diff --git a/paddlex/cv/datasets/coco.py b/paddlex/cv/datasets/coco.py index 0a1cfb90d34c086e85ed5e9ced40509e29607599..e2d3ac4e7d4525071e884bdbdea157b406ecd86e 100644 --- a/paddlex/cv/datasets/coco.py +++ b/paddlex/cv/datasets/coco.py @@ -112,7 +112,7 @@ class CocoDetection(VOCDetection): im_info = { 'im_id': np.array([img_id]).astype('int32'), - 'origin_shape': np.array([im_h, im_w]).astype('int32'), + 'image_shape': np.array([im_h, im_w]).astype('int32'), } label_info = { 'is_crowd': is_crowd, diff --git a/paddlex/cv/datasets/easydata_det.py b/paddlex/cv/datasets/easydata_det.py index 1525c336d5d404846c4c9e300d751992c6dae3eb..173720f0d5df56eb553b5e4a828962e0554403ad 100644 --- a/paddlex/cv/datasets/easydata_det.py +++ b/paddlex/cv/datasets/easydata_det.py @@ -143,7 +143,7 @@ class EasyDataDet(VOCDetection): ann_ct += 1 im_info = { 'im_id': im_id, - 'origin_shape': np.array([im_h, im_w]).astype('int32'), + 'image_shape': np.array([im_h, im_w]).astype('int32'), } label_info = { 'is_crowd': is_crowd, diff --git a/paddlex/cv/datasets/voc.py b/paddlex/cv/datasets/voc.py index 588015ca1e306024c406306d4203466a49cc89b7..3a4c9ae9e4868adb24e1ae8ca24f978bdc406e98 100644 --- a/paddlex/cv/datasets/voc.py +++ b/paddlex/cv/datasets/voc.py @@ -146,7 +146,7 @@ class VOCDetection(Dataset): im_info = { 'im_id': im_id, - 'origin_shape': np.array([im_h, im_w]).astype('int32'), + 'image_shape': np.array([im_h, im_w]).astype('int32'), } label_info = { 'is_crowd': is_crowd, diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index 2914fdb42a027860be30f661ed3d39fb3f75fe46..f4aff4ee8de3382ff2911b2959e5808f5191e585 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -58,8 +58,8 @@ class Compose: im (str/np.ndarray): 图像路径/图像np.ndarray数据。 im_info (dict): 存储与图像相关的信息，dict中的字段如下： - im_id (np.ndarray): 图像序列号，形状为(1,)。 - - origin_shape (np.ndarray): 图像原始大小，形状为(2,)， - origin_shape[0]为高，origin_shape[1]为宽。 + - image_shape (np.ndarray): 图像原始大小，形状为(2,)， + image_shape[0]为高，image_shape[1]为宽。 - mixup (list): list为[im, im_info, label_info]，分别对应 与当前图像进行mixup的图像np.ndarray数据、图像相关信息、标注框相关信息； 注意，当前epoch若无需进行mixup，则无该字段。 @@ -93,9 +93,6 @@ class Compose: # make default im_info with [h, w, 1] im_info['im_resize_info'] = np.array( [im.shape[0], im.shape[1], 1.], dtype=np.float32) - # copy augment_shape from origin_shape - im_info['augment_shape'] = np.array([im.shape[0], - im.shape[1]]).astype('int32') if not self.use_mixup: if 'mixup' in im_info: del im_info['mixup'] @@ -387,16 +384,13 @@ class RandomHorizontalFlip: raise TypeError( 'Cannot do RandomHorizontalFlip! ' + 'Becasuse the im_info and label_info can not be None!') - if 'augment_shape' not in im_info: - raise TypeError('Cannot do RandomHorizontalFlip! ' + \ - 'Becasuse augment_shape is not in im_info!') if 'gt_bbox' not in label_info: raise TypeError('Cannot do RandomHorizontalFlip! ' + \ 'Becasuse gt_bbox is not in label_info!') - augment_shape = im_info['augment_shape'] + image_shape = im_info['image_shape'] gt_bbox = label_info['gt_bbox'] - height = augment_shape[0] - width = augment_shape[1] + height = image_shape[0] + width = image_shape[1] if np.random.uniform(0, 1) < self.prob: im = horizontal_flip(im) @@ -567,7 +561,7 @@ class MixupImage: （2）拼接原图像标注框和mixup图像标注框。 （3）拼接原图像标注框类别和mixup图像标注框类别。 （4）原图像标注框混合得分乘以factor，mixup图像标注框混合得分乘以(1-factor)，叠加2个结果。 - 3. 更新im_info中的augment_shape信息。 + 3. 更新im_info中的image_shape信息。 Args: alpha (float): 随机beta分布的下限。默认为1.5。 @@ -610,7 +604,7 @@ class MixupImage: 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、 存储与标注框相关信息的字典。 其中，im_info更新字段为： - - augment_shape (np.ndarray): mixup后的图像高、宽二者组成的np.ndarray，形状为(2,)。 + - image_shape (np.ndarray): mixup后的图像高、宽二者组成的np.ndarray，形状为(2,)。 im_info删除的字段： - mixup (list): 与当前字段进行mixup的图像相关信息。 label_info更新字段为： @@ -674,8 +668,8 @@ class MixupImage: label_info['gt_score'] = gt_score label_info['gt_class'] = gt_class label_info['is_crowd'] = is_crowd - im_info['augment_shape'] = np.array([im.shape[0], - im.shape[1]]).astype('int32') + im_info['image_shape'] = np.array([im.shape[0], + im.shape[1]]).astype('int32') im_info.pop('mixup') if label_info is None: return (im, im_info) @@ -721,7 +715,7 @@ class RandomExpand: 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、 存储与标注框相关信息的字典。 其中，im_info更新字段为： - - augment_shape (np.ndarray): 扩张后的图像高、宽二者组成的np.ndarray，形状为(2,)。 + - image_shape (np.ndarray): 扩张后的图像高、宽二者组成的np.ndarray，形状为(2,)。 label_info更新字段为： - gt_bbox (np.ndarray): 随机扩张后真实标注框坐标，形状为(n, 4)， 其中n代表真实标注框的个数。 @@ -734,9 +728,6 @@ class RandomExpand: raise TypeError( 'Cannot do RandomExpand! ' + 'Becasuse the im_info and label_info can not be None!') - if 'augment_shape' not in im_info: - raise TypeError('Cannot do RandomExpand! ' + \ - 'Becasuse augment_shape is not in im_info!') if 'gt_bbox' not in label_info or \ 'gt_class' not in label_info: raise TypeError('Cannot do RandomExpand! ' + \ @@ -744,9 +735,9 @@ class RandomExpand: if np.random.uniform(0., 1.) < self.prob: return (im, im_info, label_info) - augment_shape = im_info['augment_shape'] - height = int(augment_shape[0]) - width = int(augment_shape[1]) + image_shape = im_info['image_shape'] + height = int(image_shape[0]) + width = int(image_shape[1]) expand_ratio = np.random.uniform(1., self.ratio) h = int(height * expand_ratio) @@ -759,7 +750,7 @@ class RandomExpand: canvas *= np.array(self.fill_value, dtype=np.float32) canvas[y:y + height, x:x + width, :] = im - im_info['augment_shape'] = np.array([h, w]).astype('int32') + im_info['image_shape'] = np.array([h, w]).astype('int32') if 'gt_bbox' in label_info and len(label_info['gt_bbox']) > 0: label_info['gt_bbox'] += np.array([x, y] * 2, dtype=np.float32) if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0: @@ -815,12 +806,14 @@ class RandomCrop: tuple: 当label_info为空时，返回的tuple为(im, im_info)，分别对应图像np.ndarray数据、存储与图像相关信息的字典； 当label_info不为空时，返回的tuple为(im, im_info, label_info)，分别对应图像np.ndarray数据、 存储与标注框相关信息的字典。 - 其中，label_info更新字段为： - - gt_bbox (np.ndarray): 随机裁剪后真实标注框坐标，形状为(n, 4)， + 其中，im_info更新字段为： + - image_shape (np.ndarray): 扩裁剪的图像高、宽二者组成的np.ndarray，形状为(2,)。 + label_info更新字段为： + - gt_bbox (np.ndarray): 随机裁剪后真实标注框坐标，形状为(n, 4)， 其中n代表真实标注框的个数。 - - gt_class (np.ndarray): 随机裁剪后每个真实标注框对应的类别序号，形状为(n, 1)， + - gt_class (np.ndarray): 随机裁剪后每个真实标注框对应的类别序号，形状为(n, 1)， 其中n代表真实标注框的个数。 - - gt_score (np.ndarray): 随机裁剪后每个真实标注框对应的混合得分，形状为(n, 1)， + - gt_score (np.ndarray): 随机裁剪后每个真实标注框对应的混合得分，形状为(n, 1)， 其中n代表真实标注框的个数。 