id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
13,006 | from termcolor import colored
import os
from os.path import join
import shutil
import subprocess
import time
import datetime
def log_time(text):
strf = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
print(colored(strf, 'yellow'), colored(text, 'green')) | null |
13,007 | from termcolor import colored
import os
from os.path import join
import shutil
import subprocess
import time
import datetime
def myprint(cmd, level):
color = {'run': 'blue', 'info': 'green', 'warn': 'yellow', 'error': 'red'}[level]
print(colored(cmd, color))
warning_infos = set()
def oncewarn(text):
if tex... | null |
13,008 | from termcolor import colored
import os
from os.path import join
import shutil
import subprocess
import time
import datetime
def mkdir(path):
if os.path.exists(path):
return 0
log('mkdir {}'.format(path))
os.makedirs(path, exist_ok=True)
def cp(srcname, dstname):
mkdir(join(os.path.dirname(dstn... | null |
13,009 | from termcolor import colored
import os
from os.path import join
import shutil
import subprocess
import time
import datetime
def print_table(header, contents):
from tabulate import tabulate
length = len(contents[0])
tables = [[] for _ in range(length)]
mean = ['Mean']
for icnt, content in enumerate... | null |
13,010 | import cv2
import numpy as np
import os
from os.path import join
def camera_from_img(img):
height, width = img.shape[0], img.shape[1]
# focal = 1.2*max(height, width) # as colmap
focal = 1.2*min(height, width) # as colmap
K = np.array([focal, 0., width/2, 0., focal, height/2, 0. ,0., 1.]).reshape(3, 3)... | null |
13,011 | import cv2
import numpy as np
import os
from os.path import join
def unproj(kpts, invK):
homo = np.hstack([kpts[:, :2], np.ones_like(kpts[:, :1])])
homo = homo @ invK.T
return np.hstack([homo[:, :2], kpts[:, 2:]]) | null |
13,012 | import cv2
import numpy as np
import os
from os.path import join
def get_Pall(cameras, camnames):
Pall = np.stack([cameras[cam]['K'] @ np.hstack((cameras[cam]['R'], cameras[cam]['T'])) for cam in camnames])
return Pall | null |
13,013 | import cv2
import numpy as np
import os
from os.path import join
def get_fundamental_matrix(cameras, basenames):
skew_op = lambda x: np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]], [-x[1], x[0], 0]])
fundamental_op = lambda K_0, R_0, T_0, K_1, R_1, T_1: np.linalg.inv(K_0).T @ (
R_0 @ R_1.T) @ K_1.T @... | null |
13,014 | import cv2
import numpy as np
import os
from os.path import join
def interp_cameras(cameras, keys, step=20, loop=True, allstep=-1, **kwargs):
from scipy.spatial.transform import Rotation as R
from scipy.spatial.transform import Slerp
if allstep != -1:
tall = np.linspace(0., 1., allstep+1)[:-1].resh... | null |
13,015 | import numpy as np
def simple_reprojection_error(kpts1, kpts1_proj):
# (N, 3)
error = np.mean((kpts1[:, :2] - kpts1_proj[:, :2])**2)
return error | null |
13,016 | import numpy as np
def solveZ(A):
u, s, v = np.linalg.svd(A)
X = v[-1, :]
X = X / X[3]
return X[:3]
def simple_triangulate(kpts, Pall):
# kpts: (nViews, 3)
# Pall: (nViews, 3, 4)
# return: kpts3d(3,), conf: float
nViews = len(kpts)
A = np.zeros((nViews*2, 4), dtype=np.float)
r... | null |
13,017 | import numpy as np
def projectN3(kpts3d, Pall):
# kpts3d: (N, 3)
nViews = len(Pall)
kp3d = np.hstack((kpts3d[:, :3], np.ones((kpts3d.shape[0], 1))))
kp2ds = []
for nv in range(nViews):
kp2d = Pall[nv] @ kp3d.T
kp2d[:2, :] /= kp2d[2:, :]
kp2ds.append(kp2d.T[None, :, :])
kp... | null |
13,018 | import numpy as np
def check_limb(keypoints3d, limb_means, thres=0.5):
# keypoints3d: (nJ, 4)
valid = True
cnt = 0
for (src, dst), val in limb_means.items():
if not (keypoints3d[src, 3] > 0 and keypoints3d[dst, 3] > 0):
continue
cnt += 1
# 计算骨长
l_est = np.li... | null |
13,019 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
MAX_I... | null |
13,020 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
MAX_I... | null |
13,021 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
IS_PY... | null |
13,022 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
IS_PY... | null |
13,023 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
Came... | null |
13,024 | import shutil
import sys
import os
import sqlite3
import numpy as np
from os.path import join
import cv2
