add eaasy data

paddlex/cv/datasets/__init__.py

@@ -16,3 +16,6 @@ from .imagenet import ImageNet
 from .voc import VOCDetection
 from .coco import CocoDetection
 from .seg_dataset import SegDataset
+from .easydata_cls import EasyDataCls
+from .easydata_det import EasyDataDet
+from .easydata_seg import EasyDataSeg

paddlex/cv/datasets/easydata_cls.py

@@ -0,0 +1,86 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+import os.path as osp
+import random
+import copy
+import json
+import paddlex.utils.logging as logging
+from .imagenet import ImageNet
+from .dataset import is_pic
+from .dataset import get_encoding
+
+
+class EasyDataCls(ImageNet):
+    """读取EasyDataCls格式的分类数据集，并对样本进行相应的处理。
+
+    Args:
+        data_dir (str): 数据集所在的目录路径。
+        file_list (str): 描述数据集图片文件和类别id的文件路径（文本内每行路径为相对data_dir的相对路）。
+        label_list (str): 描述数据集包含的类别信息文件路径。
+        transforms (paddlex.cls.transforms): 数据集中每个样本的预处理/增强算子。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核
+            数的一半。
+        buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
+        parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
+            线程和'process'进程两种方式。默认为'thread'（Windows和Mac下会强制使用thread，该参数无效）。
+        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
+    """
+    
+    def __init__(self,
+                 data_dir,
+                 file_list,
+                 label_list,
+                 transforms=None,
+                 num_workers='auto',
+                 buffer_size=100,
+                 parallel_method='process',
+                 shuffle=False):
+        super(ImageNet, self).__init__(
+            transforms=transforms,
+            num_workers=num_workers,
+            buffer_size=buffer_size,
+            parallel_method=parallel_method,
+            shuffle=shuffle)
+        self.file_list = list()
+        self.labels = list()
+        self._epoch = 0
+        
+        with open(label_list, encoding=get_encoding(label_list)) as f:
+            for line in f:
+                item = line.strip()
+                self.labels.append(item)
+        logging.info("Starting to read file list from dataset...")
+        with open(file_list, encoding=get_encoding(file_list)) as f:
+            for line in f:
+                img_file, json_file = [osp.join(data_dir, x) \
+                        for x in line.strip().split()[:2]]
+                if not is_pic(img_file):
+                    continue
+                if not osp.isfile(json_file):
+                    continue
+                if not osp.exists(img_file):
+                    raise IOError(
+                        'The image file {} is not exist!'.format(img_file))
+                with open(json_file, mode='r', \
+                          encoding=get_encoding(label_list)) as j:
+                    json_info = json.load(j)
+                label = json_info['labels'][0]['name']
+                self.file_list.append([img_file, self.labels.index(label)])
+        self.num_samples = len(self.file_list)
+        logging.info("{} samples in file {}".format(
+            len(self.file_list), file_list))
+    

