id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
13,106 | import torch
from torch.nn import functional as F
import numpy as np
def quaternion_to_axis_angle(quaternion):
"""
Convert quaternion to axis angle.
based on: https://github.com/facebookresearch/QuaterNet/blob/master/common/quaternion.py#L138
Args:
quaternion: torch tensor of shape (batch_size, ... | @q1: torch tensor of shape (frame, joints, 4) quaternion @q2: same as q1 @output: torch tensor of shape (frame, joints) |
13,107 | import torch
from torch.nn import functional as F
import numpy as np
def get_extrinsic(translation, rotation):
batch_size = translation.shape[0]
pose = torch.zeros((batch_size, 4, 4))
pose[:,:3, :3] = rotation
pose[:,:3, 3] = translation
pose[:,3, 3] = 1
extrinsic = torch.inverse(pose)
retu... | null |
13,108 | import torch
from torch.nn import functional as F
import numpy as np
def euler_fix_old(euler):
frame_num = euler.shape[0]
joint_num = euler.shape[1]
for l in range(3):
for j in range(joint_num):
overall_add = 0.
for i in range(1,frame_num):
add1 = overall_add... | null |
13,109 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def dict_of_tensor_to_numpy(body_params):
body_params = {key:val.d... | null |
13,110 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def grad_require(params, flag=False):
if isinstance(params, list):... | null |
13,111 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def rel_change(prev_val, curr_val):
return (prev_val - curr_val) /... | null |
13,112 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
class LBFGS(Optimizer):
def __init__(self,
p... | null |
13,113 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def load_object(module_name, module_args, **extra_args):
module_pa... | null |
13,114 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def load_object(module_name, module_args, **extra_args):
module_pa... | null |
13,115 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def dict_of_numpy_to_tensor(body_model, body_params, *args, **kwargs):
... | null |
13,116 | from ..annotator.file_utils import read_json
from ..mytools import Timer
from .lossbase import print_table
from ..config.baseconfig import load_object
from ..bodymodel.base import Params
from torch.utils.data import DataLoader
from tqdm import tqdm
def plot_meshes(img, meshes, K, R, T):
import cv2
mesh_camera ... | null |
13,117 | import torch
from ..bodymodel.lbs import batch_rodrigues
from .torchgeometry import rotation_matrix_to_axis_angle, rotation_matrix_to_quaternion, quaternion_to_rotation_matrix, quaternion_to_axis_angle
import numpy as np
from .base_ops import BeforeAfterBase
def quaternion_to_rotation_matrix(quaternion):
"""
C... | Compute the twist component of given rotation and twist axis https://stackoverflow.com/questions/3684269/component-of-a-quaternion-rotation-around-an-axis Parameters ---------- rotation_matrix : Tensor (B, 3, 3,) The rotation to convert twist_axis : Tensor (B, 3,) The twist axis Returns ------- Tensor (B, 3, 3) The twi... |
13,118 | import numpy as np
from os.path import join
import os
import cv2
def flipPoint2D(point):
def mirrorPoint3D(point, M):
point_homo = np.hstack([point, np.ones([point.shape[0], 1])])
point_m = (M @ point_homo.T).T[..., :3]
return flipPoint2D(point_m) | null |
13,119 | import numpy as np
from os.path import join
import os
import cv2
def get_rotation_from_two_directions(direc0, direc1):
direc0 = direc0/np.linalg.norm(direc0)
direc1 = direc1/np.linalg.norm(direc1)
rotdir = np.cross(direc0, direc1)
if np.linalg.norm(rotdir) < 1e-2:
return np.eye(3)
rotdir = ... | null |
13,120 | import numpy as np
class ComposedFilter:
def __init__(self, filters, min_conf) -> None:
self.filters = filters
self.min_conf = min_conf
def __call__(self, keypoints, **kwargs) -> bool:
conf = keypoints[:, 2]
conf[conf<self.min_conf] = 0
valid = conf>self.min_conf
... | null |
13,121 | import numpy as np
CONFIG = {
'points': {
'nJoints': 1,
'kintree': []
}
}
CONFIG['smpl'] = {'nJoints': 24, 'kintree':
[
[ 0, 1 ],
[ 0, 2 ],
[ 0, 3 ],
[ 1, 4 ],
[ 2, 5 ],
[ 3, 6 ],
[ 4, 7 ],
[ 5, 8 ],
[ 6, 9 ],
[... | null |
13,122 | import numpy as np
CONFIG = {
'points': {
'nJoints': 1,
'kintree': []
}
}
CONFIG['smpl'] = {'nJoints': 24, 'kintree':
