From e4351408b42a0a5308a66e4ff05a1fcde0c1db81 Mon Sep 17 00:00:00 2001 From: FEIJINTI <83849113+FEIJINTI@users.noreply.github.com> Date: Tue, 28 Mar 2023 13:20:01 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B4=E4=BA=86=E6=95=B0=E6=8D=AE?= =?UTF-8?q?=E7=9A=84=E9=A1=BA=E5=BA=8F=EF=BC=8C=E6=8C=89=E7=85=A7=E5=9B=BE?= =?UTF-8?q?=E7=89=87=E5=90=8D=E7=A7=B0=E5=A4=A7=E5=B0=8F=E6=8E=92=E5=BA=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- classifer.py | 33 +++++++++++++++++++-------------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/classifer.py b/classifer.py index 28d9121..f6f98db 100644 --- a/classifer.py +++ b/classifer.py @@ -16,7 +16,6 @@ from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix - from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from scipy.stats import binom @@ -31,7 +30,7 @@ sys.path.append(os.getcwd()) from root_dir import ROOT_DIR import utils -FEATURE_INDEX = [0,1,2]#因为显示需要，所以0,1,2分别为lab，是必须使用的，不然会影响图片显示 +FEATURE_INDEX = [0, 1, 2] # 因为显示需要，所以0,1,2分别为lab，是必须使用的，不然会影响图片显示 delete_columns = 10 # 已弃用 num_bins = 10 @@ -127,7 +126,7 @@ class WoodClass(object): self.model.fit(x_train, y_train) y_pred = self.model.predict(x_test) - #将y_pred中1和2转化为1 + # 将y_pred中1和2转化为1 # y_pred[y_pred == 2] = 1 # y_test[y_test == 2] = 1 @@ -150,9 +149,7 @@ class WoodClass(object): pre_score = accuracy_score(y, y_pred) self.log.log("Total accuracy is:" + str(pre_score * 100) + "%.") - #显示结果报告 - - + # 显示结果报告 return int(pre_score * 100) @@ -308,7 +305,6 @@ class WoodClass(object): # x = x[np.argsort(x[:, 0])] # x = x[-self.k:, :] - hist, bins = np.histogram(x[:, 0], bins=num_bins) # hist = hist[1:] # bins = bins[1:] @@ -316,8 +312,7 @@ class WoodClass(object): hist_number = sorted_indices[-1] second_hist_number = sorted_indices[-2] x = x[((x[:, 0] > bins[hist_number]) & (x[:, 0] < bins[hist_number + 1])) | ( - (x[:, 0] > bins[second_hist_number]) & (x[:, 0] < bins[second_hist_number + 1])), :] - + (x[:, 0] > bins[second_hist_number]) & (x[:, 0] < bins[second_hist_number + 1])), :] # hist, bins = np.histogram(x[:, 0], bins=9) # sorted_indices = np.argsort(hist) @@ -326,8 +321,6 @@ class WoodClass(object): # x = x[((x[:, 0] > bins[hist_number]) & (x[:, 0] < bins[hist_number + 1])) | ( # (x[:, 0] > bins[second_hist_number]) & (x[:, 0] < bins[second_hist_number + 1])), :] - - if debug_mode: # self.log.log(x) self.log.log(x.shape) @@ -531,7 +524,6 @@ class WoodClass(object): for i in range(labels.shape[0]): labels[i] = sorted_cluster_indices[labels[i]] - if plot_2d: plt.figure() plt.scatter(x_data[:, 0], x_data[:, 1], c=labels) @@ -539,7 +531,21 @@ class WoodClass(object): return x_data, y_data, labels, img_names - + def data_adjustments(self, x_data, y_data, labels, img_names): + ''' + 数据调整 + :param x_data: 提取的特征 + :param y_data: 初始的类别 + :param labels: 聚类后的类别 + :param img_names: 文件名 + :return: 调整后的数据 + ''' + sorted_idx = sorted(range(len(img_names)), key=lambda x: int(img_names[x][3:-4])) + x_data = x_data[sorted_idx, [0, 1, 2]] + y_data = y_data[sorted_idx] + labels = labels[sorted_idx] + img_names = img_names[sorted_idx] + return x_data, y_data, labels, img_names if __name__ == '__main__': @@ -557,7 +563,6 @@ if __name__ == '__main__': wood.get_kmeans_data(data_path, plot_2d=True) - # 测试单张图片的预测，predict_mode=True表示导入本地的model, False为现场训练的 pic = cv2.imread(r"data/318/dark/rgb89.png") start_time = time.time()