class coco(imdb):
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'use_salt': True, 'cleanup': True}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(
list(zip(self.classes, list(range(self.num_classes)))))
self._class_to_coco_cat_id = dict(
list(zip([c['name'] for c in cats], self._COCO.getCatIds())))
self._image_index = self._load_image_set_index()
# Default to roidb handler
self.set_proposal_method('gt')
self.competition_mode(False)
# Some image sets are "views" (i.e. subsets) into others.
# For example, minival2014 is a random 5000 image subset of val2014.
# This mapping tells us where the view's images and proposals come from.
self._view_map = {
'minival2014': 'val2014', # 5k val2014 subset
'valminusminival2014': 'val2014', # val2014 \setminus minival2014
'test-dev2015': 'test2015',
'valminuscapval2014': 'val2014',
'capval2014': 'val2014',
'captest2014': 'val2014'
}
coco_name = image_set + year # e.g., "val2014"
self._data_name = (self._view_map[coco_name]
if coco_name in self._view_map else coco_name)
# Dataset splits that have ground-truth annotations (test splits
# do not have gt annotations)
self._gt_splits = ('train', 'val', 'minival')
def _get_ann_file(self):
prefix = 'instances' if self._image_set.find('test') == -1 \
else 'image_info'
return osp.join(self._data_path, 'annotations',
prefix + '_' + self._image_set + self._year + '.json')
def _load_image_set_index(self):
"""
Load image ids.
"""
image_ids = self._COCO.getImgIds()
return image_ids
def _get_widths(self):
anns = self._COCO.loadImgs(self._image_index)
widths = [ann['width'] for ann in anns]
return widths
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_id_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self._image_index[i]
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
# Example image path for index=119993:
# images/train2014/COCO_train2014_000000119993.jpg
file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) +
'.jpg')
image_path = osp.join(self._data_path, 'images', self._data_name,
file_name)
assert osp.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
if osp.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = pickle.load(fid)
print('{} gt roidb loaded from {}'.format(self.name, cache_file))
return roidb
gt_roidb = [
self._load_coco_annotation(index) for index in self._image_index
]
with open(cache_file, 'wb') as fid:
pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote gt roidb to {}'.format(cache_file))
return gt_roidb
def _load_coco_annotation(self, index):
"""
Loads COCO bounding-box instance annotations. Crowd instances are
handled by marking their overlaps (with all categories) to -1. This
overlap value means that crowd "instances" are excluded from training.
"""
im_ann = self._COCO.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
objs = self._COCO.loadAnns(annIds)
# Sanitize bboxes -- some are invalid
valid_objs = []
for obj in objs:
x1 = np.max((0, obj['bbox'][0]))
y1 = np.max((0, obj['bbox'][1]))
x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
objs = valid_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Lookup table to map from COCO category ids to our internal class
# indices
coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
self._class_to_ind[cls])
for cls in self._classes[1:]])
for ix, obj in enumerate(objs):
cls = coco_cat_id_to_class_ind[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
overlaps[ix, :] = -1.0
else:
overlaps[ix, cls] = 1.0
ds_utils.validate_boxes(boxes, width=width, height=height)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {
'width': width,
'height': height,
'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas
}
def _get_widths(self):
return [r['width'] for r in self.roidb]
def append_flipped_images(self):
num_images = self.num_images
widths = self._get_widths()
for i in range(num_images):
boxes = self.roidb[i]['boxes'].copy()
oldx1 = boxes[:, 0].copy()
oldx2 = boxes[:, 2].copy()
boxes[:, 0] = widths[i] - oldx2 - 1
boxes[:, 2] = widths[i] - oldx1 - 1
assert (boxes[:, 2] >= boxes[:, 0]).all()
entry = {
