From 63ac758d8c450fa2aac0f6b983e80b2fe4af38df Mon Sep 17 00:00:00 2001 From: sunyanfang01 Date: Fri, 8 May 2020 12:02:06 +0800 Subject: [PATCH] add eaasy data --- paddlex/cv/datasets/__init__.py | 3 + paddlex/cv/datasets/easydata_cls.py | 86 +++++++++++ paddlex/cv/datasets/easydata_det.py | 190 ++++++++++++++++++++++++ paddlex/cv/datasets/easydata_seg.py | 116 +++++++++++++++ paddlex/cv/models/mask_rcnn.py | 5 +- paddlex/cv/transforms/seg_transforms.py | 4 +- 6 files changed, 400 insertions(+), 4 deletions(-) create mode 100644 paddlex/cv/datasets/easydata_cls.py create mode 100644 paddlex/cv/datasets/easydata_det.py create mode 100644 paddlex/cv/datasets/easydata_seg.py diff --git a/paddlex/cv/datasets/__init__.py b/paddlex/cv/datasets/__init__.py index c686e45..b8bf8f9 100644 --- a/paddlex/cv/datasets/__init__.py +++ b/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 \ No newline at end of file diff --git a/paddlex/cv/datasets/easydata_cls.py b/paddlex/cv/datasets/easydata_cls.py new file mode 100644 index 0000000..ecf0f75 --- /dev/null +++ b/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)) + \ No newline at end of file diff --git a/paddlex/cv/datasets/easydata_det.py b/paddlex/cv/datasets/easydata_det.py new file mode 100644 index 0000000..d22cb39 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/paddlex/cv/datasets/easydata_seg.py b/paddlex/cv/datasets/easydata_seg.py new file mode 100644 index 0000000..7828524 --- /dev/null +++ b/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 diff --git a/paddlex/cv/models/mask_rcnn.py b/paddlex/cv/models/mask_rcnn.py index bfdc9f1..521ddeb 100644 --- a/paddlex/cv/models/mask_rcnn.py +++ b/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: diff --git a/paddlex/cv/transforms/seg_transforms.py b/paddlex/cv/transforms/seg_transforms.py index 0b535e7..fbf97c8 100644 --- a/paddlex/cv/transforms/seg_transforms.py +++ b/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] -- GitLab