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uti_tool.py
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uti_tool.py
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# @Time : 06/05/2021
# @Author : Wei Chen
# @Project : Pycharm
import numpy as np
import torch
import cv2
import math
import struct
import os
import random
def get_rotation(x_,y_,z_):
# print(math.cos(math.pi/2))
x=float(x_/180)*math.pi
y=float(y_/180)*math.pi
z=float(z_/180)*math.pi
R_x=np.array([[1, 0, 0 ],
[0, math.cos(x), -math.sin(x)],
[0, math.sin(x), math.cos(x)]])
R_y=np.array([[math.cos(y), 0, math.sin(y)],
[0, 1, 0],
[-math.sin(y), 0, math.cos(y)]])
R_z=np.array([[math.cos(z), -math.sin(z), 0 ],
[math.sin(z), math.cos(z), 0],
[0, 0, 1]])
return np.dot(R_z,np.dot(R_y,R_x))
def trans_3d(pc,Rt,Tt):
Tt=np.reshape(Tt,(3,1))
pcc=np.zeros((4,pc.shape[0]),dtype=np.float32)
pcc[0:3,:]=pc.T
pcc[3,:]=1
TT=np.zeros((3,4),dtype=np.float32)
TT[:,0:3]=Rt
TT[:,3]=Tt[:,0]
trans=np.dot(TT,pcc)
return trans
def data_augment(points, Rs, Ts, num_c, target_seg,ax=5, ay=5, az=25, a=15):
centers = np.zeros((points.shape[0], 3))
corners = np.zeros((points.shape[0], 3 * num_c))
pts_recon= torch.zeros(points.shape[0], 1000, 3)
pts_noTs = points.copy()
pts0 = points.copy()
for ii in range(points.shape[0]):
# idx = idxs[ii].item()
Rt = Rs[ii].numpy().reshape(3,3)
Tt = Ts[ii].numpy().reshape(1,3)
# res = np.mean(points[ii], 0)
res = np.mean(points[ii], 0)
points[ii, :, 0:3] = points[ii, :, 0:3] - np.array([res[0], res[1], res[2]])
points[ii, :, 0:3] = cv2.ppf_match_3d.addNoisePC(np.float32(points[ii, :, 0:3]), 0.1)
dx = np.random.randint(-ax, ax)
dy = np.random.randint(-ay, ay)
dz = np.random.randint(-az, az)
points[ii, :, 0] = points[ii, :, 0] + dx
points[ii, :, 1] = points[ii, :, 1] + dy
points[ii, :, 2] = points[ii, :, 2] + dz
Rm = get_rotation(np.random.uniform(-a, a), np.random.uniform(-a, a), np.random.uniform(-a, a))
points[ii, :, 0:3] = np.dot(Rm, points[ii, :, 0:3].T).T
if target_seg[0].sum().item()<1:
print('target_seg.numpy()[ii, :] == 1)[0].sum()',(target_seg.numpy()[ii, :] ==1)[0].sum())
pts_seg = pts0[ii, np.where(target_seg.numpy()[ii, :] == 1)[0], 0:3]
centers[ii,:]=Tt-np.mean(pts_seg,0)
Tt_c = np.array([0, 0, 0]).T
corners_ = np.array([[0,0,0],[0,200, 0],[200, 0, 0]])
pts_rec = pts_seg - Tt
choice = np.random.choice(len(pts_rec), 1000, replace=True)
pts_rec = pts_rec[choice, :]
pts_recon[ii, :] = torch.Tensor(np.dot(Rm, pts_rec.T).T)
pts_nT = pts_noTs[ii, :] - Tt
pts_noTs[ii, :] = np.dot(Rm, pts_nT.T).T
# corners_=kps2
corners[ii, :] = (trans_3d(corners_, np.dot(Rm, Rt), Tt_c).T).flatten()
return points,corners, centers, pts_recon
def load_ply(path):
"""
Loads a 3D mesh model from a PLY file.
:param path: A path to a PLY file.
:return: The loaded model given by a dictionary with items:
'pts' (nx3 ndarray), 'normals' (nx3 ndarray), 'colors' (nx3 ndarray),
'faces' (mx3 ndarray) - the latter three are optional.