[
[ 0, 1 ],
[ 0, 2 ],
[ 0, 3 ],
[ 1, 4 ],
[ 2, 5 ],
[ 3, 6 ],
[ 4, 7 ],
[ 5, 8 ],
[ 6, 9 ],
[... | null |
13,123 | import numpy as np
COCO17_IN_BODY25 = [0,16,15,18,17,5,2,6,3,7,4,12,9,13,10,14,11]
def coco17tobody25(points2d):
dim = 3
if len(points2d.shape) == 2:
points2d = points2d[None, :, :]
dim = 2
kpts = np.zeros((points2d.shape[0], 25, 3))
kpts[:, COCO17_IN_BODY25, :2] = points2d[:, :, :2]
... | null |
13,124 | import os
from os.path import join
from glob import glob
import cv2
import os, sys
import numpy as np
from ..mytools.camera_utils import read_camera, get_fundamental_matrix, Undistort
from ..mytools import FileWriter, read_annot, getFileList, save_json
from ..mytools.reader import read_keypoints3d, read_json, read_smpl... | null |
13,125 | import os
from os.path import join
from glob import glob
import cv2
import os, sys
import numpy as np
from ..mytools.camera_utils import read_camera, get_fundamental_matrix, Undistort
from ..mytools import FileWriter, read_annot, getFileList, save_json
from ..mytools.reader import read_keypoints3d, read_json, read_smpl... | null |
13,126 | import os
from os.path import join
from glob import glob
import cv2
import os, sys
import numpy as np
from ..mytools.camera_utils import read_camera, get_fundamental_matrix, Undistort
from ..mytools import FileWriter, read_annot, getFileList, save_json
from ..mytools.reader import read_keypoints3d, read_json, read_smpl... | null |
13,127 |
def load_weight_pose2d(model, opts):
if model == 'smpl':
weight = {
'k2d': 2e-4,
'init_poses': 1e-3, 'init_shapes': 1e-2,
'smooth_body': 5e-1, 'smooth_poses': 1e-1,
}
elif model == 'smplh':
raise NotImplementedError
elif model == 'smplx':
... | null |
13,128 | from ..pyfitting import optimizeShape, optimizePose2D, optimizePose3D
from ..mytools import Timer
from ..dataset import CONFIG
from .weight import load_weight_pose, load_weight_shape
from .config import Config
class Config:
OPT_R = False
OPT_T = False
OPT_POSE = False
OPT_SHAPE = False
OPT_HAND = F... | null |
13,129 | from ..pyfitting import optimizeShape, optimizePose2D, optimizePose3D
from ..mytools import Timer
from ..dataset import CONFIG
from .weight import load_weight_pose, load_weight_shape
from .config import Config
def multi_stage_optimize(body_model, params, kp3ds, kp2ds=None, bboxes=None, Pall=None, weight={}, cfg=None):
... | null |
13,130 | from ..pyfitting import optimizeShape, optimizePose2D, optimizePose3D
from ..mytools import Timer
from ..dataset import CONFIG
from .weight import load_weight_pose, load_weight_shape
from .config import Config
def multi_stage_optimize(body_model, params, kp3ds, kp2ds=None, bboxes=None, Pall=None, weight={}, cfg=None):
... | null |
13,131 | from .yacs import CfgNode as CN
import importlib
def load_object(module_name, module_args, **extra_args):
def load_renderer(cfg, network):
if cfg.split == 'mesh':
return load_object(cfg.renderer_mesh_module, cfg.renderer_mesh_args, net=network)
else:
return load_object(cfg.renderer_module, cfg.... | null |
13,132 | from .yacs import CfgNode as CN
import importlib
def load_object(module_name, module_args, **extra_args):
module_path = '.'.join(module_name.split('.')[:-1])
module = importlib.import_module(module_path)
name = module_name.split('.')[-1]
obj = getattr(module, name)(**extra_args, **module_args)
retur... | null |
13,133 | from .yacs import CfgNode as CN
import importlib
def load_object(module_name, module_args, **extra_args):
module_path = '.'.join(module_name.split('.')[:-1])
module = importlib.import_module(module_path)
name = module_name.split('.')[-1]
obj = getattr(module, name)(**extra_args, **module_args)
retur... | null |
13,134 | from .yacs import CfgNode as CN
class Config:
def load_from_args(cls, default_cfg='config/vis/base.yml'):
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default=default_cfg)
parser.add_argument('--local_rank', type=int, default=0)
pa... | null |
13,135 | import copy
import io
import logging
import os
from ast import literal_eval
import yaml
try:
_FILE_TYPES = (file, io.IOBase)
_PY2 = True
except NameError:
_FILE_TYPES = (io.IOBase,)
def _load_cfg_from_file(file_obj):
"""Load a config from a YAML file or a Python source file."""
