id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
13,206 | import torch
import torch.nn as nn
from torch import searchsorted
def sample_pdf(bins, weights, N_samples, det=False):
# Get pdf
weights = weights + 1e-5 # prevent nans
pdf = weights / torch.sum(weights, -1, keepdim=True)
cdf = torch.cumsum(pdf, -1)
cdf = torch.cat([torch.zeros_like(cdf[...,:1]), c... | null |
13,207 | import torch
import torch.nn as nn
from torch import searchsorted
def get_near_far(ray_o, ray_d, bounds):
def get_near_far_RTBBox(ray_o, ray_d, bounds, R, T):
# sample the near far in canonical coordinate
ray_o_rt = (ray_o - T) @ R
ray_d_rt = ray_d @ R
near, far, mask_at_box = get_near_far(ray_o_rt, ra... | null |
13,208 | import torch
import torch.nn as nn
from torch import searchsorted
def concat(retlist, dim=0, unsqueeze=True, mask=None):
res = {}
if len(retlist) == 0:
return res
for key in retlist[0].keys():
val = torch.cat([r[key] for r in retlist], dim=dim)
if mask is not None and val.shape[0] !... | null |
13,209 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `pts_to_can_pts` function. Write a Python function `def pts_to_can_pts(pts, sp_input)` to solve the following problem:
transfo... | transform pts from the world coordinate to the smpl coordinate |
13,210 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
import torch.nn as nn
import torch.nn.functional as F
def get_grid_coords(pts, sp_input, voxel_size):
# convert xyz to the voxel coordinate dhw
dhw = pts[..., [2, 1, 0]]
# min_dhw = sp_input['bounds'][:, 0, [2, 1, 0]]
min_dhw = sp_input['min_d... | null |
13,211 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
try:
if spconv.__version__.split('.')[0] == '2':
import spconv.pytorch as spconv
except:
pass
import torch.nn as nn
import torch.nn.functional as F
def encode_sparse_voxels(xyzc_net, sp_input, code):
coord = sp_input['coord']
out_sh = ... | null |
13,212 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
import torch.nn as nn
import torch.nn.functional as F
def my_grid_sample(feat, grid, mode='bilinear', align_corners=True, padding_mode='border'):
B, C, ID, IH, IW = feat.shape
assert(B==1)
feat = feat[0]
grid = grid[0, 0, 0]
N_g, _ = grid.... | null |
13,213 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
import torch.nn as nn
import torch.nn.functional as F
def interpolate_features(grid_coords, feature_volume, padding_mode):
features = []
for volume in feature_volume:
feature = F.grid_sample(volume,
grid_coords,... | null |
13,214 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
import torch.nn as nn
import torch.nn.functional as F
def prepare_sp_input(batch, voxel_pad, voxel_size):
vertices = batch['vertices'][0]
R, Th = batch['R'][0], batch['Th'][0]
# Here: R^-1 @ (X - T) => (X - T) @ R^-1.T
can_xyz = torch.matmul(v... | null |
13,215 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
try:
if spconv.__version__.split('.')[0] == '2':
import spconv.pytorch as spconv
except:
pass
import torch.nn as nn
import torch.nn.functional as F
def single_conv(in_channels, out_channels, indice_key=None):
return spconv.SparseSequential... | null |
13,216 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
try:
if spconv.__version__.split('.')[0] == '2':
import spconv.pytorch as spconv
except:
pass
import torch.nn as nn
import torch.nn.functional as F
def double_conv(in_channels, out_channels, indice_key=None):
return spconv.SparseSequential... | null |
13,217 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
try:
if spconv.__version__.split('.')[0] == '2':
import spconv.pytorch as spconv
except:
pass
import torch.nn as nn
import torch.nn.functional as F
def triple_conv(in_channels, out_channels, indice_key=None):
return spconv.SparseSequential... | null |
13,218 | from .nerf import Nerf, EmbedMLP
import torch
import spconv
try:
if spconv.__version__.split('.')[0] == '2':
import spconv.pytorch as spconv
except:
pass
