def __init__(self,
data_dir,
dictionary_file,
split,
split_ratio=1.0,
img_size=512,
padding=31):
super(COCOSEGMSNAKE, self).__init__()
self.num_classes = 80
self.class_name = COCO_NAMES
self.valid_ids = COCO_IDS
self.cat_ids = {v: i for i, v in enumerate(self.valid_ids)}
self.data_rng = np.random.RandomState(99)
self.eig_val = np.array(COCO_EIGEN_VALUES, dtype=np.float32)
self.eig_vec = np.array(COCO_EIGEN_VECTORS, dtype=np.float32)
self.mean = np.array(COCO_MEAN, dtype=np.float32)[None, None, :]
self.std = np.array(COCO_STD, dtype=np.float32)[None, None, :]
self.split = split
self.dictionary_file = dictionary_file
self.data_dir = data_dir
self.img_dir = os.path.join(self.data_dir, '%s2017' % split)
if split == 'test':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'image_info_test-dev2017.json')
else:
self.annot_path = os.path.join(self.data_dir, 'annotations',
'instances_%s2017.json' % split)
self.max_objs = 128
self.padding = padding
self.down_ratio = 4
self.img_size = {'h': img_size, 'w': img_size}
self.fmap_size = {
'h': img_size // self.down_ratio,
'w': img_size // self.down_ratio
}
self.rand_scales = np.arange(0.6, 1.3, 0.1)
self.gaussian_iou = 0.7
self.n_vertices = 32
self.n_codes = 64
self.sparse_alpha = 0.01
print('==> initializing coco 2017 %s data.' % split)
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.dictionary = np.load(
self.dictionary_file) # ndarray, shape (n_coeffs, n_vertices * 2)
if 0 < split_ratio < 1:
split_size = int(
np.clip(split_ratio * len(self.images), 1, len(self.images)))
self.images = self.images[:split_size]
self.num_samples = len(self.images)
print('Loaded %d %s samples' % (self.num_samples, split))
def __init__(self, opt, split):
super(COCO, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, "images",
'{}2014'.format(split))
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'image_info_test-dev2017.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'instances_{}2014.json').format(split)
self.max_objs = 128
self.class_name = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase',
'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife',
'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
self._valid_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 84, 85, 86, 87, 88, 89, 90
]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def evaluate_coco():
c = coco.COCO(minival_gt_file)
cocoDt = c.loadRes(minival_det_file)
cocoEval = COCOeval(c, cocoDt, 'bbox')
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print(cocoEval.stats[0])
def __init__(self, cfg, split='train', augment=True):
super(COCO, self).__init__()
self.data_dir = cfg.data_dir
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
self.annot_path = os.path.join(self.data_dir, 'annotations',
'instances_{}2017.json').format(split)
self.split = split
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
self.class_name = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase',
'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife',
'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
self._valid_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 84, 85, 86, 87, 88, 89, 90
]
if cfg.class_name != '*':
self._valid_ids = [self.class_name.index(cfg.class_name)]
self.class_name = [cfg.class_name]
catIds = self.coco.getCatIds(self.class_name[-1])
assert catIds == self._valid_ids
self.images = self.coco.getImgIds(self.images, catIds)
self.num_samples = len(self.images)
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.input_w = cfg.input_w
self.input_h = cfg.input_h
self.base_stride = cfg.base_stride
self.base_window = cfg.base_window
self.k = cfg.k
self.num_class = len(self.class_name)
self.augment = augment
self.max_objs = cfg.max_objs
self.jitter = cfg.jitter
self.cfg = cfg
if not self.augment:
self.jitter = 0
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(KITTI, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'kitti')
