Merge pull request #54 from PaddlePaddle/develop_0515

modify imgaug support

paddlex/cv/datasets/voc.py

@@ -95,8 +95,8 @@ class VOCDetection(Dataset):
                 if not osp.isfile(xml_file):
                     continue
                 if not osp.exists(img_file):
-                    raise IOError(
-                        'The image file {} is not exist!'.format(img_file))
+                    raise IOError('The image file {} is not exist!'.format(
+                        img_file))
                 tree = ET.parse(xml_file)
                 if tree.find('id') is None:
                     im_id = np.array([ct])
@@ -122,25 +122,20 @@ class VOCDetection(Dataset):
                     y2 = float(obj.find('bndbox').find('ymax').text)
                     x1 = max(0, x1)
                     y1 = max(0, y1)
-                    x2 = min(im_w - 1, x2)
-                    y2 = min(im_h - 1, y2)
+                    if im_w > 0.5 and im_h > 0.5:
+                        x2 = min(im_w - 1, x2)
+                        y2 = min(im_h - 1, y2)
                     gt_bbox[i] = [x1, y1, x2, y2]
                     is_crowd[i][0] = 0
                     difficult[i][0] = _difficult
                     annotations['annotations'].append({
-                        'iscrowd':
-                        0,
-                        'image_id':
-                        int(im_id[0]),
+                        'iscrowd': 0,
+                        'image_id': int(im_id[0]),
                         'bbox': [x1, y1, x2 - x1 + 1, y2 - y1 + 1],
-                        'area':
-                        float((x2 - x1 + 1) * (y2 - y1 + 1)),
-                        'category_id':
-                        cname2cid[cname],
-                        'id':
-                        ann_ct,
-                        'difficult':
-                        _difficult
+                        'area': float((x2 - x1 + 1) * (y2 - y1 + 1)),
+                        'category_id': cname2cid[cname],
+                        'id': ann_ct,
+                        'difficult': _difficult
                     })
                     ann_ct += 1
 
@@ -160,14 +155,10 @@ class VOCDetection(Dataset):
                     self.file_list.append([img_file, voc_rec])
                     ct += 1
                     annotations['images'].append({
-                        'height':
-                        im_h,
-                        'width':
-                        im_w,
-                        'id':
-                        int(im_id[0]),
-                        'file_name':
-                        osp.split(img_file)[1]
+                        'height': im_h,
+                        'width': im_w,
+                        'id': int(im_id[0]),
+                        'file_name': osp.split(img_file)[1]
                     })
 
         if not len(self.file_list) > 0:
@@ -198,8 +189,7 @@ class VOCDetection(Dataset):
             else:
                 mix_pos = 0
             im_info['mixup'] = [
-                files[mix_pos][0],
-                copy.deepcopy(files[mix_pos][1][0]),
+                files[mix_pos][0], copy.deepcopy(files[mix_pos][1][0]),
                 copy.deepcopy(files[mix_pos][1][1])
             ]
             self._pos += 1

paddlex/cv/transforms/det_transforms.py

@@ -111,8 +111,8 @@ class Compose(DetTransform):
                 try:
                     im = cv2.imread(im_file).astype('float32')
                 except:
-                    raise TypeError(
-                        'Can\'t read The image file {}!'.format(im_file))
+                    raise TypeError('Can\'t read The image file {}!'.format(
+                        im_file))
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
             # make default im_info with [h, w, 1]
             im_info['im_resize_info'] = np.array(
@@ -145,19 +145,10 @@ class Compose(DetTransform):
                 outputs = op(im, im_info, label_info)
                 im = outputs[0]
             else:
+                im = execute_imgaug(op, im)
                 if label_info is not None:
-                    gt_poly = label_info.get('gt_poly', None)
-                    gt_bbox = label_info['gt_bbox']
-                    if gt_poly is None:
-                        im, aug_bbox = execute_imgaug(op, im, bboxes=gt_bbox)
-                    else:
-                        im, aug_bbox, aug_poly = execute_imgaug(
-                            op, im, bboxes=gt_bbox, polygons=gt_poly)
-                        label_info['gt_poly'] = aug_poly
-                    label_info['gt_bbox'] = aug_bbox
                     outputs = (im, im_info, label_info)
                 else:
-                    im, = execute_imgaug(op, im)
                     outputs = (im, im_info)
         return outputs
 
