From 562fc4142afd3171309467ec14b24e4969834bc5 Mon Sep 17 00:00:00 2001 From: yukun-hh Date: Sun, 17 May 2026 18:43:28 +0800 Subject: [PATCH] =?UTF-8?q?=E6=96=B0=E5=A2=9E=20HOG=20+=20LogisticRegressi?= =?UTF-8?q?on=20=E7=BA=AF=E4=BC=A0=E7=BB=9F=E5=9F=BA=E7=BA=BF=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=EF=BC=88=E9=9B=B6=E7=A5=9E=E7=BB=8F=E7=BD=91=E7=BB=9C?= =?UTF-8?q?=E4=BE=9D=E8=B5=96=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 + baseline/HOG_Baseline.py | 147 +++++++++++++++++++++++++++++++++++++ baseline/compare_models.py | 8 +- 3 files changed, 153 insertions(+), 3 deletions(-) create mode 100644 baseline/HOG_Baseline.py diff --git a/.gitignore b/.gitignore index ad051ec..8bacd63 100644 --- a/.gitignore +++ b/.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 diff --git a/baseline/HOG_Baseline.py b/baseline/HOG_Baseline.py new file mode 100644 index 0000000..dde61fc --- /dev/null +++ b/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}") diff --git a/baseline/compare_models.py b/baseline/compare_models.py index eb4332e..89d98f8 100644 --- a/baseline/compare_models.py +++ b/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 (10% Fine-tune)', get_resnet34_10pct_preds), - ('VGG16 + KNN (K=5)', get_vgg_knn_preds), + ('ResNet-34', get_resnet34_preds), + ('ResNet-34 (10% Fine-tune)', get_resnet34_10pct_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),