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a490ef3 8e24178 a490ef3 b3010da a490ef3 2482d63 a490ef3 2482d63 a490ef3 8e24178 a490ef3 8e24178 a490ef3 b3010da a490ef3 b3010da a490ef3 b3010da a490ef3 8e24178 a490ef3 b3010da a490ef3 8e24178 a490ef3 | 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 | from pathlib import Path
import albumentations as A
from PIL import Image
try:
import numpy as np
except ImportError:
np = None
try:
import torch
from torch.utils.data import Dataset
except ImportError:
torch = None
class Dataset:
pass
def resolve_split_root(root, split):
root = Path(root)
candidates = [root / split, root / split / split, root]
for candidate in candidates:
if (candidate / "videos").is_dir() and (candidate / "labels").is_dir():
return candidate
checked = "\n".join(str(candidate) for candidate in candidates)
raise FileNotFoundError(f"Could not resolve split '{split}'. Checked:\n{checked}")
def label_path_for(image_path, image_dir, label_dir):
return (label_dir / image_path.relative_to(image_dir)).with_suffix(".txt")
def read_varroa_boxes(label_path):
if not label_path.exists():
return []
lines = [line.strip() for line in label_path.read_text().splitlines() if line.strip()]
if not lines:
return []
boxes = []
for line in lines[1:]:
values = [float(x) for x in line.replace(",", " ").split()]
for i in range(0, len(values) - 3, 4):
boxes.append(values[i : i + 4])
return boxes
def clamp_boxes_xyxy(boxes, width, height):
clamped = []
for x1, y1, x2, y2 in boxes:
left, right = sorted((max(0.0, min(float(x1), width)), max(0.0, min(float(x2), width))))
top, bottom = sorted((max(0.0, min(float(y1), height)), max(0.0, min(float(y2), height))))
if right > left and bottom > top:
clamped.append([left, top, right, bottom])
return clamped
def letterbox_image_and_boxes(image, boxes, input_height, input_width):
if np is None:
raise ImportError("VarroaDetectionDataset requires numpy. Install numpy in the training environment.")
width, height = image.size
scale = min(input_width / width, input_height / height)
resized_width = int(round(width * scale))
resized_height = int(round(height * scale))
pad_x = (input_width - resized_width) // 2
pad_y = (input_height - resized_height) // 2
if (resized_width, resized_height) != (width, height):
image = image.resize((resized_width, resized_height), Image.BILINEAR)
if resized_width == input_width and resized_height == input_height:
canvas = image
else:
canvas = Image.new("RGB", (input_width, input_height), (0, 0, 0))
canvas.paste(image, (pad_x, pad_y))
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
if len(boxes):
boxes[:, [0, 2]] = boxes[:, [0, 2]] * scale + pad_x
boxes[:, [1, 3]] = boxes[:, [1, 3]] * scale + pad_y
boxes = clamp_boxes_xyxy(boxes, input_width, input_height)
boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
return canvas, boxes, scale, pad_x, pad_y
class VarroaDetectionDataset(Dataset):
"""Varroa bbox dataset.
Original layout:
train|val|test/
videos/<video-id>/*.png
labels/<video-id>/*.txt
Label files contain a first line with object count, then xyxy boxes in pixels.
"""
def __init__(
self,
root=".",
split="train",
input_size=(288, 160),
train=False,
include_empty=True,
normalize=True,
hflip_prob=0.5,
color_jitter_prob=0.25,
color_jitter_brightness=0.2,
color_jitter_contrast=0.0,
color_jitter_saturation=0.0,
color_jitter_hue=0.0,
):
self.root = Path(root)
self.split = split
self.input_height, self.input_width = input_size
self.train = train
self.include_empty = include_empty
self.normalize = normalize
self.hflip_prob = hflip_prob
self.color_jitter_prob = color_jitter_prob
self.color_jitter_brightness = color_jitter_brightness
self.color_jitter_contrast = color_jitter_contrast
self.color_jitter_saturation = color_jitter_saturation
self.color_jitter_hue = color_jitter_hue
self.transform = self._build_transform()
split_root = resolve_split_root(self.root, split)
self.image_dir = split_root / "videos"
self.label_dir = split_root / "labels"
all_images = sorted(self.image_dir.rglob("*.png"))
if not all_images:
raise FileNotFoundError(f"No PNG images found under {self.image_dir}")
all_boxes = [
read_varroa_boxes(label_path_for(path, self.image_dir, self.label_dir))
for path in all_images
]
if include_empty:
self.images = all_images
self.raw_boxes = all_boxes
else:
kept = [(path, boxes) for path, boxes in zip(all_images, all_boxes) if boxes]
self.images = [path for path, _ in kept]
self.raw_boxes = [boxes for _, boxes in kept]
def _build_transform(self):
if not self.train:
return None
return A.Compose(
[
A.HorizontalFlip(p=self.hflip_prob),
A.ColorJitter(
brightness=self.color_jitter_brightness,
contrast=self.color_jitter_contrast,
saturation=self.color_jitter_saturation,
hue=self.color_jitter_hue,
p=self.color_jitter_prob,
),
],
bbox_params=A.BboxParams(
format="pascal_voc",
label_fields=["labels"],
min_area=0.0,
min_visibility=0.0,
),
)
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
if torch is None:
raise ImportError("VarroaDetectionDataset requires torch. Install torch in the training environment.")
if np is None:
raise ImportError("VarroaDetectionDataset requires numpy. Install numpy in the training environment.")
image_path = self.images[idx]
image = Image.open(image_path).convert("RGB")
orig_width, orig_height = image.size
boxes = clamp_boxes_xyxy(
self.raw_boxes[idx],
orig_width,
orig_height,
)
image, boxes, scale, pad_x, pad_y = letterbox_image_and_boxes(
image, boxes, self.input_height, self.input_width
)
if self.transform is not None:
labels = [1] * len(boxes)
transformed = self.transform(image=np.asarray(image), bboxes=boxes.tolist(), labels=labels)
image = Image.fromarray(transformed["image"])
boxes = np.asarray(transformed["bboxes"], dtype=np.float32).reshape(-1, 4)
array = np.asarray(image, dtype=np.float32) / 255.0
if self.normalize:
mean = np.asarray([0.485, 0.456, 0.406], dtype=np.float32)
std = np.asarray([0.229, 0.224, 0.225], dtype=np.float32)
array = (array - mean) / std
tensor = torch.from_numpy(array).permute(2, 0, 1).contiguous()
boxes_tensor = torch.as_tensor(boxes, dtype=torch.float32)
target = {
"boxes": boxes_tensor,
"labels": torch.ones((boxes_tensor.shape[0],), dtype=torch.long),
"image_id": torch.tensor(idx, dtype=torch.long),
"orig_size": torch.tensor([orig_height, orig_width], dtype=torch.long),
"scale_pad": torch.tensor([scale, pad_x, pad_y], dtype=torch.float32),
"path": str(image_path),
}
return tensor, target
def detection_collate(batch):
if torch is None:
raise ImportError("detection_collate requires torch. Install torch in the training environment.")
images, targets = zip(*batch)
return torch.stack(images, dim=0), list(targets)
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