import torch.nn as nn
import torch.nn.functional as F
def stride_conv(in_channels, out_channels, indice_key=None):
return spconv.SparseSequential... | null |
13,219 | import torch
import torch.nn as nn
from .nerf import Nerf, EmbedMLP, MultiLinear
from os.path import join
from ...mytools.file_utils import read_json
import numpy as np
class EmbedMLP(nn.Module):
def __init__(self, input_ch, output_ch, multi_res, W, D, bounds) -> None:
super().__init__()
self.embed... | null |
13,220 | import numpy as np
import cv2
import os
from os.path import join
from ..mytools import plot_cross, plot_line, plot_bbox, plot_keypoints, get_rgb, merge
from ..mytools.file_utils import get_bbox_from_pose
from ..dataset import CONFIG
def plot_bbox_body(img, annots, **kwargs):
annots = annots['annots']
for data ... | null |
13,221 | import os
import json
import numpy as np
from os.path import join
import shutil
from ..mytools.file_utils import myarray2string
def read_json(path):
with open(path, 'r') as f:
data = json.load(f)
return data
def load_annot_to_tmp(annotname):
if annotname is None:
return {}
if not os.pat... | null |
13,222 | import cv2
The provided code snippet includes necessary dependencies for implementing the `point_callback` function. Write a Python function `def point_callback(event, x, y, flags, param)` to solve the following problem:
OpenCV使用的简单的回调函数，主要实现两个基础功能： 1. 对于按住拖动的情况，记录起始点与终止点（当前点） 2. 对于点击的情况，记录选择的点 3. 记录当前是否按住了键
Here is ... | OpenCV使用的简单的回调函数，主要实现两个基础功能： 1. 对于按住拖动的情况，记录起始点与终止点（当前点） 2. 对于点击的情况，记录选择的点 3. 记录当前是否按住了键 |
13,223 | import numpy as np
import cv2
from func_timeout import func_set_timeout
colors_chessboard_bar = [
[0, 0, 255],
[0, 128, 255],
[0, 200, 200],
[0, 255, 0],
[200, 200, 0],
[255, 0, 0],
[255, 0, 250]
]
def get_lines_chessboard(pattern=(9, 6)):
w, h = pattern[0], pattern[1]
lines = []
... | null |
13,224 | import numpy as np
import cv2
from func_timeout import func_set_timeout
def detect_charuco(image, aruco_type, long, short, squareLength, aruco_len):
ARUCO_DICT = {
"4X4_50": cv2.aruco.DICT_4X4_50,
"4X4_100": cv2.aruco.DICT_4X4_100,
"5X5_100": cv2.aruco.DICT_5X5_100,
"5X5_250": cv2.a... | null |
13,225 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
def print_help(annotator, **kwargs):
"""print the help"""
print('Here is the help:')
print( '------------------')
for key, val in annotator.register_keys.items():
if isinstance(val, list):
print(' {}:... | null |
13,226 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
The provided code snippet includes necessary dependencies for implementing the `close` function. Write a Python function `def close(annotator, **kwargs)` to solve the following problem:
quit the annotation
Here is the function:
def close... | quit the annotation |
13,227 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
for key in 'wasdfg':
register_keys[key] = get_move(key)
The provided code snippet includes necessary dependencies for implementing the `close_wo_save` function. Write a Python function `def close_wo_save(annotator, **kwargs)` to solve ... | quit the annotation without saving |
13,228 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
for key in 'wasdfg':
register_keys[key] = get_move(key)
The provided code snippet includes necessary dependencies for implementing the `skip` function. Write a Python function `def skip(annotator, **kwargs)` to solve the following prob... | skip the annotation |
13,229 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
def get_move(wasd):
get_frame = {
'a': lambda x, f: f - 1,
'd': lambda x, f: f + 1,
'w': lambda x, f: f - 10,
's': lambda x, f: f + 10,
'f': lambda x, f: f + 100,
'g': lambda x, f: f - 10... | null |
13,230 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
def set_personID(i):
def func(self, param, **kwargs):
active = param['select']['bbox']
if active == -1 and active >= len(param['annots']['annots']):
return 0
else:
