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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
-
- """SSD dataset"""
-
- from __future__ import division
-
- import os
- import json
- import xml.etree.ElementTree as et
- import numpy as np
- import cv2
-
- import mindspore.dataset as de
- import mindspore.dataset.vision.c_transforms as C
- from mindspore.mindrecord import FileWriter
- from .config import config
- from .box_utils import jaccard_numpy, ssd_bboxes_encode
-
-
- def _rand(a=0., b=1.):
- """Generate random."""
- return np.random.rand() * (b - a) + a
-
-
- def get_imageId_from_fileName(filename, id_iter):
- """Get imageID from fileName if fileName is int, else return id_iter."""
- filename = os.path.splitext(filename)[0]
- if filename.isdigit():
- return int(filename)
- return id_iter
-
-
- def random_sample_crop(image, boxes):
- """Random Crop the image and boxes"""
- height, width, _ = image.shape
- min_iou = np.random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9])
-
- if min_iou is None:
- return image, boxes
-
- # max trails (50)
- for _ in range(50):
- image_t = image
-
- w = _rand(0.3, 1.0) * width
- h = _rand(0.3, 1.0) * height
-
- # aspect ratio constraint b/t .5 & 2
- if h / w < 0.5 or h / w > 2:
- continue
-
- left = _rand() * (width - w)
- top = _rand() * (height - h)
-
- rect = np.array([int(top), int(left), int(top + h), int(left + w)])
- overlap = jaccard_numpy(boxes, rect)
-
- # dropout some boxes
- drop_mask = overlap > 0
- if not drop_mask.any():
- continue
-
- if overlap[drop_mask].min() < min_iou and overlap[drop_mask].max() > (min_iou + 0.2):
- continue
-
- image_t = image_t[rect[0]:rect[2], rect[1]:rect[3], :]
-
- centers = (boxes[:, :2] + boxes[:, 2:4]) / 2.0
-
- m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
- m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
-
- # mask in that both m1 and m2 are true
- mask = m1 * m2 * drop_mask
-
- # have any valid boxes? try again if not
- if not mask.any():
- continue
-
- # take only matching gt boxes
- boxes_t = boxes[mask, :].copy()
-
- boxes_t[:, :2] = np.maximum(boxes_t[:, :2], rect[:2])
- boxes_t[:, :2] -= rect[:2]
- boxes_t[:, 2:4] = np.minimum(boxes_t[:, 2:4], rect[2:4])
- boxes_t[:, 2:4] -= rect[:2]
-
- return image_t, boxes_t
- return image, boxes
-
-
- def preprocess_fn(img_id, image, box, is_training):
- """Preprocess function for dataset."""
- cv2.setNumThreads(2)
-
- def _infer_data(image, input_shape):
- img_h, img_w, _ = image.shape
- input_h, input_w = input_shape
-
- image = cv2.resize(image, (input_w, input_h))
-
- # When the channels of image is 1
- if len(image.shape) == 2:
- image = np.expand_dims(image, axis=-1)
- image = np.concatenate([image, image, image], axis=-1)
-
- return img_id, image, np.array((img_h, img_w), np.float32)
-
- def _data_aug(image, box, is_training, image_size=(300, 300)):
- """Data augmentation function."""
- ih, iw, _ = image.shape
- h, w = image_size
-
- if not is_training:
- return _infer_data(image, image_size)
-
- # Random crop
- box = box.astype(np.float32)
- image, box = random_sample_crop(image, box)
- ih, iw, _ = image.shape
-
- # Resize image
- image = cv2.resize(image, (w, h))
-
- # Flip image or not
- flip = _rand() < .5
- if flip:
- image = cv2.flip(image, 1, dst=None)
-
- # When the channels of image is 1
- if len(image.shape) == 2:
- image = np.expand_dims(image, axis=-1)
- image = np.concatenate([image, image, image], axis=-1)
-
- box[:, [0, 2]] = box[:, [0, 2]] / ih
- box[:, [1, 3]] = box[:, [1, 3]] / iw
-
- if flip:
- box[:, [1, 3]] = 1 - box[:, [3, 1]]
-
- box, label, num_match = ssd_bboxes_encode(box)
- return image, box, label, num_match
-
- return _data_aug(image, box, is_training, image_size=config.img_shape)
-
-
- def create_voc_label(is_training):
- """Get image path and annotation from VOC."""
- voc_root = config.voc_root
- cls_map = {name: i for i, name in enumerate(config.classes)}
- sub_dir = 'train' if is_training else 'eval'
- voc_dir = os.path.join(voc_root, sub_dir)
- if not os.path.isdir(voc_dir):
- raise ValueError(f'Cannot find {sub_dir} dataset path.')