paddlex/cv/datasets/easydata_det.py

@@ -0,0 +1,190 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+import os.path as osp
+import random
+import copy
+import json
+import cv2
+import numpy as np
+from pycocotools.coco import COCO
+from pycocotools.mask import decode
+import paddlex.utils.logging as logging
+from .voc import VOCDetection
+from .dataset import is_pic
+from .dataset import get_encoding
+
+class EasyDataDet(VOCDetection):
+    """读取EasyDataDet格式的检测数据集，并对样本进行相应的处理。
+
+    Args:
+        data_dir (str): 数据集所在的目录路径。
+        file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
+        label_list (str): 描述数据集包含的类别信息文件路径。
+        transforms (paddlex.det.transforms): 数据集中每个样本的预处理/增强算子。
+        num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
+            系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的
+            一半。
+        buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
+        parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
+            线程和'process'进程两种方式。默认为'thread'（Windows和Mac下会强制使用thread，该参数无效）。
+        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
+    """
+    
+    def __init__(self,
+                 data_dir,
+                 file_list,
+                 label_list,
+                 transforms=None,
+                 num_workers='auto',
+                 buffer_size=100,
+                 parallel_method='process',
+                 shuffle=False):
+        super(VOCDetection, self).__init__(
+            transforms=transforms,
+            num_workers=num_workers,
+            buffer_size=buffer_size,
+            parallel_method=parallel_method,
+            shuffle=shuffle)
+        self.file_list = list()
+        self.labels = list()
+        self._epoch = 0
+        
+        annotations = {}
+        annotations['images'] = []
+        annotations['categories'] = []
+        annotations['annotations'] = []
+        
+        cname2cid = {}
+        label_id = 1
+        with open(label_list, encoding=get_encoding(label_list)) as fr:
+            for line in fr.readlines():
+                cname2cid[line.strip()] = label_id
+                label_id += 1
+                self.labels.append(line.strip())
+        logging.info("Starting to read file list from dataset...")
+        for k, v in cname2cid.items():
+            annotations['categories'].append({
+                'supercategory': 'component',
+                'id': v,
+                'name': k
+            })
+            
+        ct = 0
+        ann_ct = 0
+        with open(file_list, encoding=get_encoding(file_list)) as f:
+            for line in f:
+                img_file, json_file = [osp.join(data_dir, x) \
+                        for x in line.strip().split()[:2]]
+                if not is_pic(img_file):
+                    continue
+                if not osp.isfile(json_file):
+                    continue
+                if not osp.exists(img_file):
+                    raise IOError(
+                        'The image file {} is not exist!'.format(img_file))
+                with open(json_file, mode='r', \
+                          encoding=get_encoding(label_list)) as j:
+                    json_info = json.load(j)
+                im_id = np.array([ct])
+                im = cv2.imread(img_file)
+                im_w = im.shape[1]
+                im_h = im.shape[0]
+                objs = json_info['labels']
+                gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
+                gt_class = np.zeros((len(objs), 1), dtype=np.int32)
+                gt_score = np.ones((len(objs), 1), dtype=np.float32)
+                is_crowd = np.zeros((len(objs), 1), dtype=np.int32)
+                difficult = np.zeros((len(objs), 1), dtype=np.int32)
+                gt_poly = [None] * len(objs)
+                for i, obj in enumerate(objs):
+                    cname = obj['name']
+                    gt_class[i][0] = cname2cid[cname]
+                    x1 = max(0, obj['x1'])
+                    y1 = max(0, obj['y1'])
+                    x2 = min(im_w - 1, obj['x2'])
+                    y2 = min(im_h - 1, obj['y2'])
+                    gt_bbox[i] = [x1, y1, x2, y2]
+                    is_crowd[i][0] = 0
+                    if 'mask' in obj:
+                        mask_dict = {}
+                        mask_dict['size'] = [im_h, im_w]
+                        mask_dict['counts'] = obj['mask'].encode()
+                        mask = decode(mask_dict)
+                        gt_poly[i] = self.mask2polygon(mask)
+                    annotations['annotations'].append({
+                        '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)),
+                        'segmentation':
+                        [] if gt_poly[i] is None else gt_poly[i],
+                        'category_id':
+                        cname2cid[cname],
+                        'id':
+                        ann_ct,
+                        'difficult':
+                        0
+                    })
+                    ann_ct += 1
+                im_info = {
+                    'im_id': im_id,
+                    'origin_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
+                }
+                if None not in gt_poly:
+                    label_info['gt_poly'] = gt_poly
+                voc_rec = (im_info, label_info)
+                if len(objs) != 0:
+                    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]
+                    })
+
+        if not len(self.file_list) > 0:
+            raise Exception('not found any voc record in %s' % (file_list))
+        logging.info("{} samples in file {}".format(
+            len(self.file_list), file_list))
+        self.num_samples = len(self.file_list)
+        self.coco_gt = COCO()
+        self.coco_gt.dataset = annotations
+        self.coco_gt.createIndex()
+        
+    def mask2polygon(self, mask):
+        contours, hierarchy = cv2.findContours(
+            (mask).astype(np.uint8), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+        segmentation = []
+        for contour in contours:
+            contour_list = contour.flatten().tolist()
+            if len(contour_list) > 4:
+                segmentation.append(contour_list)
+        return segmentation

