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from os import path as osp
from typing import Callable, Optional
import torch
from torch.utils.data import Dataset
from torchvision.transforms import functional as TF
from PIL import Image
import pandas as pd
from . import augmentation
from .masking import MaskGenerator
from . import data_utils as utils
class GazeFollow(Dataset):
def __init__(
self,
image_root: str,
anno_root: str,
head_root: str,
transform: Callable,
input_size: int,
output_size: int,
quant_labelmap: bool = True,
is_train: bool = True,
*,
mask_generator: Optional[MaskGenerator] = None,
bbox_jitter: float = 0.5,
rand_crop: float = 0.5,
rand_flip: float = 0.5,
color_jitter: float = 0.5,
rand_rotate: float = 0.0,
rand_lsj: float = 0.0,
):
if is_train:
column_names = [
"path",
"idx",
"body_bbox_x",
"body_bbox_y",
"body_bbox_w",
"body_bbox_h",
"eye_x",
"eye_y",
"gaze_x",
"gaze_y",
"bbox_x_min",
"bbox_y_min",
"bbox_x_max",
"bbox_y_max",
"inout",
"meta0",
"meta1",
]
df = pd.read_csv(
anno_root,
sep=",",
names=column_names,
index_col=False,
encoding="utf-8-sig",
)
df = df[
df["inout"] != -1
] # only use "in" or "out "gaze. (-1 is invalid, 0 is out gaze)
df.reset_index(inplace=True)
self.y_train = df[
[
"bbox_x_min",
"bbox_y_min",
"bbox_x_max",
"bbox_y_max",
"eye_x",
"eye_y",
"gaze_x",
"gaze_y",
"inout",
]
]
self.X_train = df["path"]
self.length = len(df)
else:
column_names = [
"path",
"idx",
"body_bbox_x",
"body_bbox_y",
"body_bbox_w",
"body_bbox_h",
"eye_x",
"eye_y",
"gaze_x",
"gaze_y",
"bbox_x_min",
"bbox_y_min",
"bbox_x_max",
"bbox_y_max",
"meta0",
"meta1",
]
df = pd.read_csv(
anno_root,
sep=",",
names=column_names,
index_col=False,
encoding="utf-8-sig",
)
df = df[
[
"path",
"eye_x",
"eye_y",
"gaze_x",
"gaze_y",
"bbox_x_min",
"bbox_y_min",
"bbox_x_max",
"bbox_y_max",
]
].groupby(["path", "eye_x"])
self.keys = list(df.groups.keys())
self.X_test = df
self.length = len(self.keys)
self.data_dir = image_root
self.head_dir = head_root
self.transform = transform
self.is_train = is_train
self.input_size = input_size
self.output_size = output_size
self.draw_labelmap = (
utils.draw_labelmap if quant_labelmap else utils.draw_labelmap_no_quant
)
if self.is_train:
## data augmentation
self.augment = augmentation.AugmentationList(
[
augmentation.ColorJitter(color_jitter),
augmentation.BoxJitter(bbox_jitter),
augmentation.RandomCrop(rand_crop),
augmentation.RandomFlip(rand_flip),
augmentation.RandomRotate(rand_rotate),
augmentation.RandomLSJ(rand_lsj),
]
)
self.mask_generator = mask_generator
def __getitem__(self, index):
if not self.is_train:
g = self.X_test.get_group(self.keys[index])
cont_gaze = []
for _, row in g.iterrows():
path = row["path"]
x_min = row["bbox_x_min"]
y_min = row["bbox_y_min"]
x_max = row["bbox_x_max"]
y_max = row["bbox_y_max"]
eye_x = row["eye_x"]
eye_y = row["eye_y"]
gaze_x = row["gaze_x"]
gaze_y = row["gaze_y"]
cont_gaze.append(
[gaze_x, gaze_y]
) # all ground truth gaze are stacked up
for _ in range(len(cont_gaze), 20):
cont_gaze.append(
[-1, -1]
) # pad dummy gaze to match size for batch processing
cont_gaze = torch.FloatTensor(cont_gaze)
gaze_inside = True # always consider test samples as inside
else:
path = self.X_train.iloc[index]
(
x_min,
y_min,
x_max,
y_max,
eye_x,
eye_y,
gaze_x,
gaze_y,
inout,
) = self.y_train.iloc[index]
gaze_inside = bool(inout)
img = Image.open(osp.join(self.data_dir, path))
img = img.convert("RGB")
head_mask = Image.open(osp.join(self.head_dir, path))
width, height = img.size
x_min, y_min, x_max, y_max = map(float, [x_min, y_min, x_max, y_max])
if x_max < x_min:
x_min, x_max = x_max, x_min
if y_max < y_min:
y_min, y_max = y_max, y_min
# expand face bbox a bit
k = 0.1
x_min = max(x_min - k * abs(x_max - x_min), 0)
y_min = max(y_min - k * abs(y_max - y_min), 0)
x_max = min(x_max + k * abs(x_max - x_min), width - 1)
y_max = min(y_max + k * abs(y_max - y_min), height - 1)
if self.is_train:
img, bbox, gaze, head_mask, size = self.augment(
img,
(x_min, y_min, x_max, y_max),
(gaze_x, gaze_y),
head_mask,
(width, height),
)
x_min, y_min, x_max, y_max = bbox
gaze_x, gaze_y = gaze
width, height = size
head_channel = utils.get_head_box_channel(
x_min,
y_min,
x_max,
y_max,
width,
height,
resolution=self.input_size,
coordconv=False,
).unsqueeze(0)
if self.is_train and self.mask_generator is not None:
image_mask = self.mask_generator(
x_min / width,
y_min / height,
x_max / width,
y_max / height,
head_channel,
)
if self.transform is not None:
img = self.transform(img)
head_mask = TF.to_tensor(
TF.resize(head_mask, (self.input_size, self.input_size))
)
# generate the heat map used for deconv prediction
gaze_heatmap = torch.zeros(
self.output_size, self.output_size
) # set the size of the output
if not self.is_train: # aggregated heatmap
num_valid = 0
for gaze_x, gaze_y in cont_gaze:
if gaze_x != -1:
num_valid += 1
gaze_heatmap += self.draw_labelmap(
torch.zeros(self.output_size, self.output_size),
[gaze_x * self.output_size, gaze_y * self.output_size],
3,
type="Gaussian",
)
gaze_heatmap /= num_valid
else:
# if gaze_inside:
gaze_heatmap = self.draw_labelmap(
gaze_heatmap,
[gaze_x * self.output_size, gaze_y * self.output_size],
3,
type="Gaussian",
)
imsize = torch.IntTensor([width, height])
if self.is_train:
out_dict = {
"images": img,
"head_channels": head_channel,
"heatmaps": gaze_heatmap,
"gazes": torch.FloatTensor([gaze_x, gaze_y]),
"gaze_inouts": torch.FloatTensor([gaze_inside]),
"head_masks": head_mask,
"imsize": imsize,
}
if self.mask_generator is not None:
out_dict["image_masks"] = image_mask
return out_dict
else:
return {
"images": img,
"head_channels": head_channel,
"heatmaps": gaze_heatmap,
"gazes": cont_gaze,
"gaze_inouts": torch.FloatTensor([gaze_inside]),
"head_masks": head_mask,
"imsize": imsize,
}
def __len__(self):
return self.length
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