尝试加个功能

classifer.py

@@ -31,7 +31,7 @@ sys.path.append(os.getcwd())
 from root_dir import ROOT_DIR
 import utils
 
-FEATURE_INDEX = [0,1,2,3,4,5]
+FEATURE_INDEX = [0,1,2]
 delete_columns = 10  # 已弃用
 num_bins = 10
 
@@ -367,6 +367,16 @@ class WoodClass(object):
             data = self.extract_feature(train_img)
             img_data.append(data)
         img_data = np.array(img_data)
+
+        # 提取图像名称
+        img_name = [os.path.splitext(file)[0] for file in files]
+        # 提取每个图像名称中的数字
+        img_name = [name[3:] for name in img_name]
+        # 将图像名称个位数前补零
+        img_name = [name.zfill(2) for name in img_name]
+        # 打印图像名称
+        print('img_name:', img_name)
+
         return img_data
 
     def get_train_data(self, data_dir=None, plot_2d=False, plot_data_3d=False, save_data=False):
@@ -386,6 +396,46 @@ class WoodClass(object):
         light_label = 2 * np.ones(len(light_data)).T
         y_data = np.hstack((dark_label, middle_label, light_label))
         x_data = x_data[:, FEATURE_INDEX]
+
+        # 使用KMeans算法对图片数据进行聚类
+        kmeans = KMeans(n_clusters=3, random_state=0).fit(x_data)
+        z = kmeans.predict(x_data)
+        # 获取聚类后的数据
+        dark = x_data[kmeans.labels_ == 0]
+        middle = x_data[kmeans.labels_ == 1]
+        light = x_data[kmeans.labels_ == 2]
+        # 获取数据的均值
+        dark_mean = np.mean(dark, axis=0)
+        middle_mean = np.mean(middle, axis=0)
+        light_mean = np.mean(light, axis=0)
+
+        # 按照平均值从小到大排序
+        sorted_cluster_indices = np.argsort([dark_mean[0], middle_mean[0], light_mean[0]])
+        print('sorted_cluster_indices:', sorted_cluster_indices)
+        # 重新编号聚类标签
+        sorted_labels = np.zeros(len(kmeans.labels_), dtype=int)
+        for i, label in enumerate(kmeans.labels_):
+            sorted_labels[i] = sorted_cluster_indices[label]
+        # 更新kmeans.labels_
+        kmeans.labels_ = sorted_labels
+        print('kmeans.labels_:', kmeans.labels_)
+        # 获取更新聚类后的数据
+        dark_new = x_data[kmeans.labels_ == 0]
+        middle_new = x_data[kmeans.labels_ == 1]
+        light_new = x_data[kmeans.labels_ == 2]
+        # 获取更新数据的均值
+        dark_mean_new = np.mean(dark_new, axis=0)
+        middle_mean_new = np.mean(middle_new, axis=0)
+        light_mean_new = np.mean(light_new, axis=0)
+        # 打印每个聚类的平均值
+        print('Dark cluster mean:', dark_mean_new)
+        print('Middle cluster mean:', middle_mean_new)
+        print('Light cluster mean:', light_mean_new)
+        # plot_2d
+        plt.figure()
+        plt.scatter(x_data[:, 0], x_data[:, 1], c=z)
+        plt.show()
+
         # 进行色彩数据校正
         if self.isCorrect:
             x_data = x_data / (self.correct_color + 1e-4)

socket_detector.py

@@ -45,6 +45,8 @@ def process_cmd(cmd: str, data: any, connected_sock: socket.socket, detector: Wo
         settings.model_path = data
         detector.load(path=settings.model_path)
         response = simple_sock(connected_sock, cmd_type=cmd)
+    elif cmd == 'DT':
+        pass
     else:
         logging.error(f'错误指令，指令为{cmd}')
         response = False