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ucla_data.py
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import numpy as np
import pickle
import json
import random
import math
import mmcv
import sys
class Feeder():
def __init__(self, data_path, label_path, repeat=1, random_choose=False, random_shift=False, random_move=False,
window_size=-1, normalization=False, debug=False, use_mmap=True):
if 'val' in label_path:
self.train_val = 'val'
self.data_dict = [{"file_name": "a05_s04_e02_v03", "length": 21, "label": 5}, {"file_name": "a12_s09_e04_v03", "length": 26, "label": 10}, {"file_name": "a03_s03_e04_v03", "length": 35, "label": 3}, {"file_name": "a08_s02_e01_v03", "length": 101, "label": 7}, {"file_name": "a03_s05_e03_v03", "length": 26, "label": 3}, {"file_name": "a12_s10_e01_v03", "length": 21, "label": 10}, {"file_name": "a01_s07_e03_v03", "length": 31, "label": 1}, {"file_name": "a03_s08_e02_v03", "length": 21, "label": 3}, {"file_name": "a11_s10_e03_v03", "length": 51, "label": 9}, {"file_name": "a11_s03_e00_v03", "length": 46, "label": 9}, {"file_name": "a03_s02_e00_v03", "length": 32, "label": 3}, {"file_name": "a11_s01_e04_v03", "length": 16, "label": 9}, {"file_name": "a09_s08_e04_v03", "length": 63, "label": 8}, {"file_name": "a09_s06_e01_v03", "length": 41, "label": 8}, {"file_name": "a09_s07_e01_v03", "length": 51, "label": 8}, {"file_name": "a02_s08_e01_v03", "length": 21, "label": 2}, {"file_name": "a01_s04_e01_v03", "length": 23, "label": 1}, {"file_name": "a02_s02_e02_v03", "length": 31, "label": 2}, {"file_name": "a02_s07_e05_v03", "length": 31, "label": 2}, {"file_name": "a06_s02_e00_v03", "length": 16, "label": 6}, {"file_name": "a03_s02_e02_v03", "length": 22, "label": 3}, {"file_name": "a11_s09_e04_v03", "length": 22, "label": 9}, {"file_name": "a09_s03_e04_v03", "length": 61, "label": 8}, {"file_name": "a04_s01_e02_v03", "length": 23, "label": 4}, {"file_name": "a12_s01_e01_v03", "length": 17, "label": 10}, {"file_name": "a02_s07_e03_v03", "length": 9, "label": 2}, {"file_name": "a05_s08_e04_v03", "length": 19, "label": 5}, {"file_name": "a02_s07_e02_v03", "length": 31, "label": 2}, {"file_name": "a04_s07_e02_v03", "length": 16, "label": 4}, {"file_name": "a01_s08_e03_v03", "length": 27, "label": 1}, {"file_name": "a08_s03_e01_v03", "length": 68, "label": 7}, {"file_name": "a04_s08_e03_v03", "length": 21, "label": 4}, {"file_name": "a03_s10_e00_v03", "length": 17, "label": 3}, {"file_name": "a04_s03_e03_v03", "length": 21, "label": 4}, {"file_name": "a06_s06_e02_v03", "length": 21, "label": 6}, {"file_name": "a09_s03_e00_v03", "length": 81, "label": 8}, {"file_name": "a09_s03_e03_v03", "length": 46, "label": 8}, {"file_name": "a04_s02_e02_v03", "length": 21, "label": 4}, {"file_name": "a08_s01_e02_v03", "length": 78, "label": 7}, {"file_name": "a04_s04_e00_v03", "length": 11, "label": 4}, {"file_name": "a03_s02_e03_v03", "length": 39, "label": 3}, {"file_name": "a05_s04_e00_v03", "length": 21, "label": 5}, {"file_name": "a05_s07_e03_v03", "length": 36, "label": 5}, {"file_name": "a06_s10_e00_v03", "length": 31, "label": 6}, {"file_name": "a11_s07_e00_v03", "length": 31, "label": 9}, {"file_name": "a03_s01_e01_v03", "length": 24, "label": 3}, {"file_name": "a04_s06_e01_v03", "length": 16, "label": 4}, {"file_name": "a08_s02_e04_v03", "length": 96, "label": 7}, {"file_name": "a09_s08_e03_v03", "length": 46, "label": 8}, {"file_name": "a05_s07_e00_v03", "length": 36, "label": 5}, {"file_name": "a05_s02_e02_v03", "length": 21, "label": 5}, {"file_name": "a04_s06_e04_v03", "length": 21, "label": 4}, {"file_name": "a05_s09_e03_v03", "length": 21, "label": 5}, {"file_name": "a03_s06_e02_v03", "length": 15, "label": 3}, {"file_name": "a01_s01_e00_v03", "length": 27, "label": 1}, {"file_name": "a06_s06_e03_v03", "length": 11, "label": 