@@ -218,8 +209,8 @@ class ResizeByShort(DetTransform):
         im_short_size = min(im.shape[0], im.shape[1])
         im_long_size = max(im.shape[0], im.shape[1])
         scale = float(self.short_size) / im_short_size
-        if self.max_size > 0 and np.round(
-                scale * im_long_size) > self.max_size:
+        if self.max_size > 0 and np.round(scale *
+                                          im_long_size) > self.max_size:
             scale = float(self.max_size) / float(im_long_size)
         resized_width = int(round(im.shape[1] * scale))
         resized_height = int(round(im.shape[0] * scale))
@@ -302,8 +293,8 @@ class Padding(DetTransform):
         if isinstance(self.target_size, int):
             padding_im_h = self.target_size
             padding_im_w = self.target_size
-        elif isinstance(self.target_size, list) or isinstance(
-                self.target_size, tuple):
+        elif isinstance(self.target_size, list) or isinstance(self.target_size,
+                                                              tuple):
             padding_im_w = self.target_size[0]
             padding_im_h = self.target_size[1]
         elif self.coarsest_stride > 0:
@@ -321,8 +312,8 @@ class Padding(DetTransform):
             raise ValueError(
                 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
                 .format(im_w, im_h, padding_im_w, padding_im_h))
-        padding_im = np.zeros((padding_im_h, padding_im_w, im_c),
-                              dtype=np.float32)
+        padding_im = np.zeros(
+            (padding_im_h, padding_im_w, im_c), dtype=np.float32)
         padding_im[:im_h, :im_w, :] = im
         if label_info is None:
             return (padding_im, im_info)
@@ -932,8 +923,9 @@ class RandomCrop(DetTransform):
                 crop_y = np.random.randint(0, h - crop_h)
                 crop_x = np.random.randint(0, w - crop_w)
                 crop_box = [crop_x, crop_y, crop_x + crop_w, crop_y + crop_h]
-                iou = iou_matrix(gt_bbox, np.array([crop_box],
-                                                   dtype=np.float32))
+                iou = iou_matrix(
+                    gt_bbox, np.array(
+                        [crop_box], dtype=np.float32))
                 if iou.max() < thresh:
                     continue
 
@@ -941,16 +933,21 @@ class RandomCrop(DetTransform):
                     continue
 
                 cropped_box, valid_ids = crop_box_with_center_constraint(
-                    gt_bbox, np.array(crop_box, dtype=np.float32))
+                    gt_bbox, np.array(
+                        crop_box, dtype=np.float32))
                 if valid_ids.size > 0:
                     found = True
                     break
 
             if found:
                 if 'gt_poly' in label_info and len(label_info['gt_poly']) > 0:
-                    crop_polys = crop_segms(label_info['gt_poly'], valid_ids,
-                                            np.array(crop_box, dtype=np.int64),
-                                            h, w)
+                    crop_polys = crop_segms(
+                        label_info['gt_poly'],
+                        valid_ids,
+                        np.array(
+                            crop_box, dtype=np.int64),
+                        h,
+                        w)
                     if [] in crop_polys:
                         delete_id = list()
                         valid_polys = list()

paddlex/cv/transforms/imgaug_support.py

@@ -13,36 +13,41 @@
 # limitations under the License.
 
 import numpy as np
+import copy
 
 
 def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
                    segment_map=None):
     # 预处理，将bboxes, polygons转换成imgaug格式
-    import imgaug.augmentables.polys as polys
+    import imgaug.augmentables.kps as kps
     import imgaug.augmentables.bbs as bbs
 
     aug_im = im.astype('uint8')
+    aug_im = augmenter.augment(image=aug_im)
+    return aug_im
 
