mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 10:26:36 +00:00
Oleksandr Bezdieniezhnykh
142c6c4de8
- 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.
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import os
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from pathlib import Path
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import cv2
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from dto.annotationClass import AnnotationClass
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from dto.imageLabel import ImageLabel
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from preprocessing import read_labels
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from matplotlib import pyplot as plt
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import constants
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annotation_classes = AnnotationClass.read_json()
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def visualise_dataset():
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cur_dataset = os.path.join(constants.config.datasets_dir, f'{constants.prefix}2024-06-18', 'train')
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images_dir = os.path.join(cur_dataset, 'images')
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labels_dir = os.path.join(cur_dataset, 'labels')
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for f in os.listdir(images_dir)[35247:]:
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image_path = os.path.join(images_dir, f)
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labels_path = os.path.join(labels_dir, f'{Path(f).stem}.txt')
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img = ImageLabel(
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image_path=image_path,
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image=cv2.imread(image_path),
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labels_path=labels_path,
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labels=read_labels(labels_path)
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)
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img.visualize(annotation_classes)
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print(f'visualizing {image_path}')
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plt.close()
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key = input('Press any key to continue')
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def visualise_processed_folder():
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def show_image(img):
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image_path = os.path.join(constants.config.processed_images_dir, img)
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labels_path = os.path.join(constants.config.processed_labels_dir, f'{Path(img).stem}.txt')
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img = ImageLabel(
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image_path=image_path,
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image=cv2.imread(image_path),
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labels_path=labels_path,
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labels=read_labels(labels_path)
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)
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img.visualize(annotation_classes)
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images = os.listdir(constants.config.processed_images_dir)
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cur = 0
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show_image(images[cur])
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pass
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if __name__ == '__main__':
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visualise_processed_folder()
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