"""
f = open(path, 'r')
n_pts = 0
n_faces = 0
face_n_corners = 3 # Only triangular faces are supported
pt_props = []
face_props = []
text_props = []
is_binary = False
header_vertex_section = False
header_face_section = False
# Read header
while True:
line = f.readline().rstrip('\n').rstrip('\r') # Strip the newline character(s)
if line.startswith('element vertex'):
n_pts = int(line.split(' ')[-1])
header_vertex_section = True
header_face_section = False
elif line.startswith('element face'):
n_faces = int(line.split(' ')[-1])
header_vertex_section = False
header_face_section = True
elif line.startswith('element'): # Some other element
header_vertex_section = False
header_face_section = False
elif line.startswith('property') and header_vertex_section:
# (name of the property, data type)
pt_props.append((line.split(' ')[-1], line.split(' ')[-2]))
elif line.startswith('property list') and header_face_section:
elems = line.split(' ')
# (name of the property, data type)
face_props.append(('n_corners', elems[2]))
for i in range(face_n_corners):
face_props.append(('ind_' + str(i), elems[3]))
elif line.startswith('property2 list') and header_face_section:
elems = line.split(' ')
# (name of the property, data type)
text_props.append(('n_corners', elems[2]))
for i in range(3):
text_props.append(('ind_' + str(i), elems[3]))
elif line.startswith('format'):
if 'binary' in line:
is_binary = True
elif line.startswith('end_header'):
break
# Prepare data structures
model = {}
model['pts'] = np.zeros((n_pts, 3), np.float)
if n_faces > 0:
model['faces'] = np.zeros((n_faces, face_n_corners), np.float)
pt_props_names = [p[0] for p in pt_props]
is_normal = False
if {'nx', 'ny', 'nz'}.issubset(set(pt_props_names)):
is_normal = True
model['normals'] = np.zeros((n_pts, 3), np.float)
is_color = False
if {'red', 'green', 'blue'}.issubset(set(pt_props_names)):
is_color = True
model['colors'] = np.zeros((n_pts, 3), np.float)
formats = { # For binary format
'float': ('f', 4),
'double': ('d', 8),
'int': ('i', 4),
'uchar': ('B', 1)
}
# Load vertices
for pt_id in range(n_pts):
prop_vals = {}
load_props = ['x', 'y', 'z', 'nx', 'ny', 'nz', 'red', 'green', 'blue']
if is_binary:
for prop in pt_props:
format = formats[prop[1]]
val = struct.unpack(format[0], f.read(format[1]))[0]
if prop[0] in load_props:
prop_vals[prop[0]] = val
else:
elems = f.readline().rstrip('\n').rstrip('\r').split(' ')
for prop_id, prop in enumerate(pt_props):
if prop[0] in load_props:
prop_vals[prop[0]] = elems[prop_id]
model['pts'][pt_id, 0] = float(prop_vals['x'])
model['pts'][pt_id, 1] = float(prop_vals['y'])
model['pts'][pt_id, 2] = float(prop_vals['z'])
if is_normal:
model['normals'][pt_id, 0] = float(prop_vals['nx'])
model['normals'][pt_id, 1] = float(prop_vals['ny'])
model['normals'][pt_id, 2] = float(prop_vals['nz'])
if is_color:
model['colors'][pt_id, 0] = float(prop_vals['red'])
model['colors'][pt_id, 1] = float(prop_vals['green'])
model['colors'][pt_id, 2] = float(prop_vals['blue'])
# Load faces
for face_id in range(n_faces):
prop_vals = {}
if is_binary:
for prop in face_props:
format = formats[prop[1]]
val = struct.unpack(format[0], f.read(format[1]))[0]
if prop[0] == 'n_corners':
if val != face_n_corners:
print ('Error: Only triangular faces are supported.')
print ('Number of face corners:', val)
exit(-1)
else:
prop_vals[prop[0]] = val
else:
elems = f.readline().rstrip('\n').rstrip('\r').split(' ')
for prop_id, prop in enumerate(face_props):
if prop[0] == 'n_corners':
if int(elems[prop_id]) != face_n_corners:
print ('Error: Only triangular faces are supported.')