'width': widths[i],
'height': self.roidb[i]['height'],
'boxes': boxes,
'gt_classes': self.roidb[i]['gt_classes'],
'gt_overlaps': self.roidb[i]['gt_overlaps'],
'flipped': True,
'seg_areas': self.roidb[i]['seg_areas']
}
self.roidb.append(entry)
self._image_index = self._image_index * 2
def _get_box_file(self, index):
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) +
'.mat')
return osp.join(file_name[:14], file_name[:22], file_name)
def _print_detection_eval_metrics(self, coco_eval):
IoU_lo_thresh = 0.5
IoU_hi_thresh = 0.95
def _get_thr_ind(coco_eval, thr):
ind = np.where((coco_eval.params.iouThrs > thr - 1e-5)
& (coco_eval.params.iouThrs < thr + 1e-5))[0][0]
iou_thr = coco_eval.params.iouThrs[ind]
assert np.isclose(iou_thr, thr)
return ind
ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh)
ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh)
# precision has dims (iou, recall, cls, area range, max dets)
# area range index 0: all area ranges
# max dets index 2: 100 per image
precision = \
coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
ap_default = np.mean(precision[precision > -1])
print(('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] '
'~~~~').format(IoU_lo_thresh, IoU_hi_thresh))
print('{:.1f}'.format(100 * ap_default))
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
# minus 1 because of __background__
precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :,
cls_ind - 1, 0, 2]
ap = np.mean(precision[precision > -1])
print('{:.1f}'.format(100 * ap))
print('~~~~ Summary metrics ~~~~')
coco_eval.summarize()
def _do_detection_eval(self, res_file, output_dir):
ann_type = 'bbox'
coco_dt = self._COCO.loadRes(res_file)
coco_eval = COCOeval(self._COCO, coco_dt)
coco_eval.params.useSegm = (ann_type == 'segm')
coco_eval.evaluate()
coco_eval.accumulate()
self._print_detection_eval_metrics(coco_eval)
eval_file = osp.join(output_dir, 'detection_results.pkl')
with open(eval_file, 'wb') as fid:
pickle.dump(coco_eval, fid, pickle.HIGHEST_PROTOCOL)
print('Wrote COCO eval results to: {}'.format(eval_file))
def _coco_results_one_category(self, boxes, cat_id):
results = []
for im_ind, index in enumerate(self.image_index):
dets = boxes[im_ind].astype(np.float)
if dets == []:
continue
scores = dets[:, -1]
xs = dets[:, 0]
ys = dets[:, 1]
ws = dets[:, 2] - xs + 1
hs = dets[:, 3] - ys + 1
results.extend([{
'image_id': index,
'category_id': cat_id,
'bbox': [xs[k], ys[k], ws[k], hs[k]],
'score': scores[k]
} for k in range(dets.shape[0])])
return results
def _write_coco_results_file(self, all_boxes, res_file):
# [{"image_id": 42,
# "category_id": 18,
# "bbox": [258.15,41.29,348.26,243.78],
# "score": 0.236}, ...]
results = []
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Collecting {} results ({:d}/{:d})'.format(
cls, cls_ind, self.num_classes - 1))
coco_cat_id = self._class_to_coco_cat_id[cls]
results.extend(
self._coco_results_one_category(all_boxes[cls_ind],
coco_cat_id))
print('Writing results json to {}'.format(res_file))
with open(res_file, 'w') as fid:
json.dump(results, fid)
def evaluate_detections(self, all_boxes, output_dir):
res_file = osp.join(
output_dir,
('detections_' + self._image_set + self._year + '_results'))
if self.config['use_salt']:
res_file += '_{}'.format(str(uuid.uuid4()))
res_file += '.json'
self._write_coco_results_file(all_boxes, res_file)
# Only do evaluation on non-test sets
if self._image_set.find('test') == -1:
self._do_detection_eval(res_file, output_dir)
# Optionally cleanup results json file
if self.config['cleanup']:
os.remove(res_file)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True
def load_coco(self,
dataset_dir,
subsets,
class_ids=None,
class_map=None,
return_coco=False):
"""Load a subset of the COCO dataset.
dataset_dir: The root directory of the COCO dataset.
subset: What to load (train, val, minival, val35k)
class_ids: If provided, only loads images that have the given classes.
class_map: TODO: Not implemented yet. Supports maping classes from
different datasets to the same class ID.
return_coco: If True, returns the COCO object.
