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import os
import glob
import cv2
import scipy.misc as misc
from skimage.transform import resize
import numpy as np
from functools import reduce
from operator import mul
import torch
from torch import nn
import matplotlib.pyplot as plt
import re
try:
import cynetworkx as netx
except ImportError:
import networkx as netx
from scipy.ndimage import gaussian_filter
from skimage.feature import canny
import collections
import shutil
import imageio
import copy
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import time
from scipy.interpolate import interp1d
from collections import namedtuple
def path_planning(num_frames, x, y, z, path_type=''):
if path_type == 'straight-line':
corner_points = np.array([[0, 0, 0], [(0 + x) * 0.5, (0 + y) * 0.5, (0 + z) * 0.5], [x, y, z]])
corner_t = np.linspace(0, 1, len(corner_points))
t = np.linspace(0, 1, num_frames)
cs = interp1d(corner_t, corner_points, axis=0, kind='quadratic')
spline = cs(t)
xs, ys, zs = [xx.squeeze() for xx in np.split(spline, 3, 1)]
elif path_type == 'double-straight-line':
corner_points = np.array([[-x, -y, -z], [0, 0, 0], [x, y, z]])
corner_t = np.linspace(0, 1, len(corner_points))
t = np.linspace(0, 1, num_frames)
cs = interp1d(corner_t, corner_points, axis=0, kind='quadratic')
spline = cs(t)
xs, ys, zs = [xx.squeeze() for xx in np.split(spline, 3, 1)]
elif path_type == 'circle':
xs, ys, zs = [], [], []
for frame_id, bs_shift_val in enumerate(np.arange(-2.0, 2.0, (4./num_frames))):
xs += [np.cos(bs_shift_val * np.pi) * 1 * x]
ys += [np.sin(bs_shift_val * np.pi) * 1 * y]
zs += [np.cos(bs_shift_val * np.pi/2.) * 1 * z]
xs, ys, zs = np.array(xs), np.array(ys), np.array(zs)
return xs, ys, zs
def open_small_mask(mask, context, open_iteration, kernel):
np_mask = mask.cpu().data.numpy().squeeze().astype(np.uint8)
raw_mask = np_mask.copy()
np_context = context.cpu().data.numpy().squeeze().astype(np.uint8)
np_input = np_mask + np_context
for _ in range(open_iteration):
np_input = cv2.erode(cv2.dilate(np_input, np.ones((kernel, kernel)), iterations=1), np.ones((kernel,kernel)), iterations=1)
np_mask[(np_input - np_context) > 0] = 1
out_mask = torch.FloatTensor(np_mask).to(mask)[None, None, ...]
return out_mask
def filter_irrelevant_edge_new(self_edge, comp_edge, other_edges, other_edges_with_id, current_edge_id, context, depth, mesh, context_cc, spdb=False):
other_edges = other_edges.squeeze().astype(np.uint8)
other_edges_with_id = other_edges_with_id.squeeze()
self_edge = self_edge.squeeze()
dilate_bevel_self_edge = cv2.dilate((self_edge + comp_edge).astype(np.uint8), np.array([[1,1,1],[1,1,1],[1,1,1]]), iterations=1)
dilate_cross_self_edge = cv2.dilate((self_edge + comp_edge).astype(np.uint8), np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8), iterations=1)
edge_ids = np.unique(other_edges_with_id * context + (-1) * (1 - context)).astype(np.int)
end_depth_maps = np.zeros_like(self_edge)
self_edge_ids = np.sort(np.unique(other_edges_with_id[self_edge > 0]).astype(np.int))
self_edge_ids = self_edge_ids[1:] if self_edge_ids.shape[0] > 0 and self_edge_ids[0] == -1 else self_edge_ids
self_comp_ids = np.sort(np.unique(other_edges_with_id[comp_edge > 0]).astype(np.int))
self_comp_ids = self_comp_ids[1:] if self_comp_ids.shape[0] > 0 and self_comp_ids[0] == -1 else self_comp_ids
edge_ids = edge_ids[1:] if edge_ids[0] == -1 else edge_ids
other_edges_info = []
extend_other_edges = np.zeros_like(other_edges)
if spdb is True:
f, ((ax1, ax2, ax3)) = plt.subplots(1, 3, sharex=True, sharey=True); ax1.imshow(self_edge); ax2.imshow(context); ax3.imshow(other_edges_with_id * context + (-1) * (1 - context)); plt.show()
import pdb; pdb.set_trace()
filter_self_edge = np.zeros_like(self_edge)
for self_edge_id in self_edge_ids:
filter_self_edge[other_edges_with_id == self_edge_id] = 1
dilate_self_comp_edge = cv2.dilate(comp_edge, kernel=np.ones((3, 3)), iterations=2)
valid_self_comp_edge = np.zeros_like(comp_edge)
for self_comp_id in self_comp_ids:
valid_self_comp_edge[self_comp_id == other_edges_with_id] = 1
self_comp_edge = dilate_self_comp_edge * valid_self_comp_edge
filter_self_edge = (filter_self_edge + self_comp_edge).clip(0, 1)
for edge_id in edge_ids:
other_edge_locs = (other_edges_with_id == edge_id).astype(np.uint8)
condition = (other_edge_locs * other_edges * context.astype(np.uint8))
end_cross_point = dilate_cross_self_edge * condition * (1 - filter_self_edge)
end_bevel_point = dilate_bevel_self_edge * condition * (1 - filter_self_edge)
if end_bevel_point.max() != 0:
end_depth_maps[end_bevel_point != 0] = depth[end_bevel_point != 0]
if end_cross_point.max() == 0:
nxs, nys = np.where(end_bevel_point != 0)
for nx, ny in zip(nxs, nys):
bevel_node = [xx for xx in context_cc if xx[0] == nx and xx[1] == ny][0]
for ne in mesh.neighbors(bevel_node):
if other_edges_with_id[ne[0], ne[1]] > -1 and dilate_cross_self_edge[ne[0], ne[1]] > 0:
extend_other_edges[ne[0], ne[1]] = 1
break
else:
other_edges[other_edges_with_id == edge_id] = 0
other_edges = (other_edges + extend_other_edges).clip(0, 1) * context
return other_edges, end_depth_maps, other_edges_info
def clean_far_edge_new(input_edge, end_depth_maps, mask, context, global_mesh, info_on_pix, self_edge, inpaint_id, config):
mesh = netx.Graph()
hxs, hys = np.where(input_edge * mask > 0)
valid_near_edge = (input_edge != 0).astype(np.uint8) * context
valid_map = mask + context
invalid_edge_ids = []
for hx, hy in zip(hxs, hys):
node = (hx ,hy)
mesh.add_node((hx, hy))
eight_nes = [ne for ne in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1), \
(hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)]\
if 0 <= ne[0] < input_edge.shape[0] and 0 <= ne[1] < input_edge.shape[1] and 0 < input_edge[ne[0], ne[1]]] # or end_depth_maps[ne[0], ne[1]] != 0]
for ne in eight_nes:
mesh.add_edge(node, ne, length=np.hypot(ne[0] - hx, ne[1] - hy))
if end_depth_maps[ne[0], ne[1]] != 0:
mesh.nodes[ne[0], ne[1]]['cnt'] = True
if end_depth_maps[ne[0], ne[1]] == 0:
import pdb; pdb.set_trace()
mesh.nodes[ne[0], ne[1]]['depth'] = end_depth_maps[ne[0], ne[1]]
elif mask[ne[0], ne[1]] != 1:
four_nes = [nne for nne in [(ne[0] + 1, ne[1]), (ne[0] - 1, ne[1]), (ne[0], ne[1] + 1), (ne[0], ne[1] - 1)]\
if nne[0] < end_depth_maps.shape[0] and nne[0] >= 0 and nne[1] < end_depth_maps.shape[1] and nne[1] >= 0]
for nne in four_nes:
if end_depth_maps[nne[0], nne[1]] != 0:
mesh.add_edge(nne, ne, length=np.hypot(nne[0] - ne[0], nne[1] - ne[1]))