print ('Number of face corners:', int(elems[prop_id]))
exit(-1)
else:
prop_vals[prop[0]] = elems[prop_id]
#print(prop_vals.keys())
model['faces'][face_id, 0] = int(prop_vals['ind_0'])
model['faces'][face_id, 1] = int(prop_vals['ind_1'])
model['faces'][face_id, 2] = int(prop_vals['ind_2'])
f.close()
return model
def get_3d_bbox(size, shift=0):
"""
Args:
size: [3] or scalar
shift: [3] or scalar
Returns:
bbox_3d: [3, N]
"""
bbox_3d = np.array([[+size[0] / 2, +size[1] / 2, +size[2] / 2],
[+size[0] / 2, +size[1] / 2, -size[2] / 2],
[-size[0] / 2, +size[1] / 2, +size[2] / 2],
[-size[0] / 2, +size[1] / 2, -size[2] / 2],
[+size[0] / 2, -size[1] / 2, +size[2] / 2],
[+size[0] / 2, -size[1] / 2, -size[2] / 2],
[-size[0] / 2, -size[1] / 2, +size[2] / 2],
[-size[0] / 2, -size[1] / 2, -size[2] / 2]]) + shift
bbox_3d = bbox_3d.transpose()
return bbox_3d
def transform_coordinates_3d(coordinates, sRT):
"""
Args:
coordinates: [3, N]
sRT: [4, 4]
Returns:
new_coordinates: [3, N]
"""
assert coordinates.shape[0] == 3
coordinates = np.vstack([coordinates, np.ones((1, coordinates.shape[1]), dtype=np.float32)])
new_coordinates = sRT @ coordinates
new_coordinates = new_coordinates[:3, :] / new_coordinates[3, :]
return new_coordinates
def compute_3d_IoU(sRT_1, sRT_2, size_1, size_2, class_name_1, class_name_2, handle_visibility):
'''
Args:
sRT_1: 4x4
sRT_2: 4x4
size_1: 3x8
size_2: 3
class_name_1: str
class_name_2: str
handle_visibility: bool
Returns:
'''
""" Computes IoU overlaps between two 3D bboxes. """
def asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2):
noc_cube_1 = get_3d_bbox(size_1, 0)
bbox_3d_1 = transform_coordinates_3d(noc_cube_1, sRT_1)
noc_cube_2 = get_3d_bbox(size_2, 0)
bbox_3d_2 = transform_coordinates_3d(noc_cube_2, sRT_2)
bbox_1_max = np.amax(bbox_3d_1, axis=0)
bbox_1_min = np.amin(bbox_3d_1, axis=0)
bbox_2_max = np.amax(bbox_3d_2, axis=0)
bbox_2_min = np.amin(bbox_3d_2, axis=0)
overlap_min = np.maximum(bbox_1_min, bbox_2_min)
overlap_max = np.minimum(bbox_1_max, bbox_2_max)
# intersections and union
if np.amin(overlap_max - overlap_min) < 0:
intersections = 0
else:
intersections = np.prod(overlap_max - overlap_min)
union = np.prod(bbox_1_max - bbox_1_min) + np.prod(bbox_2_max - bbox_2_min) - intersections
overlaps = intersections / union
return overlaps
if sRT_1 is None or sRT_2 is None:
return -1
if (class_name_1 in ['bottle', 'bowl', 'can'] and class_name_1 == class_name_2) or (class_name_1 == 'mug' and class_name_1 == class_name_2 and handle_visibility==0):
def y_rotation_matrix(theta):
return np.array([[ np.cos(theta), 0, np.sin(theta), 0],
[ 0, 1, 0, 0],
[-np.sin(theta), 0, np.cos(theta), 0],
[ 0, 0, 0, 1]])
n = 20
max_iou = 0
for i in range(n):
rotated_RT_1 = sRT_1 @ y_rotation_matrix(2 * math.pi * i / float(n))
max_iou = max(max_iou, asymmetric_3d_iou(rotated_RT_1, sRT_2, size_1, size_2))
else:
max_iou = asymmetric_3d_iou(sRT_1, sRT_2, size_1, size_2)
return max_iou
def get_change_3D(x_r,y_r,z_r):
ext1=np.array([0,x_r,y_r,z_r])
or1=np.array([-ext1[1]/2,-ext1[2]/2,ext1[3]/2])
or2=np.array([ext1[1]/2,-ext1[2]/2,ext1[3]/2])