_, file_extension = o... | Load a cfg. Supports loading from: - A file object backed by a YAML file - A file object backed by a Python source file that exports an attribute "cfg" that is either a dict or a CfgNode - A string that can be parsed as valid YAML |
13,136 | import copy
import io
import logging
import os
from ast import literal_eval
import yaml
_VALID_TYPES = {tuple, list, str, int, float, bool}
class CfgNode(dict):
"""
CfgNode represents an internal node in the configuration tree. It's a simple
dict-like container that allows for attribute-based access to keys... | Recursively convert all CfgNode objects to dict objects. |
13,137 | import copy
import io
import logging
import os
from ast import literal_eval
import yaml
class CfgNode(dict):
"""
CfgNode represents an internal node in the configuration tree. It's a simple
dict-like container that allows for attribute-based access to keys.
"""
IMMUTABLE = "__immutable__"
DEPREC... | Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. |
13,138 | from .optimize_simple import _optimizeSMPL, deepcopy_tensor, get_prepare_smplx, dict_of_tensor_to_numpy
from .lossfactory import LossRepro, LossInit, LossSmoothBody, LossSmoothPoses, LossSmoothBodyMulti, LossSmoothPosesMulti
from ..dataset.mirror import flipSMPLPoses, flipPoint2D, flipSMPLParams
import torch
import num... | From mirror vector to mirror matrix Args: m (bn, 4): (a, b, c, d) Returns: M: (bn, 3, 4) |
13,139 | from .optimize_simple import _optimizeSMPL, deepcopy_tensor, get_prepare_smplx, dict_of_tensor_to_numpy
from .lossfactory import LossRepro, LossInit, LossSmoothBody, LossSmoothPoses, LossSmoothBodyMulti, LossSmoothPosesMulti
from ..dataset.mirror import flipSMPLPoses, flipPoint2D, flipSMPLParams
import torch
import num... | simple function for optimizing mirror # 先写图片的 Args: body_model (SMPL model) params (DictParam): poses(2, 72), shapes(1, 10), Rh(2, 3), Th(2, 3) bboxes (nFrames, nViews, nJoints, 4): 2D bbox of each view，输入的时候是按照时序叠起来的 keypoints2d (nFrames, nViews, nJoints, 4): 2D keypoints of each view，输入的时候是按照时序叠起来的 weight (Dict): str... |
13,140 | from .optimize_simple import _optimizeSMPL, deepcopy_tensor, get_prepare_smplx, dict_of_tensor_to_numpy
from .lossfactory import LossRepro, LossInit, LossSmoothBody, LossSmoothPoses, LossSmoothBodyMulti, LossSmoothPosesMulti
from ..dataset.mirror import flipSMPLPoses, flipPoint2D, flipSMPLParams
import torch
import num... | simple function for optimizing mirror Args: body_model (SMPL model) params (DictParam): poses(2, 72), shapes(1, 10), Rh(2, 3), Th(2, 3) bboxes (nViews, nFrames, 5): 2D bbox of each view，输入的时候是按照时序叠起来的 keypoints2d (nViews, nFrames, nJoints, 3): 2D keypoints of each view，输入的时候是按照时序叠起来的 weight (Dict): string:float cfg (Co... |
13,141 | import torch
from functools import reduce
from torch.optim.optimizer import Optimizer
def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
# ported from https://github.com/torch/optim/blob/master/polyinterp.lua
# Compute bounds of interpolation area
if bounds is not None:
xmin_bound, xmax_bo... | null |
13,142 | import numpy as np
import torch
from .lbfgs import LBFGS
from .optimize import FittingMonitor, grad_require, FittingLog
from .lossfactory import LossSmoothBodyMean, LossRegPoses
from .lossfactory import LossKeypoints3D, LossKeypointsMV2D, LossSmoothBody, LossRegPosesZero, LossInit, LossSmoothPoses
class LBFGS(Optimiz... | simple function for optimizing model shape given 3d keypoints Args: body_model (SMPL model) params_init (DictParam): poses(1, 72), shapes(1, 10), Rh(1, 3), Th(1, 3) keypoints (nFrames, nJoints, 3): 3D keypoints weight (Dict): string:float kintree ([[src, dst]]): list of list:int cfg (Config): Config Node controling run... |
13,143 | import numpy as np