mesh.nodes[nne[0], nne[1]]['cnt'] = True
mesh.nodes[nne[0], nne[1]]['depth'] = end_depth_maps[nne[0], nne[1]]
ccs = [*netx.connected_components(mesh)]
end_pts = []
for cc in ccs:
end_pts.append(set())
for node in cc:
if mesh.nodes[node].get('cnt') is not None:
end_pts[-1].add((node[0], node[1], mesh.nodes[node]['depth']))
predef_npaths = [None for _ in range(len(ccs))]
fpath_map = np.zeros_like(input_edge) - 1
npath_map = np.zeros_like(input_edge) - 1
npaths, fpaths = dict(), dict()
break_flag = False
end_idx = 0
while end_idx < len(end_pts):
end_pt, cc = [*zip(end_pts, ccs)][end_idx]
end_idx += 1
sorted_end_pt = []
fpath = []
iter_fpath = []
if len(end_pt) > 2 or len(end_pt) == 0:
if len(end_pt) > 2:
continue
continue
if len(end_pt) == 2:
ravel_end = [*end_pt]
tmp_sub_mesh = mesh.subgraph(list(cc)).copy()
tmp_npath = [*netx.shortest_path(tmp_sub_mesh, (ravel_end[0][0], ravel_end[0][1]), (ravel_end[1][0], ravel_end[1][1]), weight='length')]
fpath_map1, npath_map1, disp_diff1 = plan_path(mesh, info_on_pix, cc, ravel_end[0:1], global_mesh, input_edge, mask, valid_map, inpaint_id, npath_map=None, fpath_map=None, npath=tmp_npath)
fpath_map2, npath_map2, disp_diff2 = plan_path(mesh, info_on_pix, cc, ravel_end[1:2], global_mesh, input_edge, mask, valid_map, inpaint_id, npath_map=None, fpath_map=None, npath=tmp_npath)
tmp_disp_diff = [disp_diff1, disp_diff2]
self_end = []
edge_len = []
ds_edge = cv2.dilate(self_edge.astype(np.uint8), np.ones((3, 3)), iterations=1)
if ds_edge[ravel_end[0][0], ravel_end[0][1]] > 0:
self_end.append(1)
else:
self_end.append(0)
if ds_edge[ravel_end[1][0], ravel_end[1][1]] > 0:
self_end.append(1)
else:
self_end.append(0)
edge_len = [np.count_nonzero(npath_map1), np.count_nonzero(npath_map2)]
sorted_end_pts = [xx[0] for xx in sorted(zip(ravel_end, self_end, edge_len, [disp_diff1, disp_diff2]), key=lambda x: (x[1], x[2]), reverse=True)]
re_npath_map1, re_fpath_map1 = (npath_map1 != -1).astype(np.uint8), (fpath_map1 != -1).astype(np.uint8)
re_npath_map2, re_fpath_map2 = (npath_map2 != -1).astype(np.uint8), (fpath_map2 != -1).astype(np.uint8)
if np.count_nonzero(re_npath_map1 * re_npath_map2 * mask) / \
(np.count_nonzero((re_npath_map1 + re_npath_map2) * mask) + 1e-6) > 0.5\
and np.count_nonzero(re_fpath_map1 * re_fpath_map2 * mask) / \
(np.count_nonzero((re_fpath_map1 + re_fpath_map2) * mask) + 1e-6) > 0.5\
and tmp_disp_diff[0] != -1 and tmp_disp_diff[1] != -1:
my_fpath_map, my_npath_map, npath, fpath = \
plan_path_e2e(mesh, cc, sorted_end_pts, global_mesh, input_edge, mask, valid_map, inpaint_id, npath_map=None, fpath_map=None)
npath_map[my_npath_map != -1] = my_npath_map[my_npath_map != -1]
fpath_map[my_fpath_map != -1] = my_fpath_map[my_fpath_map != -1]
if len(fpath) > 0:
edge_id = global_mesh.nodes[[*sorted_end_pts][0]]['edge_id']
fpaths[edge_id] = fpath
npaths[edge_id] = npath
invalid_edge_ids.append(edge_id)
else:
if tmp_disp_diff[0] != -1:
ratio_a = tmp_disp_diff[0] / (np.sum(tmp_disp_diff) + 1e-8)
else:
ratio_a = 0
if tmp_disp_diff[1] != -1:
ratio_b = tmp_disp_diff[1] / (np.sum(tmp_disp_diff) + 1e-8)
else:
ratio_b = 0
npath_len = len(tmp_npath)
if npath_len > config['depth_edge_dilate_2'] * 2:
npath_len = npath_len - (config['depth_edge_dilate_2'] * 1)
tmp_npath_a = tmp_npath[:int(np.floor(npath_len * ratio_a))]
tmp_npath_b = tmp_npath[::-1][:int(np.floor(npath_len * ratio_b))]
tmp_merge = []
if len(tmp_npath_a) > 0 and sorted_end_pts[0][0] == tmp_npath_a[0][0] and sorted_end_pts[0][1] == tmp_npath_a[0][1]:
if len(tmp_npath_a) > 0 and mask[tmp_npath_a[-1][0], tmp_npath_a[-1][1]] > 0:
tmp_merge.append([sorted_end_pts[:1], tmp_npath_a])
if len(tmp_npath_b) > 0 and mask[tmp_npath_b[-1][0], tmp_npath_b[-1][1]] > 0:
tmp_merge.append([sorted_end_pts[1:2], tmp_npath_b])
elif len(tmp_npath_b) > 0 and sorted_end_pts[0][0] == tmp_npath_b[0][0] and sorted_end_pts[0][1] == tmp_npath_b[0][1]:
if len(tmp_npath_b) > 0 and mask[tmp_npath_b[-1][0], tmp_npath_b[-1][1]] > 0:
tmp_merge.append([sorted_end_pts[:1], tmp_npath_b])
if len(tmp_npath_a) > 0 and mask[tmp_npath_a[-1][0], tmp_npath_a[-1][1]] > 0:
tmp_merge.append([sorted_end_pts[1:2], tmp_npath_a])
for tmp_idx in range(len(tmp_merge)):
if len(tmp_merge[tmp_idx][1]) == 0:
continue
end_pts.append(tmp_merge[tmp_idx][0])
ccs.append(set(tmp_merge[tmp_idx][1]))
if len(end_pt) == 1:
sub_mesh = mesh.subgraph(list(cc)).copy()
pnodes = netx.periphery(sub_mesh)
if len(end_pt) == 1:
ends = [*end_pt]
elif len(sorted_end_pt) == 1:
ends = [*sorted_end_pt]
else:
import pdb; pdb.set_trace()
try:
edge_id = global_mesh.nodes[ends[0]]['edge_id']
except:
import pdb; pdb.set_trace()
pnodes = sorted(pnodes,
key=lambda x: np.hypot((x[0] - ends[0][0]), (x[1] - ends[0][1])),
reverse=True)[0]
npath = [*netx.shortest_path(sub_mesh, (ends[0][0], ends[0][1]), pnodes, weight='length')]
for np_node in npath:
npath_map[np_node[0], np_node[1]] = edge_id
fpath = []
if global_mesh.nodes[ends[0]].get('far') is None:
print("None far")
else:
fnodes = global_mesh.nodes[ends[0]].get('far')
dmask = mask + 0
did = 0
while True:
did += 1
dmask = cv2.dilate(dmask, np.ones((3, 3)), iterations=1)
if did > 3:
break
ffnode = [fnode for fnode in fnodes if (dmask[fnode[0], fnode[1]] > 0 and mask[fnode[0], fnode[1]] == 0 and\
global_mesh.nodes[fnode].get('inpaint_id') != inpaint_id + 1)]
if len(ffnode) > 0:
fnode = ffnode[0]
break
if len(ffnode) == 0:
continue
fpath.append((fnode[0], fnode[1]))
barrel_dir = np.array([[1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0], [-1, -1], [0, -1], [1, -1]])
n2f_dir = (int(fnode[0] - npath[0][0]), int(fnode[1] - npath[0][1]))
while True:
if barrel_dir[0, 0] == n2f_dir[0] and barrel_dir[0, 1] == n2f_dir[1]:
n2f_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
for step in range(0, len(npath)):
if step == 0:
continue
elif step == 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_dir[0, 0] == next_dir[0] and barrel_dir[0, 1] == next_dir[1]:
next_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
barrel_pair = np.stack((n2f_barrel, next_barrel), axis=0)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
elif step > 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_pair[1, 0, 0] == next_dir[0] and barrel_pair[1, 0, 1] == next_dir[1]:
next_barrel = barrel_pair.copy()
break
barrel_pair = np.roll(barrel_pair, 1, axis=1)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
new_locs = []
if abs(n2f_dir[0]) == 1:
new_locs.append((npath[step][0] + n2f_dir[0], npath[step][1]))