from .debug_utils import mkdir, run_cmd, log, mywarn
from .colmap_structure import Camera, Image, CAMERA_MODEL_NAMES
from .colmap_structure import rotmat2qvec
from .colmap_structure import read_points3d_binary
clas... | null |
13,025 | import os
import json
import numpy as np
from os.path import join
def save_numpy_dict(file, data):
if not os.path.exists(os.path.dirname(file)):
os.makedirs(os.path.dirname(file))
res = {}
for key, val in data.items():
res[key] = val.tolist()
with open(file, 'w') as f:
json.dump... | null |
13,026 | import os
import json
import numpy as np
from os.path import join
def read_numpy_dict(path):
assert os.path.exists(path), path
with open(path) as f:
data = json.load(f)
for key, val in data.items():
data[key] = np.array(val, dtype=np.float32)
return data | null |
13,027 | import os
import json
import numpy as np
from os.path import join
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 append_json(fi... | null |
13,028 | import os
import json
import numpy as np
from os.path import join
def getFileList(root, ext='.jpg'):
files = []
dirs = os.listdir(root)
while len(dirs) > 0:
path = dirs.pop()
fullname = join(root, path)
if os.path.isfile(fullname) and fullname.endswith(ext):
files.append... | null |
13,029 | import os
import json
import numpy as np
from os.path import join
def array2raw(array, separator=' ', fmt='%.3f'):
assert len(array.shape) == 2, 'Only support MxN matrix, {}'.format(array.shape)
res = []
for data in array:
res.append(separator.join([fmt%(d) for d in data])) | null |
13,030 | import os
import json
import numpy as np
from os.path import join
def write_common_results(dumpname=None, results=[], keys=[], fmt='%2.3f'):
format_out = {'float_kind':lambda x: fmt % x}
out_text = []
out_text.append('[\n')
for idata, data in enumerate(results):
out_text.append(' {\n')
... | null |
13,031 | import os
import json
import numpy as np
from os.path import join
def write_common_results(dumpname=None, results=[], keys=[], fmt='%2.3f'):
format_out = {'float_kind':lambda x: fmt % x}
out_text = []
out_text.append('[\n')
for idata, data in enumerate(results):
out_text.append(' {\n')
... | null |
13,032 | import os
import json
import numpy as np
from os.path import join
def batch_bbox_from_pose(keypoints2d, height, width, rate=0.1):
# TODO:write this in batch
bboxes = np.zeros((keypoints2d.shape[0], 5), dtype=np.float32)
border = 20
for bn in range(keypoints2d.shape[0]):
valid = keypoints2d[bn, ... | null |
13,033 | import os
import json
import numpy as np
from os.path import join
def merge_params(param_list, share_shape=True):
output = {}
for key in ['poses', 'shapes', 'Rh', 'Th', 'expression']:
if key in param_list[0].keys():
output[key] = np.vstack([v[key] for v in param_list])
if share_shape:
... | null |
13,034 | import os
import sys
import collections
import numpy as np
import struct
import cv2
def read_cameras_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
cameras = ... | null |
13,035 | import os
import sys
import collections
import numpy as np
import struct
import cv2
def qvec2rotmat(qvec):
return np.array([
[1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
[2 * qvec[1] * qvec[2] ... | null |
13,036 | import os
import sys
import collections
import numpy as np
import struct
import cv2
The provided code snippet includes necessary dependencies for implementing the `write_cameras_text` function. Write a Python function `def write_cameras_text(cameras, path)` to solve the following problem:
see: src/base/reconstruction.... | see: src/base/reconstruction.cc void Reconstruction::WriteCamerasText(const std::string& path) void Reconstruction::ReadCamerasText(const std::string& path) |
13,037 | import os
import sys
import collections
import numpy as np
import struct
import cv2
CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
for camera_model in CAMERA_MODELS])
def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
"""pack and write to a bi... | see: src/base/reconstruction.cc void Reconstruction::WriteCamerasBinary(const std::string& path) void Reconstruction::ReadCamerasBinary(const std::string& path) |
13,038 | import os
import sys
import collections
import numpy as np
import struct
import cv2
def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
"""pack and write to a binary file.
