File size: 5,154 Bytes
d483542 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | import os
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
# ========================================================
import cv2
import numpy as np
import xml.etree.ElementTree as ET
from skimage.feature import hog, local_binary_pattern
from sklearn.manifold import TSNE
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
# ================= 配置路径 =================
IMG_DIR = r'./JPEGImages'
XML_DIR = r'./Annotations'
RESIZE_W, RESIZE_H = 64, 128
def extract_features(img_dir, xml_dir):
features_hog = []
features_lbp = []
labels = []
# 检查路径是否存在
if not os.path.exists(xml_dir):
print(f"错误：找不到路径 {xml_dir}")
return [], [], []
xml_files = [f for f in os.listdir(xml_dir) if f.endswith('.xml')]
print(f"正在处理 {len(xml_files)} 个XML文件...")
valid_count = 0
for xml_file in xml_files:
try:
tree = ET.parse(os.path.join(xml_dir, xml_file))
root = tree.getroot()
filename_node = root.find('filename').text
base_name = os.path.splitext(filename_node)[0]
img_path = None
for ext in ['.jpg', '.JPG', '.png', '.jpeg']:
temp_path = os.path.join(img_dir, base_name + ext)
if os.path.exists(temp_path):
img_path = temp_path
break
if img_path is None: continue
img = cv2.imread(img_path)
if img is None: continue
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
for obj in root.findall('object'):
cls_name = obj.find('name').text
bndbox = obj.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
roi = gray[max(0, ymin):min(gray.shape[0], ymax),
max(0, xmin):min(gray.shape[1], xmax)]
if roi.size == 0: continue
roi_resized = cv2.resize(roi, (RESIZE_W, RESIZE_H))
# HOG
fd_hog = hog(roi_resized, orientations=9, pixels_per_cell=(16, 16),
cells_per_block=(2, 2), visualize=False)
# LBP
radius = 3
n_points = 8 * radius
lbp = local_binary_pattern(roi_resized, n_points, radius, method='uniform')
(hist, _) = np.histogram(lbp.ravel(),
bins=np.arange(0, n_points + 3),
range=(0, n_points + 2))
hist = hist.astype("float")
hist /= (hist.sum() + 1e-7)
features_hog.append(fd_hog)
features_lbp.append(hist)
labels.append(cls_name)
valid_count += 1
except Exception:
continue
print(f"成功提取特征: {valid_count} 个")
return np.array(features_hog), np.array(features_lbp), np.array(labels)
def plot_combined_tsne(X_hog, X_lbp, y, save_name="Fig6_Feature_Visualization.png"):
print("正在计算 t-SNE...")
tsne = TSNE(n_components=2, random_state=42, init='pca', learning_rate='auto')
hog_embedded = tsne.fit_transform(X_hog)
lbp_embedded = tsne.fit_transform(X_lbp)
fig, axes = plt.subplots(1, 2, figsize=(16, 7))
unique_labels = np.unique(y)
palette = sns.color_palette("bright", len(unique_labels))
# HOG
df_hog = pd.DataFrame(hog_embedded, columns=['Dim1', 'Dim2'])
df_hog['Class'] = y
sns.scatterplot(data=df_hog, x='Dim1', y='Dim2', hue='Class', style='Class',
palette=palette, ax=axes[0], s=80, alpha=0.8)
axes[0].set_title('(a) t-SNE of HOG Features')
axes[0].legend_.remove()
# LBP
df_lbp = pd.DataFrame(lbp_embedded, columns=['Dim1', 'Dim2'])
df_lbp['Class'] = y
sns.scatterplot(data=df_lbp, x='Dim1', y='Dim2', hue='Class', style='Class',
palette=palette, ax=axes[1], s=80, alpha=0.8)
axes[1].set_title('(b) t-SNE of LBP Features')
axes[1].legend_.remove()
handles, labels = axes[0].get_legend_handles_labels()
fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=len(unique_labels))
plt.tight_layout()
print(f"正在保存图片到当前目录: {save_name}")
# 注意：这里不需要 plt.show()，Agg模式下 show() 会报错或无效
plt.savefig(save_name, dpi=300, bbox_inches='tight')
print("✅ 保存成功！请在文件夹中查看图片。")
if __name__ == "__main__":
feat_hog, feat_lbp, labels = extract_features(IMG_DIR, XML_DIR)
if len(labels) > 0:
plot_combined_tsne(feat_hog, feat_lbp, labels)
else:
print("未检测到数据，请检查路径。") |