if abs(n2f_dir[1]) == 1:
new_locs.append((npath[step][0], npath[step][1] + n2f_dir[1]))
if len(new_locs) > 1:
new_locs = sorted(new_locs, key=lambda xx: np.hypot((xx[0] - fpath[-1][0]), (xx[1] - fpath[-1][1])))
break_flag = False
for new_loc in new_locs:
new_loc_nes = [xx for xx in [(new_loc[0] + 1, new_loc[1]), (new_loc[0] - 1, new_loc[1]),
(new_loc[0], new_loc[1] + 1), (new_loc[0], new_loc[1] - 1)]\
if xx[0] >= 0 and xx[0] < fpath_map.shape[0] and xx[1] >= 0 and xx[1] < fpath_map.shape[1]]
if np.all([(fpath_map[nlne[0], nlne[1]] == -1) for nlne in new_loc_nes]) != True:
break
if npath_map[new_loc[0], new_loc[1]] != -1:
if npath_map[new_loc[0], new_loc[1]] != edge_id:
break_flag = True
break
else:
continue
if valid_map[new_loc[0], new_loc[1]] == 0:
break_flag = True
break
fpath.append(new_loc)
if break_flag is True:
break
if step != len(npath) - 1:
for xx in npath[step:]:
if npath_map[xx[0], xx[1]] == edge_id:
npath_map[xx[0], xx[1]] = -1
npath = npath[:step]
if len(fpath) > 0:
for fp_node in fpath:
fpath_map[fp_node[0], fp_node[1]] = edge_id
fpaths[edge_id] = fpath
npaths[edge_id] = npath
fpath_map[valid_near_edge != 0] = -1
if len(fpath) > 0:
iter_fpath = copy.deepcopy(fpaths[edge_id])
for node in iter_fpath:
if valid_near_edge[node[0], node[1]] != 0:
fpaths[edge_id].remove(node)
return fpath_map, npath_map, False, npaths, fpaths, invalid_edge_ids
def plan_path_e2e(mesh, cc, end_pts, global_mesh, input_edge, mask, valid_map, inpaint_id, npath_map=None, fpath_map=None):
my_npath_map = np.zeros_like(input_edge) - 1
my_fpath_map = np.zeros_like(input_edge) - 1
sub_mesh = mesh.subgraph(list(cc)).copy()
ends_1, ends_2 = end_pts[0], end_pts[1]
edge_id = global_mesh.nodes[ends_1]['edge_id']
npath = [*netx.shortest_path(sub_mesh, (ends_1[0], ends_1[1]), (ends_2[0], ends_2[1]), weight='length')]
for np_node in npath:
my_npath_map[np_node[0], np_node[1]] = edge_id
fpath = []
if global_mesh.nodes[ends_1].get('far') is None:
print("None far")
else:
fnodes = global_mesh.nodes[ends_1].get('far')
dmask = mask + 0
while True:
dmask = cv2.dilate(dmask, np.ones((3, 3)), iterations=1)
ffnode = [fnode for fnode in fnodes if (dmask[fnode[0], fnode[1]] > 0 and mask[fnode[0], fnode[1]] == 0 and\
global_mesh.nodes[fnode].get('inpaint_id') != inpaint_id + 1)]
if len(ffnode) > 0:
fnode = ffnode[0]
break
e_fnodes = global_mesh.nodes[ends_2].get('far')
dmask = mask + 0
while True:
dmask = cv2.dilate(dmask, np.ones((3, 3)), iterations=1)
e_ffnode = [e_fnode for e_fnode in e_fnodes if (dmask[e_fnode[0], e_fnode[1]] > 0 and mask[e_fnode[0], e_fnode[1]] == 0 and\
global_mesh.nodes[e_fnode].get('inpaint_id') != inpaint_id + 1)]
if len(e_ffnode) > 0:
e_fnode = e_ffnode[0]
break
fpath.append((fnode[0], fnode[1]))
if len(e_ffnode) == 0 or len(ffnode) == 0:
return my_npath_map, my_fpath_map, [], []
barrel_dir = np.array([[1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0], [-1, -1], [0, -1], [1, -1]])
n2f_dir = (int(fnode[0] - npath[0][0]), int(fnode[1] - npath[0][1]))
while True:
if barrel_dir[0, 0] == n2f_dir[0] and barrel_dir[0, 1] == n2f_dir[1]:
n2f_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
for step in range(0, len(npath)):
if step == 0:
continue
elif step == 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_dir[0, 0] == next_dir[0] and barrel_dir[0, 1] == next_dir[1]:
next_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
barrel_pair = np.stack((n2f_barrel, next_barrel), axis=0)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
elif step > 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_pair[1, 0, 0] == next_dir[0] and barrel_pair[1, 0, 1] == next_dir[1]:
next_barrel = barrel_pair.copy()
break
barrel_pair = np.roll(barrel_pair, 1, axis=1)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
new_locs = []
if abs(n2f_dir[0]) == 1:
new_locs.append((npath[step][0] + n2f_dir[0], npath[step][1]))
if abs(n2f_dir[1]) == 1:
new_locs.append((npath[step][0], npath[step][1] + n2f_dir[1]))
if len(new_locs) > 1:
new_locs = sorted(new_locs, key=lambda xx: np.hypot((xx[0] - fpath[-1][0]), (xx[1] - fpath[-1][1])))
break_flag = False
for new_loc in new_locs:
new_loc_nes = [xx for xx in [(new_loc[0] + 1, new_loc[1]), (new_loc[0] - 1, new_loc[1]),
(new_loc[0], new_loc[1] + 1), (new_loc[0], new_loc[1] - 1)]\
if xx[0] >= 0 and xx[0] < my_fpath_map.shape[0] and xx[1] >= 0 and xx[1] < my_fpath_map.shape[1]]
if fpath_map is not None and np.sum([fpath_map[nlne[0], nlne[1]] for nlne in new_loc_nes]) != 0:
break_flag = True
break
if my_npath_map[new_loc[0], new_loc[1]] != -1:
continue
if npath_map is not None and npath_map[new_loc[0], new_loc[1]] != edge_id:
break_flag = True
break
fpath.append(new_loc)
if break_flag is True:
break
if (e_fnode[0], e_fnode[1]) not in fpath:
fpath.append((e_fnode[0], e_fnode[1]))
if step != len(npath) - 1:
for xx in npath[step:]:
if my_npath_map[xx[0], xx[1]] == edge_id:
my_npath_map[xx[0], xx[1]] = -1
npath = npath[:step]
if len(fpath) > 0:
for fp_node in fpath:
my_fpath_map[fp_node[0], fp_node[1]] = edge_id
return my_fpath_map, my_npath_map, npath, fpath
def plan_path(mesh, info_on_pix, cc, end_pt, global_mesh, input_edge, mask, valid_map, inpaint_id, npath_map=None, fpath_map=None, npath=None):
my_npath_map = np.zeros_like(input_edge) - 1
my_fpath_map = np.zeros_like(input_edge) - 1
sub_mesh = mesh.subgraph(list(cc)).copy()
pnodes = netx.periphery(sub_mesh)
ends = [*end_pt]
edge_id = global_mesh.nodes[ends[0]]['edge_id']
pnodes = sorted(pnodes,
key=lambda x: np.hypot((x[0] - ends[0][0]), (x[1] - ends[0][1])),
reverse=True)[0]
if npath is None:
npath = [*netx.shortest_path(sub_mesh, (ends[0][0], ends[0][1]), pnodes, weight='length')]
else:
if (ends[0][0], ends[0][1]) == npath[0]:
npath = npath
elif (ends[0][0], ends[0][1]) == npath[-1]:
npath = npath[::-1]
else:
import pdb; pdb.set_trace()
for np_node in npath:
my_npath_map[np_node[0], np_node[1]] = edge_id
fpath = []
if global_mesh.nodes[ends[0]].get('far') is None:
print("None far")
else:
fnodes = global_mesh.nodes[ends[0]].get('far')
dmask = mask + 0
did = 0
while True:
did += 1
if did > 3:
return my_fpath_map, my_npath_map, -1
dmask = cv2.dilate(dmask, np.ones((3, 3)), iterations=1)
ffnode = [fnode for fnode in fnodes if (dmask[fnode[0], fnode[1]] > 0 and mask[fnode[0], fnode[1]] == 0 and\
global_mesh.nodes[fnode].get('inpaint_id') != inpaint_id + 1)]
if len(ffnode) > 0:
fnode = ffnode[0]
break
fpath.append((fnode[0], fnode[1]))
disp_diff = 0.