:param fid:
:param data: data to send, if multiple elements are sent at the same time,
they should be encap... | see: src/base/reconstruction.cc void Reconstruction::ReadImagesBinary(const std::string& path) void Reconstruction::WriteImagesBinary(const std::string& path) |
13,039 | import os
import sys
import collections
import numpy as np
import struct
import cv2
The provided code snippet includes necessary dependencies for implementing the `write_images_text` function. Write a Python function `def write_images_text(images, path)` to solve the following problem:
see: src/base/reconstruction.cc ... | see: src/base/reconstruction.cc void Reconstruction::ReadImagesText(const std::string& path) void Reconstruction::WriteImagesText(const std::string& path) |
13,040 | import os
import sys
import collections
import numpy as np
import struct
import cv2
The provided code snippet includes necessary dependencies for implementing the `write_points3D_text` function. Write a Python function `def write_points3D_text(points3D, path)` to solve the following problem:
see: src/base/reconstructi... | see: src/base/reconstruction.cc void Reconstruction::ReadPoints3DText(const std::string& path) void Reconstruction::WritePoints3DText(const std::string& path) |
13,041 | import os
import sys
import collections
import numpy as np
import struct
import cv2
def write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
"""pack and write to a binary file.
:param fid:
:param data: data to send, if multiple elements are sent at the same time,
they should be encap... | see: src/base/reconstruction.cc void Reconstruction::ReadPoints3DBinary(const std::string& path) void Reconstruction::WritePoints3DBinary(const std::string& path) |
13,042 | import os
import argparse
from os.path import join
def load_parser():
parser = argparse.ArgumentParser('EasyMocap commond line tools')
parser.add_argument('path', type=str)
parser.add_argument('--out', type=str, default=None)
parser.add_argument('--cfg', type=str, default=None)
parser.add_argument(... | null |
13,043 | import os
import argparse
from os.path import join
def save_parser(args):
import yaml
res = vars(args)
os.makedirs(args.out, exist_ok=True)
with open(join(args.out, 'exp.yml'), 'w') as f:
yaml.dump(res, f)
def parse_parser(parser):
args = parser.parse_args()
if args.out is None:
... | null |
13,044 | import cv2
import numpy as np
import json
def generate_colorbar(N = 20, cmap = 'jet', rand=True,
ret_float=False, ret_array=False, ret_rgb=False):
bar = ((np.arange(N)/(N-1))*255).astype(np.uint8).reshape(-1, 1)
colorbar = cv2.applyColorMap(bar, cv2.COLORMAP_JET).squeeze()
if False:
colorbar =... | null |
13,045 | import cv2
import numpy as np
import json
def get_rgb(index):
if isinstance(index, int):
if index == -1:
return (255, 255, 255)
if index < -1:
return (0, 0, 0)
# elif index == 0:
# return (245, 150, 150)
col = list(colors_bar_rgb[index%len(colors_b... | null |
13,046 | import cv2
import numpy as np
import json
def plot_point(img, x, y, r, col, pid=-1, font_scale=-1, circle_type=-1):
cv2.circle(img, (int(x+0.5), int(y+0.5)), r, col, circle_type)
if font_scale == -1:
font_scale = img.shape[0]/4000
if pid != -1:
cv2.putText(img, '{}'.format(pid), (int(x+0.5)... | null |
13,047 | import cv2
import numpy as np
import json
def get_rgb(index):
def plot_keypoints(img, points, pid, config, vis_conf=False, use_limb_color=True, lw=2, fliplr=False):
lw = max(lw, 2)
H, W = img.shape[:2]
for ii, (i, j) in enumerate(config['kintree']):
if i >= len(points) or j >= len(points):
... | null |
13,048 | import cv2
import numpy as np
import json
def plot_bbox(img, bbox, pid, scale=1, vis_id=True):
# 画bbox: (l, t, r, b)
x1, y1, x2, y2, c = bbox
if c < 0.01:return img
x1 = int(round(x1*scale))
x2 = int(round(x2*scale))
y1 = int(round(y1*scale))
y2 = int(round(y2*scale))
color = get_rgb(pid... | null |
13,049 | import cv2
import numpy as np
import json
def plot_line(img, pt1, pt2, lw, col):
cv2.line(img, (int(pt1[0]+0.5), int(pt1[1]+0.5)), (int(pt2[0]+0.5), int(pt2[1]+0.5)),
col, lw)