paddlex/cv/datasets/easydata_seg.py

@@ -0,0 +1,116 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+import os.path as osp
+import random
+import copy
+import json
+import cv2
+import numpy as np
+from pycocotools.mask import decode
+import paddlex.utils.logging as logging
+from .dataset import Dataset
+from .dataset import get_encoding
+from .dataset import is_pic
+
+class EasyDataSeg(Dataset):
+    """读取EasyDataSeg语义分割任务数据集，并对样本进行相应的处理。
+
+    Args:
+        data_dir (str): 数据集所在的目录路径。
+        file_list (str): 描述数据集图片文件和对应标注文件的文件路径（文本内每行路径为相对data_dir的相对路）。
+        label_list (str): 描述数据集包含的类别信息文件路径。
+        transforms (list): 数据集中每个样本的预处理/增强算子。
+        num_workers (int): 数据集中样本在预处理过程中的线程或进程数。默认为4。
+        buffer_size (int): 数据集中样本在预处理过程中队列的缓存长度，以样本数为单位。默认为100。
+        parallel_method (str): 数据集中样本在预处理过程中并行处理的方式，支持'thread'
+            线程和'process'进程两种方式。默认为'thread'（Windows和Mac下会强制使用thread，该参数无效）。
+        shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
+    """
+
+    def __init__(self,
+                 data_dir,
+                 file_list,
+                 label_list,
+                 transforms=None,
+                 num_workers='auto',
+                 buffer_size=100,
+                 parallel_method='process',
+                 shuffle=False):
+        super(EasyDataSeg, self).__init__(
+            transforms=transforms,
+            num_workers=num_workers,
+            buffer_size=buffer_size,
+            parallel_method=parallel_method,
+            shuffle=shuffle)
+        self.file_list = list()
+        self.labels = list()
+        self._epoch = 0
+
+        cname2cid = {}
+        label_id = 0
+        with open(label_list, encoding=get_encoding(label_list)) as fr:
+            for line in fr.readlines():
+                cname2cid[line.strip()] = label_id
+                label_id += 1
+                self.labels.append(line.strip())
+                
+        with open(file_list, encoding=get_encoding(file_list)) as f:
+            for line in f:
+                img_file, json_file = [osp.join(data_dir, x) \
+                        for x in line.strip().split()[:2]]
+                if not is_pic(img_file):
+                    continue
+                if not osp.isfile(json_file):
+                    continue
+                if not osp.exists(img_file):
+                    raise IOError(
+                        'The image file {} is not exist!'.format(img_file))
+                with open(json_file, mode='r', \
+                          encoding=get_encoding(label_list)) as j:
+                    json_info = json.load(j)
+                im = cv2.imread(img_file)
+                im_w = im.shape[1]
+                im_h = im.shape[0]
+                objs = json_info['labels']
+                lable_npy = np.zeros([im_h, im_w]).astype('uint8')
+                for i, obj in enumerate(objs):
+                    cname = obj['name']
+                    cid = cname2cid[cname]
+                    mask_dict = {}
+                    mask_dict['size'] = [im_h, im_w]
+                    mask_dict['counts'] = obj['mask'].encode()
+                    mask = decode(mask_dict)
+                    mask *= cid
+                    conflict_index = np.where(((lable_npy > 0) & (mask == cid)) == True)
+                    mask[conflict_index] = 0
+                    lable_npy += mask
+                self.file_list.append([img_file, lable_npy])
+        self.num_samples = len(self.file_list)
+        logging.info("{} samples in file {}".format(
+            len(self.file_list), file_list))
+
+    def iterator(self):
+        self._epoch += 1
+        self._pos = 0
+        files = copy.deepcopy(self.file_list)
+        if self.shuffle:
+            random.shuffle(files)
+        files = files[:self.num_samples]
+        self.num_samples = len(files)
+        for f in files:
+            lable_npy = f[1]
+            sample = [f[0], None, lable_npy]
+            yield sample

paddlex/cv/models/mask_rcnn.py

@@ -157,11 +157,12 @@ class MaskRCNN(FasterRCNN):
             ValueError: 模型从inference model进行加载。
         """
         if metric is None:
-            if isinstance(train_dataset, paddlex.datasets.CocoDetection):
+            if isinstance(train_dataset, paddlex.datasets.CocoDetection) or \
+                    isinstance(train_dataset, paddlex.datasets.EasyDataDet):
                 metric = 'COCO'
             else:
                 raise Exception(
-                    "train_dataset should be datasets.COCODetection.")
+                    "train_dataset should be datasets.COCODetection or datasets.EasyDataDet.")
         assert metric in ['COCO', 'VOC'], "Metric only support 'VOC' or 'COCO'"
         self.metric = metric
         if not self.trainable:

paddlex/cv/transforms/seg_transforms.py

@@ -66,8 +66,8 @@ class Compose:
         if self.to_rgb:
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
         if label is not None:
-            label = np.asarray(Image.open(label))
-
+            if not isinstance(label, np.ndarray):
+                label = np.asarray(Image.open(label))
         for op in self.transforms:
             outputs = op(im, im_info, label)
             im = outputs[0]