diff --git a/simpledatasetloader.py b/simpledatasetloader.py
new file mode 100644
index 0000000..bb64fad
--- /dev/null
+++ b/simpledatasetloader.py
@@ -0,0 +1,46 @@
+import numpy as np
+import cv2
+import os
+
+class SimplePreprocessor:
+    def __init__(self, width, height, inter=cv2.INTER_AREA):
+        self.width = width
+        self.height = height
+        self.inter = inter
+    def preprocess(self, image):
+        return cv2.resize(image, (self.width, self.height), interpolation=self.inter)
+
+class SimpleDatasetLoader:
+    def __init__(self, preprocessors=None):
+        self.preprocessors = preprocessors
+
+        if self.preprocessors is None:
+            self.preprocessors = []
+
+    def load(self, imagePaths, verbose=-1):
+        data = []
+        labels = []
+        for (i, imagePath) in enumerate(imagePaths):
+            image = cv2.imread(imagePath)
+            label = imagePath.split(os.path.sep)[-2]
+            if self.preprocessors is not None:
+                for p in self.preprocessors:
+                    if(image is None):
+                        print(i)
+                        os.remove(imagePaths[i])
+                        print('file: ')
+                        print(imagePaths[i])
+                        print('is removed.')
+                        continue
+                    image = p.preprocess(image)
+            data.append(image)
+            labels.append(label)
+            if verbose > 0 and i > 0 and (i + 1 ) %verbose == 0:
+                print('[INFO] processed {}/{}'.format( i +1, len(imagePaths)))
+
+        return (np.array(data), np.array(labels))
+
+if __name__ == '__main__':
+    imagePaths = 'D:/python/LearningMaterial/pet_sample/'
+    sp = SimplePreprocessor(32, 32)
+    sdl = SimpleDatasetLoader(preprocessors=[sp])