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#!/usr/bin/env bash | |
mkdir coco | |
cd coco/ | |
wget http://images.cocodataset.org/zips/train2017.zip | |
wget http://images.cocodataset.org/zips/val2017.zip | |
unzip train2017.zip | |
unzip val2017.zip |
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import os | |
import subprocess | |
from multiprocessing import Process | |
NUM_PROCESS = 1 # define number of processes to speed up | |
VIDEOS_DIR = 'Videos' # path to Human3.6M videos | |
def find_files(): | |
SUBJECTS = [1, 5, 6, 7, 8, 9, 11] |
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import dgl | |
import torch | |
import torch.nn as nn | |
def build_pose_graph(): | |
g = dgl.DGLGraph() | |
g.add_nodes(16) | |
edge_list = [(0, 1), (1, 2), (2, 6), (6, 3), (3, 4), |
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import numpy as np | |
import json | |
import math | |
import matplotlib.pyplot as plt | |
def showAnns(jts): | |
# c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0] | |
c = [0.7, 0.9, 0.8] |
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import numpy as np | |
import cv2 | |
import src.data_utils as data_utils | |
from src.data_utils import load_data, project_to_cameras | |
import os | |
import matplotlib.pyplot as plt | |
from src import cameras, viz | |
def drawlines(img1,img2,lines,pts1,pts2): |
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from __future__ import print_function | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
class SoftArgmax(nn.Module): | |
def __init__(self): | |
super(SoftArgmax, self).__init__() |
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from __future__ import print_function, absolute_import | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torchvision.models as models | |
class FPN(nn.Module): | |
def __init__(self, | |
backbone='resnet50', |
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import torch | |
import torch.nn.functional as F | |
from torch.nn.parameter import Parameter | |
import numpy as np | |
class SpatialSoftmax(torch.nn.Module): | |
def __init__(self, height, width, channel, temperature=None, data_format='NCHW', unnorm=False): | |
super(SpatialSoftmax, self).__init__() | |
self.data_format = data_format |
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import torch | |
# Original author: Francisco Massa: | |
# https://github.com/fmassa/object-detection.torch | |
# Ported to PyTorch by Max deGroot (02/01/2017) | |
def nms(boxes, scores, overlap=0.5, top_k=200): | |
"""Apply non-maximum suppression at test time to avoid detecting too many | |
overlapping bounding boxes for a given object. | |
Args: | |
boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. |
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from mininet.net import Mininet | |
from random import randint | |
from scipy import spatial | |
import numpy as np | |
import scipy as scp | |
import math | |
## Mininet must run as ROOT !!! | |
# add function for nodes to move randomly when invoked |