def plot_cross(img, x, y, col, width=-1, lw=-1):
if lw == -1:
lw = max(1, int(round(img.shape[0]/1000)))
width =... | null |
13,050 | import cv2
import numpy as np
import json
def get_row_col(l, square):
def merge(images, row=-1, col=-1, resize=False, ret_range=False, square=False, **kwargs):
if row == -1 and col == -1:
row, col = get_row_col(len(images), square)
height = images[0].shape[0]
width = images[0].shape[1]
# specia... | null |
13,051 | import numpy as np
import os
from os.path import join
from glob import glob
from .file_utils import read_json, read_annot
def read_annot(annotname, mode='body25'):
data = read_json(annotname)
if not isinstance(data, list):
data = data['annots']
for i in range(len(data)):
if 'id' not in data... | null |
13,052 | import numpy as np
import os
from os.path import join
from glob import glob
from .file_utils import read_json, read_annot
def read_json(path):
def read_keypoints3d_dict(filename):
data = read_json(filename)
res_ = {}
for d in data:
pid = d['id'] if 'id' in d.keys() else d['personID']
pose3... | null |
13,053 | import numpy as np
import os
from os.path import join
from glob import glob
from .file_utils import read_json, read_annot
def read_keypoints3d_a4d(outname):
res_ = []
with open(outname, "r") as file:
lines = file.readlines()
if len(lines) < 2:
return res_
nPerson, nJoints = ... | null |
13,054 | import time
import tabulate
def dummyfunc():
time.sleep(1) | null |
13,055 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def make_Cnk(n, k):
import itertools
res = {}
for n_ ... | null |
13,056 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def batch_triangulate(keypoints_, Pall, min_view=2):
""" trian... | null |
13,057 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def skew_op(x):
skew_op = lambda x: np.array([[0, -x[2], x[1]]... | null |
13,058 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
The provided code snippet includes necessary dependencies for imp... | img1 - image on which we draw the epilines for the points in img2 lines - corresponding epilines |
13,059 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def check_cluster(affinity, row, views, dimGroups, indices, p2dAs... | null |
13,060 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def views_from_dimGroups(dimGroups):
views = np.zeros(dimGrou... | null |
13,061 | import numpy as np
import cv2
from easymocap.datasets.base import crop_image
from easymocap.estimator.wrapper_base import bbox_from_keypoints
from easymocap.mytools.vis_base import merge, plot_keypoints_auto
from .debug_utils import log, mywarn, myerror
def SimpleConstrain(dimGroups):
class SimpleMatchAndTriangulator(S... | null |
13,062 | import cv2
import numpy as np
from ..mytools.file_utils import write_common_results
def write_common_results(dumpname=None, results=[], keys=[], fmt='%2.3f'):
def encode_detect(data):
res = write_common_results(None, data, ['keypoints3d'])
res = res.replace('\r', '').replace('\n', '').replace(' ', '')
ret... | null |
13,063 | import cv2
import numpy as np
from ..mytools.file_utils import write_common_results
def write_common_results(dumpname=None, results=[], keys=[], fmt='%2.3f'):
format_out = {'float_kind':lambda x: fmt % x}
out_text = []
out_text.append('[\n')
for idata, data in enumerate(results):
out_text.appen... | null |
13,064 | import cv2
import numpy as np
from ..mytools.file_utils import write_common_results
def encode_image(image):
fourcc = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
#frame을 binary 형태로 변환 jpg로 decoding
result, img_encode = cv2.imencode('.jpg', image, fourcc)
data = np.array(img_encode) # numpy array로 안바꿔주면 ERROR
... | null |
13,065 | import socket
import time
from threading import Thread
from queue import Queue
def log(x):
from datetime import datetime
time_now = datetime.now().strftime("%m-%d-%H:%M:%S.%f ")
print(time_now + x) | null |
13,066 | import open3d as o3d
from ..config import load_object
from ..visualize.o3dwrapper import Vector3dVector, create_mesh, load_mesh
from ..mytools import Timer
from ..mytools.vis_base import get_rgb_01
