添加了dt方法

02_classification.py

@@ -7,7 +7,8 @@
 
 
 import numpy as np
-
+import scipy
+from imblearn.under_sampling import RandomUnderSampler
 from models import AnonymousColorDetector
 from utils import read_labeled_img
 
@@ -17,8 +18,15 @@ from utils import read_labeled_img
 
 
 data_dir = "data/dataset"
-color_dict = {(0, 0, 255): "yangeng"}
+color_dict = {(0, 0, 255): "yangeng", (255, 0, 0): 'beijing'}
+label_index = {"yangeng": 1, "beijing": 0}
 dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
+rus = RandomUnderSampler(random_state=0)
+x_list, y_list = np.concatenate([v for k, v in dataset.items()], axis=0).tolist(), \
+                 np.concatenate([np.ones((v.shape[0],)) * label_index[k] for k, v in dataset.items()], axis=0).tolist()
+
+x_resampled, y_resampled = rus.fit_resample(x_list, y_list)
+dataset = {"inside": np.array(x_resampled)}
 
 # ## 模型训练
 
@@ -32,10 +40,14 @@ negative_sample_num = None  # None或者一个数字
 world_boundary = np.array([0, 0, 0, 255, 255, 255])
 # 对数据进行预处理
 x = np.concatenate([v for k, v in dataset.items()], axis=0)
-negative_sample_num = int(x.shape[0] * 0.7) if negative_sample_num is None else negative_sample_num
+negative_sample_num = int(x.shape[0] * 1.2) if negative_sample_num is None else negative_sample_num
 
 model = AnonymousColorDetector()
 
-model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7)
+model.fit(x, world_boundary, threshold, negative_sample_size=negative_sample_num, train_size=0.7,
+          is_save_dataset=True, model_selection='dt')
+# data = scipy.io.loadmat('dataset_2022-07-19_15-07.mat')
+# x, y = data['x'], data['y'].ravel()
+# model.fit(x, y=y, is_generate_negative=False, model_selection='dt')
 
 model.save()

main_test.py

@@ -10,15 +10,17 @@ import matplotlib.pyplot as plt
 import numpy as np
 
 from models import Detector, AnonymousColorDetector
+from utils import read_labeled_img
 
 
-def virtual_main(detector: Detector, test_img=None, test_img_dir=None):
+def virtual_main(detector: Detector, test_img=None, test_img_dir=None, test_model=False):
     """
     虚拟读图测试程序
 
     :param detector: 杂质探测器，需要继承Detector类
     :param test_img: 测试图像，rgb格式的图片或者路径
     :param test_img_dir: 测试图像文件夹
+    :param test_model: 是否进行模型约束性测试
     :return:
     """
     if test_img is not None:
@@ -29,8 +31,10 @@ def virtual_main(detector: Detector, test_img=None, test_img_dir=None):
         else:
             raise TypeError("test img should be np.ndarray or str")
         t1 = time.time()
-        result = detector.predict(img)
+        img = cv2.resize(img, (1024, 256))
         t2 = time.time()
+        result = 1 - detector.predict(img)
+        t3 = time.time()
         fig, axs = plt.subplots(3, 1)
         axs[0].imshow(img)
         axs[1].imshow(result)
@@ -38,10 +42,18 @@ def virtual_main(detector: Detector, test_img=None, test_img_dir=None):
         mask_color[result > 0] = (0, 0, 255)
         result_show = cv2.addWeighted(img, 1, mask_color, 0.5, 0)
         axs[2].imshow(result_show)
-        plt.title(f'{(t2 - t1) * 1000:.2f} ms')
+        axs[0].set_title(
+            f' resize {(t2 - t1) * 1000:.2f} ms, predict {(t3 - t2) * 1000:.2f} ms, total {(t3 - t1) * 1000:.2f} ms')
         plt.show()
+    if test_model:
+        data_dir = "data/dataset"
+        color_dict = {(0, 0, 255): "yangeng"}
+        dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
+        ground_truth = dataset['yangeng']
+        world_boundary = np.array([0, 0, 0, 255, 255, 255])
+        detector.visualize(world_boundary, sample_size=50000, class_max_num=5000, ground_truth=ground_truth)
 
