def __init__(self, gts, results, num_coors=4):
"""
Args:
gts (str or COCO): DOTA-COCO兼容格式, img_info中必须含crop字段
results (str or dict): 子图检测结果，保存在文件中，
results:
{
image_id: dets, # image_id必须是anns中有效的id
image_id: dets,
...
}
dets:
{
类别：[[位置坐标，得分], [...], ...], # np.array
类别: [[位置坐标，得分], [...], ...],
...
}，
位置坐标支持左上角右下角两个点表示和四个点的表示方式
num_coors (int): 4表示左上角右下角box， 8表示四个点的box
"""
assert num_coors in (4, 8), "不支持的检测位置表示"
self.coco = gts
self.results = results
if cvtools.is_str(gts):
self.coco = cvtools.COCO(gts)
if cvtools.is_str(results):
self.results = cvtools.load_pkl(results)
self.num_coors = num_coors
self.img_to_results = {} # merge返回结果保存
def __init__(self, anns_file, num_coors=4):
assert num_coors in (4, 8), "不支持的检测位置表示"
self.coco = anns_file
if cvtools.is_str(anns_file):
self.coco = cvtools.COCO(anns_file)
self.results = defaultdict() # 动态创建嵌套字典
self.num_coors = num_coors
def test_MergeCropDetResults():
ann_file = current_path + '/data/DOTA/dota_crop_1024.json'
results = current_path + '/out/DOTA/dets.pkl'
gt = cvtools.COCO2Dets(ann_file)
gt.convert(to_file=results)
dets = MergeCropDetResults(ann_file, results)
merge_dets = dets.merge()
original_ann_file = current_path + '/data/DOTA/dota_x1y1wh_polygon.json'
coco = cvtools.COCO(original_ann_file)
gt_bboxes = []
gt_labels = []
det_results = []
for img_id, img_info in coco.imgs.items():
ann_ids = coco.getAnnIds(imgIds=[img_id])
anns = coco.loadAnns(ann_ids)
gt_bboxes.append(np.array([ann['bbox'] for ann in anns]))
gt_labels.append(np.array([ann['category_id'] for ann in anns]))
bboxes, labels, _ = merge_dets[img_info['file_name']]
dets = [[] for _ in range(len(coco.cats))]
for cls, bbox in zip(labels, bboxes):
dets[cls].append(bbox)
for cls, det in enumerate(dets):
dets[cls] = np.array(det)
det_results.append(dets)
def __init__(self,
img_prefix=None,
ann_file=None,
iof_th=0.7,
small_prop=0.5,
max_objs=100,
overlap=0.1,
size_th=1333):
assert 1.0 >= iof_th >= 0.5
self.img_prefix = img_prefix
if ann_file is not None:
self.ann_file = ann_file
self.coco_dataset = cvtools.load_json(ann_file)
self.COCO = cvtools.COCO(ann_file)
self.catToAnns = defaultdict(list)
if 'annotations' in self.coco_dataset:
for ann in self.coco_dataset['annotations']:
self.catToAnns[ann['category_id']].append(ann)
self.iof_th = iof_th
self.small_prop = small_prop
self.max_objs = max_objs
self.overlap = overlap
self.size_th = size_th
self.imgs = list()
self.img_to_objs = list()
def __init__(self, img_prefix, ann_file=None):
self.img_prefix = img_prefix
if ann_file is not None:
self.ann_file = ann_file
self.coco_dataset = cvtools.load_json(ann_file)
self.COCO = cvtools.COCO(ann_file)
self.catToAnns = defaultdict(list)
if 'annotations' in self.coco_dataset:
for ann in self.coco_dataset['annotations']:
self.catToAnns[ann['category_id']].append(ann)
def __init__(self, img_prefix, ann_file):
super(CocoDatasetForCrop, self).__init__()
self.img_prefix = img_prefix
self.ann_file = ann_file
self.crop_dataset = cvtools.load_json(ann_file)
self.COCO = cvtools.COCO(ann_file)
image_ids = self.COCO.getImgIds()
image_ids.sort()
self.roidb = self.COCO.loadImgs(image_ids)
if cvtools._DEBUG:
self.roidb = self.roidb[:cvtools._NUM_DATA]
def __init__(self,
img_prefix=None,
ann_file=None,
width_size=1920,
height_size=1080,
overlap=0.):
self.img_prefix = img_prefix
if ann_file is not None:
self.ann_file = ann_file
self.coco_dataset = cvtools.load_json(ann_file)
self.COCO = cvtools.COCO(ann_file)
self.catToAnns = defaultdict(list)
if 'annotations' in self.coco_dataset:
for ann in self.coco_dataset['annotations']:
self.catToAnns[ann['category_id']].append(ann)
assert 1920 >= width_size >= 800 and 1080 >= height_size >= 800 and 0.5 >= overlap >= 0.
self.width_size = int(width_size)
self.height_size = int(height_size)
self.overlap = overlap
def __init__(self, ann_file, crop_ann_file, results=None, num_coors=4):
assert num_coors in (4, 8), "不支持的检测位置表示"
self.num_coors = num_coors
self.coco = cvtools.COCO(ann_file)
self.anns = self.coco.anns
self.calc_ious = (bbox_overlaps
if self.num_coors == 4 else poly_overlaps)
if self.num_coors == 4:
self.nms = cvtools.py_cpu_nms
else:
self.nms = poly_nms.poly_gpu_nms
self.results = results
if cvtools.is_str(crop_ann_file):
if results is None:
gt = cvtools.COCO2Dets(crop_ann_file)
self.results = gt.convert()
else:
self.results = crop_ann_file
dets = MergeCropDetResults(crop_ann_file, self.results, self.num_coors)
self.merge_dets = dets.merge(self.nms)
def __init__(self, ann_file):
self.ann_file = ann_file
self.coco_dataset = cvtools.load_json(ann_file)
self.COCO = cvtools.COCO(ann_file)