# todo: check if this works
self.img_dir = os.path.join(self.data_dir, 'training', 'image_2')
if opt.trainval:
split = 'trainval' if split == 'train' else 'test'
self.img_dir = os.path.join(self.data_dir, 'images', split)
self.annot_path = os.path.join(self.data_dir, 'annotations',
'kitti_{}.json').format(split)
elif split == 'test':
self.img_dir = os.path.join(self.data_dir, 'testing', 'image_2')
self.annot_path = os.path.join(self.data_dir, 'annotations',
'kitti_{}.json').format(split)
elif split == "video":
self.img_dir = os.path.join(self.data_dir,
'2011_09_30_drive_0027_sync', 'data')
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'kitti_2011_09_30_drive_0027_sync.json')
else:
self.annot_path = os.path.join(self.data_dir, 'annotations',
'kitti_{}_{}.json').format(
opt.kitti_split, split)
self.max_objs = 30
self.class_name = ['__background__', 'Car', 'Pedestrian', 'Cyclist']
# Pedestrian: 1, Car: 2, Cyclist: 3
self.cat_ids = {
1: 1,
2: 0,
3: 2,
4: -3,
5: -3,
6: -2,
7: -99,
8: -99,
9: -1
}
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
self.split = split
self.opt = opt
self.alpha_in_degree = False
print('==> initializing kitti {}, {} data.'.format(
opt.kitti_split, split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(FoggyCityscapes, self).__init__()
self.data_dir = os.path.join(
opt.data_dir,
'foggy_cityscapes_data/coco_foggy_cityscapes') # check
self.img_dir = os.path.join(self.data_dir, 'images') # check
if split == 'val':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'foggy_instancesonly_filtered_gtFine_val.json') # check
elif split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'foggy_instancesonly_filtered_gtFine_val.json') # check
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'foggy_instancesonly_filtered_gtFine_train.json') # check
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'foggy_instancesonly_filtered_gtFine_train.json') # check
self.max_objs = 128
self.class_name = [ # check
'person',
'rider',
'car',
'truck',
'bus',
'train',
'motorcycle',
'bicycle',
]
self._valid_ids = [1, 2, 3, 4, 5, 6, 7, 8] # check
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco_foggy_cityscapes {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def test_single_scale():
cocoGt = cc.COCO("annotations/person_keypoints_val2017.json")
cocoDt = cocoGt.loadRes("coco_result.json")
cocoEval = ce.COCOeval(cocoGt, cocoDt, 'keypoints')
cocoEval.params.imgIds = cocoGt.getImgIds()
cocoEval.evaluate()
cocoEval.accumulate()
print("Single Scale")
cocoEval.summarize()
def init_coco(self):
# only import this dependency on demand
import pycocotools.coco as coco
self.coco = coco.COCO(self.annotation_file)
ann_ids = self.coco.getAnnIds([])
self.anns = self.coco.loadAnns(ann_ids)
self.label_map = {k - 1: v for k, v in self.coco.cats.items()}
self.filename_to_anns = dict()
self.build_filename_to_anns_dict()
def coco_bbox_eval(result_file, annotation_file):
ann_type = 'bbox'
coco_gt = COCO.COCO(annotation_file)
coco_dt = coco_gt.loadRes(result_file)
cocoevaler = COCOeval.COCOeval(coco_gt, coco_dt, ann_type)
cocoevaler.evaluate()
cocoevaler.accumulate()
cocoevaler.summarize()
def adjust_detections():
c = coco.COCO(minival_gt_file)
keys = list(c.cats.keys())
detections_list = json.load(open(minival_det_file))
for det in detections_list:
det['category_id'] = keys[det['category_id']]
json.dump(
detections_list,
open("/home/krause/vision/savitar2/forwarded/temp_edited.json", 'w'))
def __init__(self, opt, split):
super(BDD_Daytime, self).__init__()
self.data_dir = os.path.join(opt.data_dir,
'bdd_data\\bdd_daytime_city') # check
self.img_dir = os.path.join(self.data_dir, 'images') # check
if split == 'val':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'bdd_daytime_val.json') # check
elif split == 'test':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'bdd_daytime_val.json') # check
