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增加未知类别负样本

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zhangpu
2025-10-23 16:05:17 +08:00
parent 1c8ceef5b0
commit 519bd1c189
4 changed files with 101 additions and 44 deletions
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classifier/embedding_food_classifier_app.py
+13 -10
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@@ -65,13 +65,13 @@ class EmbeddingFoodClassifierApp:
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# model_path = "../model/embedding_20251011_133653/best_embedding_model.pth"
# model_path = os.path.join(BASE_DIR, "../model/embedding_20251011_133653/best_embedding_model.pth")
model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/embedding_20251021_085915/best_embedding_model.pth")
model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/embedding_20251023_120531/best_embedding_model.pth")
# model_path = os.path.join(BASE_DIR, "../model/DishClassification/embedding_20251022_093635/best_embedding_model.pth")
# FAISS索引目录
# index_dir = "../faiss_vector_db/faiss_index"
# index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/faiss_index")
index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index")
index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/WholeIngredientRecognition/faiss_index102402")
# index_dir = os.path.join(BASE_DIR, "../faiss_vector_db/DishClassification/faiss_index")
if os.path.exists(model_path) and os.path.exists(index_dir):
@@ -1087,12 +1087,13 @@ class EmbeddingFoodClassifierApp:
"""识别所有图片"""
try:
self.current_results.clear()
score_list = []
for i, img_info in enumerate(self.uploaded_images):
# 使用embedding相似度识别
if self.model is not None and self.faiss_index is not None:
# 使用真实的embedding模型和FAISS索引
predicted_class, confidence, similar_images = self.predict_with_embedding(img_info['image'])
predicted_class, confidence, similar_images,score = self.predict_with_embedding(img_info['image'])
score_list.append(score)
else:
# 模拟预测结果
predicted_class = np.random.choice(self.class_names)
@@ -1122,7 +1123,7 @@ class EmbeddingFoodClassifierApp:
# 更新UI（在主线程中）
self.root.after(0, self.update_progress, i + 1, len(self.uploaded_images))
print('socre_list',score_list)
# 识别完成，更新UI
self.root.after(0, self.recognition_completed)
@@ -1144,9 +1145,11 @@ class EmbeddingFoodClassifierApp:
# 在FAISS索引中搜索最相似的k张图片
scores, indices = self.faiss_index.search(query_embedding, k)
print("最相似的图片索引:", indices)
print("最相似的图片分数:", scores)
# print("最相似的图片索引:", indices)
# print("最相似的图片分数:", type(scores[0]))
# print("最相似的图片分数:", scores[0,0])
# print("最相似的图片分数:", type(scores[0,0]))
# 收集相似图片的类别
similar_classes = []
similar_images = []
@@ -1179,12 +1182,12 @@ class EmbeddingFoodClassifierApp:
vote_ratio = class_counts[predicted_class] / len(similar_classes)
confidence = max_score * vote_ratio
return predicted_class, confidence, similar_images
return predicted_class, confidence, similar_images,round(scores[0,0],3)
else:
# 如果没有找到相似图片，随机选择一个类别
predicted_class = np.random.choice(self.class_names)
confidence = 0.1
return predicted_class, confidence, []
return predicted_class, confidence, [],scores[0,0]
except Exception as e:
print(f"Embedding预测出错: {e}")