param['annots']['annots... | null |
13,231 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
def choose_personID(i):
def func(self, param, **kwargs):
for idata, data in enumerate(param['annots']['annots']):
if data['personID'] == i:
param['select']['bbox'] = idata
return 0
func._... | null |
13,232 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
remain = 0
keys_pre = []
for key in 'wasdfg':
register_keys[key] = get_move(key)
def get_key():
k = cv2.waitKey(10) & 0xFF
if k == CV_KEY.LSHIFT:
key1 = cv2.waitKey(500) & 0xFF
if key1 == CV_KEY.NONE:
... | continue automatic |
13,233 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
remain = 0
keys_pre = []
for key in 'wasdfg':
register_keys[key] = get_move(key)
def get_key():
k = cv2.waitKey(10) & 0xFF
if k == CV_KEY.LSHIFT:
key1 = cv2.waitKey(500) & 0xFF
if key1 == CV_KEY.NONE:
... | Automatic running |
13,234 | from glob import glob
from tqdm import tqdm
from .basic_callback import get_key
The provided code snippet includes necessary dependencies for implementing the `set_keyframe` function. Write a Python function `def set_keyframe(self, param)` to solve the following problem:
set/unset the key-frame
Here is the function:
... | set/unset the key-frame |
13,235 | import numpy as np
from ..dataset.config import CONFIG
def findNearestPoint(points, click):
def callback_select_bbox_corner(start, end, annots, select, bbox_name, **kwargs):
if start is None or end is None:
select['corner'] = -1
return 0
if start[0] == end[0] and start[1] == end[1]:
ret... | null |
13,236 | import numpy as np
from ..dataset.config import CONFIG
def get_auto_track(mode='kpts'):
MAX_SPEED = 100
if mode == 'bbox':
MAX_SPEED = 0.2
def auto_track(self, param, **kwargs):
if self.frame == 0:
return 0
previous = self.previous()
annots = param['annots']['ann... | null |
13,237 | import numpy as np
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `copy_previous_missing` function. Write a Python function `def copy_previous_missing(self, param, **kwargs)` to solve the following problem:
copy the missing person of previous frame
H... | copy the missing person of previous frame |
13,238 | import numpy as np
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `copy_previous_bbox` function. Write a Python function `def copy_previous_bbox(self, param, **kwargs)` to solve the following problem:
copy the annots of previous frame
Here is the fun... | copy the annots of previous frame |
13,239 | import numpy as np
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `create_bbox` function. Write a Python function `def create_bbox(self, param, **kwargs)` to solve the following problem:
add new boundbox
Here is the function:
def create_bbox(self, p... | add new boundbox |
13,240 | import numpy as np
from ..dataset.config import CONFIG
CONFIG = {
'points': {
'nJoints': 1,
'kintree': []
}
}
CONFIG['smpl'] = {'nJoints': 24, 'kintree':
[
[ 0, 1 ],
[ 0, 2 ],
[ 0, 3 ],
[ 1, 4 ],
[ 2, 5 ],
[ 3, 6 ],
[ 4, 7 ],
... | add new boundbox |
13,241 | import numpy as np
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `delete_bbox` function. Write a Python function `def delete_bbox(self, param, **kwargs)` to solve the following problem:
delete the person
Here is the function:
def delete_bbox(self, ... | delete the person |
13,242 | import numpy as np
from ..dataset.config import CONFIG
The provided code snippet includes necessary dependencies for implementing the `delete_all_bbox` function. Write a Python function `def delete_all_bbox(self, param, **kwargs)` to solve the following problem:
delete the person
Here is the function:
def delete_all... | delete the person |
13,243 | import numpy as np
from ..dataset.config import CONFIG
def callback_select_image(click, select, ranges, **kwargs):
if click is None:
return 0
ranges = np.array(ranges)
click = np.array(click).reshape(1, -1)
res = (click[:, 0]>ranges[:, 0])&(click[:, 0]<ranges[:, 2])&(click[:, 1]>ranges[:, 1])&(... | null |