for n_loc in npath:
if mask[n_loc[0], n_loc[1]] != 0:
disp_diff = abs(abs(1. / info_on_pix[(n_loc[0], n_loc[1])][0]['depth']) - abs(1. / ends[0][2]))
break
barrel_dir = np.array([[1, 0], [1, 1], [0, 1], [-1, 1], [-1, 0], [-1, -1], [0, -1], [1, -1]])
n2f_dir = (int(fnode[0] - npath[0][0]), int(fnode[1] - npath[0][1]))
while True:
if barrel_dir[0, 0] == n2f_dir[0] and barrel_dir[0, 1] == n2f_dir[1]:
n2f_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
for step in range(0, len(npath)):
if step == 0:
continue
elif step == 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_dir[0, 0] == next_dir[0] and barrel_dir[0, 1] == next_dir[1]:
next_barrel = barrel_dir.copy()
break
barrel_dir = np.roll(barrel_dir, 1, axis=0)
barrel_pair = np.stack((n2f_barrel, next_barrel), axis=0)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
elif step > 1:
next_dir = (npath[step][0] - npath[step - 1][0], npath[step][1] - npath[step - 1][1])
while True:
if barrel_pair[1, 0, 0] == next_dir[0] and barrel_pair[1, 0, 1] == next_dir[1]:
next_barrel = barrel_pair.copy()
break
barrel_pair = np.roll(barrel_pair, 1, axis=1)
n2f_dir = (barrel_pair[0, 0, 0], barrel_pair[0, 0, 1])
new_locs = []
if abs(n2f_dir[0]) == 1:
new_locs.append((npath[step][0] + n2f_dir[0], npath[step][1]))
if abs(n2f_dir[1]) == 1:
new_locs.append((npath[step][0], npath[step][1] + n2f_dir[1]))
if len(new_locs) > 1:
new_locs = sorted(new_locs, key=lambda xx: np.hypot((xx[0] - fpath[-1][0]), (xx[1] - fpath[-1][1])))
break_flag = False
for new_loc in new_locs:
new_loc_nes = [xx for xx in [(new_loc[0] + 1, new_loc[1]), (new_loc[0] - 1, new_loc[1]),
(new_loc[0], new_loc[1] + 1), (new_loc[0], new_loc[1] - 1)]\
if xx[0] >= 0 and xx[0] < my_fpath_map.shape[0] and xx[1] >= 0 and xx[1] < my_fpath_map.shape[1]]
if fpath_map is not None and np.all([(fpath_map[nlne[0], nlne[1]] == -1) for nlne in new_loc_nes]) != True:
break_flag = True
break
if np.all([(my_fpath_map[nlne[0], nlne[1]] == -1) for nlne in new_loc_nes]) != True:
break_flag = True
break
if my_npath_map[new_loc[0], new_loc[1]] != -1:
continue
if npath_map is not None and npath_map[new_loc[0], new_loc[1]] != edge_id:
break_flag = True
break
if valid_map[new_loc[0], new_loc[1]] == 0:
break_flag = True
break
fpath.append(new_loc)
if break_flag is True:
break
if step != len(npath) - 1:
for xx in npath[step:]:
if my_npath_map[xx[0], xx[1]] == edge_id:
my_npath_map[xx[0], xx[1]] = -1
npath = npath[:step]
if len(fpath) > 0:
for fp_node in fpath:
my_fpath_map[fp_node[0], fp_node[1]] = edge_id
return my_fpath_map, my_npath_map, disp_diff
def refresh_node(old_node, old_feat, new_node, new_feat, mesh, stime=False):
mesh.add_node(new_node)
mesh.nodes[new_node].update(new_feat)
mesh.nodes[new_node].update(old_feat)
for ne in mesh.neighbors(old_node):
mesh.add_edge(new_node, ne)
if mesh.nodes[new_node].get('far') is not None:
tmp_far_nodes = mesh.nodes[new_node]['far']
for far_node in tmp_far_nodes:
if mesh.has_node(far_node) is False:
mesh.nodes[new_node]['far'].remove(far_node)
continue
if mesh.nodes[far_node].get('near') is not None:
for idx in range(len(mesh.nodes[far_node].get('near'))):
if mesh.nodes[far_node]['near'][idx][0] == new_node[0] and mesh.nodes[far_node]['near'][idx][1] == new_node[1]:
if len(mesh.nodes[far_node]['near'][idx]) == len(old_node):
mesh.nodes[far_node]['near'][idx] = new_node
if mesh.nodes[new_node].get('near') is not None:
tmp_near_nodes = mesh.nodes[new_node]['near']
for near_node in tmp_near_nodes:
if mesh.has_node(near_node) is False:
mesh.nodes[new_node]['near'].remove(near_node)
continue
if mesh.nodes[near_node].get('far') is not None:
for idx in range(len(mesh.nodes[near_node].get('far'))):
if mesh.nodes[near_node]['far'][idx][0] == new_node[0] and mesh.nodes[near_node]['far'][idx][1] == new_node[1]:
if len(mesh.nodes[near_node]['far'][idx]) == len(old_node):
mesh.nodes[near_node]['far'][idx] = new_node
if new_node != old_node:
mesh.remove_node(old_node)
if stime is False:
return mesh
else:
return mesh, None, None
def create_placeholder(context, mask, depth, fpath_map, npath_map, mesh, inpaint_id, edge_ccs, extend_edge_cc, all_edge_maps, self_edge_id):
add_node_time = 0
add_edge_time = 0
add_far_near_time = 0
valid_area = context + mask
H, W = mesh.graph['H'], mesh.graph['W']
edge_cc = edge_ccs[self_edge_id]
num_com = len(edge_cc) + len(extend_edge_cc)
hxs, hys = np.where(mask > 0)
for hx, hy in zip(hxs, hys):
mesh.add_node((hx, hy), inpaint_id=inpaint_id + 1, num_context=num_com)
for hx, hy in zip(hxs, hys):
four_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] if\
0 <= x < mesh.graph['H'] and 0 <= y < mesh.graph['W'] and valid_area[x, y] != 0]
for ne in four_nes:
if mask[ne[0], ne[1]] != 0:
if not mesh.has_edge((hx, hy), ne):
mesh.add_edge((hx, hy), ne)
elif depth[ne[0], ne[1]] != 0:
if mesh.has_node((ne[0], ne[1], depth[ne[0], ne[1]])) and\
not mesh.has_edge((hx, hy), (ne[0], ne[1], depth[ne[0], ne[1]])):
mesh.add_edge((hx, hy), (ne[0], ne[1], depth[ne[0], ne[1]]))
else:
print("Undefined context node.")
import pdb; pdb.set_trace()
near_ids = np.unique(npath_map)
if near_ids[0] == -1: near_ids = near_ids[1:]
for near_id in near_ids:
hxs, hys = np.where((fpath_map == near_id) & (mask > 0))
if hxs.shape[0] > 0:
mesh.graph['max_edge_id'] = mesh.graph['max_edge_id'] + 1
else:
break
for hx, hy in zip(hxs, hys):
mesh.nodes[(hx, hy)]['edge_id'] = int(round(mesh.graph['max_edge_id']))
four_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] if\
x < mesh.graph['H'] and x >= 0 and y < mesh.graph['W'] and y >= 0 and npath_map[x, y] == near_id]
for xx in four_nes:
xx_n = copy.deepcopy(xx)
if not mesh.has_node(xx_n):
if mesh.has_node((xx_n[0], xx_n[1], depth[xx_n[0], xx_n[1]])):
xx_n = (xx_n[0], xx_n[1], depth[xx_n[0], xx_n[1]])
if mesh.has_edge((hx, hy), xx_n):
# pass
mesh.remove_edge((hx, hy), xx_n)
if mesh.nodes[(hx, hy)].get('near') is None:
mesh.nodes[(hx, hy)]['near'] = []
mesh.nodes[(hx, hy)]['near'].append(xx_n)
connect_point_exception = set()
hxs, hys = np.where((npath_map == near_id) & (all_edge_maps > -1))
for hx, hy in zip(hxs, hys):
unknown_id = int(round(all_edge_maps[hx, hy]))
if unknown_id != near_id and unknown_id != self_edge_id:
unknown_node = set([xx for xx in edge_ccs[unknown_id] if xx[0] == hx and xx[1] == hy])
connect_point_exception |= unknown_node
hxs, hys = np.where((npath_map == near_id) & (mask > 0))
if hxs.shape[0] > 0:
mesh.graph['max_edge_id'] = mesh.graph['max_edge_id'] + 1
else:
break
for hx, hy in zip(hxs, hys):
mesh.nodes[(hx, hy)]['edge_id'] = int(round(mesh.graph['max_edge_id']))
mesh.nodes[(hx, hy)]['connect_point_id'] = int(round(near_id))
mesh.nodes[(hx, hy)]['connect_point_exception'] = connect_point_exception
four_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] if\
x < mesh.graph['H'] and x >= 0 and y < mesh.graph['W'] and y >= 0 and fpath_map[x, y] == near_id]
for xx in four_nes:
xx_n = copy.deepcopy(xx)
if not mesh.has_node(xx_n):
if mesh.has_node((xx_n[0], xx_n[1], depth[xx_n[0], xx_n[1]])):
xx_n = (xx_n[0], xx_n[1], depth[xx_n[0], xx_n[1]])
if mesh.has_edge((hx, hy), xx_n):
mesh.remove_edge((hx, hy), xx_n)
if mesh.nodes[(hx, hy)].get('far') is None:
mesh.nodes[(hx, hy)]['far'] = []
mesh.nodes[(hx, hy)]['far'].append(xx_n)
return mesh, add_node_time, add_edge_time, add_far_near_time
def clean_far_edge(mask_edge, mask_edge_with_id, context_edge, mask, info_on_pix, global_mesh, anchor):
if isinstance(mask_edge, torch.Tensor):
if mask_edge.is_cuda:
mask_edge = mask_edge.cpu()
mask_edge = mask_edge.data
mask_edge = mask_edge.numpy()
if isinstance(context_edge, torch.Tensor):
if context_edge.is_cuda:
context_edge = context_edge.cpu()
context_edge = context_edge.data
context_edge = context_edge.numpy()
if isinstance(mask, torch.Tensor):
if mask.is_cuda:
mask = mask.cpu()
mask = mask.data
mask = mask.numpy()
mask = mask.squeeze()
mask_edge = mask_edge.squeeze()
context_edge = context_edge.squeeze()
valid_near_edge = np.zeros_like(mask_edge)
far_edge = np.zeros_like(mask_edge)
far_edge_with_id = np.ones_like(mask_edge) * -1
near_edge_with_id = np.ones_like(mask_edge) * -1
uncleaned_far_edge = np.zeros_like(mask_edge)