import torch
from .lbfgs import LBFGS
from .optimize import FittingMonitor, grad_require, FittingLog
from .lossfactory import LossSmoothBodyMean, LossRegPoses
from .lossfactory import LossKeypoints3D, LossKeypointsMV2D, LossSmoothBody, LossRegPosesZero, LossInit, LossSmoothPoses
def interp(left_valu... | null |
13,144 | import numpy as np
import torch
from .lbfgs import LBFGS
from .optimize import FittingMonitor, grad_require, FittingLog
from .lossfactory import LossSmoothBodyMean, LossRegPoses
from .lossfactory import LossKeypoints3D, LossKeypointsMV2D, LossSmoothBody, LossRegPosesZero, LossInit, LossSmoothPoses
def get_interp_by_ke... | simple function for optimizing model pose given 3d keypoints Args: body_model (SMPL model) params (DictParam): poses(1, 72), shapes(1, 10), Rh(1, 3), Th(1, 3) keypoints3d (nFrames, nJoints, 4): 3D keypoints weight (Dict): string:float cfg (Config): Config Node controling running mode |
13,145 | import numpy as np
import torch
from .lbfgs import LBFGS
from .optimize import FittingMonitor, grad_require, FittingLog
from .lossfactory import LossSmoothBodyMean, LossRegPoses
from .lossfactory import LossKeypoints3D, LossKeypointsMV2D, LossSmoothBody, LossRegPosesZero, LossInit, LossSmoothPoses
def get_interp_by_ke... | simple function for optimizing model pose given 3d keypoints Args: body_model (SMPL model) params (DictParam): poses(1, 72), shapes(1, 10), Rh(1, 3), Th(1, 3) keypoints2d (nFrames, nViews, nJoints, 4): 2D keypoints of each view bboxes: (nFrames, nViews, 5) weight (Dict): string:float cfg (Config): Config Node controlin... |
13,146 | import numpy as np
import torch
from .operation import projection, batch_rodrigues
The provided code snippet includes necessary dependencies for implementing the `gmof` function. Write a Python function `def gmof(squared_res, sigma_squared)` to solve the following problem:
Geman-McClure error function
Here is the fun... | Geman-McClure error function |
13,147 | import numpy as np
import torch
from .operation import projection, batch_rodrigues
def projection(points3d, camera_intri, R=None, T=None, distance=None):
""" project the 3d points to camera coordinate
Arguments:
points3d {Tensor} -- (bn, N, 3)
camera_intri {Tensor} -- (bn, 3, 3)
distan... | null |
13,148 | import numpy as np
import torch
from .operation import projection, batch_rodrigues
def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):
''' Calculates the rotation matrices for a batch of rotation vectors
Parameters
----------
rot_vecs: torch.tensor Nx3
array of N a... | null |
13,149 | import numpy as np
import torch
from .operation import projection, batch_rodrigues
def RegularizationLoss(body_params, body_params_init, weight_loss):
loss_dict = {}
for key in ['poses', 'shapes', 'Th', 'hands', 'head', 'expression']:
if 'init_'+key in weight_loss.keys() and weight_loss['init_'+key] > ... | null |
13,150 | import numpy as np
import os
from tqdm import tqdm
import torch
import json
def rel_change(prev_val, curr_val):
return (prev_val - curr_val) / max([np.abs(prev_val), np.abs(curr_val), 1]) | null |
13,151 | import os
import numpy as np
import cv2
import pyrender
import trimesh
import copy7, 1.),
colors_table = {
# colorblind/print/copy safe:
'_blue': [0.65098039, 0.74117647, 0.85882353],
'_pink': [.9, .7, .7],
'_mint': [ 166/255., 229/255., 204/255.],
'_mint2': [ 202/255., 229/255., 223/255.],
... | null |
13,152 | import os
import numpy as np
import cv2
import pyrender
import trimesh
import copy7, 1.),
from pyrender import RenderFlags
class Renderer(object):
def __init__(self, focal_length=1000, height=512, width=512, faces=None,
bg_color=[1.0, 1.0, 1.0, 0.0], down_scale=1, # render 配置
extra_mesh=... | null |
13,153 | import numpy as np
import cv2
from os.path import join
