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| import warnings
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| import numpy as np
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| import torch
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| from tqdm import tqdm
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| from evaluation.base_eval import BaseEval
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| from evaluation.utils.json_helpers import json_to_dict
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| warnings.filterwarnings("ignore")
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| class JsonScoreEvaluator(BaseEval):
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| """
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| Evaluates anomaly detection performance based on pre-computed scores stored in JSON files.
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| This class extends the BaseEval class and specializes in reading scores from JSON files,
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| computing evaluation metrics, and optionally saving results to CSV or JSON format.
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| Notes:
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| - Score files are expected to follow the exact dataset structure.
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| `{category}/{split}/{anomaly_type}/{image_name}_scores.json`
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| e.g., `photovoltaic_module/test/good/037_scores.json`
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| - Score files are expected to be at `self.scores_dir`.
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| Example usage:
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| >>> evaluator = JsonScoreEvaluator(cfg)
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| >>> results = evaluator.main()
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| """
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| def __init__(self, cfg):
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| super().__init__(cfg)
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| def get_scores_for_image(self, image_path):
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| image_scores_path = self.get_scores_path_for_image(image_path)
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| image_scores = json_to_dict(image_scores_path)
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| return image_scores
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| def load_category_scores(self, category):
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| cls_scores_list = []
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| anomaly_maps = []
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| gt_list = []
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| img_masks = []
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| image_path_list = []
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| test_dataset = self.load_datasets(category)
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| test_dataloader = torch.utils.data.DataLoader(
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| test_dataset,
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| batch_size=1,
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| shuffle=False,
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| num_workers=0,
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| pin_memory=True,
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| )
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| for image_info in tqdm(test_dataloader):
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| if not isinstance(image_info, dict):
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| raise ValueError("Encountered non-dict image in dataloader")
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| del image_info["image"]
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| image_path = image_info["image_path"][0]
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| image_path_list.extend(image_path)
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| img_masks.append(image_info["mask"])
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| gt_list.extend(list(image_info["is_anomaly"].numpy()))
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| image_scores = self.get_scores_for_image(image_path)
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| cls_scores = image_scores["img_level_score"]
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| anomaly_maps_iter = image_scores["pix_level_score"]
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| cls_scores_list.append(cls_scores)
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| anomaly_maps.append(anomaly_maps_iter)
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| pr_sp = np.array(cls_scores_list)
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| gt_sp = np.array(gt_list)
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| pr_px = np.array(anomaly_maps)
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| gt_px = torch.cat(img_masks, dim=0).numpy().astype(np.int32)
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| print(pr_px.shape)
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| assert pr_px.shape[1:] == (
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| 1,
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| 224,
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| 224,
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| ), "Predicted output scores do not meet the expected shape!"
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| assert gt_px.shape[1:] == (
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| 1,
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| 224,
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| 224,
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| ), "Loaded ground truth maps do not meet the expected shape!"
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| return gt_sp, pr_sp, gt_px, pr_px, image_path_list
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|
