File size: 5,070 Bytes
bc45d7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | import json
import glob
import os
import torch
from torch.utils import data
import numpy as np
import pandas as pd
from augment import (
Augmentation,
OneOf,
plus7rotation,
minus7rotation,
gaussSample,
cutout,
upsample,
downsample,
)
class KeypointsDataset(data.Dataset):
def __init__(
self,
keypoints_dir,
use_augs,
label_map,
mode="train",
max_frame_len=200,
frame_length=1080,
frame_width=1920,
):
self.files = sorted(glob.glob(os.path.join(keypoints_dir, "*.json")))
self.mode = mode
self.use_augs = use_augs
self.label_map = label_map
self.max_frame_len = max_frame_len
self.frame_length = frame_length
self.frame_width = frame_width
# Filter out files with labels not in label_map
valid_files = []
for file_path in self.files:
row = pd.read_json(file_path, typ="series")
label = "".join([i for i in row.label if i.isalpha()]).lower()
if label in self.label_map:
valid_files.append(file_path)
self.files = valid_files
self.augs = [
Augmentation(OneOf(plus7rotation, minus7rotation), p=0.4),
Augmentation(gaussSample, p=0.4),
Augmentation(cutout, p=0.4),
Augmentation(OneOf(upsample, downsample), p=0.4),
]
def augment(self, df):
for aug in self.augs:
df = aug(df)
return df
def interpolate(self, arr):
arr_x = arr[:, :, 0]
arr_x = pd.DataFrame(arr_x)
arr_x = arr_x.interpolate(method="linear", limit_direction="both").to_numpy()
arr_y = arr[:, :, 1]
arr_y = pd.DataFrame(arr_y)
arr_y = arr_y.interpolate(method="linear", limit_direction="both").to_numpy()
if np.count_nonzero(~np.isnan(arr_x)) == 0:
arr_x = np.zeros(arr_x.shape)
if np.count_nonzero(~np.isnan(arr_y)) == 0:
arr_y = np.zeros(arr_y.shape)
arr_x = arr_x * self.frame_width
arr_y = arr_y * self.frame_length
return np.stack([arr_x, arr_y], axis=-1)
def combine_xy(self, x, y):
x, y = np.array(x), np.array(y)
_, length = x.shape
x = x.reshape((-1, length, 1))
y = y.reshape((-1, length, 1))
return np.concatenate((x, y), -1).astype(np.float32)
def __getitem__(self, idx):
file_path = self.files[idx]
row = pd.read_json(file_path, typ="series")
label = row.label
label = "".join([i for i in label if i.isalpha()]).lower()
pose = self.combine_xy(row.pose_x, row.pose_y)
h1 = self.combine_xy(row.hand1_x, row.hand1_y)
h2 = self.combine_xy(row.hand2_x, row.hand2_y)
pose = self.interpolate(pose)
h1 = self.interpolate(h1)
h2 = self.interpolate(h2)
df = pd.DataFrame.from_dict(
{
"uid": row.uid,
"pose": pose.tolist(),
"hand1": h1.tolist(),
"hand2": h2.tolist(),
"label": label,
}
)
if self.mode == "train" and self.use_augs:
df = self.augment(df)
pose = (
np.array(list(map(np.array, df.pose.values)))
.reshape(-1, 50)
.astype(np.float32)
)
h1 = (
np.array(list(map(np.array, df.hand1.values)))
.reshape(-1, 42)
.astype(np.float32)
)
h2 = (
np.array(list(map(np.array, df.hand2.values)))
.reshape(-1, 42)
.astype(np.float32)
)
final_data = np.concatenate((pose, h1, h2), -1)
final_data = np.pad(
final_data,
((0, self.max_frame_len - final_data.shape[0]), (0, 0)),
"constant",
)
return {
"uid": row.uid,
"data": torch.FloatTensor(final_data),
"label": self.label_map[label],
"lablel_string": label,
}
def __len__(self):
return len(self.files)
class FeaturesDatset(data.Dataset):
def __init__(self, features_dir, label_map, mode="train", max_frame_len=200):
self.features_dir = features_dir
self.file_paths = sorted(glob.glob(os.path.join(features_dir, "*.npy")))
self.label_map = label_map
self.mode = mode
self.max_frame_len = max_frame_len
def __getitem__(self, i):
file_path = self.file_paths[i]
data = np.load(file_path)
data = np.pad(
data,
((0, self.max_frame_len - data.shape[0]), (0, 0)),
"constant",
)
label = os.path.basename(file_path).split("_")[0]
return {
"uid": os.path.basename(file_path).split(".")[0],
"data": torch.FloatTensor(data),
"label": self.label_map[label],
"lablel_string": label,
}
def __len__(self):
return len(self.file_paths)
|