charlie880624
7716a0060f
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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>
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import mmcv
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import numpy as np
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from mmcv.utils import print_log
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from PIL import Image
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from .builder import DATASETS
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from .custom import CustomDataset
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@DATASETS.register_module()
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class GolfDataset(CustomDataset):
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"""GolfDataset for semantic segmentation with four classes: car, grass, people, and road."""
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# ✅ 固定的類別與調色盤（不從 config 接收）
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CLASSES = ('car', 'grass', 'people', 'road')
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PALETTE = [
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[246, 14, 135], # car
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[233, 81, 78], # grass
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[220, 148, 21], # people
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[207, 215, 220], # road
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]
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def __init__(self,
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img_suffix='_leftImg8bit.png',
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seg_map_suffix='_gtFine_labelIds.png',
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**kwargs):
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super(GolfDataset, self).__init__(
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img_suffix=img_suffix,
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seg_map_suffix=seg_map_suffix,
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**kwargs)
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# ✅ DEBUG：初始化時印出 CLASSES 與 PALETTE
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print("✅ [GolfDataset] 初始化完成")
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print(f" ➤ CLASSES: {self.CLASSES}")
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print(f" ➤ PALETTE: {self.PALETTE}")
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print(f" ➤ img_suffix: {img_suffix}")
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print(f" ➤ seg_map_suffix: {seg_map_suffix}")
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print(f" ➤ img_dir: {self.img_dir}")
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print(f" ➤ ann_dir: {self.ann_dir}")
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print(f" ➤ dataset length: {len(self)}")
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def results2img(self, results, imgfile_prefix, indices=None):
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"""Write the segmentation results to images."""
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if indices is None:
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indices = list(range(len(self)))
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mmcv.mkdir_or_exist(imgfile_prefix)
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result_files = []
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for result, idx in zip(results, indices):
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filename = self.img_infos[idx]['filename']
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basename = osp.splitext(osp.basename(filename))[0]
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png_filename = osp.join(imgfile_prefix, f'{basename}.png')
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output = Image.fromarray(result.astype(np.uint8)).convert('P')
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palette = np.zeros((len(self.PALETTE), 3), dtype=np.uint8)
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for label_id, color in enumerate(self.PALETTE):
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palette[label_id] = color
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output.putpalette(palette)
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output.save(png_filename)
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result_files.append(png_filename)
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return result_files
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def format_results(self, results, imgfile_prefix, indices=None):
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"""Format the results into dir (for evaluation or visualization)."""
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return self.results2img(results, imgfile_prefix, indices)
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def evaluate(self,
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results,
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metric='mIoU',
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logger=None,
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imgfile_prefix=None):
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"""Evaluate the results with the given metric."""
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# ✅ DEBUG：評估時印出目前 CLASSES 使用狀況
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print("🧪 [GolfDataset.evaluate] 被呼叫")
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print(f" ➤ 當前 CLASSES: {self.CLASSES}")
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print(f" ➤ 評估 metric: {metric}")
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print(f" ➤ 結果數量: {len(results)}")
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metrics = metric if isinstance(metric, list) else [metric]
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eval_results = super(GolfDataset, self).evaluate(results, metrics, logger)
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# ✅ DEBUG：印出最終的 eval_results keys
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print(f" ➤ 返回評估指標: {list(eval_results.keys())}")
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return eval_results
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