or3=np.array([ext1[1]/2,ext1[2]/2,ext1[3]/2])
or4=np.array([-ext1[1]/2,ext1[2]/2,ext1[3]/2])
or5=np.array([-ext1[1]/2,-ext1[2]/2,-ext1[3]/2])
or6=np.array([ext1[1]/2,-ext1[2]/2,-ext1[3]/2])
or7=np.array([ext1[1]/2,ext1[2]/2,-ext1[3]/2])
or8=np.array([-ext1[1]/2,ext1[2]/2,-ext1[3]/2])
OR=np.array([or1,or2,or3,or4,or5,or6,or7,or8])
return OR
def get_3D_corner(pc):
# pc=move_2_C(pc)
x_r=max(pc[:,0])-min(pc[:,0])
y_r=max(pc[:,1])-min(pc[:,1])
z_r=max(pc[:,2])-min(pc[:,2])
# print(max(pc[:,0]))
# pdb.set_trace()
ext1=np.array([0,x_r,y_r,z_r])
or1=np.array([-ext1[1]/2,-ext1[2]/2,ext1[3]/2])
or2=np.array([ext1[1]/2,-ext1[2]/2,ext1[3]/2])
or3=np.array([ext1[1]/2,ext1[2]/2,ext1[3]/2])
or4=np.array([-ext1[1]/2,ext1[2]/2,ext1[3]/2])
or5=np.array([-ext1[1]/2,-ext1[2]/2,-ext1[3]/2])
or6=np.array([ext1[1]/2,-ext1[2]/2,-ext1[3]/2])
or7=np.array([ext1[1]/2,ext1[2]/2,-ext1[3]/2])
or8=np.array([-ext1[1]/2,ext1[2]/2,-ext1[3]/2])
OR=np.array([or1,or2,or3,or4,or5,or6,or7,or8])
return OR, x_r,y_r,z_r
def draw_cors_withsize(img_,K,R_,T_,color,xr,yr,zr, lindwidth=2):
T_=T_.reshape((3,1))
img=np.zeros(img_.shape)
np.copyto(img,img_)
R=R_
OR=get_change_3D(xr,yr,zr)
OR_temp=OR
OR[:,0]=OR_temp[:,0]
OR[:,1]=OR_temp[:,1]
OR[:,2]=OR_temp[:,2]
pcc=np.zeros((4,len(OR)),dtype='float32')
pcc[0:3,:]=OR.T
pcc[3,:]=1
TT=np.zeros((3,4),dtype='float32')
TT[:,0:3]=R
TT[:,3]=T_[:,0]
camMat=K
pc_t = np.dot(TT, pcc) # 3xN
pc_tt = np.dot(camMat, pc_t)
pc_t=np.transpose(pc_tt)
x=pc_t[:,0]/pc_t[:,2]
y=pc_t[:,1]/pc_t[:,2]
cv2.line(img, (np.int(x[0]),np.int(y[0])), (np.int(x[1]), np.int(y[1])), color, lindwidth)
cv2.line(img, (np.int(x[1]),np.int(y[1])), (np.int(x[2]), np.int(y[2])), color, lindwidth)
cv2.line(img, (np.int(x[2]),np.int(y[2])), (np.int(x[3]), np.int(y[3])), color, lindwidth)
cv2.line(img, (np.int(x[3]),np.int(y[3])), (np.int(x[0]), np.int(y[0])), color, lindwidth)
cv2.line(img, (np.int(x[0]),np.int(y[0])), (np.int(x[4]), np.int(y[4])), color, lindwidth)
cv2.line(img, (np.int(x[1]),np.int(y[1])), (np.int(x[5]), np.int(y[5])), color, lindwidth)
cv2.line(img, (np.int(x[2]),np.int(y[2])), (np.int(x[6]), np.int(y[6])), color, lindwidth)
cv2.line(img, (np.int(x[3]),np.int(y[3])), (np.int(x[7]), np.int(y[7])), color, lindwidth)
cv2.line(img, (np.int(x[4]),np.int(y[4])), (np.int(x[5]), np.int(y[5])), color, lindwidth)
cv2.line(img, (np.int(x[5]),np.int(y[5])), (np.int(x[6]), np.int(y[6])), color, lindwidth)
cv2.line(img, (np.int(x[6]),np.int(y[6])), (np.int(x[7]), np.int(y[7])), color, lindwidth)
cv2.line(img, (np.int(x[7]),np.int(y[7])), (np.int(x[4]), np.int(y[4])), color, lindwidth)
return img
def move_2_C(pc):
x_c=(max(pc[:,0])+min(pc[:,0]))/2
y_c=(max(pc[:,1])+min(pc[:,1]))/2
z_c=(max(pc[:,2])+min(pc[:,2]))/2
pc_t=pc
pc[:,0]=pc_t[:,0]-x_c
pc[:,1]=pc_t[:,1]-y_c
pc[:,2]=pc_t[:,2]-z_c
return pc
def draw_cors(img_,pc,K,R_,T_,color, lindwidth=2):
pc = move_2_C(pc)
T_=T_.reshape((3,1))
img=np.zeros(img_.shape)
np.copyto(img,img_)
R=R_
# R_m=get_rotation(0,0,-90)
R_m=get_rotation(0,0,0)
# print(R_m)
R=np.dot(R,R_m)
