You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
414 lines
15 KiB
414 lines
15 KiB
# Copyright 2020 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):
|
|
"""Get imageID from fileName"""
|
|
try:
|
|
filename = os.path.splitext(filename)[0]
|
|
return int(filename)
|
|
except:
|
|
raise NotImplementedError('Filename %s is supposed to be an integer.'%(filename))
|
|
|
|
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."""
|
|
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
|
|
w, h = 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_dir = config.voc_dir
|
|
cls_map = {name: i for i, name in enumerate(config.coco_classes)}
|
|
sub_dir = 'train' if is_training else 'eval'
|
|
#sub_dir = 'train'
|
|
voc_dir = os.path.join(voc_dir, 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:
|
|
data_dir = config.voc_root
|
|
json_file = os.path.join(data_dir, config.instances_set.format(sub_dir))
|
|
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 = []
|
|
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)
|
|
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.coco_classes}"')
|
|
continue
|
|
bnd_box = obj.find('bndbox')
|
|
x_min = int(bnd_box.find('xmin').text) - 1
|
|
y_min = int(bnd_box.find('ymin').text) - 1
|
|
x_max = int(bnd_box.find('xmax').text) - 1
|
|
y_max = int(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.coco_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=4):
|
|
"""Creatr 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(input_columns=["image"], operations=decode)
|
|
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(input_columns=["img_id", "image", "annotation"],
|
|
output_columns=output_columns, column_order=output_columns,
|
|
operations=compose_map_func, python_multiprocessing=is_training,
|
|
num_parallel_workers=num_parallel_workers)
|
|
ds = ds.map(input_columns=["image"], operations=trans, python_multiprocessing=is_training,
|
|
num_parallel_workers=num_parallel_workers)
|
|
ds = ds.batch(batch_size, drop_remainder=True)
|
|
ds = ds.repeat(repeat_num)
|
|
return ds
|