_lst) - 1):
sample = floorplan_sub[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]]
score = 0 if sample.size == 0 else sample.mean()
if score >= 0.3:
ans[y_lst[j]:y_lst[j + 1], x_lst[i]:x_lst[i + 1]] = 1
if debug_show:
plt.figure(dpi=300)
plt.axis('off')
a = cv2.drawMarker(ans.copy() * 0.5, tuple(sub_center.astype(int)), [1],
markerType=0, markerSize=10, thickness=2)
plt.imshow(cv2.drawContours(a, [poly], 0, 1, 1))
# plt.show()
plt.savefig('src/3.png', bbox_inches='tight', transparent=True, pad_inches=0)
plt.show()
pred = np.uint8(ans)
pred_polys = cv2.findContours(pred, 1, 3)
if isinstance(pred_polys, tuple):
if len(pred_polys) == 3:
pred_polys = pred_polys[1]
else:
pred_polys = pred_polys[0]
pred_polys.sort(key=lambda x: cv2.contourArea(x), reverse=True)
pred_poly = pred_polys[0]
# findContours may produce errors, which are enforced here
for i in range(len(pred_poly)):
p1 = pred_poly[i][0]
p2 = pred_poly[(i+1)%len(pred_poly)][0]
if abs(p1[0] - p2[0]) < abs(p1[1] - p2[1]):
p1[0] = p2[0]
else:
p1[1] = p2[1]
if debug_show:
plt.figure(dpi=300)
plt.axis('off')
a = cv2.drawMarker(ans.copy() * 0.5, tuple(sub_center.astype(int)), [1],
markerType=0, markerSize=10, thickness=2)
a = cv2.drawContours(a, [poly], 0, 0.8, 1)
a = cv2.drawContours(a, [pred_poly], 0, 1, 1)
plt.imshow(a)
# plt.show()
plt.savefig('src/4.png', bbox_inches='tight', transparent=True, pad_inches=0)
plt.show()
polygon = [(p[0][1], p[0][0]) for p in pred_poly[::-1]]
v = np.array([p[0] + sub_y for p in polygon])
u = np.array([p[1] + sub_x for p in polygon])
# side_l
# v<-----------|o
# | | |
# | ----|----z | side_l
# | | |
# | x \|/
# |------------u
side_l = floorplan.shape[0]
pred_xz = np.concatenate((u[:, np.newaxis] - side_l // 2, side_l // 2 - v[:, np.newaxis]), axis=1)
pred_xz = pred_xz * show_radius / (side_l // 2)
if show:
draw_floorplan(pred_xz, show_radius=show_radius, show=show)
show_process = False
if show_process:
img = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[1], 3))
for x in x_lst:
cv2.line(img, (x, 0), (x, floorplan_sub.shape[0]), (0, 255, 0), 1)
for y in y_lst:
cv2.line(img, (0, y), (floorplan_sub.shape[1], y), (255, 0, 0), 1)
fig = plt.figure()
plt.axis('off')
ax1 = fig.add_subplot(2, 2, 1)
ax1.imshow(floorplan)
ax3 = fig.add_subplot(2, 2, 2)
ax3.imshow(floorplan_sub)
ax4 = fig.add_subplot(2, 2, 3)
ax4.imshow(img)
ax5 = fig.add_subplot(2, 2, 4)
ax5.imshow(ans)
plt.show()
return pred_xz
if __name__ == '__main__':
# processed_xz = fit_layout(floor_xz=np.load('/room_layout_estimation/lgt_net/floor_xz.npy'), need_cube=False, show=False)
from utils.conversion import uv2xyz
pano_img = np.zeros([512, 1024, 3])
corners = np.array([[0.1, 0.7],
[0.4, 0.7],
[0.3, 0.6],
[0.6, 0.6],
[0.8, 0.7]])
xz = uv2xyz(corners)[..., ::2]
draw_floorplan(xz, show=True, marker_color=None, center_color=0.8)
xz = fit_layout(xz)
draw_floorplan(xz, show=True, marker_color=None, center_color=0.8)