From b12bb52a6d71479e358a1f51889ff4cca0906449 Mon Sep 17 00:00:00 2001
From: FEIJINTI <83849113+FEIJINTI@users.noreply.github.com>
Date: Mon, 6 Mar 2023 15:35:40 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86=E5=8F=82=E6=95=B0?=
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---
 classifer.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/classifer.py b/classifer.py
index 07ddfb2..fc2a5a1 100644
--- a/classifer.py
+++ b/classifer.py
@@ -14,6 +14,7 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
 from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
 from scipy.stats import binom
 import matplotlib.pyplot as plt
 import time
@@ -26,7 +27,7 @@ sys.path.append(os.getcwd())
 from root_dir import ROOT_DIR
 import utils
 
-FEATURE_INDEX = [1, 2]
+FEATURE_INDEX = [0, 1]
 
 
 class WoodClass(object):
@@ -53,6 +54,7 @@ class WoodClass(object):
             self.set_purity(self.pur)
             self.change_pick_mode(single_pick_mode)
             self.model = LogisticRegression(C=1e5)
+            # self.model = KNeighborsClassifier()
         else:
             self.load(load_from)
         self.isCorrect = False
@@ -102,7 +104,7 @@ class WoodClass(object):
         :return:
         """
         # 训练数据文件位置
-        result = self.get_train_data(data_path, plot_2d=False)
+        result = self.get_train_data(data_path, plot_2d=True)
         if result is False:
             return 0
         x, y = result
@@ -150,6 +152,9 @@ class WoodClass(object):
         feature = feature.reshape(1, -1)[:, FEATURE_INDEX]
         if self.isCorrect:
             feature = feature / (self.correct_color+1e-4)
+        plt.figure()
+        plt.scatter(feature[:, 0], feature[:, 1])
+        plt.show()
         pred_color = self.model.predict(feature)
         if self.debug_mode:
             self.log.log(feature)
@@ -360,20 +365,20 @@ if __name__ == '__main__':
     from config import Config
     settings = Config()
     # 初始化wood
-    wood = WoodClass(w=4096, h=1200, n=5000, p1=0.4, debug_mode=False)
+    wood = WoodClass(w=4096, h=1200, n=12500, p1=0.4, debug_mode=False)
     print("色彩纯度控制量{}/{}".format(wood.k, wood.n))
-    data_path = r"C:\Users\FEIJINTI\PycharmProjects\wood_color\data\data1015"
+    data_path = settings.data_path
     # wood.correct()
     # wood.load()
     # fit 相应的文件夹
-    settings.model_path = ROOT_DIR / 'models' / wood.fit_pictures(data_path=data_path)
+    settings.model_path = str(ROOT_DIR / 'models' / wood.fit_pictures(data_path=data_path))
 
     # 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/dark/rgb60.png")
-    # start_time = time.time()
+    pic = cv2.imread(r"data/duizhao/rgb7.png")
+    start_time = time.time()
     # for i in range(100):
-    #     wood_color = wood.predict(pic)
-    # end_time = time.time()
-    # print("time consume:"+str((end_time - start_time)/100))
-    # print("wood_color:"+str(wood_color))
+    wood_color = wood.predict(pic)
+    end_time = time.time()
+    print("time consume:"+str((end_time - start_time)/100))
+    print("wood_color:"+str(wood_color))