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'bdd_daytime_train.json') # check
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'bdd_daytime_train.json') # check
self.max_objs = 128
self.class_name = [ # check
"person",
"rider",
"car",
"bus",
"truck",
"bike",
"motor",
"traffic light",
"traffic sign",
"train",
]
self._valid_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # check
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco_bdd_daytime {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(CAMERA, self).__init__()
self.data_dir = opt.data_dir
self.img_dir = {}
for sensor in self.sensor_list:
self.img_dir[sensor] = os.path.join(self.data_dir, sensor, 'images')
if split == 'val':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'val.json')
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'train.json')
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
opt.test_dataset+'.json')
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'train.json')
self.max_objs = 100
if len(cf.categories)==5:
self.class_name = [
'__background__', 'bike', 'car', 'car_stop', 'color_cone', 'person']
self._valid_ids = [0, 1, 2, 3, 4, 5]
else:
self.class_name = [
'__background__', 'bike', 'car', 'color_cone', 'person']
self._valid_ids = [0, 1, 2, 3, 4]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([
[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]
], dtype=np.float32)
self.split = split
self.opt = opt
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(MHP, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'image_info_test-dev2017.json').format(split)
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'instances_extreme_{}2017.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'instances_{}2017.json').format(split)
self.max_objs = 128
self.class_name = [
"cap/hat", "helmet", "face", "hair", "left-arm", "right-arm",
"left-hand", "right-hand", "protector", "bikini-bra",
"jacket-windbreaker-hoodie", "t-shirt", "polo-shirt", "sweater",
"sin-glet", "torso-skin", "pants", "shorts-swim-shorts", "skirt",
"stock-ings", "socks", "left-boot", "right-boot", "left-shoe",
"right-shoe", "left-highheel", "right-highheel", "left-sandal",
"right-sandal", "left-leg", "right-leg", "left-foot", "right-foot",
"coat", "dress", "robe", "jumpsuits", "other-full-body-clothes",
"headwear", "backpack", "ball", "bats", "belt", "bottle",
"carrybag", "cases", "sunglasses", "eyewear", "gloves", "scarf",
"umbrella", "wallet-purse", "watch", "wristband", "tie",
"other-accessaries", "other-upper-body-clothes",
"other-lower-body-clothes"
]
self._valid_ids = range(1, 59)
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(Graduation, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'graduation')
self.img_dir = os.path.join(self.data_dir, 'images')
if split == 'val':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'train.json').format(split)
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'train.json').format(split)
else:
self.annot_path = os.path.join(self.data_dir, 'annotations',
'train.json').format(split)
self.max_objs = 128
self.class_name = [
'__background__',
'fishing_boat',
'river_boat',
'container_ship',
'speedboat',
'official_ship',
'bulker',
'cruise',
'ferry',
'tug',
'tanker',
'engineering_ship',
'RoRo_ship',
'timber_ship',
'LPG_ship',
]
self._valid_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, config, split):
super(COCO, self).__init__()
config = config.DATASET
self.data_dir = config.DATA_DIR
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
self.annot_path = os.path.join(self.data_dir, 'annotations',
'instances_{}2017.json').format(split)
self.class_name = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase',
'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife',
'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
self._valid_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 84, 85, 86, 87, 88, 89, 90
]
self.max_objs = 80