# pc_temp=pc
# pc[:,0]=pc_temp[:,0]
# pc[:,1]=pc_temp[:,1]
#print(pc.shape)
OR1,xr,yr,zr=get_3D_corner(pc)
#print(xr,yr,zr)
#dfd
OR=get_change_3D(xr,yr,zr)
OR_temp=OR
OR[:,0]=OR_temp[:,0]
OR[:,1]=OR_temp[:,1]
OR[:,2]=OR_temp[:,2]
# OR[:,0]=OR_temp[:,0]
# OR[:,1]=OR_temp[:,1]
# OR[:,2]=OR_temp[:,2]
pcc=np.zeros((4,len(OR)),dtype='float32')
pcc[0:3,:]=OR.T
pcc[3,:]=1
TT=np.zeros((3,4),dtype='float32')
TT[:,0:3]=R
TT[:,3]=T_[:,0]
#print('s: ',TT)
# etrs
#aa=TT*pcc
#print(aa.shape)
camMat=K
#pdb.set_trace()
pc_tt=np.dot(camMat,np.dot(TT,pcc))
pc_t=np.transpose(pc_tt)
x=pc_t[:,0]/pc_t[:,2]
y=pc_t[:,1]/pc_t[:,2]
cv2.line(img, (np.int(x[0]),np.int(y[0])), (np.int(x[1]), np.int(y[1])), color, lindwidth)
cv2.line(img, (np.int(x[1]),np.int(y[1])), (np.int(x[2]), np.int(y[2])), color, lindwidth)
cv2.line(img, (np.int(x[2]),np.int(y[2])), (np.int(x[3]), np.int(y[3])), color, lindwidth)
cv2.line(img, (np.int(x[3]),np.int(y[3])), (np.int(x[0]), np.int(y[0])), color, lindwidth)
cv2.line(img, (np.int(x[0]),np.int(y[0])), (np.int(x[4]), np.int(y[4])), color, lindwidth)
cv2.line(img, (np.int(x[1]),np.int(y[1])), (np.int(x[5]), np.int(y[5])), color, lindwidth)
cv2.line(img, (np.int(x[2]),np.int(y[2])), (np.int(x[6]), np.int(y[6])), color, lindwidth)
cv2.line(img, (np.int(x[3]),np.int(y[3])), (np.int(x[7]), np.int(y[7])), color, lindwidth)
cv2.line(img, (np.int(x[4]),np.int(y[4])), (np.int(x[5]), np.int(y[5])), color, lindwidth)
cv2.line(img, (np.int(x[5]),np.int(y[5])), (np.int(x[6]), np.int(y[6])), color, lindwidth)
cv2.line(img, (np.int(x[6]),np.int(y[6])), (np.int(x[7]), np.int(y[7])), color, lindwidth)
cv2.line(img, (np.int(x[7]),np.int(y[7])), (np.int(x[4]), np.int(y[4])), color, lindwidth)
# plt.imshow(img)
# plt.plot([x[0],x[1]],[y[0],y[1]],marker = 'o',color='red')
# plt.plot([x[1],x[2]],[y[1],y[2]],marker = 'o',color='red')
# plt.plot([x[2],x[3]],[y[2],y[3]],marker = 'o',color='red')
# plt.plot([x[3],x[0]],[y[3],y[0]],marker = 'o',color='red')
#
# plt.plot([x[0],x[4]],[y[0],y[4]],marker = 'o',color='red')
# plt.plot([x[1],x[5]],[y[1],y[5]],marker = 'o',color='red')
# plt.plot([x[2],x[6]],[y[2],y[6]],marker = 'o',color='red')
# plt.plot([x[3],x[7]],[y[3],y[7]],marker = 'o',color='red')
#
# plt.plot([x[4],x[5]],[y[4],y[5]],marker = 'o',color='red')
# plt.plot([x[5],x[6]],[y[5],y[6]],marker = 'o',color='red')
# plt.plot([x[6],x[7]],[y[6],y[7]],marker = 'o',color='red')
# plt.plot([x[7],x[4]],[y[7],y[4]],marker = 'o',color='red')
return img
def kabsch(P, Q):
"""
Using the Kabsch algorithm with two sets of paired point P and Q, centered
around the centroid. Each vector set is represented as an NxD
matrix, where D is the the dimension of the space.
The algorithm works in three steps:
- a centroid translation of P and Q (assumed done before this function
call)
- the computation of a covariance matrix C
- computation of the optimal rotation matrix U
For more info see http://en.wikipedia.org/wiki/Kabsch_algorithm
Parameters
----------
P : array
(N,D) matrix, where N is points and D is dimension.
Q : array
(N,D) matrix, where N is points and D is dimension.
