support addding background images for detection training

paddlex/cv/datasets/coco.py

@@ -128,7 +128,6 @@ class CocoDetection(VOCDetection):
 
             coco_rec = (im_info, label_info)
             self.file_list.append([im_fname, coco_rec])
-
         if not len(self.file_list) > 0:
             raise Exception('not found any coco record in %s' % (ann_file))
         logging.info("{} samples in file {}".format(

paddlex/cv/datasets/voc.py

@@ -14,6 +14,7 @@
 
 from __future__ import absolute_import
 import copy
+import os
 import os.path as osp
 import random
 import numpy as np
@@ -170,6 +171,43 @@ class VOCDetection(Dataset):
         self.coco_gt.dataset = annotations
         self.coco_gt.createIndex()
 
+    def append_backgrounds(self, image_dir):
+        import cv2
+        if not osp.exists(image_dir):
+           raise Exception("{} background images directory does not exist.".format(image_dir))
+        image_list = os.listdir(image_dir)
+        max_img_id = max(self.coco_gt.getImgIds())
+        for image in image_list:
+            if not is_pic(image):
+                continue
+            # False ground truth
+            gt_bbox = np.array([[0, 0, 1e-05, 1e-05]], dtype=np.float32)
+            gt_class = np.array([[0]], dtype=np.int32)
+            gt_score = np.ones((1, 1), dtype=np.float32)
+            is_crowd = np.array([[0]], dtype=np.int32)
+            difficult = np.zeros((1, 1), dtype=np.int32)
+            gt_poly = [[[0, 0, 0, 1e-05, 1e-05, 1e-05, 1e-05, 0]]]
+            
+            max_img_id += 1
+            im_fname = osp.join(image_dir, image)
+            img_data = cv2.imread(im_fname)
+            im_h, im_w, im_c = img_data.shape
+            im_info = {
+                'im_id': np.array([max_img_id]).astype('int32'),
+                'image_shape': np.array([im_h, im_w]).astype('int32'),
+            }
+            label_info = {
+                'is_crowd': is_crowd,
+                'gt_class': gt_class,
+                'gt_bbox': gt_bbox,
+                'gt_score': gt_score,
+                'difficult': difficult,
+                'gt_poly': gt_poly
+            }
+            coco_rec = (im_info, label_info)
+            self.file_list.append([im_fname, coco_rec])
+        self.num_samples = len(self.file_list)
+
     def iterator(self):
         self._epoch += 1
         self._pos = 0

paddlex/cv/nets/detection/mask_rcnn.py

@@ -204,7 +204,7 @@ class MaskRCNN(object):
                 bg_thresh_hi=self.bg_thresh_hi,
                 bg_thresh_lo=self.bg_thresh_lo,
                 bbox_reg_weights=self.bbox_reg_weights,
-                calass_nums=self.num_classes,
+                class_nums=self.num_classes,
                 use_random=self.rpn_head.use_random)
 
             rois = outputs[0]

paddlex/cv/transforms/det_transforms.py

@@ -722,22 +722,38 @@ class MixupImage(DetTransform):
                             'Becasuse gt_bbox/gt_class/gt_score is not in label_info!')
         gt_bbox1 = label_info['gt_bbox']
         gt_bbox2 = im_info['mixup'][2]['gt_bbox']
-        gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
         gt_class1 = label_info['gt_class']
         gt_class2 = im_info['mixup'][2]['gt_class']
-        gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
-
         gt_score1 = label_info['gt_score']
         gt_score2 = im_info['mixup'][2]['gt_score']
-        gt_score = np.concatenate(
-            (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
         if 'gt_poly' in label_info:
             gt_poly1 = label_info['gt_poly']
             gt_poly2 = im_info['mixup'][2]['gt_poly']
-            label_info['gt_poly'] = gt_poly1 + gt_poly2
         is_crowd1 = label_info['is_crowd']
         is_crowd2 = im_info['mixup'][2]['is_crowd']
-        is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0)
+        
+        if 0 not in gt_class1 and 0 not in gt_class2:
+            gt_bbox = np.concatenate((gt_bbox1, gt_bbox2), axis=0)
+            gt_class = np.concatenate((gt_class1, gt_class2), axis=0)
+            gt_score = np.concatenate(
+                (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0)
+            if 'gt_poly' in label_info:
+                label_info['gt_poly'] = gt_poly1 + gt_poly2
+            is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0)
+        elif 0 in gt_class1:
+            gt_bbox = gt_bbox2
+            gt_class = gt_class2
+            gt_score = gt_score2 * (1. - factor)
+            if 'gt_poly' in label_info:
+                label_info['gt_poly'] = gt_poly2
+            is_crowd = is_crowd2
+        else:
+            gt_bbox = gt_bbox1
+            gt_class = gt_class1
+            gt_score = gt_score1 * factor
+            if 'gt_poly' in label_info:
+                label_info['gt_poly'] = gt_poly1
+            is_crowd = is_crowd1
         label_info['gt_bbox'] = gt_bbox
         label_info['gt_score'] = gt_score
         label_info['gt_class'] = gt_class
@@ -809,6 +825,8 @@ class RandomExpand(DetTransform):
         if np.random.uniform(0., 1.) < self.prob:
             return (im, im_info, label_info)
 
+        if 'gt_class' in label_info and 0 in label_info['gt_class']:
+            return (im, im_info, label_info)
         image_shape = im_info['image_shape']
         height = int(image_shape[0])
         width = int(image_shape[1])
@@ -904,6 +922,8 @@ class RandomCrop(DetTransform):
 
         if len(label_info['gt_bbox']) == 0:
             return (im, im_info, label_info)
+        if 'gt_class' in label_info and 0 in label_info['gt_class']:
+            return (im, im_info, label_info)
 
         image_shape = im_info['image_shape']
         w = image_shape[1]
@@ -1199,9 +1219,10 @@ class ArrangeYOLOv3(DetTransform):
             if gt_num > 0:
                 label_info['gt_class'][:gt_num, 0] = label_info[
                     'gt_class'][:gt_num, 0] - 1
-                gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
-                gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
-                gt_score[:gt_num] = label_info['gt_score'][:gt_num, 0]
+                if -1 not in label_info['gt_class']:
+                    gt_bbox[:gt_num, :] = label_info['gt_bbox'][:gt_num, :]
+                    gt_class[:gt_num] = label_info['gt_class'][:gt_num, 0]
+                    gt_score[:gt_num] = label_info['gt_score'][:gt_num, 0]
             # parse [x1, y1, x2, y2] to [x, y, w, h]
             gt_bbox[:, 2:4] = gt_bbox[:, 2:4] - gt_bbox[:, :2]
             gt_bbox[:, :2] = gt_bbox[:, :2] + gt_bbox[:, 2:4] / 2.