# Detect if there is any valid pixel mask_edge, if not ==> return default value
if mask_edge.sum() == 0:
return far_edge, uncleaned_far_edge, far_edge_with_id, near_edge_with_id
mask_edge_ids = dict(collections.Counter(mask_edge_with_id.flatten())).keys()
for edge_id in mask_edge_ids:
if edge_id < 0:
continue
specific_edge_map = (mask_edge_with_id == edge_id).astype(np.uint8)
_, sub_specific_edge_maps = cv2.connectedComponents(specific_edge_map.astype(np.uint8), connectivity=8)
for sub_edge_id in range(1, sub_specific_edge_maps.max() + 1):
specific_edge_map = (sub_specific_edge_maps == sub_edge_id).astype(np.uint8)
edge_pxs, edge_pys = np.where(specific_edge_map > 0)
edge_mesh = netx.Graph()
for edge_px, edge_py in zip(edge_pxs, edge_pys):
edge_mesh.add_node((edge_px, edge_py))
for ex in [edge_px-1, edge_px, edge_px+1]:
for ey in [edge_py-1, edge_py, edge_py+1]:
if edge_px == ex and edge_py == ey:
continue
if ex < 0 or ex >= specific_edge_map.shape[0] or ey < 0 or ey >= specific_edge_map.shape[1]:
continue
if specific_edge_map[ex, ey] == 1:
if edge_mesh.has_node((ex, ey)):
edge_mesh.add_edge((ex, ey), (edge_px, edge_py))
periphery_nodes = netx.periphery(edge_mesh)
path_diameter = netx.diameter(edge_mesh)
start_near_node = None
for node_s in periphery_nodes:
for node_e in periphery_nodes:
if node_s != node_e:
if netx.shortest_path_length(edge_mesh, node_s, node_e) == path_diameter:
if np.any(context_edge[node_s[0]-1:node_s[0]+2, node_s[1]-1:node_s[1]+2].flatten()):
start_near_node = (node_s[0], node_s[1])
end_near_node = (node_e[0], node_e[1])
break
if np.any(context_edge[node_e[0]-1:node_e[0]+2, node_e[1]-1:node_e[1]+2].flatten()):
start_near_node = (node_e[0], node_e[1])
end_near_node = (node_s[0], node_s[1])
break
if start_near_node is not None:
break
if start_near_node is None:
continue
new_specific_edge_map = np.zeros_like(mask)
for path_node in netx.shortest_path(edge_mesh, start_near_node, end_near_node):
new_specific_edge_map[path_node[0], path_node[1]] = 1
context_near_pxs, context_near_pys = np.where(context_edge[start_near_node[0]-1:start_near_node[0]+2, start_near_node[1]-1:start_near_node[1]+2] > 0)
distance = np.abs((context_near_pxs - 1)) + np.abs((context_near_pys - 1))
if (np.where(distance == distance.min())[0].shape[0]) > 1:
closest_pxs = context_near_pxs[np.where(distance == distance.min())[0]]
closest_pys = context_near_pys[np.where(distance == distance.min())[0]]
closest_depths = []
for closest_px, closest_py in zip(closest_pxs, closest_pys):
if info_on_pix.get((closest_px + start_near_node[0] - 1 + anchor[0], closest_py + start_near_node[1] - 1 + anchor[2])) is not None:
for info in info_on_pix.get((closest_px + start_near_node[0] - 1 + anchor[0], closest_py + start_near_node[1] - 1 + anchor[2])):
if info['synthesis'] is False:
closest_depths.append(abs(info['depth']))
context_near_px, context_near_py = closest_pxs[np.array(closest_depths).argmax()], closest_pys[np.array(closest_depths).argmax()]
else:
context_near_px, context_near_py = context_near_pxs[distance.argmin()], context_near_pys[distance.argmin()]
context_near_node = (start_near_node[0]-1 + context_near_px, start_near_node[1]-1 + context_near_py)
far_node_list = []
global_context_near_node = (context_near_node[0] + anchor[0], context_near_node[1] + anchor[2])
if info_on_pix.get(global_context_near_node) is not None:
for info in info_on_pix[global_context_near_node]:
if info['synthesis'] is False:
context_near_node_3d = (global_context_near_node[0], global_context_near_node[1], info['depth'])
if global_mesh.nodes[context_near_node_3d].get('far') is not None:
for far_node in global_mesh.nodes[context_near_node_3d].get('far'):
far_node = (far_node[0] - anchor[0], far_node[1] - anchor[2], far_node[2])
if mask[far_node[0], far_node[1]] == 0:
far_node_list.append([far_node[0], far_node[1]])
if len(far_node_list) > 0:
far_nodes_dist = np.sum(np.abs(np.array(far_node_list) - np.array([[edge_px, edge_py]])), axis=1)
context_far_node = tuple(far_node_list[far_nodes_dist.argmin()])
corresponding_far_edge = np.zeros_like(mask_edge)
corresponding_far_edge[context_far_node[0], context_far_node[1]] = 1
surround_map = cv2.dilate(new_specific_edge_map.astype(np.uint8),
np.array([[1,1,1],[1,1,1],[1,1,1]]).astype(np.uint8),
iterations=1)
specific_edge_map_wo_end_pt = new_specific_edge_map.copy()
specific_edge_map_wo_end_pt[end_near_node[0], end_near_node[1]] = 0
surround_map_wo_end_pt = cv2.dilate(specific_edge_map_wo_end_pt.astype(np.uint8),
np.array([[1,1,1],[1,1,1],[1,1,1]]).astype(np.uint8),
iterations=1)
surround_map_wo_end_pt[new_specific_edge_map > 0] = 0
surround_map_wo_end_pt[context_near_node[0], context_near_node[1]] = 0
surround_map = surround_map_wo_end_pt.copy()
_, far_edge_cc = cv2.connectedComponents(surround_map.astype(np.uint8), connectivity=4)
start_far_node = None
accompany_far_node = None
if surround_map[context_far_node[0], context_far_node[1]] == 1:
start_far_node = context_far_node
else:
four_nes = [(context_far_node[0] - 1, context_far_node[1]),
(context_far_node[0] + 1, context_far_node[1]),
(context_far_node[0], context_far_node[1] - 1),
(context_far_node[0], context_far_node[1] + 1)]
candidate_bevel = []
for ne in four_nes:
if surround_map[ne[0], ne[1]] == 1:
start_far_node = (ne[0], ne[1])
break
elif (ne[0] != context_near_node[0] or ne[1] != context_near_node[1]) and \
(ne[0] != start_near_node[0] or ne[1] != start_near_node[1]):
candidate_bevel.append((ne[0], ne[1]))
if start_far_node is None:
for ne in candidate_bevel:
if ne[0] == context_far_node[0]:
bevel_xys = [[ne[0] + 1, ne[1]], [ne[0] - 1, ne[1]]]
if ne[1] == context_far_node[1]:
bevel_xys = [[ne[0], ne[1] + 1], [ne[0], ne[1] - 1]]
for bevel_x, bevel_y in bevel_xys:
if surround_map[bevel_x, bevel_y] == 1:
start_far_node = (bevel_x, bevel_y)
accompany_far_node = (ne[0], ne[1])
break
if start_far_node is not None:
break
if start_far_node is not None:
for far_edge_id in range(1, far_edge_cc.max() + 1):
specific_far_edge = (far_edge_cc == far_edge_id).astype(np.uint8)
if specific_far_edge[start_far_node[0], start_far_node[1]] == 1:
if accompany_far_node is not None:
specific_far_edge[accompany_far_node] = 1
far_edge[specific_far_edge > 0] = 1
far_edge_with_id[specific_far_edge > 0] = edge_id
end_far_candidates = np.zeros_like(far_edge)
end_far_candidates[end_near_node[0], end_near_node[1]] = 1
end_far_candidates = cv2.dilate(end_far_candidates.astype(np.uint8),
np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8),
iterations=1)
end_far_candidates[end_near_node[0], end_near_node[1]] = 0
invalid_nodes = (((far_edge_cc != far_edge_id).astype(np.uint8) * \
(far_edge_cc != 0).astype(np.uint8)).astype(np.uint8) + \
(new_specific_edge_map).astype(np.uint8) + \
(mask == 0).astype(np.uint8)).clip(0, 1)
end_far_candidates[invalid_nodes > 0] = 0
far_edge[end_far_candidates > 0] = 1
far_edge_with_id[end_far_candidates > 0] = edge_id
far_edge[context_far_node[0], context_far_node[1]] = 1
far_edge_with_id[context_far_node[0], context_far_node[1]] = edge_id
near_edge_with_id[(mask_edge_with_id == edge_id) > 0] = edge_id
uncleaned_far_edge = far_edge.copy()
far_edge[mask == 0] = 0
return far_edge, uncleaned_far_edge, far_edge_with_id, near_edge_with_id
def get_MiDaS_samples(image_folder, depth_folder, config, specific=None, aft_certain=None):
lines = [os.path.splitext(os.path.basename(xx))[0] for xx in glob.glob(os.path.join(image_folder, '*' + config['img_format']))]
samples = []
generic_pose = np.eye(4)
assert len(config['traj_types']) == len(config['x_shift_range']) ==\
len(config['y_shift_range']) == len(config['z_shift_range']) == len(config['video_postfix']), \
"The number of elements in 'traj_types', 'x_shift_range', 'y_shift_range', 'z_shift_range' and \
'video_postfix' should be equal."
tgt_pose = [[generic_pose * 1]]
tgts_poses = []
for traj_idx in range(len(config['traj_types'])):
tgt_poses = []
sx, sy, sz = path_planning(config['num_frames'], config['x_shift_range'][traj_idx], config['y_shift_range'][traj_idx],
config['z_shift_range'][traj_idx], path_type=config['traj_types'][traj_idx])
for xx, yy, zz in zip(sx, sy, sz):
tgt_poses.append(generic_pose * 1.)