import os
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `calTransformation` function. Write a Python function `def calTransformation(v_i, v_j, r, adaptr=False, ratio=10)` to solve the following p... | from to vertices to T Arguments: v_i {} -- [description] v_j {[type]} -- [description] |
13,154 | import numpy as np
import cv2
import numpy as np
from tqdm import tqdm
from os.path import join
def load_sphere():
cur_dir = os.path.dirname(__file__)
faces = np.loadtxt(join(cur_dir, 'sphere_faces_20.txt'), dtype=int)
vertices = np.loadtxt(join(cur_dir, 'sphere_vertices_20.txt'))
return vertices, faces... | create sphere Args: points (array): (N, 3)/(N, 4) r (float, optional): radius. Defaults to 0.01. |
13,155 | import numpy as np
import cv2
import numpy as np
from tqdm import tqdm
from os.path import join
import os
def create_ground(
center=[0, 0, 0], xdir=[1, 0, 0], ydir=[0, 1, 0], # 位置
step=1, xrange=10, yrange=10, # 尺寸
white=[1., 1., 1.], black=[0.,0.,0.], # 颜色
two_sides=True
):
if isinstance(cente... | null |
13,156 | import numpy as np
import cv2
import numpy as np
from tqdm import tqdm
from os.path import join
def get_rotation_from_two_directions(direc0, direc1):
direc0 = direc0/np.linalg.norm(direc0)
direc1 = direc1/np.linalg.norm(direc1)
rotdir = np.cross(direc0, direc1)
if np.linalg.norm(rotdir) < 1e-2:
... | null |
13,157 | import numpy as np
import cv2
import numpy as np
from tqdm import tqdm
from os.path import join
import os
current_dir = os.path.dirname(os.path.realpath(__file__))
def create_cameras_texture(cameras, imgnames, scale=5e-3):
import trimesh
import pyrender
from PIL import Image
from os.path import join
... | null |
13,158 | import numpy as np
import cv2
import numpy as np
from tqdm import tqdm
from os.path import join
import os
def create_mesh_pyrender(vert, faces, col):
import trimesh
import pyrender
mesh = trimesh.Trimesh(vert, faces, process=False)
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0... | null |
13,159 | import pyrender
import numpy as np
import trimesh
import cv2
from .pyrender_flags import get_flags
from ..mytools.vis_base import get_rgb
def offscree_render(renderer, scene, img, flags):
rend_rgba, rend_depth = renderer.render(scene, flags=flags)
assert rend_depth.max() < 65, 'depth should less than 65.536: {... | null |
13,160 | import pyrender
import numpy as np
import trimesh
import cv2
from .pyrender_flags import get_flags
from ..mytools.vis_base import get_rgb
class Renderer:
def __init__(self, bg_color=[1.0, 1.0, 1.0, 0.0], ambient_light=[0.5, 0.5, 0.5], flags={}) -> None:
self.bg_color = bg_color
self.ambient_light = ... | null |
13,161 | import pyrender
import numpy as np
import trimesh
import cv2
from .pyrender_flags import get_flags
from ..mytools.vis_base import get_rgb
colors_table = {
# colorblind/print/copy safe:
'_blue': [0.65098039, 0.74117647, 0.85882353],
'_pink': [.9, .7, .7],
'_mint': [ 166/255., 229/255., 204/255.],
'... | null |
13,162 | from glob import glob
from os.path import join
import numpy as np
from ..mytools.file_utils import read_json
from ..mytools.debug_utils import log
from ..mytools.reader import read_keypoints3d, read_smpl
import os
from ..mytools.camera_utils import read_cameras, Undistort
import cv2
from ..mytools.vis_base import merge... | null |
13,163 | from glob import glob
from os.path import join
import numpy as np
from ..mytools.file_utils import read_json
from ..mytools.debug_utils import log
from ..mytools.reader import read_keypoints3d, read_smpl
import os
from ..mytools.camera_utils import read_cameras, Undistort
import cv2
from ..mytools.vis_base import merge... | null |
13,164 | from pyrender import RenderFlags
render_flags_default = {
'flip_wireframe': False,
'all_wireframe': False,