"""
# Path
self._view_map = {
'minival2014': 'val2014', # 5k val2014 subset
'valminusminival2014': 'val2014', # val2014 \setminus minival2014
'test-dev2015': 'test2015',
}
self.image_ids = []
for subset in subsets:
image_dir = os.path.join(
dataset_dir, 'images',
"train2014" if subset == "train" else "val2014")
# Create COCO object
json_path_dict = {
"train": "annotations/instances_train2014.json",
"val": "annotations/instances_val2014.json",
"minival": "annotations/instances_minival2014.json",
"val35k": "annotations/instances_valminusminival2014.json",
}
coco = COCO(os.path.join(dataset_dir, json_path_dict[subset]))
image_ids = []
# Load all classes or a subset?
if not class_ids:
# All classes
class_ids = sorted(coco.getCatIds())
# All images or a subset?
if class_ids:
for id in class_ids:
image_ids.extend(list(coco.getImgIds(catIds=[id])))
# Remove duplicates
image_ids = list(set(image_ids))
else:
# All images
image_ids = list(coco.imgs.keys())
# Add classes
for i in class_ids:
self.add_class("coco", i, coco.loadCats(i)[0]["name"])
# Add images
self.image_ids += image_ids
for i in image_ids:
self.add_image("coco",
image_id=i,
path=os.path.join(image_dir,
coco.imgs[i]['file_name']),
width=coco.imgs[i]["width"],
height=coco.imgs[i]["height"],
annotations=coco.loadAnns(
coco.getAnnIds(imgIds=[i],
catIds=class_ids,
iscrowd=None)))
print(subset)
print(len(self.image_info))
if return_coco:
return coco
def load_coco(self,
dataset_dir,
subset,
year=DEFAULT_DATASET_YEAR,
class_ids=None,
class_map=None,
return_coco=False,
auto_download=False):
"""Load a subset of the COCO dataset.
dataset_dir: The root directory of the COCO dataset.
subset: What to load (train, val, minival, valminusminival)
year: What dataset year to load (2014, 2017) as a string, not an integer
class_ids: If provided, only loads images that have the given classes.
class_map: TODO: Not implemented yet. Supports maping classes from
different datasets to the same class ID.
return_coco: If True, returns the COCO object.
auto_download: Automatically download and unzip MS-COCO images and annotations
"""
if auto_download is True:
self.auto_download(dataset_dir, subset, year)
coco = COCO("{}/annotations/instances_{}{}.json".format(
dataset_dir, subset, year))
if subset == "minival" or subset == "valminusminival":
subset = "val"
image_dir = "{}/{}{}".format(dataset_dir, subset, year)
# Load all classes or a subset?
if not class_ids:
# All classes
class_ids = sorted(coco.getCatIds())
# All images or a subset?
if class_ids:
image_ids = []
for id in class_ids:
image_ids.extend(list(coco.getImgIds(catIds=[id])))
# Remove duplicates
image_ids = list(set(image_ids))
else:
# All images
image_ids = list(coco.imgs.keys())
# Add classes
for i in class_ids:
self.add_class("coco", i, coco.loadCats(i)[0]["name"])
# Add images
for i in image_ids:
self.add_image("coco",
image_id=i,
path=os.path.join(image_dir,
coco.imgs[i]['file_name']),
width=coco.imgs[i]["width"],
height=coco.imgs[i]["height"],
annotations=coco.loadAnns(
coco.getAnnIds(imgIds=[i],
catIds=class_ids,
iscrowd=None)))
if return_coco:
return coco
class CocoDetection(Dataset):
"""
Adapted from the torchvision code
"""
def __init__(self, mode):
"""
:param mode: train2014 or val2014
"""
self.mode = mode
self.root = os.path.join(COCO_PATH, mode)
self.ann_file = os.path.join(COCO_PATH, 'annotations',
'instances_{}.json'.format(mode))
self.coco = COCO(self.ann_file)
self.ids = [
k for k in self.coco.imgs.keys() if len(self.coco.imgToAnns[k]) > 0
]
tform = []
if self.is_train:
tform.append(
RandomOrder([
Grayscale(),
Brightness(),
Contrast(),
Sharpness(),
Hue(),
]))
tform += [
SquarePad(),
Resize(IM_SCALE),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
self.transform_pipeline = Compose(tform)
self.ind_to_classes = ['__background__'] + [
v['name'] for k, v in self.coco.cats.items()
]