tgt_poses[-1][:3, -1] = np.array([xx, yy, zz])
tgts_poses += [tgt_poses]
tgt_pose = generic_pose * 1
aft_flag = True
if aft_certain is not None and len(aft_certain) > 0:
aft_flag = False
for seq_dir in lines:
if specific is not None and len(specific) > 0:
if specific != seq_dir:
continue
if aft_certain is not None and len(aft_certain) > 0:
if aft_certain == seq_dir:
aft_flag = True
if aft_flag is False:
continue
samples.append({})
sdict = samples[-1]
sdict['depth_fi'] = os.path.join(depth_folder, seq_dir + config['depth_format'])
sdict['ref_img_fi'] = os.path.join(image_folder, seq_dir + config['img_format'])
H, W = imageio.imread(sdict['ref_img_fi']).shape[:2]
sdict['int_mtx'] = np.array([[max(H, W), 0, W//2], [0, max(H, W), H//2], [0, 0, 1]]).astype(np.float32)
if sdict['int_mtx'].max() > 1:
sdict['int_mtx'][0, :] = sdict['int_mtx'][0, :] / float(W)
sdict['int_mtx'][1, :] = sdict['int_mtx'][1, :] / float(H)
sdict['ref_pose'] = np.eye(4)
sdict['tgt_pose'] = tgt_pose
sdict['tgts_poses'] = tgts_poses
sdict['video_postfix'] = config['video_postfix']
sdict['tgt_name'] = [os.path.splitext(os.path.basename(sdict['depth_fi']))[0]]
sdict['src_pair_name'] = sdict['tgt_name'][0]
return samples
def get_valid_size(imap):
x_max = np.where(imap.sum(1).squeeze() > 0)[0].max() + 1
x_min = np.where(imap.sum(1).squeeze() > 0)[0].min()
y_max = np.where(imap.sum(0).squeeze() > 0)[0].max() + 1
y_min = np.where(imap.sum(0).squeeze() > 0)[0].min()
size_dict = {'x_max':x_max, 'y_max':y_max, 'x_min':x_min, 'y_min':y_min}
return size_dict
def dilate_valid_size(isize_dict, imap, dilate=[0, 0]):
osize_dict = copy.deepcopy(isize_dict)
osize_dict['x_min'] = max(0, osize_dict['x_min'] - dilate[0])
osize_dict['x_max'] = min(imap.shape[0], osize_dict['x_max'] + dilate[0])
osize_dict['y_min'] = max(0, osize_dict['y_min'] - dilate[0])
osize_dict['y_max'] = min(imap.shape[1], osize_dict['y_max'] + dilate[1])
return osize_dict
def crop_maps_by_size(size, *imaps):
omaps = []
for imap in imaps:
omaps.append(imap[size['x_min']:size['x_max'], size['y_min']:size['y_max']].copy())
return omaps
def smooth_cntsyn_gap(init_depth_map, mask_region, context_region, init_mask_region=None):
if init_mask_region is not None:
curr_mask_region = init_mask_region * 1
else:
curr_mask_region = mask_region * 0
depth_map = init_depth_map.copy()
for _ in range(2):
cm_mask = context_region + curr_mask_region
depth_s1 = np.roll(depth_map, 1, 0)
depth_s2 = np.roll(depth_map, -1, 0)
depth_s3 = np.roll(depth_map, 1, 1)
depth_s4 = np.roll(depth_map, -1, 1)
mask_s1 = np.roll(cm_mask, 1, 0)
mask_s2 = np.roll(cm_mask, -1, 0)
mask_s3 = np.roll(cm_mask, 1, 1)
mask_s4 = np.roll(cm_mask, -1, 1)
fluxin_depths = (depth_s1 * mask_s1 + depth_s2 * mask_s2 + depth_s3 * mask_s3 + depth_s4 * mask_s4) / \
((mask_s1 + mask_s2 + mask_s3 + mask_s4) + 1e-6)
fluxin_mask = (fluxin_depths != 0) * mask_region
init_mask = (fluxin_mask * (curr_mask_region >= 0).astype(np.float32) > 0).astype(np.uint8)
depth_map[init_mask > 0] = fluxin_depths[init_mask > 0]
if init_mask.shape[-1] > curr_mask_region.shape[-1]:
curr_mask_region[init_mask.sum(-1, keepdims=True) > 0] = 1
else:
curr_mask_region[init_mask > 0] = 1
depth_map[fluxin_mask > 0] = fluxin_depths[fluxin_mask > 0]
return depth_map
def read_MiDaS_depth(disp_fi, disp_rescale=10., h=None, w=None):
if 'npy' in os.path.splitext(disp_fi)[-1]:
disp = np.load(disp_fi)
else:
disp = imageio.imread(disp_fi).astype(np.float32)
disp = disp - disp.min()
disp = cv2.blur(disp / disp.max(), ksize=(3, 3)) * disp.max()
disp = (disp / disp.max()) * disp_rescale
if h is not None and w is not None:
disp = resize(disp / disp.max(), (h, w), order=1) * disp.max()
depth = 1. / np.maximum(disp, 0.05)
return depth
def follow_image_aspect_ratio(depth, image):
H, W = image.shape[:2]
image_aspect_ratio = H / W
dH, dW = depth.shape[:2]
depth_aspect_ratio = dH / dW
if depth_aspect_ratio > image_aspect_ratio:
resize_H = dH
resize_W = dH / image_aspect_ratio
else:
resize_W = dW
resize_H = dW * image_aspect_ratio
depth = resize(depth / depth.max(),
(int(resize_H),
int(resize_W)),
order=0) * depth.max()
return depth
def depth_resize(depth, origin_size, image_size):
if origin_size[0] is not 0:
max_depth = depth.max()
depth = depth / max_depth
depth = resize(depth, origin_size, order=1, mode='edge')
depth = depth * max_depth
else:
max_depth = depth.max()
depth = depth / max_depth
depth = resize(depth, image_size, order=1, mode='edge')
depth = depth * max_depth
return depth
def filter_irrelevant_edge(self_edge, other_edges, other_edges_with_id, current_edge_id, context, edge_ccs, mesh, anchor):
other_edges = other_edges.squeeze()
other_edges_with_id = other_edges_with_id.squeeze()
self_edge = self_edge.squeeze()
dilate_self_edge = cv2.dilate(self_edge.astype(np.uint8), np.array([[1,1,1],[1,1,1],[1,1,1]]).astype(np.uint8), iterations=1)
edge_ids = collections.Counter(other_edges_with_id.flatten()).keys()
other_edges_info = []
# import ipdb
# ipdb.set_trace()
for edge_id in edge_ids:
edge_id = int(edge_id)
if edge_id >= 0:
condition = ((other_edges_with_id == edge_id) * other_edges * context).astype(np.uint8)
if dilate_self_edge[condition > 0].sum() == 0:
other_edges[other_edges_with_id == edge_id] = 0
else:
num_condition, condition_labels = cv2.connectedComponents(condition, connectivity=8)
for condition_id in range(1, num_condition):
isolate_condition = ((condition_labels == condition_id) > 0).astype(np.uint8)
num_end_group, end_group = cv2.connectedComponents(((dilate_self_edge * isolate_condition) > 0).astype(np.uint8), connectivity=8)
if num_end_group == 1:
continue
for end_id in range(1, num_end_group):
end_pxs, end_pys = np.where((end_group == end_id))
end_px, end_py = end_pxs[0], end_pys[0]
other_edges_info.append({})
other_edges_info[-1]['edge_id'] = edge_id
# other_edges_info[-1]['near_depth'] = None
other_edges_info[-1]['diff'] = None
other_edges_info[-1]['edge_map'] = np.zeros_like(self_edge)
other_edges_info[-1]['end_point_map'] = np.zeros_like(self_edge)
other_edges_info[-1]['end_point_map'][(end_group == end_id)] = 1
other_edges_info[-1]['forbidden_point_map'] = np.zeros_like(self_edge)
