38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
import json
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import os
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from pprint import pprint
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from app.service.outfit_matcher.dataset import FashionDataset
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from app.service.outfit_matcher.outfit_evaluator import OutfitMaterTypeAware
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if __name__ == '__main__':
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with open("./test_param/recommendation_test.json", "r") as f:
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param = json.load(f)
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fashion_dataset = FashionDataset(param["database"])
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service = OutfitMaterTypeAware()
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best_list = []
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bad_list = []
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for item in param["query"]:
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outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
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scores, features = service.get_result(outfits)
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# save features
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# 链接milvus
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# 存入数据库
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# 关闭链接
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# print(scores)
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# print(len(scores))
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best_outfits, best_scores = service.visualize(outfits, scores, param["topk"], best=True,
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# output_path=os.path.join(r"E:\workspace\outfit_matcher\2024 SS Outfit", f"{item['item_name']}_best_{param['topk']}.png")
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)
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bad_outfits, bad_scores = service.visualize(outfits, scores, param["topk"], best=False,
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# output_path=os.path.join(r"E:\workspace\outfit_matcher\2024 SS Outfit", f"{item['item_name']}_worst_{param['topk']}.png")
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)
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best_list.append({"best_outfits": best_outfits, "best_scores": best_scores})
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bad_list.append({"bad_outfits": bad_outfits, "bad_scores": bad_scores})
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pprint(best_list)
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pprint(bad_list)
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