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7279c87 bc45d7d 7279c87 bc45d7d 7279c87 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 | import pandas as pd
import numpy as np
class Augmentation:
def __init__(self, aug_func, p=1):
self.aug_func = aug_func
self.p = p
def __call__(self, df):
if np.random.rand() <= self.p:
return self.aug_func(df)
return df
def OneOf(aug_a, aug_b):
if np.random.rand() < 0.5:
return aug_a
return aug_b
def plus7rotation(df):
# +7 degree rotation
df_augmented = pd.DataFrame(
index=df.index,
columns=["uid", "pose", "hand1", "hand2", "label"],
dtype="object",
)
df_augmented["uid"] = df["uid"].astype("object")
df_augmented["label"] = df["label"].astype("object")
theta = 7 * (np.pi / 180)
c, s = np.cos(theta), np.sin(theta)
rotation_matrix = np.array([[c, -s], [s, c]])
for i in range(df.shape[0]):
for col in ["pose", "hand1", "hand2"]:
matrix = np.array(df.loc[i, col], dtype=np.float64)
matrix = np.matmul(matrix, rotation_matrix)
matrix = np.where(np.isnan(matrix), None, matrix).tolist()
df_augmented.at[i, col] = matrix
return df_augmented
def minus7rotation(df):
# -7 degree rotation
df_augmented = pd.DataFrame(
index=df.index,
columns=["uid", "pose", "hand1", "hand2", "label"],
dtype="object",
)
df_augmented["uid"] = df["uid"].astype("object")
df_augmented["label"] = df["label"].astype("object")
theta = -7 * (np.pi / 180)
c, s = np.cos(theta), np.sin(theta)
rotation_matrix = np.array([[c, -s], [s, c]])
for i in range(df.shape[0]):
for col in ["pose", "hand1", "hand2"]:
matrix = np.array(df.loc[i, col], dtype=np.float64)
matrix = np.matmul(matrix, rotation_matrix)
matrix = np.where(np.isnan(matrix), None, matrix).tolist()
df_augmented.at[i, col] = matrix
return df_augmented
def gaussSample(df):
# Random Gaussian sampling
df_augmented = df.copy()
dv = 0.05 * 10 ** -2
sv = 0.08 * 10 ** -2
lv = 0.08 * 10 ** -1
sigma = [
sv,
dv,
dv,
dv,
dv,
dv,
dv,
sv,
sv,
sv,
sv,
lv,
lv,
lv,
lv,
sv,
sv,
sv,
sv,
sv,
sv,
sv,
sv,
lv,
lv,
]
## Check if keypoints is range [0, 1]
x_width = 1920
y_height = 1080
for i in range(df.shape[0]):
if np.count_nonzero(df.loc[i, "pose"]) == 0:
break
pose = np.array(df.loc[i, "pose"], dtype=np.float64)
pose[:, 0] /= x_width
pose[:, 1] /= y_height
pose_variance = np.column_stack((sigma, sigma))
pose = np.random.normal(pose, pose_variance)
pose[:, 0] *= x_width
pose[:, 1] *= y_height
pose = np.where(np.isnan(pose), None, pose).tolist()
hand1 = np.array(df.loc[i, "hand1"], dtype=np.float64)
hand1[:, 0] /= x_width
hand1[:, 1] /= y_height
hand1 = np.random.normal(hand1, dv)
hand1[:, 0] *= x_width
hand1[:, 1] *= y_height
hand1 = np.where(np.isnan(hand1), None, hand1).tolist()
hand2 = np.array(df.loc[i, "hand2"], dtype=np.float64)
hand2[:, 0] /= x_width
hand2[:, 1] /= y_height
hand2 = np.random.normal(hand2, dv)
hand2[:, 0] *= x_width
hand2[:, 1] *= y_height
hand2 = np.where(np.isnan(hand2), None, hand2).tolist()
df_augmented.at[i, "pose"] = pose
df_augmented.at[i, "hand1"] = hand1
df_augmented.at[i, "hand2"] = hand2
return df_augmented
def cutout(df):
# cutout
df_augmented = df.copy()
pad_idx = 0
for i in range(df.shape[0]):
if np.count_nonzero(df.loc[i, "pose"]) == 0:
pad_idx = i
break
for i in range(df.shape[0]):
if np.count_nonzero(df.loc[i, "pose"]) == 0:
break
if i < pad_idx:
pose = np.array(df.loc[i, "pose"])
hand1 = np.array(df.loc[i, "hand1"])
hand2 = np.array(df.loc[i, "hand2"])
pose_zero_idx = np.random.choice(25, 3, replace=False)
hand1_zero_idx = np.random.choice(21, 3, replace=False)
hand2_zero_idx = np.random.choice(21, 3, replace=False)
for i in pose_zero_idx:
pose[i] = [0, 0]
for i in hand1_zero_idx:
hand1[i] = [0, 0]
for i in hand2_zero_idx:
hand2[i] = [0, 0]
pose = pose.tolist()
hand1 = hand1.tolist()
hand2 = hand2.tolist()
df_augmented.at[i, "pose"] = pose
df_augmented.at[i, "hand1"] = hand1
df_augmented.at[i, "hand2"] = hand2
return df_augmented
def downsample(df):
# downsample
frame_len = df.shape[0]
if frame_len < 15:
return df.copy()
df_augmented = df.copy()
drop_idx = np.random.choice(frame_len, 15) # 154 frames , 15 frames
df_augmented = df_augmented.drop(index=drop_idx)
return df_augmented
def upsample(df):
# upsample
def get_avg(df, idx, col):
aug_points = (
(
np.array(df.loc[idx - 1, col], dtype=np.float64)
+ np.array(df.loc[idx, col], dtype=np.float64)
)
/ 2
).tolist()
return np.where(np.isnan(aug_points), None, aug_points).tolist()
frame_length = df.shape[0]
additional_frames = frame_length // 10
df_augmented = pd.DataFrame(
index=np.arange(frame_length + additional_frames),
columns=["uid", "pose", "hand1", "hand2", "label"],
)
df_augmented["uid"] = df.iloc[0].loc["uid"]
j = 0
for i in range(df_augmented.shape[0]):
if i % 10 != 0 or i == 0:
df_augmented.at[i, "pose"] = df.loc[j, "pose"]
df_augmented.at[i, "hand1"] = df.loc[j, "hand1"]
df_augmented.at[i, "hand2"] = df.loc[j, "hand2"]
j += 1
continue
df_augmented.at[i, "pose"] = get_avg(df, j, "pose")
df_augmented.at[i, "hand1"] = get_avg(df, j, "hand1")
df_augmented.at[i, "hand2"] = get_avg(df, j, "hand2")
df_augmented["label"] = df.iloc[0].loc["label"]
return df_augmented
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