6}, {"file_name": "a06_s10_e02_v03", "length": 25, "label": 6}, {"file_name": "a02_s07_e04_v03", "length": 36, "label": 2}, {"file_name": "a09_s06_e00_v03", "length": 80, "label": 8}, {"file_name": "a04_s07_e04_v03", "length": 16, "label": 4}, {"file_name": "a05_s02_e01_v03", "length": 19, "label": 5}, {"file_name": "a01_s06_e04_v03", "length": 17, "label": 1}, {"file_name": "a04_s08_e01_v03", "length": 17, "label": 4}, {"file_name": "a01_s09_e00_v03", "length": 31, "label": 1}, {"file_name": "a08_s03_e03_v03", "length": 67, "label": 7}, {"file_name": "a12_s03_e00_v03", "length": 21, 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"a06_s01_e01_v02", "length": 21, "label": 6}, {"file_name": "a12_s03_e02_v02", "length": 39, "label": 10}, {"file_name": "a08_s05_e02_v02", "length": 51, "label": 7}, {"file_name": "a03_s04_e00_v02", "length": 26, "label": 3}, {"file_name": "a11_s01_e03_v02", "length": 31, "label": 9}, {"file_name": "a03_s08_e01_v02", "length": 21, "label": 3}, {"file_name": "a11_s04_e00_v02", "length": 32, "label": 9}, {"file_name": "a04_s05_e00_v02", "length": 36, "label": 4}, {"file_name": "a12_s05_e01_v02", "length": 31, "label": 10}, {"file_name": "a02_s05_e02_v02", "length": 26, "label": 2}, {"file_name": "a06_s06_e01_v02", "length": 16, "label": 6}, {"file_name": "a03_s03_e02_v02", "length": 32, "label": 3}, {"file_name": "a11_s07_e02_v02", "length": 21, "label": 9}, {"file_name": "a11_s01_e02_v02", "length": 21, "label": 9}]
self.nw_ucla_root = '/data/lhd/NW-UCLA/all_sqe/'
self.time_steps = 52
self.label = []
for index in range(len(self.data_dict)):
info = self.data_dict[index]
self.label.append(int(info['label']) - 1)
self.debug = debug
self.data_path = data_path
self.label_path = label_path
self.random_choose = random_choose
self.random_shift = random_shift
self.random_move = random_move
self.window_size = window_size
self.normalization = normalization
self.use_mmap = use_mmap
self.repeat = repeat
self.load_data()
if normalization:
self.get_mean_map()
def load_data(self):
# data: N C V T M
self.data = []
self.filename = []
for data in self.data_dict:
file_name = data['file_name']
with open(self.nw_ucla_root + file_name + '.json', 'r') as f:
json_file = json.load(f)
skeletons = json_file['skeletons']
value = np.array(skeletons)
self.filename.append(file_name)
self.data.append(value)
def get_mean_map(self):
data = self.data
N, C, T, V, M = data.shape
self.mean_map = data.mean(axis=2, keepdims=True).mean(axis=4, keepdims=True).mean(axis=0)
self.std_map = data.transpose((0, 2, 4, 1, 3)).reshape((N * T * M, C * V)).std(axis=0).reshape((C, 1, V, 1))
def __len__(self):
return len(self.data_dict)*self.repeat
def __iter__(self):
return self
def rand_view_transform(self,X, agx, agy, s):
agx = math.radians(agx)
agy = math.radians(agy)
Rx = np.asarray([[1,0,0], [0,math.cos(agx),math.sin(agx)], [0, -math.sin(agx),math.cos(agx)]])
Ry = np.asarray([[math.cos(agy), 0, -math.sin(agy)], [0,1,0], [math.sin(agy), 0, math.cos(agy)]])
Ss = np.asarray([[s,0,0],[0,s,0],[0,0,s]])
X0 = np.dot(np.reshape(X,(-1,3)), np.dot(Ry,np.dot(Rx,Ss)))
X = np.reshape(X0, X.shape)
return X
def getdata(self, index):
frame_dir = self.data_dict[index]['file_name']
total_frames = 0
label = self.label[index % len(self.data_dict)]
value = self.data[index % len(self.data_dict)]
if self.train_val == 'train':
random.random()
agx = random.randint(-60, 60)
agy = random.randint(-60, 60)
s = random.uniform(0.5, 1.5)
center = value[0,1,:]
value = value - center
scalerValue = self.rand_view_transform(value, agx, agy, s)
scalerValue = np.reshape(scalerValue, (-1, 3))
scalerValue = (scalerValue - np.min(scalerValue,axis=0)) / (np.max(scalerValue,axis=0) - np.min(scalerValue,axis=0))
scalerValue = scalerValue*2-1
scalerValue = np.reshape(scalerValue, (-1, 20, 3))
value = scalerValue[:,:,:]
total_frames= value.shape[0]