'all_solid': True,
'shadows': False, # TODO:bug exists in shadow mode
'vertex_normals': False,
'face_normals': False,
'cull_faces': True, # set to False
'point_size': 1.0,
'rgb... | null |
13,165 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def _create_cylinder():
# create_cylinder(radius=1.0, height=2.0, resolution=20, split=4, crea... | null |
13,166 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
load_mesh = o3d.io.read_triangle_mesh
def read_mesh(filename):
mesh = load_mesh(filename)
... | null |
13,167 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
Vector3dVector = o3d.utility.Vector3dVector
def create_pcd(xyz, color=None, colors=None):
pcd ... | null |
13,168 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_mesh(vertices, faces, colors=None, normal=True, **kwargs):
mesh = TriangleMesh()
... | null |
13,169 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_mesh(vertices, faces, colors=None, normal=True, **kwargs):
mesh = TriangleMesh()
... | null |
13,170 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
TriangleMesh = o3d.geometry.TriangleMesh
def create_coord(camera = [0,0,0], radius=1, scale=1):
... | null |
13,171 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_bbox(min_bound=(-3., -3., 0), max_bound=(3., 3., 2), flip=False):
if flip:
... | null |
13,172 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_line(**kwargs):
return create_mesh(**create_line_(**kwargs))
def get_bound_corners(b... | null |
13,173 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_my_bbox(min_bound=(-3., -3., 0), max_bound=(3., 3., 2)):
# 使用圆柱去创建一个mesh
bbox =... | null |
13,174 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
def create_mesh(vertices, faces, colors=None, normal=True, **kwargs):
mesh = TriangleMesh()
... | null |
13,175 | import open3d as o3d
import numpy as np
from .geometry import create_ground as create_ground_
from .geometry import create_point as create_point_
from .geometry import create_line as create_line_
from os.path import join
load_mesh = o3d.io.read_triangle_mesh
vis = o3d.visualization.draw_geometries
def read_and_vis(fil... | null |
13,176 | def get_ext(mode):
ext = {'image': '.jpg', 'color':'.jpg', 'blend': '.jpg',
'depth':'.png', 'mask':'.png', 'instance':'.png',
'instance-mask': '.png', 'instance-depth': '.png', 'instance-depth-twoside': '.png',
'side': '.jpg'
}.get(mode, '.jpg')
return ext
def ge... | null |
13,177 | None:
self.render = render
self.position = {}
def factory(self, mode):
if mode == 'image':
return self.render_image
elif mode == 'color':
return self.render_color
elif mode == 'depth':
return self.render_depth
elif mode == 'corner':... | null |
13,178 | from easymocap.config.baseconfig import load_object
import torch
def make_data_sampler(cfg, dataset, shuffle, is_distributed, is_train):
if not is_train and cfg.test.sampler == 'FrameSampler':
from .samplers import FrameSampler
sampler = FrameSampler(dataset)
return sampler
if is_distrib... | null |
13,179 | import torch
from collections import Counter
from bisect import bisect_right
class MultiStepLR(torch.optim.lr_scheduler._LRScheduler):
def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):
self.milestones = Counter(milestones)
self.gamma = gamma
super(MultiStepLR, self).__init... | null |
13,180 | import torch
from collections import Counter
from bisect import bisect_right
def set_lr_scheduler(cfg_scheduler, scheduler):
if cfg_scheduler.type == 'multi_step':
scheduler.milestones = Counter(cfg_scheduler.milestones)
elif cfg_scheduler.type == 'exponential':
scheduler.decay_epochs = cfg_sch... | null |
13,181 | import torch
_optimizer_factory = {
'adam': torch.optim.Adam,
'sgd': torch.optim.SGD
}
def Optimizer(net, cfg):
params = []
lr = cfg.lr
