From 7a39db597b3936da6ebbc0f9f944e1a0fb12f0c3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=BE=A1=E5=9D=82=E6=98=B4?= <gugugulaocuchen@gmail.com>
Date: Wed, 27 Nov 2024 18:11:39 +0800
Subject: [PATCH] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E8=AE=AD=E7=BB=83?=
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---
 实验六/train.py | 83 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 实验六/train.py

diff --git a/实验六/train.py b/实验六/train.py
new file mode 100644
index 0000000..3435a50
--- /dev/null
+++ b/实验六/train.py
@@ -0,0 +1,83 @@
+import cv2 as cv
+import joblib
+import numpy as np
+import tqdm
+import os
+
+from sklearn.metrics import accuracy_score
+from sklearn.svm import SVC
+
+# 提取轮廓特征
+def extract_contour_features(img):
+    contours, _ = cv.findContours(img, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contour = contours[0]
+    area = cv.contourArea(contour)
+    perimeter = cv.arcLength(contour, True)
+    return [area, perimeter]
+
+# 提取形状特征
+def extract_shape_features(contour):
+    x, y, w, h = cv.boundingRect(contour)
+    aspect_ratio = float(w) / h
+    rect_area = w * h
+    shape_factor = cv.contourArea(contour) / rect_area
+    return [aspect_ratio, shape_factor]
+
+# 计算HU矩
+def extract_hu_moments(contour):
+    moments = cv.moments(contour)
+    hu_moments = cv.HuMoments(moments)
+    return hu_moments.flatten()
+
+# 特征向量构建
+def extract_features(img_path):
+    img = cv.imread(img_path, cv.IMREAD_GRAYSCALE)
+    if img is None:
+        raise FileNotFoundError(f"无法加载图像: {img_path}")
+    _, img_bin = cv.threshold(img, 128, 255, cv.THRESH_BINARY)
+    contours, _ = cv.findContours(img_bin, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+    contour = contours[0]
+    contour_features = extract_contour_features(img_bin)
+    shape_features = extract_shape_features(contour)
+    hu_moments = extract_hu_moments(contour)
+    feature_vector = contour_features + shape_features + hu_moments.tolist()
+    return feature_vector
+
+# 加载图像路径和标签
+def load_data(dataset_path):
+    image_paths = []
+    labels = []
+    for file_name in os.listdir(dataset_path):
+        if file_name.endswith(".png"):
+            label = int(file_name.split("-")[-1].split(".")[0])
+            image_paths.append(os.path.join(dataset_path, file_name))
+            labels.append(label)
+    return image_paths, labels
+
+# 创建文件夹
+def ensure_dir_exists(directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+# 加载训练数据
+trains_paths, trains_labels = load_data("cache/pretrains/train")
+test_paths, test_labels = load_data("cache/pretrains/test")
+
+# 提取特征和标签
+X_train = np.array([extract_features(train_path) for train_path in tqdm.tqdm(trains_paths, desc="训练集特征提取中：")])
+Y_train = np.array(trains_labels)
+X_test = np.array([extract_features(test_path) for test_path in tqdm.tqdm(test_paths, desc="测试集特征提取中：")])
+Y_test = np.array(test_labels)
+
+# 训练分类器
+classifier = SVC(kernel="linear")
+classifier.fit(X_train, Y_train)
+
+# 在测试集上进行评估
+Y_pred = classifier.predict(X_test)
+accuracy = accuracy_score(Y_test, Y_pred)
+print(f"性能: {accuracy * 100:.2f}%")
+
+# 保存模型
+ensure_dir_exists("models")
+joblib.dump(classifier, "models/classifier.pkl")