 
 if __name__ == '__main__':
-    detector = AnonymousColorDetector(file_path='models/ELM_2022-07-18_17-22.mat')
-    virtual_main(detector, test_img='data/dataset/img/yangeng.bmp')
+    detector = AnonymousColorDetector(file_path='models/dt_2022-07-19_14-38.model')
+    virtual_main(detector, test_img=r'data/dataset/img/yangeng.bmp', test_model=True)

models.py

@@ -4,12 +4,17 @@
 # @File: models.py
 # @Software:PyCharm、
 import datetime
+import pickle
 
 import cv2
 import numpy as np
 import scipy.io
+import tqdm
+from sklearn.tree import DecisionTreeClassifier
 from sklearn.metrics import classification_report
 from sklearn.model_selection import train_test_split
+from utils import lab_scatter, read_labeled_img
+from tqdm import tqdm
 
 from elm import ELM
 
@@ -34,10 +39,12 @@ class Detector(object):
 class AnonymousColorDetector(Detector):
     def __init__(self, file_path: str = None):
         self.model = None
+        self.model_type = 'None'
         if file_path is not None:
-            self.model = ELM(model_path=file_path)
+            self.load(file_path)
 
-    def fit(self, x: np.ndarray, world_boundary: np.ndarray, threshold: float,
+    def fit(self, x: np.ndarray, world_boundary: np.ndarray = None, threshold: float = None,
+            is_generate_negative: bool = True, y: np.ndarray = None, model_selection='elm',
             negative_sample_size: int = 1000, train_size: float = 0.8, is_save_dataset=False, **kwargs):
         """
         拟合到指定的样本分布情况下，根据x进行分布的变化。
@@ -45,23 +52,36 @@ class AnonymousColorDetector(Detector):
         :param x: ndarray类型的正样本数据，给出的正样本形状为 n x feature_num
         :param world_boundary: 整个世界的边界，边界形状为 feature_num个下限, feature_num个上限
         :param threshold: 与正样本之间的距离阈值大于多少则不认为是指定的样本类别
+        :param is_generate_negative: 是否生成负样本
+        :param y: 给出x对应的样本y
+        :param model_selection: 模型的选择, in ['elm', 'decision tree']
         :param negative_sample_size: 负样本的数量
         :param train_size: 训练集的比例, float
         :param is_save_dataset: 是否保存数据集
         :param kwargs: 与模型相对应的参数
         :return:
         """
-        node_num = kwargs.get('node_num', 10)
-        self.model = ELM(input_size=x.shape[1], node_num=node_num, output_num=2, **kwargs)
-        negative_samples = self.generate_negative_samples(x, world_boundary, threshold,
-                                                          sample_size=negative_sample_size)
-        data_x, data_y = np.concatenate([x, negative_samples], axis=0), \
-                         np.concatenate([np.ones(x.shape[0], dtype=int),
-                                         np.zeros(negative_samples.shape[0], dtype=int)], axis=0)
+        if model_selection == 'elm':
+            node_num = kwargs.get('node_num', 10)
+            self.model = ELM(input_size=x.shape[1], node_num=node_num, output_num=2, **kwargs)
+        elif model_selection == 'dt':
+            self.model = DecisionTreeClassifier(**kwargs)
+        else:
+            raise ValueError("你看看我要的是啥")
+        self.model_type = model_selection
+        if is_generate_negative:
+            negative_samples = self.generate_negative_samples(x, world_boundary, threshold,
+                                                              sample_size=negative_sample_size)
+            data_x, data_y = np.concatenate([x, negative_samples], axis=0), \
+                             np.concatenate([np.ones(x.shape[0], dtype=int),
+                                             np.zeros(negative_samples.shape[0], dtype=int)], axis=0)
+        else:
+            data_x, data_y = x, y
         if is_save_dataset:
             path = datetime.datetime.now().strftime("dataset_%Y-%m-%d_%H-%M.mat")
             scipy.io.savemat(path, {'x': data_x, 'y': data_y})
-        x_train, x_val, y_train, y_val = train_test_split(data_x, data_y, train_size=train_size, shuffle=True)
+        x_train, x_val, y_train, y_val = train_test_split(data_x, data_y, train_size=train_size, shuffle=True,
+                                                          stratify=data_y)
         self.model.fit(x_train, y_train)
         y_predict = self.model.predict(x_val)
         print(classification_report(y_true=y_val, y_pred=y_predict))
@@ -88,9 +108,11 @@ class AnonymousColorDetector(Detector):
         :param threshold: 与正样本x之间的距离限制
         :return: 负样本形状为：(sample_size, feature_num)
         """
+
         feature_num = x.shape[1]
         negative_samples = np.zeros((sample_size, feature_num), dtype=x.dtype)
         generated_sample_num = 0
+        bar = tqdm(total=sample_size, ncols=100)
         while generated_sample_num <= sample_size:
             generated_data = np.random.uniform(world_boundary[:feature_num], world_boundary[feature_num:],
                                                size=(sample_size, feature_num))
@@ -100,20 +122,45 @@ class AnonymousColorDetector(Detector):
                 if not in_threshold:
                     negative_samples[sample_idx, :] = sample
                     generated_sample_num += 1
+                    bar.update()
                     if generated_sample_num >= sample_size:
                         break
+        bar.close()
         return negative_samples
 