weight_decay = cfg.weight_decay
for key, value in net.named_parameters():
if not value.requires_grad:
continue
params += [{"params... | null |
13,182 | import os
from termcolor import colored
import torch
def load_model(net,
optim,
scheduler,
recorder,
model_dir,
resume=True,
epoch=-1):
if not resume:
os.system('rm -rf {}'.format(model_dir))
if not os.path.exist... | null |
13,183 | import os
from termcolor import colored
import torch
def save_model(net, optim, scheduler, recorder, model_dir, epoch, last=False):
os.system('mkdir -p {}'.format(model_dir))
model = {
'net': net.state_dict(),
'optim': optim.state_dict(),
'scheduler': scheduler.state_dict(),
're... | null |
13,184 | import os
from termcolor import colored
import torch
def load_network(net, model_dir, resume=True, epoch=-1, strict=True):
if not resume:
return 0
if not os.path.exists(model_dir):
print(colored('pretrained model does not exist', 'red'))
return 0
if os.path.isdir(model_dir):
... | null |
13,185 | import numpy as np
import cv2
from termcolor import colored
import os
from os.path import join
from ..dataset.utils_reader import palette
colors_rgb = [
(1, 1, 1),
(94/255, 124/255, 226/255), # 青色
(255/255, 200/255, 87/255), # yellow
(74/255., 189/255., 172/255.), # green
(8/255, 76/255, 97/255), ... | null |
13,186 | import cv2
import numpy as np
from ...mytools.file_utils import read_json
def img_to_numpy(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = img.astype(np.float32)/255.
return img | null |
13,187 | import cv2
import numpy as np
from ...mytools.file_utils import read_json
def numpy_to_img(img):
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = (img*255).astype(np.uint8)
return img | null |
13,188 | import cv2
import numpy as np
from ...mytools.file_utils import read_json
def read_json(path):
assert os.path.exists(path), path
with open(path) as f:
try:
data = json.load(f)
except:
print('Reading error {}'.format(path))
data = []
return data
def read... | null |
13,189 | import cv2
import numpy as np
from ...mytools.file_utils import read_json
palette = get_schp_palette(semantic_dim)
The provided code snippet includes necessary dependencies for implementing the `get_schp_palette` function. Write a Python function `def get_schp_palette(num_cls=256)` to solve the following problem:
Ret... | Returns the color map for visualizing the segmentation mask. Inputs: num_cls: Number of classes. Returns: The color map. |
13,190 | import cv2
import numpy as np
from ...mytools.file_utils import read_json
semantic_dict = {
'background': 0,
'hat': 1,
'hair': 1,
'glove': 1,
'sunglasses': 1,
'upper_cloth': 2,
'dress': 1, #x
'coat': 1,
'sock': 1,
'pant': 3,
'jumpsuit': 1,
'scarf': 1,
'skirt': 1,
... | null |
13,191 | from os.path import join
import numpy as np
import cv2
from tqdm import trange
import copy
from .mvbase import BaseDataset, read_json, get_bounds
from ...multistage.mirror import calc_mirror_transform
import torch
from .utils_sample import AABBwMask
def calc_mirror_transform(m_):
""" From mirror vector to mirror m... | null |
13,192 | import numpy as np
import cv2
import math
from collections import namedtuple
def get_rays(H, W, K, R, T):
# calculate the camera origin
rays_o = -np.dot(R.T, T).ravel()
# calculate the world coodinates of pixels
i, j = np.meshgrid(np.arange(W, dtype=np.float32),
np.arange(H, dtyp... | null |
13,193 | import numpy as np
import cv2
import math
from collections import namedtuple
The provided code snippet includes necessary dependencies for implementing the `project` function. Write a Python function `def project(xyz, K, R, T)` to solve the following problem:
xyz: [N, 3] K: [3, 3] RT: [3, 4]
Here is the function:
de... | xyz: [N, 3] K: [3, 3] RT: [3, 4] |
13,194 | import numpy as np
import cv2