self.num_classes = 80
self.mean = np.array([0.40789654, 0.44719302, 0.47026115],
dtype=np.float32).reshape(1, 1, 3)
self.std = np.array([0.28863828, 0.27408164, 0.27809835],
dtype=np.float32).reshape(1, 1, 3)
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self.split = split
self.config = config
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self,
config,
subset,
coord,
fraction=1.0,
ignore_classes=IGNORE_CLASSES,
num_classes=NUM_CLASSES):
super(COCODataset, self).__init__("coco",
COCO_DEFAULT_PATH,
num_classes,
config,
subset,
coord,
INPUT_SIZE,
COCO_VOID_LABEL,
fraction,
label_load_fn=self.label_load_fn,
img_load_fn=self.img_load_fn,
ignore_classes=ignore_classes)
if subset == "train":
self.data_type = "train2014"
self.filter_crowd_images = config.bool("filter_crowd_images",
False)
self.min_box_size = config.float("min_box_size", -1.0)
else:
self.data_type = "val2014"
self.filter_crowd_images = False
self.min_box_size = config.float("min_box_size_val", -1.0)
# Use the minival split as done in https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/README.md
self.annotation_file = '%s/annotations/instances_%s.json' % (
self.data_dir, subset)
self.restricted_image_category_list = config.unicode_list(
"restricted_image_category_list", [])
if len(self.restricted_image_category_list) == 0:
self.restricted_image_category_list = None
self.restricted_annotations_category_list = config.unicode_list(
"restricted_annotations_category_list", [])
if len(self.restricted_annotations_category_list) == 0:
self.restricted_annotations_category_list = None
#either both of them or none should be specified for now to avoid unintuitive behaviour
assert (self.restricted_image_category_list is None and self.restricted_annotations_category_list is None) or \
(self.restricted_image_category_list is not None and self.restricted_annotations_category_list is not None),\
(self.restricted_image_category_list, self.restricted_annotations_category_list)
# only import this dependency on demand
import pycocotools.coco as coco
self.coco = coco.COCO(self.annotation_file)
ann_ids = self.coco.getAnnIds([])
self.anns = self.coco.loadAnns(ann_ids)
self.label_map = {k - 1: v for k, v in list(self.coco.cats.items())}
self.filename_to_anns = dict()
self.build_filename_to_anns_dict()
def __init__(self, opt, split):
super(JAC_COCO_36, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'Jacquard')
if split:
self.img_dir = os.path.join(self.data_dir, 'coco/512_cnt_angle',
split, 'grasps_{}2018'.format(split))
if opt.flag_test:
self.annot_path = os.path.join(
self.data_dir, 'coco/512_cnt_angle', split,
'instances_grasps_{}2018.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'coco/512_cnt_angle', split,
'instances_grasps_{}2018_filter.json').format(split)
self.max_objs = 128
self.avg_h = 20.
self.class_name = [
"__background__", "orient01", "orient02", "orient03", "orient04",
"orient05", "orient06", "orient07", "orient08", "orient09",
"orient10", "orient11", "orient12", "orient13", "orient14",
"orient15", "orient16", "orient17", "orient18", "orient19",
"orient20", "orient21", "orient22", "orient23", "orient24",
"orient25", "orient26", "orient27", "orient28", "orient29",
"orient30", "orient31", "orient32", "orient33", "orient34",
"orient35", "orient36"
]
self._valid_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36
]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)} # rx
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
self.split = split
self.opt = opt
print(
'==> initializing jacquard dataset in coco format {} data.'.format(
split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()[:]
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def process_coco_edge_box():
pool = Pool(processes=8)
db = coco.COCO(
'/media/zawlin/ssd/coco/annotations/captions_train2014.json')
cnt = 0
for k in db.imgs:
cnt += 1
im = db.imgs[k]
pool.apply_async(do_one_image, (im['file_name'], ))