from .base import BaseSocket, log
import json
import numpy as np
from os.path import join
import os
from ..assignment.crit... | null |
13,067 | import torch
import torch.nn as nn
from .lbs import batch_rodrigues
from .lbs import lbs, dqs
import os.path as osp
import pickle
import numpy as np
import os
def to_np(array, dtype=np.float32):
if 'scipy.sparse' in str(type(array)):
array = array.todense()
return np.array(array, dtype=dtype) | null |
13,068 | import torch
import torch.nn as nn
from .lbs import batch_rodrigues
from .lbs import lbs, dqs
import os.path as osp
import pickle
import numpy as np
import os
def to_tensor(array, dtype=torch.float32, device=torch.device('cpu')):
if 'torch.tensor' not in str(type(array)):
return torch.tensor(array, dtype=dt... | null |
13,069 | import torch
import torch.nn as nn
from .lbs import batch_rodrigues
from .lbs import lbs, dqs
import os.path as osp
import pickle
import numpy as np
import os
def load_bodydata(model_type, model_path, gender):
if osp.isdir(model_path):
model_fn = '{}_{}.{ext}'.format(model_type.upper(), gender.upper(), ext... | null |
13,070 | import numpy as np
from os.path import join
def merge_params(param_list, share_shape=True):
output = {}
for key in ['poses', 'shapes', 'Rh', 'Th', 'expression']:
if key in param_list[0].keys():
output[key] = np.vstack([v[key] for v in param_list])
if share_shape:
output['shapes'... | null |
13,071 | import numpy as np
from os.path import join
def select_nf(params_all, nf):
output = {}
for key in ['poses', 'Rh', 'Th']:
output[key] = params_all[key][nf:nf+1, :]
if 'expression' in params_all.keys():
output['expression'] = params_all['expression'][nf:nf+1, :]
if params_all['shapes'].sh... | null |
13,072 | import numpy as np
from os.path import join
class SMPLlayer(nn.Module):
def __init__(self, model_path, model_type='smpl', gender='neutral', device=None,
regressor_path=None,
use_pose_blending=True, use_shape_blending=True, use_joints=True,
with_color=False, use_lbs=True,
**kwargs) -... | null |
13,073 | import numpy as np
from os.path import join
def check_keypoints(keypoints2d, WEIGHT_DEBUFF=1, min_conf=0.3):
# keypoints2d: nFrames, nJoints, 3
#
# wrong feet
# if keypoints2d.shape[-2] > 25 + 42:
# keypoints2d[..., 0, 2] = 0
# keypoints2d[..., [15, 16, 17, 18], -1] = 0
# keypoints2d[.... | null |
13,074 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import torch
import torch.nn.functional as F
def rot_mat_to_euler(rot_mats):
# Calculates rotation matrix to euler angles
# Careful for extreme cases of eular angles like [0.0, pi, 0.0... | Compute the faces, barycentric coordinates for the dynamic landmarks To do so, we first compute the rotation of the neck around the y-axis and then use a pre-computed look-up table to find the faces and the barycentric coordinates that will be used. Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) for... |
13,075 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import torch
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `vertices2landmarks` function. Write a Python function `def vertice... | Calculates landmarks by barycentric interpolation Parameters ---------- vertices: torch.tensor BxVx3, dtype = torch.float32 The tensor of input vertices faces: torch.tensor Fx3, dtype = torch.long The faces of the mesh lmk_faces_idx: torch.tensor L, dtype = torch.long The tensor with the indices of the faces used to ca... |
13,076 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import torch
import torch.nn.functional as F
def vertices2joints(J_regressor, vertices):
''' Calculates the 3D joint locations from the vertices
Parameters
----------
J_regress... | Performs Linear Blend Skinning with the given shape and pose parameters Parameters ---------- betas : torch.tensor BxNB The tensor of shape parameters pose : torch.tensor Bx(J + 1) * 3 The pose parameters in axis-angle format v_template torch.tensor BxVx3 The template mesh that will be deformed shapedirs : torch.tensor... |
13,077 | from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import numpy as np
import torch
import torch.nn.functional as F