other_edges_info[-1]['forbidden_point_map'][(end_group != end_id) * (end_group != 0)] = 1
other_edges_info[-1]['forbidden_point_map'] = cv2.dilate(other_edges_info[-1]['forbidden_point_map'], kernel=np.array([[1,1,1],[1,1,1],[1,1,1]]), iterations=2)
for x in edge_ccs[edge_id]:
nx = x[0] - anchor[0]
ny = x[1] - anchor[1]
if nx == end_px and ny == end_py:
# other_edges_info[-1]['near_depth'] = abs(nx)
if mesh.nodes[x].get('far') is not None and len(mesh.nodes[x].get('far')) == 1:
other_edges_info[-1]['diff'] = abs(1./abs([*mesh.nodes[x].get('far')][0][2]) - 1./abs(x[2]))
else:
other_edges_info[-1]['diff'] = 0
# if end_group[nx, ny] != end_id and end_group[nx, ny] > 0:
# continue
try:
if isolate_condition[nx, ny] == 1:
other_edges_info[-1]['edge_map'][nx, ny] = 1
except:
pass
try:
other_edges_info = sorted(other_edges_info, key=lambda x : x['diff'], reverse=True)
except:
import pdb
pdb.set_trace()
# import pdb
# pdb.set_trace()
# other_edges = other_edges[..., None]
for other_edge in other_edges_info:
if other_edge['end_point_map'] is None:
import pdb
pdb.set_trace()
other_edges = other_edges * context
return other_edges, other_edges_info
def require_depth_edge(context_edge, mask):
dilate_mask = cv2.dilate(mask, np.array([[1,1,1],[1,1,1],[1,1,1]]).astype(np.uint8), iterations=1)
if (dilate_mask * context_edge).max() == 0:
return False
else:
return True
def refine_color_around_edge(mesh, info_on_pix, edge_ccs, config, spdb=False):
H, W = mesh.graph['H'], mesh.graph['W']
tmp_edge_ccs = copy.deepcopy(edge_ccs)
for edge_id, edge_cc in enumerate(edge_ccs):
if len(edge_cc) == 0:
continue
near_maps = np.zeros((H, W)).astype(np.bool)
far_maps = np.zeros((H, W)).astype(np.bool)
tmp_far_nodes = set()
far_nodes = set()
near_nodes = set()
end_nodes = set()
for i in range(5):
if i == 0:
for edge_node in edge_cc:
if mesh.nodes[edge_node].get('depth_edge_dilate_2_color_flag') is not True:
break
if mesh.nodes[edge_node].get('inpaint_id') == 1:
near_nodes.add(edge_node)
tmp_node = mesh.nodes[edge_node].get('far')
tmp_node = set(tmp_node) if tmp_node is not None else set()
tmp_far_nodes |= tmp_node
rmv_tmp_far_nodes = set()
for far_node in tmp_far_nodes:
if not(mesh.has_node(far_node) and mesh.nodes[far_node].get('inpaint_id') == 1):
rmv_tmp_far_nodes.add(far_node)
if len(tmp_far_nodes - rmv_tmp_far_nodes) == 0:
break
else:
for near_node in near_nodes:
near_maps[near_node[0], near_node[1]] = True
mesh.nodes[near_node]['refine_rgbd'] = True
mesh.nodes[near_node]['backup_depth'] = near_node[2] \
if mesh.nodes[near_node].get('real_depth') is None else mesh.nodes[near_node]['real_depth']
mesh.nodes[near_node]['backup_color'] = mesh.nodes[near_node]['color']
for far_node in tmp_far_nodes:
if mesh.has_node(far_node) and mesh.nodes[far_node].get('inpaint_id') == 1:
far_nodes.add(far_node)
far_maps[far_node[0], far_node[1]] = True
mesh.nodes[far_node]['refine_rgbd'] = True
mesh.nodes[far_node]['backup_depth'] = far_node[2] \
if mesh.nodes[far_node].get('real_depth') is None else mesh.nodes[far_node]['real_depth']
mesh.nodes[far_node]['backup_color'] = mesh.nodes[far_node]['color']
tmp_far_nodes = far_nodes
tmp_near_nodes = near_nodes
else:
tmp_far_nodes = new_tmp_far_nodes
tmp_near_nodes = new_tmp_near_nodes
new_tmp_far_nodes = None
new_tmp_near_nodes = None
new_tmp_far_nodes = set()
new_tmp_near_nodes = set()
for node in tmp_near_nodes:
for ne_node in mesh.neighbors(node):
if far_maps[ne_node[0], ne_node[1]] == False and \
near_maps[ne_node[0], ne_node[1]] == False:
if mesh.nodes[ne_node].get('inpaint_id') == 1:
new_tmp_near_nodes.add(ne_node)
near_maps[ne_node[0], ne_node[1]] = True
mesh.nodes[ne_node]['refine_rgbd'] = True
mesh.nodes[ne_node]['backup_depth'] = ne_node[2] \
if mesh.nodes[ne_node].get('real_depth') is None else mesh.nodes[ne_node]['real_depth']
mesh.nodes[ne_node]['backup_color'] = mesh.nodes[ne_node]['color']
else:
mesh.nodes[ne_node]['backup_depth'] = ne_node[2] \
if mesh.nodes[ne_node].get('real_depth') is None else mesh.nodes[ne_node]['real_depth']
mesh.nodes[ne_node]['backup_color'] = mesh.nodes[ne_node]['color']
end_nodes.add(node)
near_nodes.update(new_tmp_near_nodes)
for node in tmp_far_nodes:
for ne_node in mesh.neighbors(node):
if far_maps[ne_node[0], ne_node[1]] == False and \
near_maps[ne_node[0], ne_node[1]] == False:
if mesh.nodes[ne_node].get('inpaint_id') == 1:
new_tmp_far_nodes.add(ne_node)
far_maps[ne_node[0], ne_node[1]] = True
mesh.nodes[ne_node]['refine_rgbd'] = True
mesh.nodes[ne_node]['backup_depth'] = ne_node[2] \
if mesh.nodes[ne_node].get('real_depth') is None else mesh.nodes[ne_node]['real_depth']
mesh.nodes[ne_node]['backup_color'] = mesh.nodes[ne_node]['color']
else:
mesh.nodes[ne_node]['backup_depth'] = ne_node[2] \
if mesh.nodes[ne_node].get('real_depth') is None else mesh.nodes[ne_node]['real_depth']
mesh.nodes[ne_node]['backup_color'] = mesh.nodes[ne_node]['color']
end_nodes.add(node)
far_nodes.update(new_tmp_far_nodes)
if len(far_nodes) == 0:
tmp_edge_ccs[edge_id] = set()
continue
for node in new_tmp_far_nodes | new_tmp_near_nodes:
for ne_node in mesh.neighbors(node):
if far_maps[ne_node[0], ne_node[1]] == False and near_maps[ne_node[0], ne_node[1]] == False:
end_nodes.add(node)
mesh.nodes[ne_node]['backup_depth'] = ne_node[2] \
if mesh.nodes[ne_node].get('real_depth') is None else mesh.nodes[ne_node]['real_depth']
mesh.nodes[ne_node]['backup_color'] = mesh.nodes[ne_node]['color']
tmp_end_nodes = end_nodes
refine_nodes = near_nodes | far_nodes
remain_refine_nodes = copy.deepcopy(refine_nodes)
accum_idx = 0
while len(remain_refine_nodes) > 0:
accum_idx += 1
if accum_idx > 100:
break
new_tmp_end_nodes = None
new_tmp_end_nodes = set()
survive_tmp_end_nodes = set()
for node in tmp_end_nodes:
re_depth, re_color, re_count = 0, np.array([0., 0., 0.]), 0
for ne_node in mesh.neighbors(node):
if mesh.nodes[ne_node].get('refine_rgbd') is True:
if ne_node not in tmp_end_nodes:
new_tmp_end_nodes.add(ne_node)
else:
try:
re_depth += mesh.nodes[ne_node]['backup_depth']
re_color += mesh.nodes[ne_node]['backup_color'].astype(np.float32)
re_count += 1.
