模型测试

.gitignore

@@ -3,7 +3,7 @@
 # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
 
 data/*
-
+models/*
 # User-specific stuff
 .idea/**/workspace.xml
 .idea/**/tasks.xml

elm.py

@@ -36,10 +36,16 @@ class ELM:
         :param bias: [array] shape: 1 x node_num
         :param beta: [array] shape: node_num, output_num
         :param rand_seed: [int] the random seed
+        :param model_path: [str] the trained model path
         """
         if rand_seed is not None:
             np.random.seed(rand_seed)
 
+        if 'model_path' in kwargs:
+            data = scipy.io.loadmat(kwargs['model_path'])
+            self.w, self.b, self.beta = data['w'], data['b'], data['beta']
+            return
+
         if weight is not None:
             self.w = weight
         else:

main_test.py

@@ -0,0 +1,47 @@
+# -*- codeing = utf-8 -*-
+# Time : 2022/7/19 10:49
+# @Auther : zhouchao
+# @File: main_test.py
+# @Software:PyCharm
+import time
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from models import Detector, AnonymousColorDetector
+
+
+def virtual_main(detector: Detector, test_img=None, test_img_dir=None):
+    """
+    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
+    :return:
+    """
+    if test_img is not None:
+        if isinstance(test_img, str):
+            img = cv2.imread(test_img)[:, :, ::-1]
+        elif isinstance(test_img, np.ndarray):
+            img = test_img
+        else:
+            raise TypeError("test img should be np.ndarray or str")
+        t1 = time.time()
+        result = detector.predict(img)
+        t2 = time.time()
+        fig, axs = plt.subplots(3, 1)
+        axs[0].imshow(img)
+        axs[1].imshow(result)
+        mask_color = np.zeros_like(img)
+        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')
+        plt.show()
+
+
+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')

models.py

@@ -3,32 +3,54 @@
 # @Auther : zhouchao
 # @File: models.py
 # @Software:PyCharm、
+import datetime
+
+import cv2
 import numpy as np
+import scipy.io
 from sklearn.metrics import classification_report
 from sklearn.model_selection import train_test_split
 
 from elm import ELM
 
 
-class AnonymousColorDetector(object):
+class Detector(object):
-    def __init__(self, file_path=None):
+    def __int__(self, *args, **kwargs):
-        self.model = None
+        raise NotImplementedError
 
-    def fit(self, x: np.ndarray, world_boundary: np.ndarray, threshold: float, model_selected: str = 'elm',
+    def predict(self, *args, **kwargs):
-            negative_sample_size: int = 1000, train_size: float = 0.8, **kwargs):
+        raise NotImplementedError
+
+    def load(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def save(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def fit(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class AnonymousColorDetector(Detector):
+    def __init__(self, file_path: str = None):
+        self.model = None
+        if file_path is not None:
+            self.model = ELM(model_path=file_path)
+
+    def fit(self, x: np.ndarray, world_boundary: np.ndarray, threshold: float,
+            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。
 
         :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 model_selected: 选择模型，默认为elm
         :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:
         """
-        assert model_selected in ['elm']
-        if model_selected == 'elm':
         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,
@@ -36,14 +58,25 @@ class AnonymousColorDetector(object):
         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 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)
-
         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))
 
     def predict(self, x):
-        return self.model.predict(x)
+        """
+        输入rgb彩色图像
+
+        :param x: rgb彩色图像,np.ndarray
+        :return:
+        """
+        w, h = x.shape[1], x.shape[0]
+        x = cv2.cvtColor(x, cv2.COLOR_RGB2LAB)
+        result = self.model.predict(x.reshape(w * h, -1))
+        return result.reshape(h, w)
 
     @staticmethod
     def generate_negative_samples(x: np.ndarray, world_boundary: np.ndarray, threshold: float, sample_size: int):