diff --git a/gen_anchors.py b/gen_anchors.py
index bb3b45a8a..e1c172b13 100644
--- a/gen_anchors.py
+++ b/gen_anchors.py
@@ -1,8 +1,9 @@
+import sys
 import random
 import argparse
 import numpy as np
 
-from voc import parse_voc_annotation
+from voc import parse_voc_annotation, parse_boxpoints_annotation
 import json
 
 def IOU(ann, centroids):
@@ -86,26 +87,56 @@ def run_kmeans(ann_dims, anchor_num):
 
 def _main_(argv):
     config_path = args.conf
-    num_anchors = args.anchors
+    num_anchors = int(args.anchors)
+    xywh = not args.xy
 
     with open(config_path) as config_buffer:
         config = json.loads(config_buffer.read())
 
-    train_imgs, train_labels = parse_voc_annotation(
-        config['train']['train_annot_folder'],
-        config['train']['train_image_folder'],
-        config['train']['cache_name'],
-        config['model']['labels']
-    )
+    if xywh:
+        print('xywh format parser')
+        train_imgs, train_labels = parse_voc_annotation(
+            config['train']['train_annot_folder'],
+            config['train']['train_image_folder'],
+            config['train']['cache_name'],
+            config['model']['labels']
+        )
+    else:
+        print('boxpoints format parser')
+        train_imgs, train_labels = parse_boxpoints_annotation(
+            config['train']['train_annot_folder'],
+            config['train']['train_image_folder'],
+            config['train']['cache_name'],
+            config['model']['labels']
+        )
 
     # run k_mean to find the anchors
     annotation_dims = []
+    print(len(train_imgs))
+    if len(train_imgs) < 1:
+        print('empty train_imgs')
+        sys.exit()
     for image in train_imgs:
         print(image['filename'])
-        for obj in image['object']:
-            relative_w = (float(obj['xmax']) - float(obj['xmin']))/image['width']
-            relatice_h = (float(obj["ymax"]) - float(obj['ymin']))/image['height']
-            annotation_dims.append(tuple(map(float, (relative_w,relatice_h))))
+        if xywh:
+             for image in train_imgs:
+                print(image['filename'])
+                for obj in image['object']:
+                    relative_w = (float(obj['xmax']) - float(obj['xmin']))/image['width']
+                    relatice_h = (float(obj["ymax"]) - float(obj['ymin']))/image['height']
+                    annotation_dims.append(tuple(map(float, (relative_w,relatice_h))))
+        else:
+            for image in train_imgs:
+                print(image['filename'])
+                for obj in image['object']:
+                    # numpy.linalg.norm(a-b)
+                    relative_w = float(np.linalg.norm(
+                        np.array((float(obj["x1"]),float(obj["y1"])))-np.array((float(obj["x2"]),float(obj["y2"])))
+                        ))/image['width']
+                    relatice_h = float(np.linalg.norm(
+                        np.array((float(obj["x1"]),float(obj["y1"])))-np.array((float(obj["x3"]),float(obj["y3"])))
+                        ))/image['height']
+                    annotation_dims.append(tuple(map(float, (relative_w,relatice_h))))
 
     annotation_dims = np.array(annotation_dims)
     centroids = run_kmeans(annotation_dims, num_anchors)
@@ -127,6 +158,8 @@ def _main_(argv):
         '--anchors',
         default=9,
         help='number of anchors to use')
-
+    argparser.add_argument(
+        '-xy',
+        action="store_true")
     args = argparser.parse_args()
     _main_(args)
diff --git a/voc.py b/voc.py
index f51e5fd4a..0d0748140 100644
--- a/voc.py
+++ b/voc.py
@@ -64,4 +64,81 @@ def parse_voc_annotation(ann_dir, img_dir, cache_name, labels=[]):
         with open(cache_name, 'wb') as handle:
             pickle.dump(cache, handle, protocol=pickle.HIGHEST_PROTOCOL)    
                         
+    return all_insts, seen_labels
+
+def parse_boxpoints_annotation(ann_dir, img_dir, cache_name, labels=[]):
+    if os.path.exists(cache_name):
+        with open(cache_name, 'rb') as handle:
+            cache = pickle.load(handle)
+        all_insts, seen_labels = cache['all_insts'], cache['seen_labels']
+    else:
+        all_insts = []
+        seen_labels = {}
+        
+        for ann in sorted(os.listdir(ann_dir)):
+            img = {'object':[]}
+
+            try:
+                tree = ET.parse(ann_dir + ann)
+            except Exception as e:
+                print(e)
+                print('Ignore this bad annotation: ' + ann_dir + ann)
+                continue
+            
+            for elem in tree.iter():
+                if 'filename' in elem.tag:
+                    img['filename'] = img_dir + elem.text
+                if 'width' in elem.tag:
+                    img['width'] = int(elem.text)
+                if 'height' in elem.tag:
+                    img['height'] = int(elem.text)
+                if 'object' in elem.tag or 'part' in elem.tag:
+                    obj = {}
+                    
+                    for attr in list(elem):
+                        if 'name' in attr.tag:
+                            obj['name'] = attr.text
+
+                            if obj['name'] in seen_labels:
+                                seen_labels[obj['name']] += 1
+                            else:
+                                seen_labels[obj['name']] = 1
+                            
+                            if len(labels) > 0 and obj['name'] not in labels:
+                                break
+                            else:
+                                img['object'] += [obj]
+                                
+                        if 'bndbox' in attr.tag:
+                            for dim in list(attr):
+                                if 'x0' in dim.tag:
+                                    obj['x0'] = int(round(float(dim.text)))
+                                else:
+                                    if 'x4' in dim.tag:
+                                        obj['x4'] = int(round(float(dim.text)))
+                                if 'y0' in dim.tag:
+                                    obj['y0'] = int(round(float(dim.text)))
+                                else:
+                                    if 'y4' in dim.tag:
+                                        obj['y4'] = int(round(float(dim.text)))
+                                if 'x1' in dim.tag:
+                                    obj['x1'] = int(round(float(dim.text)))
+                                if 'y1' in dim.tag:
+                                    obj['y1'] = int(round(float(dim.text)))
+                                if 'x2' in dim.tag:
+                                    obj['x2'] = int(round(float(dim.text)))
+                                if 'y2' in dim.tag:
+                                    obj['y2'] = int(round(float(dim.text)))
+                                if 'x3' in dim.tag:
+                                    obj['x3'] = int(round(float(dim.text)))
+                                if 'y3' in dim.tag:
+                                    obj['y3'] = int(round(float(dim.text)))
+
+            if len(img['object']) > 0:
+                all_insts += [img]
+
+        cache = {'all_insts': all_insts, 'seen_labels': seen_labels}
+        with open(cache_name, 'wb') as handle:
+            pickle.dump(cache, handle, protocol=pickle.HIGHEST_PROTOCOL)    
+                        
     return all_insts, seen_labels
\ No newline at end of file