13,244 | import numpy as np
from ..dataset.config import CONFIG
MIN_PIXEL = 50
def callback_select_bbox_center(click, annots, select, bbox_name, min_pixel=-1, **kwargs):
def callback_select_image_bbox(click, start, end, select, ranges, annots, bbox_name='bbox', **kwargs):
if click is None:
return 0
ranges = np.... | null |
13,245 | import numpy as np
from ..dataset.config import CONFIG
def findNearestPoint(points, click):
# points: (N, 2)
# click : [x, y]
click = np.array(click)
if len(points.shape) == 2:
click = click[None, :]
elif len(points.shape) == 3:
click = click[None, None, :]
dist = np.linalg.norm(... | null |
13,246 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `set_face_unvisible` function. Write a Python function `def set_face_unvisible(self, param, **kwargs)` to solve the following problem:
set the face unvisible
Here is the function:
def set_face_unvisible(self, param, **k... | set the face unvisible |
13,247 | import shutil
import cv2
import os
from tqdm import tqdm
from .basic_keyboard import print_help, register_keys
from .basic_visualize import plot_text, resize_to_screen, merge
from .basic_callback import point_callback, CV_KEY, get_key
from .bbox_callback import callback_select_image
from .file_utils import load_annot_t... | null |
13,248 | import shutil
import cv2
import os
from tqdm import tqdm
from .basic_keyboard import print_help, register_keys
from .basic_visualize import plot_text, resize_to_screen, merge
from .basic_callback import point_callback, CV_KEY, get_key
from .bbox_callback import callback_select_image
from .file_utils import load_annot_t... | null |
13,249 | import shutil
import cv2
import os
from tqdm import tqdm
from .basic_keyboard import print_help, register_keys
from .basic_visualize import plot_text, resize_to_screen, merge
from .basic_callback import point_callback, CV_KEY, get_key
from .bbox_callback import callback_select_image
from .file_utils import load_annot_t... | null |
13,250 | from os.path import join
import os
from glob import glob
import numpy as np
from easymocap.dataset.config import coco17tobody25
from ..mytools.vis_base import merge, plot_keypoints_auto, plot_keypoints_total
from ..mytools.camera_utils import Undistort, unproj, read_cameras
from ..mytools.file_utils import read_json, w... | null |
13,251 | from os.path import join
import os
from glob import glob
import numpy as np
from easymocap.dataset.config import coco17tobody25
from ..mytools.vis_base import merge, plot_keypoints_auto, plot_keypoints_total
from ..mytools.camera_utils import Undistort, unproj, read_cameras
from ..mytools.file_utils import read_json, w... | null |
13,252 | from os.path import join
import os
from glob import glob
import numpy as np
from easymocap.dataset.config import coco17tobody25
from ..mytools.vis_base import merge, plot_keypoints_auto, plot_keypoints_total
from ..mytools.camera_utils import Undistort, unproj, read_cameras
from ..mytools.file_utils import read_json, w... | null |
13,253 | from os.path import join
import os
from glob import glob
import numpy as np
from easymocap.dataset.config import coco17tobody25
from ..mytools.vis_base import merge, plot_keypoints_auto, plot_keypoints_total
from ..mytools.camera_utils import Undistort, unproj, read_cameras
from ..mytools.file_utils import read_json, w... | null |
13,254 | from os.path import join
import os
from glob import glob
import numpy as np
from easymocap.dataset.config import coco17tobody25
from ..mytools.vis_base import merge, plot_keypoints_auto, plot_keypoints_total
from ..mytools.camera_utils import Undistort, unproj, read_cameras
from ..mytools.file_utils import read_json, w... | null |
13,255 | from ..annotator.file_utils import read_json
from .wrapper_base import check_result, create_annot_file, save_annot
from glob import glob
from os.path import join
from tqdm import tqdm
import os
import cv2