# COCO inds are weird (84 inds in total but a bunch of numbers are skipped)
self.id_to_ind = {
coco_id: (ind + 1)
for ind, coco_id in enumerate(self.coco.cats.keys())
}
self.id_to_ind[0] = 0
self.ind_to_id = {x: y for y, x in self.id_to_ind.items()}
@property
def is_train(self):
return self.mode.startswith('train')
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns: entry dict
"""
img_id = self.ids[index]
path = self.coco.loadImgs(img_id)[0]['file_name']
image_unpadded = Image.open(os.path.join(self.root,
path)).convert('RGB')
ann_ids = self.coco.getAnnIds(imgIds=img_id)
anns = self.coco.loadAnns(ann_ids)
gt_classes = np.array([self.id_to_ind[x['category_id']] for x in anns],
dtype=np.int64)
if np.any(gt_classes >= len(self.ind_to_classes)):
raise ValueError("OH NO {}".format(index))
if len(anns) == 0:
raise ValueError("Annotations should not be empty")
# gt_boxes = np.array((0, 4), dtype=np.float32)
# else:
gt_boxes = np.array([x['bbox'] for x in anns], dtype=np.float32)
if np.any(gt_boxes[:, [0, 1]] < 0):
raise ValueError("GT boxes empty columns")
if np.any(gt_boxes[:, [2, 3]] < 0):
raise ValueError("GT boxes empty h/w")
gt_boxes[:, [2, 3]] += gt_boxes[:, [0, 1]]
# Rescale so that the boxes are at BOX_SCALE
if self.is_train:
image_unpadded, gt_boxes = random_crop(
image_unpadded,
gt_boxes * BOX_SCALE / max(image_unpadded.size),
BOX_SCALE,
round_boxes=False,
)
else:
# Seems a bit silly because we won't be using GT boxes then but whatever
gt_boxes = gt_boxes * BOX_SCALE / max(image_unpadded.size)
w, h = image_unpadded.size
box_scale_factor = BOX_SCALE / max(w, h)
# Optionally flip the image if we're doing training
flipped = self.is_train and np.random.random() > 0.5
if flipped:
scaled_w = int(box_scale_factor * float(w))
image_unpadded = image_unpadded.transpose(Image.FLIP_LEFT_RIGHT)
gt_boxes[:, [0, 2]] = scaled_w - gt_boxes[:, [2, 0]]
img_scale_factor = IM_SCALE / max(w, h)
if h > w:
im_size = (IM_SCALE, int(w * img_scale_factor), img_scale_factor)
elif h < w:
im_size = (int(h * img_scale_factor), IM_SCALE, img_scale_factor)
else:
im_size = (IM_SCALE, IM_SCALE, img_scale_factor)
entry = {
'img': self.transform_pipeline(image_unpadded),
'img_size': im_size,
'gt_boxes': gt_boxes,
'gt_classes': gt_classes,
'scale': IM_SCALE / BOX_SCALE,
'index': index,
'image_id': img_id,
'flipped': flipped,
'fn': path,
}
return entry
@classmethod
def splits(cls, *args, **kwargs):
""" Helper method to generate splits of the dataset"""
train = cls('train2014', *args, **kwargs)
val = cls('val2014', *args, **kwargs)
return train, val
def __len__(self):
return len(self.ids)
class coco(imdb):
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {
'top_k': 2000,
'use_salt': True,
'cleanup': True,
'crowd_thresh': 0.7,
'min_size': 2
}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._class_to_coco_cat_id = dict(
zip([c['name'] for c in cats], self._COCO.getCatIds()))
self._image_index = self._load_image_set_index()
# Default to roidb handler
self.set_proposal_method('selective_search')
self.competition_mode(False)
# Some image sets are "views" (i.e. subsets) into others.
# For example, minival2014 is a random 5000 image subset of val2014.
# This mapping tells us where the view's images and proposals come from.
self._view_map = {
'minival2014': 'val2014', # 5k val2014 subset
'valminusminival2014': 'val2014', # val2014 \setminus minival2014
}
coco_name = image_set + year # e.g., "val2014"
self._data_name = (self._view_map[coco_name]
if self._view_map.has_key(coco_name) else coco_name)
# Dataset splits that have ground-truth annotations (test splits
# do not have gt annotations)
self._gt_splits = ('train', 'val', 'minival')
def _get_ann_file(self):
prefix = 'instances' if self._image_set.find('test') == -1 \
else 'image_info'
return osp.join(self._data_path, 'annotations',
prefix + '_' + self._image_set + self._year + '.json')
def _load_image_set_index(self):
"""
Load image ids.
