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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 | |
from visualization.floorplan import draw_floorplan | |
def merge_near(lst, diag): | |
group = [[0, ]] | |
for i in range(1, len(lst)): | |
if lst[i] - np.mean(group[-1]) < diag * 0.02: | |
group[-1].append(lst[i]) | |
else: | |
group.append([lst[i], ]) | |
if len(group) == 1: | |
group = [lst[0], lst[-1]] | |
else: | |
group = [int(np.mean(x)) for x in group] | |
return group | |
def fit_layout_old(floor_xz, need_cube=False, show=False, block_eps=0.05): | |
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 = [0, ] | |
y_lst = [0, ] | |
for i in range(len(poly)): | |
p1 = poly[i][0] | |
p2 = poly[(i + 1) % len(poly)][0] | |
if (p2[0] - p1[0]) == 0: | |
slope = 10 | |
else: | |
slope = abs((p2[1] - p1[1]) / (p2[0] - p1[0])) | |
if slope <= 1: | |
s = int((p1[1] + p2[1]) / 2) | |
y_lst.append(s) | |
elif slope > 1: | |
s = int((p1[0] + p2[0]) / 2) | |
x_lst.append(s) | |
x_lst.append(floorplan_sub.shape[1]) | |
y_lst.append(floorplan_sub.shape[0]) | |
x_lst.sort() | |
y_lst.sort() | |
diag = math.sqrt(math.pow(floorplan_sub.shape[1], 2) + math.pow(floorplan_sub.shape[0], 2)) | |
x_lst = merge_near(x_lst, diag) | |
y_lst = merge_near(y_lst, diag) | |
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]] | |
ans = np.zeros((floorplan_sub.shape[0], floorplan_sub.shape[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 | |
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] | |
polygon = [(p[0][1], p[0][0]) for p in pred_polys[0][::-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) | |
return pred_xz | |
if __name__ == '__main__': | |
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_old(xz) | |
draw_floorplan(xz, show=True, marker_color=None, center_color=0.8) | |