pool.close()
pool.join()
def __init__(self,
data_dir,
split,
split_ratio=1.0,
gaussian=True,
img_size=511):
super(COCO, self).__init__()
self.split = split
self.gaussian = gaussian
self.down_ratio = 4
self.img_size = {'h': img_size, 'w': img_size}
self.fmap_size = {
'h': (img_size + 1) // self.down_ratio,
'w': (img_size + 1) // self.down_ratio
}
self.padding = 128
self.data_rng = np.random.RandomState(123)
self.rand_scales = np.arange(0.6, 1.4, 0.1)
self.gaussian_iou = 0.3
self.data_dir = os.path.join(data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, '%s2017' % split)
if split == 'test':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'image_info_test-dev2017.json')
else:
self.annot_path = os.path.join(self.data_dir, 'annotations',
'instances_%s2017.json' % split)
self.num_classes = 80
self.class_name = COCO_NAMES
self.valid_ids = COCO_IDS
self.cat_ids = {v: i for i, v in enumerate(self.valid_ids)}
self.max_objs = 128
self.eig_val = np.array(COCO_EIGEN_VALUES, dtype=np.float32)
self.eig_vec = np.array(COCO_EIGEN_VECTORS, dtype=np.float32)
self.mean = np.array(COCO_MEAN, dtype=np.float32)[None, None, :]
self.std = np.array(COCO_STD, dtype=np.float32)[None, None, :]
print('==> initializing coco 2017 %s data.' % split)
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
if 0 < split_ratio < 1:
split_size = int(
np.clip(split_ratio * len(self.images), 1, len(self.images)))
self.images = self.images[:split_size]
self.num_samples = len(self.images)
print('Loaded %d %s samples' % (self.num_samples, split))
def eval(gt_file, det_file):
coco_gt = coco.COCO(gt_file)
coco_dets = coco_gt.loadRes(det_file)
coco_eval = COCOeval(coco_gt, coco_dets, "bbox")
coco_eval.params.maxDets = [200]
coco_eval.params.iouThrs = np.array([0.5])
coco_eval.params.fppiThrs = np.logspace(-2, 0, 9)
coco_eval.evaluate()
accumulate(coco_eval)
summarize(coco_eval)
return coco_eval
def LoadAnnotations(self, annotations):
"""Load annotations dictionary into COCO datastructure.
See http://mscoco.org/dataset/#format for a description of the annotations
format. As above, this function replicates the default behavior of the API
but does not require writing to external storage.
Args:
annotations: python list holding object detection results where each
detection is encoded as a dict with required keys ['image_id',
'category_id', 'score'] and one of ['bbox', 'segmentation'] based on
`detection_type`.
Returns:
a coco.COCO datastructure holding object detection annotations results
Raises:
ValueError: if annotations is not a list
ValueError: if annotations do not correspond to the images contained
in self.
"""
results = coco.COCO()
results.dataset['images'] = [img for img in self.dataset['images']]
# tf.logging.info('Loading and preparing annotation results...')
print('Loading and preparing annotation results...')
tic = time.time()
if not isinstance(annotations, list):
raise ValueError('annotations is not a list of objects')
annotation_img_ids = [ann['image_id'] for ann in annotations]
if (set(annotation_img_ids) != (set(annotation_img_ids)
& set(self.getImgIds()))):
raise ValueError('Results do not correspond to current coco set')
results.dataset['categories'] = copy.deepcopy(
self.dataset['categories'])
if self._detection_type == 'bbox':
for idx, ann in enumerate(annotations):
bb = ann['bbox']
ann['area'] = bb[2] * bb[3]
ann['id'] = idx + 1
ann['iscrowd'] = 0
elif self._detection_type == 'segmentation':
for idx, ann in enumerate(annotations):
ann['area'] = mask.area(ann['segmentation'])
ann['bbox'] = mask.toBbox(ann['segmentation'])
ann['id'] = idx + 1
ann['iscrowd'] = 0
# tf.logging.info('DONE (t=%0.2fs)', (time.time() - tic))
print('DONE (t=%0.2fs)', (time.time() - tic))
results.dataset['annotations'] = annotations
results.createIndex()
return results
def __init__(self, opt, dataset):
self.images = dataset.images
self.load_image_func = dataset.coco.loadImgs
self.img_dir = dataset.img_dir