data = np.zeros( (value.shape[0], 20, 3) )
# data[:,:,:] = value[:,:,:]
length = value.shape[0]
random_idx = random.sample(list(np.arange(length))*100, self.time_steps)
random_idx.sort()
data[:,:,:] = value[random_idx,:,:]
else:
random.random()
agx = 0
agy = 0
s = 1.0
center = value[0,1,:]
value = value - center
scalerValue = self.rand_view_transform(value, agx, agy, s)
scalerValue = np.reshape(scalerValue, (-1, 3))
scalerValue = (scalerValue - np.min(scalerValue,axis=0)) / (np.max(scalerValue,axis=0) - np.min(scalerValue,axis=0))
scalerValue = scalerValue*2-1
scalerValue = np.reshape(scalerValue, (-1, 20, 3))
value = scalerValue[:,:,:]
total_frames= value.shape[0]
data = np.zeros( (value.shape[0], 20, 3) )
# data[:,:,:] = value[:,:,:]
idx = np.linspace(0,length-1,self.time_steps).astype(np.int)
data[:,:,:] = value[idx,:,:] # T,V,C
data = np.transpose(data, (2, 0, 1))
C,T,V = data.shape
data = np.reshape(data,(C,T,V,1))
return frame_dir, total_frames, data, label
def get_long_tailed_data(self):
class_name = []
for class_i in range(10):
class_name.append([])
for idx in range(len(self.data_dict)):
label = self.data_dict[idx]['label']-1
class_name[label].append(self.data_dict[idx]['file_name'])
skeleton_max = 100
cls_num = 10
imb_factor = 0.1
skeleton_num_per_cls = []
for cls_idx in range(cls_num):
num = skeleton_max * (imb_factor**(cls_idx / (cls_num - 1.0)))
skeleton_num_per_cls.append(int(num))
new_name = []
new_label = []
rand_number = 0
np.random.seed(rand_number)
# classes = []
# for the_i in range(cls_num) :
# classes.append(the_i)
# np.random.shuffle(classes)
for the_class in range(cls_num):
the_skeleton_num = skeleton_num_per_cls[the_class]
print(the_skeleton_num)
idx = class_name[the_class]
np.random.shuffle(idx)
if len(idx) > the_skeleton_num :
for im_k in range(the_skeleton_num):
new_name.append(idx[im_k])
new_label.append(the_class)
else :
for im_k in range(len(idx)):
new_name.append(idx[im_k])
new_label.append(the_class)
# print(len(self.data_dict))
# print(sum(skeleton_num_per_cls))
# print(len(new_name))
# f = open('./NW_UCLA_exp_10.txt','w', encoding='utf-8')
# for thename in new_name:
# f.writelines(thename)
# if thename != new_name[-1]:
# f.writelines('\n')
# f.close()
with open('./NW_UCLA_exp_10.txt', 'r') as f:
ske_list = [
line.strip() for line in f.readlines()
]
count_lable = []
for class_i in range(10):
count_lable.append([])
for idx in range(len(self.data_dict)):
if self.data_dict[idx]['file_name'] in ske_list:
label = self.data_dict[idx]['label']-1
count_lable[label].append(self.data_dict[idx]['file_name'])
for k in range(len(count_lable)):
print(len(count_lable[k]))
def get_file_name(self):
return self.filename
def get_all_data(self):
self.all_data = []
for i in range(len(self.data_dict)):
frame_dir, total_frames, data, label = self.getdata(i)
data = np.transpose(data, (3, 1, 2, 0))
annotation = {'frame_dir': frame_dir, 'label': label, 'keypoint': data, 'total_frames': total_frames}
self.all_data.append(annotation)
return self.all_data
# print(frame_dir)
# print(total_frames)
# print(data)
# print(label)
# print(type(frame_dir)) # <class 'str'>
# print(type(total_frames)) # <class 'int'>
# print(type(data)) # <class 'numpy.ndarray'>
# print(type(label)) # <class 'int'>
# print(data.shape) # 3,26,20,1 C,T,V,M -> M x T x V x C 1,26,20,3
# self.all_data.append(value)
if __name__ == '__main__':
feeder1 = Feeder('joint', 'train')
train_name = feeder1.get_file_name()
the_train_annotations = feeder1.get_all_data()
# feeder1.get_long_tailed_data()
feeder2 = Feeder('joint', 'val')
val_name = feeder2.get_file_name()
the_val_annotations = feeder2.get_all_data()
split = {'train': train_name, 'val': val_name}
annotations = the_train_annotations + the_val_annotations
data = {'split': split, 'annotations': annotations}
mmcv.dump(data, '/data/lhd/pyskl_data/NW-UCLA/nw_ucla_normal_3danno.pkl')