def vertices2joints(J_regressor, vertices):
''' Calculates the 3D joint locations from the vertices
Parameters
----------
J_regress... | Performs Linear Blend Skinning with the given shape and pose parameters Parameters ---------- betas : torch.tensor BxNB The tensor of shape parameters pose : torch.tensor Bx(J + 1) * 3 The pose parameters in axis-angle format v_template torch.tensor BxVx3 The template mesh that will be deformed shapedirs : torch.tensor... |
13,078 | import numpy as np
import cv2
from ..dataset.config import CONFIG
from ..config import load_object
from ..mytools.debug_utils import log, mywarn, myerror
import torch
from tqdm import tqdm, trange
def svd_rot(src, tgt, reflection=False, debug=False):
# optimum rotation matrix of Y
A = np.matmul(src.transpose(0... | null |
13,079 | import numpy as np
import cv2
from ..dataset.config import CONFIG
from ..config import load_object
from ..mytools.debug_utils import log, mywarn, myerror
import torch
from tqdm import tqdm, trange
def batch_invRodrigues(rot):
res = []
for r in rot:
v = cv2.Rodrigues(r)[0]
res.append(v)
res ... | null |
13,080 | import numpy as np
import torch.nn as nn
import torch
from ..bodymodel.lbs import batch_rodrigues
class GMoF(nn.Module):
def __init__(self, rho=1):
def extra_repr(self):
def forward(self, est, gt=None, conf=None):
def make_loss(norm, norm_info, reduce='sum'):
reduce = torch.sum if reduce=='sum' else... | null |
13,081 | import numpy as np
import torch.nn as nn
import torch
from ..bodymodel.lbs import batch_rodrigues
def select(value, ranges, index, dim):
if len(ranges) > 0:
if ranges[1] == -1:
value = value[..., ranges[0]:]
else:
value = value[..., ranges[0]:ranges[1]]
return value
... | null |
13,082 | import numpy as np
import torch.nn as nn
import torch
from ..bodymodel.lbs import batch_rodrigues
def print_table(header, contents):
from tabulate import tabulate
length = len(contents[0])
tables = [[] for _ in range(length)]
mean = ['Mean']
for icnt, content in enumerate(contents):
for i i... | null |
13,083 | import numpy as np
import torch
from ..dataset.mirror import flipPoint2D, flipSMPLPoses, flipSMPLParams
from ..estimator.wrapper_base import bbox_from_keypoints
from .lossbase import Keypoints2D
The provided code snippet includes necessary dependencies for implementing the `calc_vanishpoint` function. Write a Python f... | keypoints2d: (2, N, 3) |
13,084 | import pickle
import os
from os.path import join
import numpy as np
import torch
from .lossbase import LossBase
The provided code snippet includes necessary dependencies for implementing the `create_prior_from_cmu` function. Write a Python function `def create_prior_from_cmu(n_gaussians, epsilon=1e-15)` to solve the f... | Load the gmm from the CMU motion database. |
13,085 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | Calculates the rotation matrices for a batch of rotation vectors Parameters ---------- rot_vecs: torch.tensor Nx3 array of N axis-angle vectors Returns ------- R: torch.tensor Nx3x3 The rotation matrices for the given axis-angle parameters |
13,086 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,087 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,088 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,089 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,090 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,091 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,092 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,093 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,094 | from collections import namedtuple
from time import time, sleep
import numpy as np
import cv2
import torch
import copy
from ..config.baseconfig import load_object_from_cmd
from ..mytools.debug_utils import log, mywarn
from ..mytools import Timer
from ..config import Config
from ..mytools.triangulator import iterative_t... | null |
13,095 | import torch
from torch.nn import functional as F
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `rot6d_to_rotation_matrix` function. Write a Python function `def rot6d_to_rotation_matrix(rot6d)` to solve the following problem:
Convert 6D rotation representation to 3x... | Convert 6D rotation representation to 3x3 rotation matrix. Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 Args: rot6d: torch tensor of shape (batch_size, 6) of 6d rotation representations. Returns: rotation_matrix: torch tensor of shape (batch_size, 3, 3) of correspo... |
13,096 | import torch
from torch.nn import functional as F
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `rotation_matrix_to_rot6d` function. Write a Python function `def rotation_matrix_to_rot6d(rotation_matrix)` to solve the following problem:
Convert 3x3 rotation matrix to... | Convert 3x3 rotation matrix to 6D rotation representation. Args: rotation_matrix: torch tensor of shape (batch_size, 3, 3) of corresponding rotation matrices. Returns: rot6d: torch tensor of shape (batch_size, 6) of 6d rotation representations. |
13,097 | import torch
from torch.nn import functional as F
import numpy as np
def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
"""
Convert rotation matrix to corresponding quaternion
Args:
rotation_matrix: torch tensor of shape (batch_size, 3, 3)
Returns:
quaternion: torch tensor of ... | null |
13,098 | import torch
from torch.nn import functional as F
import numpy as np
def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
def quaternion_to_euler(quaternion, order, epsilon=0):
def rotation_matrix_to_euler(rotation_matrix, order):
quaternion = rotation_matrix_to_quaternion(rotation_matrix)
return quat... | null |
13,099 | import torch
from torch.nn import functional as F
import numpy as np
def quaternion_to_rotation_matrix(quaternion):
"""
Convert quaternion coefficients to rotation matrix.
Args:
quaternion: torch tensor of shape (batch_size, 4) in (w, x, y, z) representation.
Returns:
rotation matrix cor... | null |
13,100 | import torch
from torch.nn import functional as F
import numpy as np
def quaternion_to_euler(quaternion, order, epsilon=0):
"""
Convert quaternion to euler angles.
Args:
quaternion: torch tensor of shape (batch_size, 4) in (w, x, y, z) representation.
order: euler angle representation order,... | null |
13,101 | import torch
from torch.nn import functional as F
import numpy as np
def euler_to_quaternion(euler, order):
"""
Convert euler angles to quaternion.
Args:
euler: torch tensor of shape (batch_size, 3) in order.
order:
Returns:
quaternion: torch tensor of shape (batch_size, 4) in (w... | null |
13,102 | import torch
from torch.nn import functional as F
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `rotate_vec_by_quaternion` function. Write a Python function `def rotate_vec_by_quaternion(v, q)` to solve the following problem:
Rotate vector(s) v about the rotation des... | Rotate vector(s) v about the rotation described by quaternion(s) q. Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v, where * denotes any number of dimensions. Returns a tensor of shape (*, 3). |
13,103 | import torch
from torch.nn import functional as F
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `quaternion_fix` function. Write a Python function `def quaternion_fix(quaternion)` to solve the following problem:
Enforce quaternion continuity across the time dimension... | Enforce quaternion continuity across the time dimension by selecting the representation (q or -q) with minimal distance (or, equivalently, maximal dot product) between two consecutive frames. Args: quaternion: torch tensor of shape (batch_size, 4) Returns: quaternion: torch tensor of shape (batch_size, 4) |
13,104 | import torch
from torch.nn import functional as F
import numpy as np
def quaternion_inverse(quaternion):
q_conjugate = quaternion.clone()
q_conjugate[::, 1:] * -1
q_norm = quaternion[::, 1:].norm(dim=-1) + quaternion[::, 0]**2
return q_conjugate/q_norm.unsqueeze(-1) | null |
13,105 | import torch
from torch.nn import functional as F
import numpy as np
def quaternion_lerp(q1, q2, t):
q = (1-t)*q1 + t*q2
q = q/q.norm(dim=-1).unsqueeze(-1)
return q | null |
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