self.get_default_calib = dataset.get_default_calib
self.opt = opt
split_name = "val"
data_dir = os.path.join(opt.data_dir, "nuscenes")
ann_path = os.path.join(data_dir, "annotations",
"{}{}.json").format(opt.dataset_version,
split_name)
self.coco = coco.COCO(ann_path)
def _test_dataset(annotation_file):
coco = cocoapi.COCO(annotation_file)
# Load categories from annotation file
cats = [cat['name'] for cat in coco.loadCats(coco.getCatIds())]
classes = ['__background__'] + cats
num_classes = len(classes)
_class_to_ind = dict(zip(classes, range(num_classes)))
_class_to_coco_ind = dict(zip(cats, coco.getCatIds()))
_coco_ind_to_class_ind = dict([(_class_to_coco_ind[cls],
_class_to_ind[cls])
for cls in classes[1:]])
def __init__(self, opt, split):
super(VisDrone, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'VISDRONE')
self.data_dir = os.path.join(self.data_dir, 'Images')
if split == 'test':
self.data_dir = os.path.join(self.data_dir, 'VisDrone2018-DET-val')
self.img_dir = os.path.join(self.data_dir, 'images')
self.annot_dir = os.path.join(self.data_dir, 'annotations')
self.annot_path = os.path.join(self.annot_dir,
'instances.json').format(split)
else:
self.data_dir = os.path.join(self.data_dir,
'VisDrone2018-DET-train')
self.img_dir = os.path.join(self.data_dir, 'images')
self.annot_dir = os.path.join(self.data_dir, 'annotations')
self.annot_path = os.path.join(self.annot_dir,
'instances.json').format(split)
self.max_objs = 512
self.class_name = [
'Ignored Regions', 'Pedestrian', 'People', 'Bicycle', 'Car', 'Van',
'Truck', 'Tricycle', 'Awning-tricycle', 'Bus', 'Motorbike', 'Other'
]
self._valid_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
self.mean = np.array([0.485, 0.456, 0.406],
np.float32).reshape(1, 1, 3)
self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing visdrone {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(COCOHP, self).__init__()
self.edges = [[0, 1], [1, 2], [2, 3], [3, 0]]
# self.edges = [[0, 1], [0, 2], [1, 3], [2, 4],
# [4, 6], [3, 5], [5, 6],
# [5, 7], [7, 9], [6, 8], [8, 10],
# [6, 12], [5, 11], [11, 12],
# [12, 14], [14, 16], [11, 13], [13, 15]]
self.acc_idxs = [1, 2, 3, 4]
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, 'images', '{}4582'.format(split))
# self.img_dir = os.path.join(self.data_dir, 'images', '{}2017'.format(split))
if split == 'test':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'parking_keypoints_{}4582.json').format(split)
# 'image_info_test-dev2017.json').format(split)
else:
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'parking_keypoints_{}4582.json').format(split)
# 'person_keypoints_{}2017.json').format(split)
self.max_objs = 64
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([
[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]
], dtype=np.float32)
self.split = split
self.opt = opt
# print('==> initializing coco 2017 {} data.'.format(split))
print('==> initializing coco 4582 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
image_ids = self.coco.getImgIds()
if split == 'train':
# if split == 'trainval':
self.images = []
for img_id in image_ids:
idxs = self.coco.getAnnIds(imgIds=[img_id])
if len(idxs) > 0:
self.images.append(img_id)
else:
self.images = image_ids
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def evaluate(args):
data_dir = args.dataset_root
data_type = 'val2014'
ann_file = "{0}/annotations/{1}_{2}.cars.json".format(data_dir, 'instances', data_type)
results_file = "predictions.json"
coco_gt = coco.COCO(ann_file)
coco_det = coco_gt.loadRes(results_file)
coco_eval = cocoeval.COCOeval(coco_gt, coco_det, 'bbox')
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
def __init__(
self,
ann_file: str,
img_dir: str,
stage: str = "train",
transforms=A.Compose([A.ToFloat()]),
):
super().__init__()
self.coco = coco.COCO(ann_file)
self.ids = list(sorted(self.coco.imgs.keys()))
self.img_dir = img_dir
self.stage = stage
