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Oleksandr Bezdieniezhnykh 142c6c4de8 Refactor constants management to use Pydantic BaseModel for configuration

- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.

2026-03-27 18:18:30 +02:00

2.5 KiB

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Module: augmentation

Purpose

Image augmentation pipeline that takes raw annotated images and produces multiple augmented variants for training data expansion. Runs continuously in a loop.

Public Interface

Augmentator

Method	Signature	Returns	Description
`__init__`	`()`	—	Initializes augmentation transforms and counters
`augment_annotations`	`(from_scratch: bool = False)`	—	Processes all unprocessed images from `data/images` → `data-processed/images`
`augment_annotation`	`(image_file)`	—	Processes a single image file: reads image + labels, augments, saves results
`augment_inner`	`(img_ann: ImageLabel) -> list[ImageLabel]`	List of augmented images	Generates 1 original + 7 augmented variants
`correct_bboxes`	`(labels) -> list`	Corrected labels	Clips bounding boxes to image boundaries, removes tiny boxes
`read_labels`	`(labels_path) -> list[list]`	Parsed YOLO labels	Reads YOLO-format label file into list of [x, y, w, h, class_id]

Internal Logic

Augmentation pipeline (albumentations Compose):
1. HorizontalFlip (p=0.6)
2. RandomBrightnessContrast (p=0.4)
3. Affine: scale 0.8–1.2, rotate ±35°, shear ±10° (p=0.8)
4. MotionBlur (p=0.1)
5. HueSaturationValue (p=0.4)
Each image produces 8 outputs: 1 original copy + 7 augmented variants
Naming: {stem}_{1..7}.jpg for augmented, original keeps its name
Bbox correction: clips bounding boxes that extend outside image borders, removes boxes smaller than correct_min_bbox_size (0.01 of image dimension)
Incremental processing: skips images already present in processed_images_dir
Concurrent: uses ThreadPoolExecutor for parallel processing
Continuous mode: __main__ runs augmentation in an infinite loop with 5-minute sleep between rounds

Dependencies

constants — directory paths (data_images_dir, data_labels_dir, processed_*)
dto/imageLabel — ImageLabel container class
albumentations (external) — augmentation transforms
cv2 (external) — image read/write
numpy (external) — image array handling
concurrent.futures, os, shutil, time, datetime, pathlib (stdlib)

Consumers

manual_run

Data Models

Uses ImageLabel from dto/imageLabel.

Configuration

Hardcoded augmentation parameters (probabilities, ranges). Directory paths from constants.

External Integrations

Filesystem I/O: reads from /azaion/data/, writes to /azaion/data-processed/.

Security

None.

Tests