Returns
-------
U : matrix
Rotation matrix (D,D)
"""
# Computation of the covariance matrix
#print(P.shape,Q.shape)
# print(np.mean(P,0))
# P= P-np.mean(P,0)
# Q =Q - np.mean(Q, 0)
# print(P)
# tests
C = np.dot(P.T, Q)
# Computation of the optimal rotation matrix
# This can be done using singular value decomposition (SVD)
# Getting the sign of the det(V)*(W) to decide
# whether we need to correct our rotation matrix to ensure a
# right-handed coordinate system.
# And finally calculating the optimal rotation matrix U
# see http://en.wikipedia.org/wiki/Kabsch_algorithm
U, S, V = np.linalg.svd(C)
#S=np.diag(S)
#print(C)
# print(S)
#print(np.dot(U,np.dot(S,V)))
d = (np.linalg.det(V.T) * np.linalg.det(U.T)) <0.0
# d = (np.linalg.det(V) * np.linalg.det(W)) < 0.0
# E = np.diag(np.array([1, 1, 1]))
# if d:
# S[-1] = -S[-1]
# V[:, -1] = -V[:, -1]
E = np.diag(np.array([1, 1, (np.linalg.det(V.T) * np.linalg.det(U.T))]))
# print(E)
# Create Rotation matrix U
#print(V)
#print(U)
R = np.dot(V.T ,np.dot(E,U.T))
return R
def gettrans(kps,h):
# print(kps.shape) ##N*3
# print(h.shape)##N,100,3
# tess
hss=[]
# print(h)
# print(kps.shape) ##3*N
# kps
# print(kps.shape)
# tess
kps=kps.reshape(-1,3)
for i in range(h.shape[1]):
# print(i)
# print(h[:,i,:].shape #N*3
# tss
P = kps.T - kps.T.mean(1).reshape((3, 1))
#
Q= h[:,i,:].T - h[:,i,:].T.mean(1).reshape((3,1))
# print(P.shape,Q.shape)
# print(kps,h[:,i,:])
# tess
# print(P.T,Q.T)
R=kabsch(P.T,Q.T) ##N*3, N*3
T=h[:,i,:]-np.dot(R,kps.T).T
# print(np.mean(T,0))
# tess
# print(T.shape)
hh = np.zeros((3, 4), dtype=np.float32)
hh[0:3,0:3]=R
hh[0:3,3]=np.mean(T,0)
# print(R)
hss.append(hh)
# print(hh)
# if i==3:
# tess
# print(hss)
return hss
def compute_RT_degree_cm_symmetry(RT_1, RT_2, class_id,hv=0):
R1 = RT_1
R2 = RT_2
# try:
# assert np.abs(np.linalg.det(R1) - 1) < 0.01
# assert np.abs(np.linalg.det(R2) - 1) < 0.01
# except AssertionError:
# print(np.linalg.det(R1), np.linalg.det(R2))
if class_id in ['bottle', 'can', 'bowl']: ## symmetric when rotating around y-axis
y = np.array([0, 1, 0])
y1 = R1 @ y
y2 = R2 @ y
theta = np.arccos(y1.dot(y2) / (np.linalg.norm(y1) * np.linalg.norm(y2)))
elif class_id == 'mug' and hv==0: ## symmetric when rotating around y-axis
y = np.array([0, 1, 0])
y1 = R1 @ y
y2 = R2 @ y
theta = np.arccos(y1.dot(y2) / (np.linalg.norm(y1) * np.linalg.norm(y2)))
elif class_id in ['phone', 'eggbox', 'glue']:
y_180_RT = np.diag([-1.0, 1.0, -1.0])
R = R1 @ R2.transpose()
R_rot = R1 @ y_180_RT @ R2.transpose()
theta = min(np.arccos((np.trace(R) - 1) / 2),
np.arccos((np.trace(R_rot) - 1) / 2))
else:
R = R1 @ R2.transpose()
theta = np.arccos((np.trace(R) - 1) / 2)
theta *= 180 / np.pi
# shift = np.linalg.norm(T1 - T2) * 100
result = theta
return result
def calcAngularDistance(Rt, R):
#print(np.transpose(Rt))
#t1=np.ones((3,3))
rotDiff = np.dot(Rt.T, R/np.linalg.det(R))
#print(rotDiff)
trace = np.trace(rotDiff)
#print(np.arccos(1)/math.pi)
trace2 = np.min([float(3.0000), np.max([float(-1.0000), float(trace)])])
#print((float(trace2) - 1.0) / 2.0)
#pdb.set_trace()
return float(180 * np.arccos((float(trace2) - 1.0) / 2.0) / math.pi)
def get6dpose1(Rt,Tt, R, T, sy=0,class_id='',hv=0):
if sy==1:
R_loss = compute_RT_degree_cm_symmetry(Rt, R,class_id,hv)