self.transforms = transforms
def __init__(self, opt, split):
super(COCO, self).__init__()
self.data_dir = os.path.join(opt.data_dir, 'coco')
self.img_dir = os.path.join(self.data_dir, '{}2017'.format(split))
if split == 'test':
self.annot_path = os.path.join(self.data_dir, "validation",
'vis_val.json')
else:
if opt.task == 'exdet':
self.annot_path = os.path.join(
self.data_dir, 'annotations',
'instances_extreme_{}2017.json').format(split)
else:
self.annot_path = os.path.join(self.data_dir, 'annotations',
'new.json').format(split)
self.max_objs = 128
self.class_name = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat'
]
self._valid_ids = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78,
79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90
]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
# print(self.coco.loadImgs([1]))
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(Driving, self).__init__()
self.data_dir = '/scratch/jl5/'
self.img_dir = os.path.join(self.data_dir, 'driving1000')
if split == 'test':
self.annot_path = '/data2/jl5/mmdetect_results/driving1000/fifth_test.json'
else:
self.annot_path = '/data2/jl5/mmdetect_results/driving1000/fifth_train.json'
self.modify_json(self.annot_path, opt.data_thresh)
self.annot_path = '/scratch/jl5/fifth_train.json'
self.max_objs = 128
self.class_name = [
'__background__', 'person', 'bicycle', 'car', 'motorcycle',
'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase',
'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife',
'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
self._valid_ids = [i for i in range(1, 81)]
self.cat_ids = {v: i for i, v in enumerate(self._valid_ids)}
self.voc_color = [(v // 32 * 64 + 64, (v // 8) % 4 * 64, v % 8 * 32) \
for v in range(1, self.num_classes + 1)]
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
# self.mean = np.array([0.485, 0.456, 0.406], np.float32).reshape(1, 1, 3)
# self.std = np.array([0.229, 0.224, 0.225], np.float32).reshape(1, 1, 3)
self.split = split
self.opt = opt
print('==> initializing coco 2017 {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = self.coco.getImgIds()
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))
def __init__(self, opt, split):
super(Cigar, self).__init__()
dt_name = 'cigar_box' # todo: rect box
self.data_dir = os.path.join(opt.data_dir, dt_name)
self.img_dir = '' # as file_name is absolute path
if split == 'test':
self.annot_path = os.path.join(self.data_dir, 'annotations',
'{}_{}_{}.json').format(
Cigar.num_classes, dt_name,
split)
else:
if opt.task == 'exdet': # train or val
self.annot_path = os.path.join(self.data_dir, 'annotations',
'{}_{}_{}.json').format(
Cigar.num_classes, dt_name,
split)
else: # ctdet,..?
self.annot_path = os.path.join(self.data_dir, 'annotations',
'{}_{}_{}.json').format(
Cigar.num_classes, dt_name,
split)
self.max_objs = 10
# self.max_objs = 128
self.class_name = [
'__background__', 'DaZhongJiu_A', 'YunYan_a', 'JiaoZi_B',
'ZhongHua_B', 'LiQun_a', 'HuangHeLou_e', 'YunYan_A', 'JiaoZi_F',
'HuangHeLou_h', 'HuangHeLou_E', 'HuangJinYe_C', '555_a',
'HongTaShan_b', 'YuXi_A', 'HuangGuoShu_a', 'JiaoZi_K',
'HuangHeLou_A', 'JiaoZi_E', 'TianZi_a', 'TianZi_A'
]
# note: _valid_ids same to real cat_id in xx.json
self._valid_ids = np.arange(1, 21, dtype=np.int32)
self.cat_ids = {v: i
for i, v in enumerate(self._valid_ids)} # value, idx
self._data_rng = np.random.RandomState(123)
self._eig_val = np.array([0.2141788, 0.01817699, 0.00341571],
dtype=np.float32)
self._eig_vec = np.array([[-0.58752847, -0.69563484, 0.41340352],
[-0.5832747, 0.00994535, -0.81221408],
[-0.56089297, 0.71832671, 0.41158938]],
dtype=np.float32)
self.split = split
self.opt = opt
print('==> initializing cigar {} data.'.format(split))
self.coco = coco.COCO(self.annot_path)
self.images = sorted(self.coco.getImgIds())
self.num_samples = len(self.images)
print('Loaded {} {} samples'.format(split, self.num_samples))