import numpy as np
def detect_frame(detector, img, pid=0, only_bbox=False):
lDetections = detector.detect([img... | null |
13,256 | from ..annotator.file_utils import read_json
from .wrapper_base import check_result, create_annot_file, save_annot
from glob import glob
from os.path import join
from tqdm import tqdm
import os
import cv2
import numpy as np
def check_result(image_root, annot_root):
if os.path.exists(annot_root):
# check th... | null |
13,257 | from ..annotator.file_utils import read_json
from .wrapper_base import check_result, create_annot_file, save_annot
from glob import glob
from os.path import join
from tqdm import tqdm
import os
import cv2
import numpy as np
def read_json(path):
with open(path, 'r') as f:
data = json.load(f)
return data... | null |
13,258 | from ..annotator.file_utils import read_json
from .wrapper_base import check_result, create_annot_file, save_annot
from glob import glob
from os.path import join
from tqdm import tqdm
import os
import cv2
import numpy as np
def create_annot_file(annotname, imgname):
assert os.path.exists(imgname), imgname
img ... | null |
13,259 | import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import numpy as np
import math
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `rot6d_to_rotmat` function. Write a Python function `def rot6d_to_rotmat(x)` to solve the foll... | Convert 6D rotation representation to 3x3 rotation matrix. Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 Input: (B,6) Batch of 6-D rotation representations Output: (B,3,3) Batch of corresponding rotation matrices |
13,260 | import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import numpy as np
import math
from torch.nn import functional as F
class Bottleneck(nn.Module):
""" Redefinition of Bottleneck residual block
Adapted from the official PyTorch implementation
"""
expansion = 4
def __in... | Constructs an HMR model with ResNet50 backbone. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
13,261 | import torch
import numpy as np
import cv2
from .models import hmr
The provided code snippet includes necessary dependencies for implementing the `normalize` function. Write a Python function `def normalize(tensor, mean, std, inplace: bool = False)` to solve the following problem:
Normalize a tensor image with mean an... | Normalize a tensor image with mean and standard deviation. .. note:: This transform acts out of place by default, i.e., it does not mutates the input tensor. See :class:`~torchvision.transforms.Normalize` for more details. Args: tensor (Tensor): Tensor image of size (C, H, W) or (B, C, H, W) to be normalized. mean (seq... |
13,262 | import torch
import numpy as np
import cv2
from .models import hmr
class constants:
FOCAL_LENGTH = 5000.
IMG_RES = 224
# Mean and standard deviation for normalizing input image
IMG_NORM_MEAN = [0.485, 0.456, 0.406]
IMG_NORM_STD = [0.229, 0.224, 0.225]
class Normalize(torch.nn.Module):
"""Normali... | Read image, do preprocessing and possibly crop it according to the bounding box. If there are bounding box annotations, use them to crop the image. If no bounding box is specified but openpose detections are available, use them to get the bounding box. |
13,263 | import torch
import numpy as np
import cv2
from .models import hmr
def solve_translation(X, x, K):
A = np.zeros((2*X.shape[0], 3))
b = np.zeros((2*X.shape[0], 1))
fx, fy = K[0, 0], K[1, 1]
cx, cy = K[0, 2], K[1, 2]
for nj in range(X.shape[0]):
A[2*nj, 0] = 1
A[2*nj + 1, 1] = 1
... | null |
13,264 | import torch
import numpy as np
import cv2
from .models import hmr
def estimate_translation_np(S, joints_2d, joints_conf, K):
def init_with_spin(body_model, spin_model, img, bbox, kpts, camera):
body_params = spin_model.forward(img.copy(), bbox)
body_params = body_model.check_params(body_params)
# only use... | null |
13,265 | import numpy as np
import cv2
import mediapipe as mp
from ..mytools import Timer
