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import torch
import numpy as np
import ipdb
import glob
import os
import io
import math
import random
import json
import pickle
import math
from torch.utils.data import Dataset, DataLoader
from lib.utils.utils_data import crop_scale
def halpe2h36m(x):
'''
Input: x (T x V x C)
//Halpe 26 body keypoints
{0, "Nose"},
{1, "LEye"},
{2, "REye"},
{3, "LEar"},
{4, "REar"},
{5, "LShoulder"},
{6, "RShoulder"},
{7, "LElbow"},
{8, "RElbow"},
{9, "LWrist"},
{10, "RWrist"},
{11, "LHip"},
{12, "RHip"},
{13, "LKnee"},
{14, "Rknee"},
{15, "LAnkle"},
{16, "RAnkle"},
{17, "Head"},
{18, "Neck"},
{19, "Hip"},
{20, "LBigToe"},
{21, "RBigToe"},
{22, "LSmallToe"},
{23, "RSmallToe"},
{24, "LHeel"},
{25, "RHeel"},
'''
T, V, C = x.shape
y = np.zeros([T,17,C])
y[:,0,:] = x[:,19,:]
y[:,1,:] = x[:,12,:]
y[:,2,:] = x[:,14,:]
y[:,3,:] = x[:,16,:]
y[:,4,:] = x[:,11,:]
y[:,5,:] = x[:,13,:]
y[:,6,:] = x[:,15,:]
y[:,7,:] = (x[:,18,:] + x[:,19,:]) * 0.5
y[:,8,:] = x[:,18,:]
y[:,9,:] = x[:,0,:]
y[:,10,:] = x[:,17,:]
y[:,11,:] = x[:,5,:]
y[:,12,:] = x[:,7,:]
y[:,13,:] = x[:,9,:]
y[:,14,:] = x[:,6,:]
y[:,15,:] = x[:,8,:]
y[:,16,:] = x[:,10,:]
return y
def read_input(json_path, vid_size, scale_range, focus):
with open(json_path, "r") as read_file:
results = json.load(read_file)
kpts_all = []
for item in results:
if focus!=None and item['idx']!=focus:
continue
kpts = np.array(item['keypoints']).reshape([-1,3])
kpts_all.append(kpts)
kpts_all = np.array(kpts_all)
kpts_all = halpe2h36m(kpts_all)
if vid_size:
w, h = vid_size
scale = min(w,h) / 2.0
kpts_all[:,:,:2] = kpts_all[:,:,:2] - np.array([w, h]) / 2.0
kpts_all[:,:,:2] = kpts_all[:,:,:2] / scale
motion = kpts_all
if scale_range:
motion = crop_scale(kpts_all, scale_range)
return motion.astype(np.float32)
class WildDetDataset(Dataset):
def __init__(self, json_path, clip_len=243, vid_size=None, scale_range=None, focus=None):
self.json_path = json_path
self.clip_len = clip_len
self.vid_all = read_input(json_path, vid_size, scale_range, focus)
def __len__(self):
'Denotes the total number of samples'
return math.ceil(len(self.vid_all) / self.clip_len)
def __getitem__(self, index):
'Generates one sample of data'
st = index*self.clip_len
end = min((index+1)*self.clip_len, len(self.vid_all))
return self.vid_all[st:end] |