-
- image_dir = anno_dir = voc_dir
- if os.path.isdir(os.path.join(voc_dir, 'Images')):
- image_dir = os.path.join(voc_dir, 'Images')
- if os.path.isdir(os.path.join(voc_dir, 'Annotations')):
- anno_dir = os.path.join(voc_dir, 'Annotations')
-
- if not is_training:
- json_file = os.path.join(config.voc_root, config.voc_json)
- file_dir = os.path.split(json_file)[0]
- if not os.path.isdir(file_dir):
- os.makedirs(file_dir)
- json_dict = {"images": [], "type": "instances", "annotations": [],
- "categories": []}
- bnd_id = 1
-
- image_files_dict = {}
- image_anno_dict = {}
- images = []
- id_iter = 0
- for anno_file in os.listdir(anno_dir):
- print(anno_file)
- if not anno_file.endswith('xml'):
- continue
- tree = et.parse(os.path.join(anno_dir, anno_file))
- root_node = tree.getroot()
- file_name = root_node.find('filename').text
- img_id = get_imageId_from_fileName(file_name, id_iter)
- id_iter += 1
- image_path = os.path.join(image_dir, file_name)
- print(image_path)
- if not os.path.isfile(image_path):
- print(f'Cannot find image {file_name} according to annotations.')
- continue
-
- labels = []
- for obj in root_node.iter('object'):
- cls_name = obj.find('name').text
- if cls_name not in cls_map:
- print(f'Label "{cls_name}" not in "{config.classes}"')
- continue
- bnd_box = obj.find('bndbox')
- x_min = int(float(bnd_box.find('xmin').text)) - 1
- y_min = int(float(bnd_box.find('ymin').text)) - 1
- x_max = int(float(bnd_box.find('xmax').text)) - 1
- y_max = int(float(bnd_box.find('ymax').text)) - 1
- labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]])
-
- if not is_training:
- o_width = abs(x_max - x_min)
- o_height = abs(y_max - y_min)
- ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \
- img_id, 'bbox': [x_min, y_min, o_width, o_height], \
- 'category_id': cls_map[cls_name], 'id': bnd_id, \
- 'ignore': 0, \
- 'segmentation': []}
- json_dict['annotations'].append(ann)
- bnd_id = bnd_id + 1
-
- if labels:
- images.append(img_id)
- image_files_dict[img_id] = image_path
- image_anno_dict[img_id] = np.array(labels)
-
- if not is_training:
- size = root_node.find("size")
- width = int(size.find('width').text)
- height = int(size.find('height').text)
- image = {'file_name': file_name, 'height': height, 'width': width,
- 'id': img_id}
- json_dict['images'].append(image)
-
- if not is_training:
- for cls_name, cid in cls_map.items():
- cat = {'supercategory': 'none', 'id': cid, 'name': cls_name}
- json_dict['categories'].append(cat)
- json_fp = open(json_file, 'w')
- json_str = json.dumps(json_dict)
- json_fp.write(json_str)
- json_fp.close()
-
- return images, image_files_dict, image_anno_dict
-
-
- def create_coco_label(is_training):
- """Get image path and annotation from COCO."""
- from pycocotools.coco import COCO
-
- coco_root = config.coco_root
- data_type = config.val_data_type
- if is_training:
- data_type = config.train_data_type
-
- # Classes need to train or test.
- train_cls = config.classes
- train_cls_dict = {}
- for i, cls in enumerate(train_cls):
- train_cls_dict[cls] = i
-
- anno_json = os.path.join(coco_root, config.instances_set.format(data_type))
-
- coco = COCO(anno_json)
- classs_dict = {}
- cat_ids = coco.loadCats(coco.getCatIds())
- for cat in cat_ids:
- classs_dict[cat["id"]] = cat["name"]
-
- image_ids = coco.getImgIds()
- images = []
- image_path_dict = {}
- image_anno_dict = {}
-
- for img_id in image_ids:
- image_info = coco.loadImgs(img_id)
- file_name = image_info[0]["file_name"]
- anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
- anno = coco.loadAnns(anno_ids)
- image_path = os.path.join(coco_root, data_type, file_name)
- annos = []
- iscrowd = False
- for label in anno:
- bbox = label["bbox"]
- class_name = classs_dict[label["category_id"]]
- iscrowd = iscrowd or label["iscrowd"]
- if class_name in train_cls:
- x_min, x_max = bbox[0], bbox[0] + bbox[2]
- y_min, y_max = bbox[1], bbox[1] + bbox[3]
- annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])
-
- if not is_training and iscrowd:
- continue
- if len(annos) >= 1:
- images.append(img_id)
- image_path_dict[img_id] = image_path
- image_anno_dict[img_id] = np.array(annos)
-
- return images, image_path_dict, image_anno_dict
-
-
- def anno_parser(annos_str):
- """Parse annotation from string to list."""
- annos = []
- for anno_str in annos_str:
- anno = list(map(int, anno_str.strip().split(',')))
- annos.append(anno)
- return annos
-
-
- def filter_valid_data(image_dir, anno_path):
- """Filter valid image file, which both in image_dir and anno_path."""
- images = []
- image_path_dict = {}
- image_anno_dict = {}
- if not os.path.isdir(image_dir):
- raise RuntimeError("Path given is not valid.")
- if not os.path.isfile(anno_path):
- raise RuntimeError("Annotation file is not valid.")