else:
R_loss=calcAngularDistance(Rt, R)
Tt = Tt.reshape(3,1)
T = T.reshape(3,1)
t_loss=cv2.norm(Tt-T,normType=cv2.NORM_L2)
#print(cv2.norm(Tt-T))
#print(np.linalg.norm(Tt-T))
#pdb.set_trace()
return R_loss, t_loss
def getFiles_cate(file_dir,suf,a,b, sort=1):
L=[]
for root, dirs, files in os.walk(file_dir):
#print('root: ',dirs)
for file in files:
if os.path.splitext(file)[0][4:] == suf:
L.append(os.path.join(root, file))
if sort==1:
L.sort(key=lambda x: int(x[b-len(suf)-a:b-len(suf)]))#0000.png
return L
def getFiles_ab_cate(file_dir,suf,a,b, sort=1):
L=[]
for root, dirs, files in os.walk(file_dir):
#print('root: ',dirs)
for file in files:
if file.split('_')[1]== suf:
#print(os.path.join(root, file))
#sdf
L.append(os.path.join(root, file))
# L.sort(key=lambda x:int(x[-9:-4])) # 0000
if sort==1:
L.sort(key=lambda x: int(x[a:b]))#0000.png
return L
def depth_2_pc(depth, K, bbx=[1, 2, 3, 4], step=1):
x1 = bbx[0]
x2 = bbx[1]
y1 = bbx[2]
y2 = bbx[3]
fx = K[0, 0]
ux = K[0, 2]
fy = K[1, 1]
uy = K[1, 2]
W = y2 - y1 + 1
H = x2 - x1 + 1
xw0 = np.arange(y1, y2, step)
xw0 = np.expand_dims(xw0, axis=0)
xw0 = np.tile(xw0.T, 2)
uu0 = np.zeros_like(xw0, dtype=np.float32)
uu0[:, 0] = ux
uu0[:, 1] = uy
mesh = np.zeros((len(range(0, H, step)) * xw0.shape[0], 3))
c = 0
for i in range(x1, x2, step):
xw = xw0.copy()
uu = uu0.copy()
xw[:, 0] = i ### W 2
z = depth[xw[:, 0], xw[:, 1]] ##W 1
xw[:, 0] = xw[:, 0] * z
xw[:, 1] = xw[:, 1] * z
uu[:, 0] = uu[:, 0] * z
uu[:, 1] = uu[:, 1] * z
X = (xw[:, 1] - uu[:, 0]) / fx
Y = (xw[:, 0] - uu[:, 1]) / fy
mesh[xw.shape[0] * c:xw.shape[0] * (c + 1), 0] = X
mesh[xw.shape[0] * c:xw.shape[0] * (c + 1), 1] = Y
mesh[xw.shape[0] * c:xw.shape[0] * (c + 1), 2] = z
c += 1
return mesh
def depth_2_mesh_bbx(depth,bbx,K, step=1, enl=0):
#bbx: r1,r2, c1,c2
x1 = int(max(bbx[0],0))-enl
x2 = int(min(bbx[1],depth.shape[0]))+enl
y1 = int(max(bbx[2],0))-enl
y2 = int(min(bbx[3],depth.shape[1]))+enl
mesh = depth_2_pc(depth, K, bbx = [x1,x2,y1,y2], step=step)
return mesh
def depth_2_mesh_all(depth,K):
#dep=np.logical_and(depth[:,:],depth[:,:])
r,c=np.where(depth>0)
r1 = r.min()
r2 = r.max()
c1 = c.min()
c2 = c.max()
mesh=depth_2_mesh_bbx(depth, [r1,r2,c1,c2], K, step=1, enl=0)
return mesh
def getFiles_ab(file_dir,suf,a,b):
L=[]
for root, dirs, files in os.walk(file_dir):
#print('root: ',dirs)
for file in files:
if os.path.splitext(file)[1] == suf:
#print(os.path.join(root, file))
#sdf
L.append(os.path.join(root, file))
# L.sort(key=lambda x:int(x[-9:-4])) # 0000
L.sort(key=lambda x: int(x[a:b]))#0000.png
return L
def shake_bbx(bbx, degrees=(0, 0), translate=(0.1, 0.1), scale=(0.9, 1.2), shear=(0, 0),W=640,H=480):
#### bbx: x1,x2,y1,y2
# random.seed(1092)
# print(random.random())
bw = bbx[1]-bbx[0]
cx = bbx[0]+bw//2
bh = bbx[3]-bbx[2]
cy = bbx[2]+bh//2
targets = np.array([bbx[0], bbx[2], bbx[1], bbx[3]])
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
s = random.random() * (scale[1] - scale[0]) + scale[0]
R = np.eye(3)
# R[:2] = cv2.getRotationMatrix2D(angle=a, center=(W / 2, H / 2), scale=s)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(int(cx), int(cy)), scale=s)
# Translation
T = np.eye(3)
T[0, 2] = (random.random() * 2 - 1) * translate[0] * bw # x translation (pixels)