except:
import pdb; pdb.set_trace()
if re_count > 0:
re_depth = re_depth / re_count
re_color = re_color / re_count
mesh.nodes[node]['backup_depth'] = re_depth
mesh.nodes[node]['backup_color'] = re_color
mesh.nodes[node]['refine_rgbd'] = False
else:
survive_tmp_end_nodes.add(node)
for node in tmp_end_nodes - survive_tmp_end_nodes:
if node in remain_refine_nodes:
remain_refine_nodes.remove(node)
tmp_end_nodes = new_tmp_end_nodes
if spdb == True:
bfrd_canvas = np.zeros((H, W))
bfrc_canvas = np.zeros((H, W, 3)).astype(np.uint8)
aftd_canvas = np.zeros((H, W))
aftc_canvas = np.zeros((H, W, 3)).astype(np.uint8)
for node in refine_nodes:
bfrd_canvas[node[0], node[1]] = abs(node[2])
aftd_canvas[node[0], node[1]] = abs(mesh.nodes[node]['backup_depth'])
bfrc_canvas[node[0], node[1]] = mesh.nodes[node]['color'].astype(np.uint8)
aftc_canvas[node[0], node[1]] = mesh.nodes[node]['backup_color'].astype(np.uint8)
f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, sharex=True, sharey=True);
ax1.imshow(bfrd_canvas);
ax2.imshow(aftd_canvas);
ax3.imshow(bfrc_canvas);
ax4.imshow(aftc_canvas);
plt.show()
import pdb; pdb.set_trace()
for node in refine_nodes:
if mesh.nodes[node].get('refine_rgbd') is not None:
mesh.nodes[node].pop('refine_rgbd')
mesh.nodes[node]['color'] = mesh.nodes[node]['backup_color']
for info in info_on_pix[(node[0], node[1])]:
if info['depth'] == node[2]:
info['color'] = mesh.nodes[node]['backup_color']
return mesh, info_on_pix
def refine_depth_around_edge(mask_depth, far_edge, uncleaned_far_edge, near_edge, mask, all_depth, config):
if isinstance(mask_depth, torch.Tensor):
if mask_depth.is_cuda:
mask_depth = mask_depth.cpu()
mask_depth = mask_depth.data
mask_depth = mask_depth.numpy()
if isinstance(far_edge, torch.Tensor):
if far_edge.is_cuda:
far_edge = far_edge.cpu()
far_edge = far_edge.data
far_edge = far_edge.numpy()
if isinstance(uncleaned_far_edge, torch.Tensor):
if uncleaned_far_edge.is_cuda:
uncleaned_far_edge = uncleaned_far_edge.cpu()
uncleaned_far_edge = uncleaned_far_edge.data
uncleaned_far_edge = uncleaned_far_edge.numpy()
if isinstance(near_edge, torch.Tensor):
if near_edge.is_cuda:
near_edge = near_edge.cpu()
near_edge = near_edge.data
near_edge = near_edge.numpy()
if isinstance(mask, torch.Tensor):
if mask.is_cuda:
mask = mask.cpu()
mask = mask.data
mask = mask.numpy()
mask = mask.squeeze()
uncleaned_far_edge = uncleaned_far_edge.squeeze()
far_edge = far_edge.squeeze()
near_edge = near_edge.squeeze()
mask_depth = mask_depth.squeeze()
dilate_far_edge = cv2.dilate(uncleaned_far_edge.astype(np.uint8), kernel=np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8), iterations=1)
near_edge[dilate_far_edge == 0] = 0
dilate_near_edge = cv2.dilate(near_edge.astype(np.uint8), kernel=np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8), iterations=1)
far_edge[dilate_near_edge == 0] = 0
init_far_edge = far_edge.copy()
init_near_edge = near_edge.copy()
for i in range(config['depth_edge_dilate_2']):
init_far_edge = cv2.dilate(init_far_edge, kernel=np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8), iterations=1)
init_far_edge[init_near_edge == 1] = 0
init_near_edge = cv2.dilate(init_near_edge, kernel=np.array([[0,1,0],[1,1,1],[0,1,0]]).astype(np.uint8), iterations=1)
init_near_edge[init_far_edge == 1] = 0
init_far_edge[mask == 0] = 0
init_near_edge[mask == 0] = 0
hole_far_edge = 1 - init_far_edge
hole_near_edge = 1 - init_near_edge
change = None
while True:
change = False
hole_far_edge[init_near_edge == 1] = 0
hole_near_edge[init_far_edge == 1] = 0
far_pxs, far_pys = np.where((hole_far_edge == 0) * (init_far_edge == 1) > 0)
current_hole_far_edge = hole_far_edge.copy()
for far_px, far_py in zip(far_pxs, far_pys):
min_px = max(far_px - 1, 0)
max_px = min(far_px + 2, mask.shape[0]-1)
min_py = max(far_py - 1, 0)
max_py = min(far_py + 2, mask.shape[1]-1)
hole_far = current_hole_far_edge[min_px: max_px, min_py: max_py]
tmp_mask = mask[min_px: max_px, min_py: max_py]
all_depth_patch = all_depth[min_px: max_px, min_py: max_py] * 0
all_depth_mask = (all_depth_patch != 0).astype(np.uint8)
cross_element = np.array([[0,1,0],[1,1,1],[0,1,0]])[min_px - (far_px - 1): max_px - (far_px - 1), min_py - (far_py - 1): max_py - (far_py - 1)]
combine_mask = (tmp_mask + all_depth_mask).clip(0, 1) * hole_far * cross_element
tmp_patch = combine_mask * (mask_depth[min_px: max_px, min_py: max_py] + all_depth_patch)
number = np.count_nonzero(tmp_patch)
if number > 0:
mask_depth[far_px, far_py] = np.sum(tmp_patch).astype(np.float32) / max(number, 1e-6)
hole_far_edge[far_px, far_py] = 1
change = True
near_pxs, near_pys = np.where((hole_near_edge == 0) * (init_near_edge == 1) > 0)
current_hole_near_edge = hole_near_edge.copy()
for near_px, near_py in zip(near_pxs, near_pys):
min_px = max(near_px - 1, 0)
max_px = min(near_px + 2, mask.shape[0]-1)
min_py = max(near_py - 1, 0)
max_py = min(near_py + 2, mask.shape[1]-1)
hole_near = current_hole_near_edge[min_px: max_px, min_py: max_py]
tmp_mask = mask[min_px: max_px, min_py: max_py]
all_depth_patch = all_depth[min_px: max_px, min_py: max_py] * 0
all_depth_mask = (all_depth_patch != 0).astype(np.uint8)
cross_element = np.array([[0,1,0],[1,1,1],[0,1,0]])[min_px - near_px + 1:max_px - near_px + 1, min_py - near_py + 1:max_py - near_py + 1]
combine_mask = (tmp_mask + all_depth_mask).clip(0, 1) * hole_near * cross_element
tmp_patch = combine_mask * (mask_depth[min_px: max_px, min_py: max_py] + all_depth_patch)
number = np.count_nonzero(tmp_patch)
if number > 0:
mask_depth[near_px, near_py] = np.sum(tmp_patch) / max(number, 1e-6)
hole_near_edge[near_px, near_py] = 1
change = True
if change is False:
break
return mask_depth
def vis_depth_edge_connectivity(depth, config):
disp = 1./depth
u_diff = (disp[1:, :] - disp[:-1, :])[:-1, 1:-1]
b_diff = (disp[:-1, :] - disp[1:, :])[1:, 1:-1]
l_diff = (disp[:, 1:] - disp[:, :-1])[1:-1, :-1]
r_diff = (disp[:, :-1] - disp[:, 1:])[1:-1, 1:]
u_over = (np.abs(u_diff) > config['depth_threshold']).astype(np.float32)
b_over = (np.abs(b_diff) > config['depth_threshold']).astype(np.float32)
l_over = (np.abs(l_diff) > config['depth_threshold']).astype(np.float32)
r_over = (np.abs(r_diff) > config['depth_threshold']).astype(np.float32)
concat_diff = np.stack([u_diff, b_diff, r_diff, l_diff], axis=-1)
concat_over = np.stack([u_over, b_over, r_over, l_over], axis=-1)
over_diff = concat_diff * concat_over
pos_over = (over_diff > 0).astype(np.float32).sum(-1).clip(0, 1)
neg_over = (over_diff < 0).astype(np.float32).sum(-1).clip(0, 1)
neg_over[(over_diff > 0).astype(np.float32).sum(-1) > 0] = 0
_, edge_label = cv2.connectedComponents(pos_over.astype(np.uint8), connectivity=8)
T_junction_maps = np.zeros_like(pos_over)
for edge_id in range(1, edge_label.max() + 1):
edge_map = (edge_label == edge_id).astype(np.uint8)
edge_map = np.pad(edge_map, pad_width=((1,1),(1,1)), mode='constant')
four_direc = np.roll(edge_map, 1, 1) + np.roll(edge_map, -1, 1) + np.roll(edge_map, 1, 0) + np.roll(edge_map, -1, 0)
eight_direc = np.roll(np.roll(edge_map, 1, 1), 1, 0) + np.roll(np.roll(edge_map, 1, 1), -1, 0) + \
np.roll(np.roll(edge_map, -1, 1), 1, 0) + np.roll(np.roll(edge_map, -1, 1), -1, 0)
eight_direc = (eight_direc + four_direc)[1:-1,1:-1]
pos_over[eight_direc > 2] = 0
T_junction_maps[eight_direc > 2] = 1
_, edge_label = cv2.connectedComponents(pos_over.astype(np.uint8), connectivity=8)
edge_label = np.pad(edge_label, 1, mode='constant')
return edge_label
def max_size(mat, value=0):
if not (mat and mat[0]): return (0, 0)
it = iter(mat)
prev = [(el==value) for el in next(it)]
max_size = max_rectangle_size(prev)
for row in it:
hist = [(1+h) if el == value else 0 for h, el in zip(prev, row)]
max_size = max(max_size, max_rectangle_size(hist), key=get_area)
prev = hist
return max_size
def max_rectangle_size(histogram):
Info = namedtuple('Info', 'start height')
stack = []
top = lambda: stack[-1]
max_size = (0, 0) # height, width of the largest rectangle
pos = 0 # current position in the histogram
for pos, height in enumerate(histogram):
start = pos # position where rectangle starts
while True:
if not stack or height > top().height:
stack.append(Info(start, height)) # push
if stack and height < top().height:
max_size = max(max_size, (top().height, (pos-top().start)),
key=get_area)
start, _ = stack.pop()
continue
break # height == top().height goes here
pos += 1
for start, height in stack:
max_size = max(max_size, (height, (pos-start)),
key=get_area)
return max_size
def get_area(size):
return reduce(mul, size)
def find_anchors(matrix):
matrix = [[*x] for x in matrix]
mh, mw = max_size(matrix)
matrix = np.array(matrix)
# element = np.zeros((mh, mw))
for i in range(matrix.shape[0] + 1 - mh):
for j in range(matrix.shape[1] + 1 - mw):
if matrix[i:i + mh, j:j + mw].max() == 0:
return i, i + mh, j, j + mw
def find_largest_rect(dst_img, bg_color=(128, 128, 128)):
valid = np.any(dst_img[..., :3] != bg_color, axis=-1)
dst_h, dst_w = dst_img.shape[:2]
ret, labels = cv2.connectedComponents(np.uint8(valid == False))
red_mat = np.zeros_like(labels)
# denoise
for i in range(1, np.max(labels)+1, 1):
x, y, w, h = cv2.boundingRect(np.uint8(labels==i))
if x == 0 or (x+w) == dst_h or y == 0 or (y+h) == dst_w:
red_mat[labels==i] = 1
# crop
t, b, l, r = find_anchors(red_mat)
return t, b, l, r
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