diff --git a/ppdet/data/transform/operators.py b/ppdet/data/transform/operators.py index dbe8a0c3fffbd4e1cc4da8714ddb104dfdcb6c83..8b3a479991889275e6185cc108c573ac9883de99 100644 --- a/ppdet/data/transform/operators.py +++ b/ppdet/data/transform/operators.py @@ -1064,10 +1064,16 @@ class MixupImage(BaseOperator): gt_score2 = sample['mixup']['gt_score'] gt_score = np.concatenate( (gt_score1 * factor, gt_score2 * (1. - factor)), axis=0) + + is_crowd1 = sample['is_crowd'] + is_crowd2 = sample['mixup']['is_crowd'] + is_crowd = np.concatenate((is_crowd1, is_crowd2), axis=0) + sample['image'] = im sample['gt_bbox'] = gt_bbox sample['gt_score'] = gt_score sample['gt_class'] = gt_class + sample['is_crowd'] = is_crowd sample['h'] = im.shape[0] sample['w'] = im.shape[1] sample.pop('mixup') @@ -1298,9 +1304,14 @@ class RandomExpand(BaseOperator): ratio (float): maximum expansion ratio. prob (float): probability to expand. fill_value (list): color value used to fill the canvas. in RGB order. + is_mask_expand(bool): whether expand the segmentation. """ - def __init__(self, ratio=4., prob=0.5, fill_value=(127.5, ) * 3): + def __init__(self, + ratio=4., + prob=0.5, + fill_value=(127.5, ) * 3, + is_mask_expand=False): super(RandomExpand, self).__init__() assert ratio > 1.01, "expand ratio must be larger than 1.01" self.ratio = ratio @@ -1312,6 +1323,7 @@ class RandomExpand(BaseOperator): if not isinstance(fill_value, tuple): fill_value = tuple(fill_value) self.fill_value = fill_value + self.is_mask_expand = is_mask_expand def expand_segms(self, segms, x, y, height, width, ratio): def _expand_poly(poly, x, y): @@ -1369,7 +1381,8 @@ class RandomExpand(BaseOperator): sample['image'] = canvas if 'gt_bbox' in sample and len(sample['gt_bbox']) > 0: sample['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32) - if 'gt_poly' in sample and len(sample['gt_poly']) > 0: + if self.is_mask_expand and 'gt_poly' in sample and len(sample[ + 'gt_poly']) > 0: sample['gt_poly'] = self.expand_segms(sample['gt_poly'], x, y, height, width, expand_ratio) return sample @@ -1388,6 +1401,7 @@ class RandomCrop(BaseOperator): num_attempts (int): number of tries before giving up. allow_no_crop (bool): allow return without actually cropping them. cover_all_box (bool): ensure all bboxes are covered in the final crop. + is_mask_crop(bool): whether crop the segmentation. """ def __init__(self, @@ -1396,7 +1410,8 @@ class RandomCrop(BaseOperator): scaling=[.3, 1.], num_attempts=50, allow_no_crop=True, - cover_all_box=False): + cover_all_box=False, + is_mask_crop=False): super(RandomCrop, self).__init__() self.aspect_ratio = aspect_ratio self.thresholds = thresholds @@ -1404,6 +1419,7 @@ class RandomCrop(BaseOperator): self.num_attempts = num_attempts self.allow_no_crop = allow_no_crop self.cover_all_box = cover_all_box + self.is_mask_crop = is_mask_crop def crop_segms(self, segms, valid_ids, crop, height, width): def _crop_poly(segm, crop): @@ -1527,7 +1543,8 @@ class RandomCrop(BaseOperator): break if found: - if 'gt_poly' in sample and len(sample['gt_poly']) > 0: + if self.is_mask_crop and 'gt_poly' in sample and len(sample[ + 'gt_poly']) > 0: crop_polys = self.crop_segms( sample['gt_poly'], valid_ids, diff --git a/requirements.txt b/requirements.txt index 3509e65abdfee3a24e31f5450c371c5d5e909633..c7d9d8093ad476856dcf643c08b10f8c5f252c30 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,4 @@ tb-paddle tensorboard >= 1.15 cython pycocotools +shapely