T[1, 2] = (random.random() * 2 - 1) * translate[1] * bh # y translation (pixels)
# T[0, 2] = translate[0] * bw # x translation (pixels)
# T[1, 2] = translate[1] * bh # y translation (pixels)
# Shear
S = np.eye(3)
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
# M = np.eye(3)
# if len(targets) > 0:
n = 1
points = targets.copy().reshape(1,4)
# print(random.random())
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = (xy @ M.T)[:, :2].reshape(n, 8)
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# apply angle-based reduction of bounding boxes
radians = a * math.pi / 180
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
x = (xy[:, 2] + xy[:, 0]) / 2
y = (xy[:, 3] + xy[:, 1]) / 2
w = (xy[:, 2] - xy[:, 0]) * reduction
h = (xy[:, 3] - xy[:, 1]) * reduction
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
# reject warped points outside of image
x1 = int(np.clip(xy[0][0], 0, W))
x2 = int(np.clip(xy[0][2], 0, W))
y1 = int(np.clip(xy[0][1], 0, H))
y2 = int(np.clip(xy[0][3], 0, H))
return np.array([x1, x2,y1, y2])
def depth_out_iou(depth, box1_yolo, box2_gt, z=0):
ioubb = get_bbxs_iou(box1_yolo, box2_gt)
if z == 0:
depth[ioubb[2]:ioubb[3], ioubb[0]:ioubb[1]] = 0
return depth, ioubb
def get_bbxs_iou(box1, box2):
'''
:param box1: x1,x2, y1 , y2
:param box2: x1 x2 y1 y2
:return:
'''
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[2], box1[1], box1[3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[2], box2[1], box2[3]
x2 = min(b1_x2, b2_x2)
x1 = max(b1_x1, b2_x1)
y1 = max(b1_y1, b2_y1)
y2 = min(b1_y2, b2_y2)
return int(x1),int(x2),int(y1),int(y2)
def defor_3D(pts,lab, R,T,pc ,scalex=(1,1),scalez=(1,1),scaley=(1,1),scale=(1,1), cate='',OR=None,bm=0.5):
## follow by xyz order
# x_nr = x_r + x_r*np.random.uniform(scalex[0], scalex[1])
# y_nr = y_r + y_r*np.random.uniform(scaley[0], scaley[1])
# z_nr = z_r + z_r*np.random.uniform(scalez[0], scalez[1])
ptsn = pts.copy()
ptsn =np.dot(R.T ,(ptsn-T).T).T # Nx3
# ptsn = np.dot(R.T, ptsn.T).T # Nx3
# show_mulit_mesh([ptsn, pts-T])
ex = random.uniform(scalex[0], scalex[1])
ez = random.uniform(scalez[0], scalez[1])
ey = random.uniform(scaley[0], scaley[1])
OR,lx,ly,lz ,miny,maxy= get_3D_corner_def(pc)
# show_mulit_mesh([pc])
ptsn[:, 0] = ptsn[:, 0] * ex
ptsn[:, 1] = ptsn[:, 1] * ey
ptsn[:, 2] = ptsn[:, 2] * ez
s=0
if cate =='mug' or cate=='bowl':
s = random.uniform(-(1 - scale[0]), -(1 - scale[1]))
mb = bm
# print(mb, s)
#
if mb>0.5:
s = s * ((maxy-ptsn[np.where(lab == 1)[0], 1]) / ly) ## bottle change (screen down)
else:
s = s * ((ptsn[np.where(lab == 1)[0], 1] - miny) / ly) ## mouth change (screen up)
ptsn[np.where(lab==1)[0], 0] = ptsn[np.where(lab==1)[0], 0]*(1+s)
ptsn[np.where(lab==1)[0], 2] = ptsn[np.where(lab==1)[0], 2]*(1+s)
ptsn = np.dot(R, ptsn.T).T+T
# print('inside func')
# show_mulit_mesh([ptsn])
return ptsn, ex,ey, ez,s
def pts_iou(pts,label, K, seg):
'''
:param pts: N,3
label: N,1
:param K: 3,3
:param seg: x1, x2, y1 y2
:return:
'''
fx = K[0,0]
ux = K[0,2]
fy = K[1,1]
uy = K[1,2]
# dep3d = dep3d[np.where(dep3d[:, 0] != 0.0)]
# dep3d = dep3d[np.where(dep3d[:, 1] != 0.0)]
# dep3d = dep3d[np.where(dep3d[:, 2] != 0.0)]
x1 = seg[0]
x2 = seg[1]
y1 = seg[2]