yixiong/FoodClassifier
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根据路径自动判断识别结果是否正确！

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This commit is contained in:
zhanghuan
2025-09-03 11:39:39 +08:00
parent c2186b8381
commit 3aabf88b1f
1 changed files with 112 additions and 47 deletions
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classifier/food_classifier_app.py
+112 -47
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@@ -125,6 +125,27 @@ class FoodClassifierApp:
print(f"模型加载失败: {e}")
self.model = None
def extract_true_class_from_path(self, file_path):
"""从文件路径中提取真实类别（上一级目录名）"""
try:
# 标准化路径
normalized_path = os.path.normpath(file_path)
# 获取目录路径
dir_path = os.path.dirname(normalized_path)
# 获取上一级目录名（即类别名）
true_class = os.path.basename(dir_path)
# 检查是否是已知的食物类别
if true_class in self.food_classes:
return true_class
else:
# 如果不是已知类别，返回None表示未知
return None
except Exception as e:
print(f"提取真实类别失败: {e}")
return None
def load_image_with_chinese_path(self, file_path):
"""使用支持中文路径的方法加载图片"""
try:
@@ -337,11 +358,15 @@ class FoodClassifierApp:
messagebox.showerror("错误", f"无法读取图片: {file_path}")
return
# 从文件路径中提取真实类别（上一级目录名）
true_class = self.extract_true_class_from_path(file_path)
# 添加到列表
image_info = {
'path': file_path,
'name': os.path.basename(file_path),
'image': image,
'true_class': true_class,
'recognized': False,
'result': None
}
@@ -390,15 +415,42 @@ class FoodClassifierApp:
)
name_label.pack(fill="x", padx=5, pady=2)
# 状态
status = "已识别" if img_info['recognized'] else "未识别"
# 状态和真实类别
if img_info['recognized'] and img_info.get('result'):
result = img_info['result']
is_correct = result.get('is_correct')
if is_correct is True:
status = "已识别 ✓"
status_color = "green"
elif is_correct is False:
status = "已识别 ✗"
status_color = "red"
else:
status = "已识别 ?"
status_color = "orange"
else:
status = "未识别"
status_color = None
status_label = ctk.CTkLabel(
info_frame,
text=f"状态: {status}",
anchor="w"
anchor="w",
text_color=status_color
)
status_label.pack(fill="x", padx=5, pady=2)
# 显示真实类别（如果有）
true_class = img_info.get('true_class')
if true_class:
true_class_label = ctk.CTkLabel(
info_frame,
text=f"真实类别: {true_class}",
anchor="w",
font=("Arial", 10)
)
true_class_label.pack(fill="x", padx=5, pady=1)
# 删除按钮
delete_button = ctk.CTkButton(
img_frame,
@@ -487,13 +539,20 @@ class FoodClassifierApp:
predicted_class = self.food_classes[prediction]
confidence = np.random.uniform(0.6, 0.95)
# 自动判断识别是否正确
true_class = img_info.get('true_class')
is_correct = None
if true_class is not None:
is_correct = (predicted_class == true_class)
# 保存结果
result = {
'image_index': i,
'image_name': img_info['name'],
'predicted_class': predicted_class,
'confidence': confidence,
'is_correct': None # 用户可以手动标记
'true_class': true_class,
'is_correct': is_correct # 自动判断结果
}
self.current_results.append(result)
@@ -636,59 +695,65 @@ class FoodClassifierApp:
)
confidence_label.pack(fill="x", padx=5, pady=2)
# 正确性标记框架
correct_frame = ctk.CTkFrame(info_frame)
correct_frame.pack(fill="x", padx=5, pady=5)
# 真实类别
true_class = result.get('true_class')
if true_class is not None:
true_class_label = ctk.CTkLabel(
info_frame,
text=f"真实类别: {true_class}",
anchor="w",
font=("Arial", 11)
)
true_class_label.pack(fill="x", padx=5, pady=2)
correct_label = ctk.CTkLabel(correct_frame, text="识别是否正确:")
correct_label.pack(side="left", padx=5)
# 正确按钮
correct_button = ctk.CTkButton(
correct_frame,
text="正确",
command=lambda idx=i: self.mark_result(idx, True),
width=60,
height=25,
fg_color="green" if result['is_correct'] is True else "gray",
hover_color="darkgreen"
)
correct_button.pack(side="left", padx=2)
# 错误按钮
incorrect_button = ctk.CTkButton(
correct_frame,
text="错误",
command=lambda idx=i: self.mark_result(idx, False),
width=60,
height=25,
fg_color="red" if result['is_correct'] is False else "gray",
hover_color="darkred"
)
incorrect_button.pack(side="left", padx=2)
# 自动判断结果
is_correct = result.get('is_correct')
if is_correct is not None:
if is_correct:
status_text = "✓ 识别正确"
status_color = "green"
else:
status_text = "✗ 识别错误"
status_color = "red"
status_label = ctk.CTkLabel(
info_frame,
text=status_text,
anchor="w",
font=("Arial", 12, "bold"),
text_color=status_color
)
status_label.pack(fill="x", padx=5, pady=5)
else:
# 如果无法自动判断，显示未知状态
status_label = ctk.CTkLabel(
info_frame,
text="? 无法自动判断（路径中未包含已知类别）",
anchor="w",
font=("Arial", 11),
text_color="orange"
)
status_label.pack(fill="x", padx=5, pady=5)
def mark_result(self, result_index, is_correct):
"""标记识别结果的正确性"""
if result_index < len(self.current_results):
self.current_results[result_index]['is_correct'] = is_correct
self.update_results_display()
self.update_stats()
def update_stats(self):
"""更新统计信息"""
total_images = len(self.uploaded_images)
recognized_images = len(self.current_results)
# 计算准确率
marked_results = [r for r in self.current_results if r['is_correct'] is not None]
if marked_results:
correct_count = sum(1 for r in marked_results if r['is_correct'])
accuracy = (correct_count / len(marked_results)) * 100
# 计算准确率（基于自动判断的结果）
auto_judged_results = [r for r in self.current_results if r.get('is_correct') is not None]
if auto_judged_results:
correct_count = sum(1 for r in auto_judged_results if r['is_correct'])
accuracy = (correct_count / len(auto_judged_results)) * 100
# 更新统计标签
stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 平均准确率: {accuracy:.1f}%"
else:
accuracy = 0
# 如果没有可自动判断的结果
stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 平均准确率: 0%"
# 更新统计标签
stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 平均准确率: {accuracy:.1f}%"
self.stats_label.configure(text=stats_text)
def main():