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- Add golf1/2/4/7/8 dataset classes for semantic segmentation - Add kneron-specific configs (meconfig series, kn_stdc1_golf4class) - Organize scripts into tools/check/ and tools/kneron/ - Add kneron_preprocessing module - Update README with quick-start guide - Update .gitignore to exclude data dirs, onnx, nef outputs Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import os
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import torch
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from mmseg.apis import inference_segmentor, init_segmentor
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def main():
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# 設定路徑
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config_file = 'configs/stdc/kn_stdc1_in1k-pre_512x1024_80k_cityscapes.py'
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checkpoint_file = 'work_dirs/kn_stdc1_in1k-pre_512x1024_80k_cityscapes/latest.pth'
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img_dir = 'data/cityscapes/leftImg8bit/val'
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out_dir = 'work_dirs/vis_results'
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# 初始化模型
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model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
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print('CLASSES：', model.CLASSES)
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print('PALETTE：', model.PALETTE)
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# 建立輸出資料夾
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os.makedirs(out_dir, exist_ok=True)
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# 找出所有圖片檔
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img_list = []
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for root, _, files in os.walk(img_dir):
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for f in files:
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if f.endswith('.png') or f.endswith('.jpg'):
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img_list.append(os.path.join(root, f))
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# 推論每一張圖片
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for img_path in img_list:
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result = inference_segmentor(model, img_path)
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filename = os.path.basename(img_path)
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out_path = os.path.join(out_dir, filename)
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model.show_result(img_path, result, out_file=out_path, opacity=0.5)
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print(f'✅ 推論完成，共處理 {len(img_list)} 張圖片，結果已輸出至：{out_dir}')
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if __name__ == '__main__':
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main()
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