-    def save(self, file_path=None):
-        self.model.save(file_path)
+    def save(self):
+        path = datetime.datetime.now().strftime(f"{self.model_type}_%Y-%m-%d_%H-%M.model")
+        with open(path, 'wb') as f:
+            pickle.dump((self.model_type, self.model), f)
 
     def load(self, file_path):
-        self.model.load(file_path)
+        with open(file_path, 'rb') as model_file:
+            data = pickle.load(model_file)
+        self.model_type, self.model = data
+
+    def visualize(self, world_boundary: np.ndarray, sample_size: int, ground_truth=None,
+                  **kwargs):
+        feature_num = world_boundary.shape[0] // 2
+        x = np.random.uniform(world_boundary[:feature_num], world_boundary[feature_num:],
+                              size=(sample_size, feature_num))
+        pred_y = self.model.predict(x)
+        draw_dataset = {'Inside': x[pred_y == 1, :], 'Outside': x[pred_y == 0, :]}
+        if ground_truth is not None:
+            draw_dataset.update({'Given': ground_truth})
+        lab_scatter(draw_dataset, is_3d=True, is_ps_color_space=False, **kwargs)
 
 
 if __name__ == '__main__':
-    detector = AnonymousColorDetector()
-    x = np.array([[10, 30, 20], [10, 35, 25], [10, 35, 36]])
-    world_boundary = np.array([0, -127, -127, 100, 127, 127])
-    detector.fit(x, world_boundary, threshold=5, negative_sample_size=2000)
-    detector.load('ELM_2022-07-18_17-01.mat')
+    data_dir = "data/dataset"
+    color_dict = {(0, 0, 255): "yangeng"}
+    dataset = read_labeled_img(data_dir, color_dict=color_dict, is_ps_color_space=False)
+    ground_truth = dataset['yangeng']
+    detector = AnonymousColorDetector(file_path='models/dt_2022-07-19_14-38.model')
+    # x = np.array([[10, 30, 20], [10, 35, 25], [10, 35, 36]])
+    world_boundary = np.array([0, 0, 0, 255, 255, 255])
+    # detector.fit(x, world_boundary, threshold=5, negative_sample_size=2000)
+    detector.visualize(world_boundary, sample_size=50000, class_max_num=5000, ground_truth=ground_truth)
+    data = scipy.io.loadmat('data/dataset_2022-07-19_11-35.mat')
+    x, y = data['x'], data['y']
+    dataset = {'inside': x[y.ravel() == 1, :], "outside": x[y.ravel() == 0, :]}
+    lab_scatter(dataset, class_max_num=5000, is_3d=True, is_ps_color_space=False)

utils.py

@@ -55,7 +55,7 @@ def read_labeled_img(dataset_dir: str, color_dict: dict, ext='.bmp', is_ps_color
     return total_dataset
 
 
-def lab_scatter(dataset: dict, class_max_num=None, is_3d=False, is_ps_color_space=True):
+def lab_scatter(dataset: dict, class_max_num=None, is_3d=False, is_ps_color_space=True, **kwargs):
     """
     在lab色彩空间内绘制3维数据分布情况