-
- with open(anno_path, "rb") as f:
- lines = f.readlines()
- for img_id, line in enumerate(lines):
- line_str = line.decode("utf-8").strip()
- line_split = str(line_str).split(' ')
- file_name = line_split[0]
- image_path = os.path.join(image_dir, file_name)
- if os.path.isfile(image_path):
- images.append(img_id)
- image_path_dict[img_id] = image_path
- image_anno_dict[img_id] = anno_parser(line_split[1:])
-
- return images, image_path_dict, image_anno_dict
-
-
- def voc_data_to_mindrecord(mindrecord_dir, is_training, prefix="ssd.mindrecord", file_num=8):
- """Create MindRecord file by image_dir and anno_path."""
- mindrecord_path = os.path.join(mindrecord_dir, prefix)
- writer = FileWriter(mindrecord_path, file_num)
- images, image_path_dict, image_anno_dict = create_voc_label(is_training)
-
- ssd_json = {
- "img_id": {"type": "int32", "shape": [1]},
- "image": {"type": "bytes"},
- "annotation": {"type": "int32", "shape": [-1, 5]},
- }
- writer.add_schema(ssd_json, "ssd_json")
-
- for img_id in images:
- image_path = image_path_dict[img_id]
- with open(image_path, 'rb') as f:
- img = f.read()
- annos = np.array(image_anno_dict[img_id], dtype=np.int32)
- img_id = np.array([img_id], dtype=np.int32)
- row = {"img_id": img_id, "image": img, "annotation": annos}
- writer.write_raw_data([row])
- writer.commit()
-
-
- def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="ssd.mindrecord", file_num=8):
- """Create MindRecord file."""
- mindrecord_dir = config.mindrecord_dir
- mindrecord_path = os.path.join(mindrecord_dir, prefix)
- writer = FileWriter(mindrecord_path, file_num)
- if dataset == "coco":
- images, image_path_dict, image_anno_dict = create_coco_label(is_training)
- else:
- images, image_path_dict, image_anno_dict = filter_valid_data(config.image_dir, config.anno_path)
-
- ssd_json = {
- "img_id": {"type": "int32", "shape": [1]},
- "image": {"type": "bytes"},
- "annotation": {"type": "int32", "shape": [-1, 5]},
- }
- writer.add_schema(ssd_json, "ssd_json")
-
- for img_id in images:
- image_path = image_path_dict[img_id]
- with open(image_path, 'rb') as f:
- img = f.read()
- annos = np.array(image_anno_dict[img_id], dtype=np.int32)
- img_id = np.array([img_id], dtype=np.int32)
- row = {"img_id": img_id, "image": img, "annotation": annos}
- writer.write_raw_data([row])
- writer.commit()
-
-
- def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num=1, rank=0,
- is_training=True, num_parallel_workers=64, use_multiprocessing=True):
- """Create SSD dataset with MindDataset."""
- ds = de.MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
- shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
- decode = C.Decode()
- ds = ds.map(operations=decode, input_columns=["image"])
- change_swap_op = C.HWC2CHW()
- normalize_op = C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
- std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
- color_adjust_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
- compose_map_func = (lambda img_id, image, annotation: preprocess_fn(img_id, image, annotation, is_training))
- if is_training:
- output_columns = ["image", "box", "label", "num_match"]
- trans = [color_adjust_op, normalize_op, change_swap_op]
- else:
- output_columns = ["img_id", "image", "image_shape"]
- trans = [normalize_op, change_swap_op]
- ds = ds.map(operations=compose_map_func, input_columns=["img_id", "image", "annotation"],
- output_columns=output_columns, column_order=output_columns,
- python_multiprocessing=use_multiprocessing,
- num_parallel_workers=num_parallel_workers)
- ds = ds.map(operations=trans, input_columns=["image"], python_multiprocessing=use_multiprocessing,
- num_parallel_workers=num_parallel_workers)
- ds = ds.batch(batch_size, drop_remainder=True)
- ds = ds.repeat(repeat_num)
- return ds
-
-
- def create_mindrecord(dataset="coco", prefix="ssd.mindrecord", is_training=True):
- """create mindrecord file"""
- print("Start create dataset!")
-
- # It will generate mindrecord file in config.mindrecord_dir,
- # and the file name is ssd.mindrecord0, 1, ... file_num.
-
- mindrecord_dir = config.mindrecord_dir
- mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
- if not os.path.exists(mindrecord_file):
- if not os.path.isdir(mindrecord_dir):
- os.makedirs(mindrecord_dir)
- if dataset == "coco":
- if os.path.isdir(config.coco_root):
- print("Create Mindrecord.")
- data_to_mindrecord_byte_image("coco", is_training, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("coco_root not exits.")
- elif dataset == "voc":
- if os.path.isdir(config.voc_root):
- print("Create Mindrecord.")
- voc_data_to_mindrecord(mindrecord_dir, is_training, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("voc_root not exits.")
- else:
- if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path):
- print("Create Mindrecord.")
- data_to_mindrecord_byte_image("other", is_training, prefix)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("image_dir or anno_path not exits.")
- return mindrecord_file
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