+    # TODO imgaug的标注处理逻辑与paddlex已存的transform存在部分差异
+    # 目前仅支持对原图进行处理，因此只能使用pixlevel的imgaug增强操作
+    # 以下代码暂不会执行
     aug_bboxes = None
     if bboxes is not None:
         aug_bboxes = list()
         for i in range(len(bboxes)):
-            x1 = bboxes[i, 0] - 1
+            x1 = bboxes[i, 0]
             y1 = bboxes[i, 1]
             x2 = bboxes[i, 2]
             y2 = bboxes[i, 3]
             aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
 
-    aug_polygons = None
-    lod_info = list()
+    aug_points = None
     if polygons is not None:
-        aug_polygons = list()
+        aug_points = list()
         for i in range(len(polygons)):
             num = len(polygons[i])
-            lod_info.append(num)
             for j in range(num):
-                points = np.reshape(polygons[i][j], (-1, 2))
-                aug_polygons.append(polys.Polygon(points))
+                tmp = np.reshape(polygons[i][j], (-1, 2))
+                for k in range(len(tmp)):
+                    aug_points.append(kps.Keypoint(tmp[k, 0], tmp[k, 1]))
 
     aug_segment_map = None
     if segment_map is not None:
@@ -56,72 +61,47 @@ def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
             raise Exception(
                 "Only support 2-dimensions for 3-dimensions for segment_map")
 
-    aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(
+    unnormalized_batch = augmenter.augment(
         image=aug_im,
         bounding_boxes=aug_bboxes,
-        polygons=aug_polygons,
-        segmentation_maps=aug_segment_map)
+        keypoints=aug_points,
+        segmentation_maps=aug_segment_map,
+        return_batch=True)
+    aug_im = unnormalized_batch.images_aug[0]
+    aug_bboxes = unnormalized_batch.bounding_boxes_aug
+    aug_points = unnormalized_batch.keypoints_aug
+    aug_seg_map = unnormalized_batch.segmentation_maps_aug
 
     aug_im = aug_im.astype('float32')
 
-    if aug_polygons is not None:
-        assert len(aug_bboxes) == len(
-            lod_info
-        ), "Number of aug_bboxes should be equal to number of aug_polygons"
-
     if aug_bboxes is not None:
-        # 裁剪掉在图像之外的bbox和polygon
-        for i in range(len(aug_bboxes)):
-            aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
-        if aug_polygons is not None:
-            for i in range(len(aug_polygons)):
-                aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
-
-        # 过滤掉无效的bbox和polygon，并转换为训练数据格式
         converted_bboxes = list()
-        converted_polygons = list()
-        poly_index = 0
         for i in range(len(aug_bboxes)):
-            # 过滤width或height不足1像素的框
-            if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
-                continue
-            if aug_polygons is None:
-                converted_bboxes.append([
-                    aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
-                    aug_bboxes[i].y2
-                ])
-                continue
-
-            # 如若有polygons，将会继续执行下面代码
-            polygons_this_box = list()
-            for ps in aug_polygons[poly_index:poly_index + lod_info[i]]:
-                if len(ps) == 0:
-                    continue
-                for p in ps:
-                    # 没有3个point的polygon被过滤
-                    if len(p.exterior) < 3:
-                        continue
-                    polygons_this_box.append(p.exterior.flatten().tolist())
-            poly_index += lod_info[i]
-
-            if len(polygons_this_box) == 0:
-                continue
             converted_bboxes.append([
                 aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
                 aug_bboxes[i].y2
             ])
-            converted_polygons.append(polygons_this_box)
-        if len(converted_bboxes) == 0:
-            aug_im = im
-            converted_bboxes = bboxes
-            converted_polygons = polygons
+        aug_bboxes = converted_bboxes
+
+    aug_polygons = None
+    if aug_points is not None:
+        aug_polygons = copy.deepcopy(polygons)
+        idx = 0
+        for i in range(len(aug_polygons)):
+            num = len(aug_polygons[i])
+            for j in range(num):
+                num_points = len(aug_polygons[i][j]) // 2
+                for k in range(num_points):
+                    aug_polygons[i][j][k * 2] = aug_points[idx].x
+                    aug_polygons[i][j][k * 2 + 1] = aug_points[idx].y
+                    idx += 1
 
