新增 HOG + LogisticRegression 纯传统基线模型（零神经网络依赖）

.gitignore

@@ -17,6 +17,7 @@
 !/baseline/VGG_KNN.py
 !/baseline/compare_models.py
 !/baseline/ResNet34_Pretrained_10pct.py
+!/baseline/HOG_Baseline.py
 !/training_log.csv
 !/confusion_matrix.png
 !/roc_curve.png

baseline/HOG_Baseline.py

@@ -0,0 +1,147 @@
+"""
+baseline/HOG_Baseline.py
+HOG + 颜色直方图特征提取 + LogisticRegression 四分类
+纯传统 CV/ML 基线，零神经网络依赖
+可独立运行，也可被 compare_models.py 导入
+author: yukun-hh
+date: 2026-5-14
+"""
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+import matplotlib
+
+from skimage.feature import hog
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    accuracy_score, f1_score,
+    confusion_matrix, ConfusionMatrixDisplay,
+    classification_report,
+)
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+# ============================================================
+# ★★★ 可配置参数 ★★★
+# ============================================================
+DATA_ROOT   = '../../trash_division_data/ultimate_4_class/'
+IMAGE_SIZE  = 128
+HOG_ORIENTATIONS = 9
+HOG_PIXELS_PER_CELL = (8, 8)
+HOG_CELLS_PER_BLOCK = (2, 2)
+COLOR_BINS  = 32
+# ============================================================
+
+CLASS_NAMES = ['厨余垃圾', '可回收物', '其他垃圾', '有害垃圾']
+NUM_CLASSES = 4
+
+
+def extract_hog_color(image):
+    img = image.convert('RGB').resize((IMAGE_SIZE, IMAGE_SIZE))
+    arr = np.array(img, dtype=np.float64) / 255.0
+
+    hog_feat = hog(arr, orientations=HOG_ORIENTATIONS,
+                   pixels_per_cell=HOG_PIXELS_PER_CELL,
+                   cells_per_block=HOG_CELLS_PER_BLOCK,
+                   channel_axis=2, feature_vector=True)
+
+    color_feat = []
+    for c in range(3):
+        hist, _ = np.histogram(arr[:, :, c], bins=COLOR_BINS, range=(0, 1))
+        color_feat.append(hist)
+    color_feat = np.concatenate(color_feat)
+
+    return np.concatenate([hog_feat, color_feat])
+
+
+class HOGLRBaseline:
+    def __init__(self, data_root=DATA_ROOT, image_size=IMAGE_SIZE):
+        self.data_root = data_root
+        self.image_size = image_size
+        self.clf = LogisticRegression(
+            C=1.0, max_iter=1000,
+            multi_class='multinomial', solver='lbfgs',
+            n_jobs=-1,
+        )
+
+    def _load_data(self, split):
+        dir_path = os.path.join(self.data_root, split)
+        features, labels = [], []
+        for class_id in range(1, NUM_CLASSES + 1):
+            class_dir = os.path.join(dir_path, str(class_id))
+            if not os.path.isdir(class_dir):
+                continue
+            files = sorted(os.listdir(class_dir))
+            for fname in tqdm(files, desc=f'{split}/class_{class_id}'):
+                fpath = os.path.join(class_dir, fname)
+                try:
+                    with Image.open(fpath) as img:
+                        feat = extract_hog_color(img)
+                    features.append(feat)
+                    labels.append(class_id - 1)
+                except Exception:
+                    pass
+        print(f"  {split}: {len(features)} 张")
+        return np.array(features, dtype=np.float32), np.array(labels)
+
+    def fit(self, train_dir=None):
+        if train_dir is None:
+            train_dir = 'train'
+        print("  提取训练集 HOG 特征 ...")
+        X, y = self._load_data(train_dir)
+        self.clf.fit(X, y)
+
+    def predict(self, val_dir=None):
+        if val_dir is None:
+            val_dir = 'val'
+        print("  提取验证集 HOG 特征 ...")
+        X, y = self._load_data(val_dir)
+        preds = self.clf.predict(X)
+        probs = self.clf.predict_proba(X)
+        return y, preds, probs
+
+
+# ============================================================
+# compare_models.py 导入接口
+# ============================================================
+
+def get_hog_lr_preds(train_loader, val_loader, device):
+    baseline = HOGLRBaseline()
+    baseline.fit('train')
+    return baseline.predict('val')
+
+
+# ============================================================
+# 独立运行入口
+# ============================================================
+
+if __name__ == '__main__':
+    out_dir = os.path.dirname(os.path.abspath(__file__))
+
+    print("HOG + LogisticRegression 基线")
+    baseline = HOGLRBaseline()
+
+    baseline.fit('train')
+    y_true, y_preds, y_probs = baseline.predict('val')
+
+    acc = accuracy_score(y_true, y_preds)
+    macro_f1 = f1_score(y_true, y_preds, average='macro')
+    print(f"\n验证集 Accuracy: {acc:.4f}")
+    print(f"验证集 Macro-F1: {macro_f1:.4f}")
+    print(f"\n分类报告:\n{classification_report(y_true, y_preds, target_names=CLASS_NAMES)}")
+
+    cm = confusion_matrix(y_true, y_preds)
+    fig, ax = plt.subplots(figsize=(8, 7))
+    ConfusionMatrixDisplay(cm, display_labels=CLASS_NAMES).plot(
+        ax=ax, cmap='Blues', values_format='d', xticks_rotation=30)
+    ax.set_title('HOG + LogisticRegression 混淆矩阵', fontsize=14)
+    plt.tight_layout()
+    cm_path = os.path.join(out_dir, 'hog_lr_confusion_matrix.png')
+    plt.savefig(cm_path, dpi=150, bbox_inches='tight')
+    plt.show()
+    print(f"混淆矩阵已保存: {cm_path}")

baseline/compare_models.py

@@ -26,6 +26,7 @@ from Model import Net
 from Dataloader import RobustImageFolder
 from baseline.VGG_KNN import VGGKNNBaseline
 from baseline.ResNet34_Pretrained_10pct import get_resnet34_10pct_preds
+from baseline.HOG_Baseline import get_hog_lr_preds
 
 matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
 matplotlib.rcParams['axes.unicode_minus'] = False
@@ -81,9 +82,10 @@ def get_vgg_knn_preds(train_loader, val_loader, device):
 # ============================================================
 
 MODELS = [
-    ('ResNet-34',                get_resnet34_preds),
+    ('ResNet-34',                  get_resnet34_preds),
-    ('ResNet-34 (10% Fine-tune)', get_resnet34_10pct_preds),
+    ('ResNet-34 (10% Fine-tune)',  get_resnet34_10pct_preds),
-    ('VGG16 + KNN  (K=5)',       get_vgg_knn_preds),
+    ('VGG16 + KNN  (K=5)',        get_vgg_knn_preds),
+    ('HOG + LogisticRegression',   get_hog_lr_preds),
     # 未来轻松扩展示例：
     # ('ResNet-18 (pretrained)', get_resnet18_preds),
     # ('ResNet-50 (pretrained)', get_resnet50_preds),