Raises: @@ -830,9 +823,6 @@ class RandomCrop: raise TypeError( 'Cannot do RandomCrop! ' + 'Becasuse the im_info and label_info can not be None!') - if 'augment_shape' not in im_info: - raise TypeError('Cannot do RandomCrop! ' + \ - 'Becasuse augment_shape is not in im_info!') if 'gt_bbox' not in label_info or \ 'gt_class' not in label_info: raise TypeError('Cannot do RandomCrop! ' + \ @@ -841,9 +831,9 @@ class RandomCrop: if len(label_info['gt_bbox']) == 0: return (im, im_info, label_info) - augment_shape = im_info['augment_shape'] - w = augment_shape[1] - h = augment_shape[0] + image_shape = im_info['image_shape'] + w = image_shape[1] + h = image_shape[0] gt_bbox = label_info['gt_bbox'] thresholds = list(self.thresholds) if self.allow_no_crop: @@ -902,7 +892,7 @@ class RandomCrop: label_info['gt_bbox'] = np.take(cropped_box, valid_ids, axis=0) label_info['gt_class'] = np.take( label_info['gt_class'], valid_ids, axis=0) - im_info['augment_shape'] = np.array( + im_info['image_shape'] = np.array( [crop_box[3] - crop_box[1], crop_box[2] - crop_box[0]]).astype('int32') if 'gt_score' in label_info: @@ -973,7 +963,7 @@ class ArrangeFasterRCNN: im_resize_info = im_info['im_resize_info'] im_id = im_info['im_id'] im_shape = np.array( - (im_info['augment_shape'][0], im_info['augment_shape'][1], 1), + (im_info['image_shape'][0], im_info['image_shape'][1], 1), dtype=np.float32) gt_bbox = label_info['gt_bbox'] gt_class = label_info['gt_class'] @@ -986,7 +976,7 @@ class ArrangeFasterRCNN: 'Becasuse the im_info can not be None!') im_resize_info = im_info['im_resize_info'] im_shape = np.array( - (im_info['augment_shape'][0], im_info['augment_shape'][1], 1), + (im_info['image_shape'][0], im_info['image_shape'][1], 1), dtype=np.float32) outputs = (im, im_resize_info, im_shape) return outputs @@ -1066,7 +1056,7 @@ class ArrangeMaskRCNN: 'Becasuse the im_info can not be None!') im_resize_info = im_info['im_resize_info'] im_shape = np.array( - (im_info['augment_shape'][0], im_info['augment_shape'][1], 1), + (im_info['image_shape'][0], im_info['image_shape'][1], 1), dtype=np.float32) if self.mode == 'eval': im_id = im_info['im_id'] @@ -1117,7 +1107,7 @@ class ArrangeYOLOv3: raise TypeError( 'Cannot do ArrangeYolov3! ' + 'Becasuse the im_info and label_info can not be None!') - im_shape = im_info['augment_shape'] + im_shape = im_info['image_shape'] if len(label_info['gt_bbox']) != len(label_info['gt_class']): raise ValueError("gt num mismatch: bbox and class.") if len(label_info['gt_bbox']) != len(label_info['gt_score']): @@ -1141,7 +1131,7 @@ class ArrangeYOLOv3: raise TypeError( 'Cannot do ArrangeYolov3! ' + 'Becasuse the im_info and label_info can not be None!') - im_shape = im_info['augment_shape'] + im_shape = im_info['image_shape'] if len(label_info['gt_bbox']) != len(label_info['gt_class']): raise ValueError("gt num mismatch: bbox and class.") im_id = im_info['im_id'] @@ -1160,6 +1150,6 @@ class ArrangeYOLOv3: if im_info is None: raise TypeError('Cannot do ArrangeYolov3! ' + 'Becasuse the im_info can not be None!') - im_shape = im_info['augment_shape'] + im_shape = im_info['image_shape'] outputs = (im, im_shape) return outputs