     result = [aug_im]
-    if bboxes is not None:
-        result.append(np.array(converted_bboxes))
-    if polygons is not None:
-        result.append(converted_polygons)
-    if segment_map is not None:
+    if aug_bboxes is not None:
+        result.append(np.array(aug_bboxes))
+    if aug_polygons is not None:
+        result.append(aug_polygons)
+    if aug_seg_map is not None:
         n, h, w, c = aug_seg_map.shape
         if len(segment_map.shape) == 2:
             aug_seg_map = np.reshape(aug_seg_map, (h, w))

paddlex/cv/transforms/seg_transforms.py

@@ -101,11 +101,10 @@ class Compose(SegTransform):
                 if len(outputs) == 3:
                     label = outputs[2]
             else:
+                im = execute_imgaug(op, im)
                 if label is not None:
-                    im, label = execute_imgaug(op, im, segment_map=label)
                     outputs = (im, im_info, label)
                 else:
-                    im, = execute_imgaug(op, im)
                     outputs = (im, im_info)
         return outputs
 
@@ -391,8 +390,8 @@ class ResizeByShort(SegTransform):
         im_short_size = min(im.shape[0], im.shape[1])
         im_long_size = max(im.shape[0], im.shape[1])
         scale = float(self.short_size) / im_short_size
-        if self.max_size > 0 and np.round(
-                scale * im_long_size) > self.max_size:
+        if self.max_size > 0 and np.round(scale *
+                                          im_long_size) > self.max_size:
             scale = float(self.max_size) / float(im_long_size)
         resized_width = int(round(im.shape[1] * scale))
         resized_height = int(round(im.shape[0] * scale))
@@ -423,8 +422,8 @@ class ResizeRangeScaling(SegTransform):
     def __init__(self, min_value=400, max_value=600):
         if min_value > max_value:
             raise ValueError('min_value must be less than max_value, '
-                             'but they are {} and {}.'.format(
-                                 min_value, max_value))
+                             'but they are {} and {}.'.format(min_value,
+                                                              max_value))
         self.min_value = min_value
         self.max_value = max_value
 
@@ -761,8 +760,8 @@ class RandomPaddingCrop(SegTransform):
                 h_off = np.random.randint(img_height - crop_height + 1)
                 w_off = np.random.randint(img_width - crop_width + 1)
 
-                im = im[h_off:(crop_height + h_off), w_off:(
-                    w_off + crop_width), :]
+                im = im[h_off:(crop_height + h_off), w_off:(w_off + crop_width
+                                                            ), :]
                 if label is not None:
                     label = label[h_off:(crop_height + h_off), w_off:(
                         w_off + crop_width)]

setup.py

@@ -27,7 +27,7 @@ setuptools.setup(
     long_description_content_type="text/plain",
     url="https://github.com/PaddlePaddle/PaddleX",
     packages=setuptools.find_packages(),
-    setup_requires=['cython', 'numpy', 'sklearn'],
+    setup_requires=['cython', 'numpy'],
     install_requires=[
         "pycocotools;platform_system!='Windows'", 'pyyaml', 'colorama', 'tqdm',
         'visualdl==1.3.0', 'paddleslim==1.0.1', 'visualdl==2.0.0a2'
@@ -38,6 +38,4 @@ setuptools.setup(
         "Operating System :: OS Independent",
     ],
     license='Apache 2.0',
-    entry_points={'console_scripts': [
-        'paddlex=paddlex.command:main',
-    ]})
+    entry_points={'console_scripts': ['paddlex=paddlex.command:main', ]})