--- a
+++ b/FastRCNN/utils/DefectDataset.py
@@ -0,0 +1,131 @@
+import numpy as np
+import os
+from skimage import exposure, filters
+import chainer
+from chainercv import utils
+from chainercv import transforms
+import warnings
+
+root = './Data/'
+root2 = './Data/'
+
+class DetectionDataset(chainer.dataset.DatasetMixin):
+    """Base class for defect defection dataset
+    """
+
+    def __init__(self, data_dir='auto', split='', img_size=1024, resize=False):
+        if data_dir == 'auto':
+            data_dir = root
+        self.data_dir = data_dir
+        self.img_size = img_size
+        self.resize = resize
+        images_file = os.path.join(self.data_dir, '{}images.txt'.format(split))
+        self.images = [line.strip() for line in open(images_file)]
+    
+    def obtain_image_name(self,i):
+        #print(i)
+        return self.images[i]
+
+    def __len__(self):
+        return len(self.images)
+
+    def get_example(self, i):
+        """Returns the i-th example.
+
+        Args:
+            i (int): The index of the example.
+
+        Returns:
+            tuple of an image and its label.
+            The image is in CHW format and its color channel is ordered in
+            RGB.
+            a bounding box is appended to the returned value.
+        """
+        img = utils.read_image(
+            os.path.join(self.data_dir, 'images', self.images[i]),
+            color=True)
+        # Add processing to the other two channels
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            img[1, :, :] = exposure.rescale_intensity(exposure.equalize_adapthist(
+                exposure.rescale_intensity(img[1, :, :])), out_range=(0, 255))
+            img[2, :, :] = exposure.rescale_intensity(filters.gaussian(
+                exposure.rescale_intensity(img[2, :, :])), out_range=(0, 255))
+
+        # bbs should be a matrix (m by 4). m is the number of bounding
+        # boxes in the image
+        # labels should be an integer array (m by 1). m is the same as the bbs
+
+        bbs_file = os.path.join(self.data_dir, 'bounding_boxes', self.images[i][0:-4]+'.txt')
+        
+        bbs = np.stack([line.strip().split() for line in open(bbs_file)]).astype(np.float32)
+        label = np.stack([0]*bbs.shape[0]).astype(np.int32)
+
+        _, H, W = img.shape
+        if self.resize and (H != self.img_size or W != self.img_size):
+            img = transforms.resize(img, (self.img_size, self.img_size))
+            bbs = transforms.resize_bbox(bbs, (H, W), (self.img_size, self.img_size))
+
+        return img, bbs, label
+
+class MultiDetectionDataset(chainer.dataset.DatasetMixin):
+    """Base class for multi defect defection dataset
+    """
+
+    def __init__(self, data_dir='auto', split='', img_size=1024, resize=False):
+        if data_dir == 'auto':
+            data_dir = root2
+        self.data_dir = data_dir
+        self.img_size = img_size
+        self.resize = resize
+        images_file = os.path.join(self.data_dir, '{}images.txt'.format(split))
+        self.images = [
+            line.strip() for line in open(images_file)]
+
+    def __len__(self):
+        return len(self.images)
+
+    def get_example(self, i):
+        """Returns the i-th example.
+
+        Args:
+            i (int): The index of the example.
+
+        Returns:
+            tuple of an image and its label.
+            The image is in CHW format and its color channel is ordered in
+            RGB.
+            a bounding box is appended to the returned value.
+        """
+        img = utils.read_image(
+            os.path.join(self.data_dir, 'images', self.images[i]),
+            color=True)
+        # Add processing to the other two channels
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            img[1, :, :] = exposure.rescale_intensity(exposure.equalize_adapthist(
+                exposure.rescale_intensity(img[1, :, :])), out_range=(0, 255))
+            img[2, :, :] = exposure.rescale_intensity(filters.gaussian(
+                exposure.rescale_intensity(img[2, :, :])), out_range=(0, 255))
+
+        # bbs should be a matrix (m by 4). m is the number of bounding
+        # boxes in the image
+        # labels should be an integer array (m by 1). m is the same as the bbs
+
+        bbs_file = os.path.join(self.data_dir, 'bounding_boxes', self.images[i][0:-4]+'.txt')
+        print(bbs_file)
+        #bbs_file = "./Data/bounding_boxes/a.txt"
+        label_bbs = np.loadtxt(bbs_file, dtype=np.float32)
+        print(label_bbs)
+        # only 1D for medical case
+        #label = label_bbs[:,0].astype(np.int32)
+        #bbs = label_bbs[:,1:5]
+        label = label_bbs[0].astype(np.int32)
+        bbs = label_bbs[1:5]
+
+        _, H, W = img.shape
+        if self.resize and (H != self.img_size or W != self.img_size):
+            img = transforms.resize(img, (self.img_size, self.img_size))
+            bbs = transforms.resize_bbox(bbs, (H, W), (self.img_size, self.img_size))
+
+        return img, bbs, label