The provided code snippet includes necessary dependencies for implementing the `bbox_from_keypoints` function. Write a Python function `def bbox_from_keypoints(keypoints, rescale=1.2, detection_thresh=0.05, MIN_PIXEL=5)` to solve the foll... | Get center and scale for bounding box from openpose detections. |
13,266 | import numpy as np
import cv2
import mediapipe as mp
from ..mytools import Timer
class Detector:
def __init__(self, nViews, to_openpose, model_type, show=False, **cfg) -> None:
def to_array(pose, W, H, start=0):
def get_body(self, pose, W, H):
def get_hand(self, pose, W, H):
def get_face(self, ... | null |
13,267 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def sigmoid(x):
return 1.0 / (np.exp(-x) + 1.) | null |
13,268 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def softmax(x):
x = np.exp(x - np.expand_dims(np.max(x, axis=1), axis=1))
x = x / np.expand_dims(x.sum(axis=1), axis=1)
return x | null |
13,269 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def bbox_iou(box1, box2, x1y1x2y2=True):
# print('iou box1:', box1)
# print('iou box2:', box2)
if x1y1x2y2:
mx = min(box1[0], box2[0])
Mx = max(box1[2], box2[2])
my = ... | null |
13,270 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def plot_boxes_cv2(img, boxes, savename=None, class_names=None, color=None):
import cv2
img = np.copy(img)
colors = np.array([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0], [1, 1, 0], [1, 0, 0]], dtyp... | null |
13,271 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def read_truths(lab_path):
if not os.path.exists(lab_path):
return np.array([])
if os.path.getsize(lab_path):
truths = np.loadtxt(lab_path)
truths = truths.reshape(truths.size /... | null |
13,272 | import sys
import os
import time
import math
import numpy as np
import itertools
import struct
import imghdr
def nms_cpu(boxes, confs, nms_thresh=0.5, min_mode=False):
# print(boxes.shape)
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1) * (y2 - y1)
ord... | null |
13,273 | import sys
import os
import time
import math
import torch
import numpy as np
from torch.autograd import Variable
def get_region_boxes(boxes_and_confs):
# print('Getting boxes from boxes and confs ...')
boxes_list = []
confs_list = []
for item in boxes_and_confs:
boxes_list.append(item[0])
... | null |
13,274 | import sys
import os
import time
import math
import torch
import numpy as np
from torch.autograd import Variable
def convert2cpu_long(gpu_matrix):
return torch.LongTensor(gpu_matrix.size()).copy_(gpu_matrix) | null |
13,275 | import sys
import os
import time
import math
import torch
import numpy as np
from torch.autograd import Variable
def do_detect(model, img, conf_thresh, nms_thresh, use_cuda=1):
model.eval()
t0 = time.time()
if type(img) == np.ndarray and len(img.shape) == 3: # cv2 image
img = torch.from_numpy(img... | null |
13,276 | import torch
from .torch_utils import convert2cpu
def parse_cfg(cfgfile):
blocks = []
fp = open(cfgfile, 'r')
block = None
line = fp.readline()
while line != '':
line = line.rstrip()
if line == '' or line[0] == '#':
line = fp.readline()
continue
elif ... | null |
13,277 | import torch
from .torch_utils import convert2cpu
def print_cfg(blocks):
print('layer filters size input output');
prev_width = 416
prev_height = 416
prev_filters = 3
out_filters = []
out_widths = []
out_heights = []
ind = -2
for block in blocks:
... | null |
13,278 | import torch
from .torch_utils import convert2cpu
def load_conv(buf, start, conv_model):
num_w = conv_model.weight.numel()
num_b = conv_model.bias.numel()
conv_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
start = start + num_b
conv_model.weight.data.copy_(torch.from_numpy(buf[... | null |
13,279 | import torch
from .torch_utils import convert2cpu
def convert2cpu(gpu_matrix):
return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix)
def save_conv(fp, conv_model):
if conv_model.bias.is_cuda:
convert2cpu(conv_model.bias.data).numpy().tofile(fp)
convert2cpu(conv_model.weight.data).numpy... | null |
13,280 | import torch
from .torch_utils import convert2cpu
def load_conv_bn(buf, start, conv_model, bn_model):
num_w = conv_model.weight.numel()
num_b = bn_model.bias.numel()
bn_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
start = start + num_b
bn_model.weight.data.copy_(torch.from_num... | null |
13,281 | import torch
from .torch_utils import convert2cpu
def convert2cpu(gpu_matrix):
return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix)
def save_conv_bn(fp, conv_model, bn_model):
if bn_model.bias.is_cuda:
convert2cpu(bn_model.bias.data).numpy().tofile(fp)
convert2cpu(bn_model.weight.data... | null |
13,282 | import torch
from .torch_utils import convert2cpu
def load_fc(buf, start, fc_model):
num_w = fc_model.weight.numel()
num_b = fc_model.bias.numel()
fc_model.bias.data.copy_(torch.from_numpy(buf[start:start + num_b]));
start = start + num_b
fc_model.weight.data.copy_(torch.from_numpy(buf[start:start ... | null |
13,283 | import torch
from .torch_utils import convert2cpu
def save_fc(fp, fc_model):
fc_model.bias.data.numpy().tofile(fp)
fc_model.weight.data.numpy().tofile(fp) | null |
13,284 | import torch.nn as nn
import torch.nn.functional as F
from .torch_utils import *
import math
import torch
from torch.autograd import Variable
def bbox_ious(boxes1, boxes2, x1y1x2y2=True):
def build_targets(pred_boxes, target, anchors, num_anchors, num_classes, nH, nW, noobject_scale, object_scale,
... | null |
13,285 | from .darknet2pytorch import Darknet
import cv2
import torch
from os.path import join
import os
import numpy as np
def load_class_names(namesfile):
class_names = []
with open(namesfile, 'r') as fp:
lines = fp.readlines()
for line in lines:
line = line.rstrip()
class_names.append(lin... | null |
13,286 | from .darknet2pytorch import Darknet
import cv2
import torch
from os.path import join
import os
import numpy as np
def nms_cpu(boxes, confs, nms_thresh=0.5, min_mode=False):
# print(boxes.shape)
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
areas = (x2 - x1) * (y2 - y1)
... | null |
13,287 | import torch.nn as nn
import torch.nn.functional as F
from .torch_utils import *
import torch
from torch.autograd import Variable
def yolo_forward(output, conf_thresh, num_classes, anchors, num_anchors, scale_x_y, only_objectness=1,
validation=False):
# Output would be invalid if it ... | null |
13,288 | import torch.nn as nn
import torch.nn.functional as F
from .torch_utils import *
import torch
from torch.autograd import Variable
def yolo_forward_dynamic(output, conf_thresh, num_classes, anchors, num_anchors, scale_x_y, only_objectness=1,
validation=False):
# Output would be invali... | null |
13,289 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
COCO17_IN_BODY25 = [0,16,15,18,17,5,2,6,3,7,4,12,9,13,10,14,11]
import math
def coco17tobody25(points2d):
kpts = np.zeros((points2d.shape[0], 25, 3))
kpts[:, COCO17_IN_BODY... | null |
13,290 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
tmp_size = sigma * 3
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
g = np.exp(- ((x - x0) ** 2 + (y ... | null |
13,291 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
import math
The provided code snippet includes necessary dependencies for implementing the `box_to_center_scale` function. Write a Python function `def box_to_center_scale(box, mod... | convert a box to center,scale information required for pose transformation Parameters ---------- box : list of tuple list of length 2 with two tuples of floats representing bottom left and top right corner of a box model_image_width : int model_image_height : int Returns ------- (numpy array, numpy array) Two numpy arr... |
13,292 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
import math
def affine_transform(pt, t):