import math
from collections import namedtuple
def get_bound_corners(bounds):
min_x, min_y, min_z = bounds[0]
max_x, max_y, max_z = bounds[1]
corners_3d = np.array([
[min_x, min_y, min_z],
[min_x, min_y, max_z],
[min_x, max_y, min_z],
[min_x, ma... | null |
13,195 | import numpy as np
import cv2
import math
from collections import namedtuple
def get_bounds(xyz, delta=0.05):
min_xyz = np.min(xyz, axis=0)
max_xyz = np.max(xyz, axis=0)
if isinstance(delta, list):
delta = np.array(delta, dtype=np.float32).reshape(1, 3)
min_xyz -= delta
max_xyz += delta
... | null |
13,196 | import numpy as np
import cv2
import math
from collections import namedtuple
def sample_rays(bound_sum, mask_back, split, nrays=1024, **kwargs):
coord_body = np.argwhere(bound_sum*mask_back > 0)
if split == 'train':
coord_body = coord_body[np.random.randint(0, len(coord_body), nrays)]
return coord_... | null |
13,197 | import numpy as np
import cv2
import math
from collections import namedtuple
def generate_weight_coords(bounds, rates, back_mask):
coords = []
for key in bounds.keys():
coord_ = np.argwhere(bounds[key]*back_mask > 0)
if rates[key] == 1.:
coords.append(coord_)
elif rates[key] ... | null |
13,198 | import numpy as np
import cv2
import math
from collections import namedtuple
def create_cameras_mean(cameras, camera_args):
Told = np.stack([d['T'] for d in cameras])
Rold = np.stack([d['R'] for d in cameras])
Kold = np.stack([d['K'] for d in cameras])
Cold = - np.einsum('bmn,bnp->bmp', Rold.transpose(... | null |
13,199 | import numpy as np
import cv2
import math
from collections import namedtuple
def create_center_radius(center, radius=5., up='y', ranges=[0, 360, 36], angle_x=0, **kwargs):
center = np.array(center).reshape(1, 3)
thetas = np.deg2rad(np.linspace(*ranges))
st = np.sin(thetas)
ct = np.cos(thetas)
zero ... | null |
13,200 | import numpy as np
import cv2
import torch.nn as nn
import torch
import time
import json
from ..model.base import augment_z_vals, concat
_time_ = 0
def tic():
global _time_
_time_ = time.time() | null |
13,201 | import numpy as np
import cv2
import torch.nn as nn
import torch
import time
import json
from ..model.base import augment_z_vals, concat
_time_ = 0
def toc(name):
global _time_
print('{:15s}: {:.1f}'.format(name, 1000*(time.time() - _time_)))
_time_ = time.time() | null |
13,202 | import numpy as np
import cv2
import torch.nn as nn
import torch
import time
import json
from ..model.base import augment_z_vals, concat
def raw2acc(raw):
alpha = raw[..., -1]
weights = alpha * torch.cumprod(
torch.cat(
[torch.ones((alpha.shape[0], 1)).to(alpha), 1. - alpha + 1e-10],
... | null |
13,203 | import numpy as np
import cv2
import torch.nn as nn
import torch
import time
import json
from ..model.base import augment_z_vals, concat
The provided code snippet includes necessary dependencies for implementing the `raw2outputs` function. Write a Python function `def raw2outputs(outputs, z_vals, rays_d, bkgd=None)` t... | Transforms model's predictions to semantically meaningful values. Args: acc: [num_rays, num_samples along ray, 1]. Prediction from model. feature: [num_rays, num_samples along ray, N]. Prediction from model. z_vals: [num_rays, num_samples along ray]. Integration time. rays_d: [num_rays, 3]. Direction of each ray. Retur... |
13,204 | import torch
class Embedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda ... | null |
13,205 | import torch
import torch.nn as nn
from torch import searchsorted
def augment_z_vals(z_vals, perturb=1):
# get intervals between samples
mids = .5 * (z_vals[..., 1:] + z_vals[..., :-1])
upper = torch.cat([mids, z_vals[..., -1:]], -1)
lower = torch.cat([z_vals[..., :1], mids], -1)
# stratified sampl... | null |
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