new_pt = np.array([pt[0], pt[1], 1.]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2] | null |
13,293 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
size = 2 * tmp_size + 1
def get_max_preds(batch_heatmaps):
'''
get predictions from score maps
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
'... | batch_image: [batch_size, channel, height, width] batch_heatmaps: ['batch_size, num_joints, height, width] file_name: saved file name |
13,294 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
def get_max_preds(batch_heatmaps):
def transform_preds(coords, center, scale, rot, output_size):
import math
def get_final_preds(batch_heatmaps, center, scale, rot=None, flip=None)... | null |
13,295 | from os.path import join
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from .hrnet import HRNet
gauss =
import math
def get_gaussian_maps(net_out, keypoints, sigma):
radius, kernel = gauss[sigma]['radius'], gauss[sigma]['kernel']
weights = np.ones(net_out.shape, dtype... | null |
13,296 | import os
import shutil
from tqdm import tqdm
from .wrapper_base import bbox_from_keypoints, create_annot_file, check_result
from ..mytools import read_json
from ..annotator.file_utils import save_annot
from os.path import join
import numpy as np
import cv2
from glob import glob
from multiprocessing import Process
def ... | null |
13,297 | import os
import shutil
from tqdm import tqdm
from .wrapper_base import bbox_from_keypoints, create_annot_file, check_result
from ..mytools import read_json
from ..annotator.file_utils import save_annot
from os.path import join
import numpy as np
import cv2
from glob import glob
from multiprocessing import Process
def... | null |
13,298 | import os
import shutil
from tqdm import tqdm
from .wrapper_base import bbox_from_keypoints, create_annot_file, check_result
from ..mytools import read_json
from ..annotator.file_utils import save_annot
from os.path import join
import numpy as np
import cv2
from glob import glob
from multiprocessing import Process
def... | null |
13,299 | import os
import shutil
from tqdm import tqdm
from .wrapper_base import bbox_from_keypoints, create_annot_file, check_result
from ..mytools import read_json
from ..annotator.file_utils import save_annot
from os.path import join
import numpy as np
import cv2
from glob import glob
from multiprocessing import Process
def... | null |
13,300 | import numpy as np
def dist_pl(query_points, line, moment):
moment_q = moment - np.cross(query_points, line)
dist = np.linalg.norm(moment_q, axis=1)
return dist | null |
13,301 | import numpy as np
def reciprocal_product(l1, m1, l2, m2):
l1 = l1[:, None]
m1 = m1[:, None]
l2 = l2[None, :]
m2 = m2[None, :]
product = np.sum(l1*m2, axis=2) + np.sum(l2*m1, axis=2)
return np.abs(product) | null |
13,302 | import numpy as np
def dist_pl_pointwise(p0, p1):
moment_q = p1[..., 3:6] - np.cross(p0[..., :3], p1[..., :3])
dist = np.linalg.norm(moment_q, axis=-1)
return dist | null |
13,303 | import numpy as np
def dist_ll_pointwise(p0, p1):
product = np.sum(p0[..., :3] * p1[..., 3:6], axis=-1) + np.sum(p1[..., :3] * p0[..., 3:6], axis=-1)
return np.abs(product)
def dist_ll_pointwise_conf(p0, p1):
dist = dist_ll_pointwise(p0, p1)
conf = np.sqrt(p0[..., -1] * p1[..., -1])
dist = np.sum(d... | null |
13,304 | import numpy as np
def plucker_from_pp(point1, point2):
line = point2 - point1
return plucker_from_pl(point1, line)
def computeRay(keypoints2d, invK, R, T):
# 将点转为世界坐标系下plucker坐标
# points: (nJoints, 3)
# invK: (3, 3)
# R: (3, 3)
# T: (3, 1)
# cam_center: (3, 1)
if len(keypoints2d.sh... | null |
13,305 | import numpy as np
def plucker_from_pp(point1, point2):
def computeRaynd(keypoints2d, invK, R, T):
# keypoints2d: (..., 3)
conf = keypoints2d[..., 2:]
# cam_center: (1, 3)
cam_center = - (R.T @ T).T
kp_pixel = np.concatenate([keypoints2d[..., :2], np.ones_like(conf)], axis=-1)
kp_all_3d = (kp_p... | null |
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