"""
image_ids = self._COCO.getImgIds()
return image_ids
def _get_widths(self):
anns = self._COCO.loadImgs(self._image_index)
widths = [ann['width'] for ann in anns]
return widths
def image_path_at(self, i):
"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self._image_index[i])
def image_path_from_index(self, index):
"""
Construct an image path from the image's "index" identifier.
"""
# Example image path for index=119993:
# images/train2014/COCO_train2014_000000119993.jpg
file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) +
'.jpg')
image_path = osp.join(self._data_path, 'images', self._data_name,
file_name)
assert osp.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def selective_search_roidb(self):
return self._roidb_from_proposals('selective_search')
def edge_boxes_roidb(self):
return self._roidb_from_proposals('edge_boxes_AR')
def mcg_roidb(self):
return self._roidb_from_proposals('MCG')
def _roidb_from_proposals(self, method):
"""
Creates a roidb from pre-computed proposals of a particular methods.
"""
top_k = self.config['top_k']
cache_file = osp.join(
self.cache_path,
self.name + '_{:s}_top{:d}'.format(method, top_k) + '_roidb.pkl')
if osp.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{:s} {:s} roidb loaded from {:s}'.format(
self.name, method, cache_file)
return roidb
if self._image_set in self._gt_splits:
gt_roidb = self.gt_roidb()
method_roidb = self._load_proposals(method, gt_roidb)
roidb = imdb.merge_roidbs(gt_roidb, method_roidb)
# Make sure we don't use proposals that are contained in crowds
roidb = _filter_crowd_proposals(roidb, self.config['crowd_thresh'])
else:
roidb = self._load_proposals(method, None)
with open(cache_file, 'wb') as fid:
cPickle.dump(roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote {:s} roidb to {:s}'.format(method, cache_file)
return roidb
def _load_proposals(self, method, gt_roidb):
"""
Load pre-computed proposals in the format provided by Jan Hosang:
http://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-
computing/research/object-recognition-and-scene-understanding/how-
good-are-detection-proposals-really/
For MCG, use boxes from http://www.eecs.berkeley.edu/Research/Projects/
CS/vision/grouping/mcg/ and convert the file layout using
lib/datasets/tools/mcg_munge.py.
"""
box_list = []
top_k = self.config['top_k']
valid_methods = [
'MCG', 'selective_search', 'edge_boxes_AR', 'edge_boxes_70'
]
assert method in valid_methods
print 'Loading {} boxes'.format(method)
for i, index in enumerate(self._image_index):
if i % 1000 == 0:
print '{:d} / {:d}'.format(i + 1, len(self._image_index))
box_file = osp.join(cfg.DATA_DIR, 'coco_proposals', method, 'mat',
self._get_box_file(index))
raw_data = sio.loadmat(box_file)['boxes']
boxes = np.maximum(raw_data - 1, 0).astype(np.uint16)
if method == 'MCG':
# Boxes from the MCG website are in (y1, x1, y2, x2) order
boxes = boxes[:, (1, 0, 3, 2)]
# Remove duplicate boxes and very small boxes and then take top k
keep = ds_utils.unique_boxes(boxes)
boxes = boxes[keep, :]
keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
boxes = boxes[keep, :]
boxes = boxes[:top_k, :]
box_list.append(boxes)
# Sanity check
im_ann = self._COCO.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
ds_utils.validate_boxes(boxes, width=width, height=height)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def gt_roidb(self):
"""
Return the database of ground-truth regions of interest.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = osp.join(self.cache_path, self.name + '_gt_roidb.pkl')
if osp.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = cPickle.load(fid)
print '{} gt roidb loaded from {}'.format(self.name, cache_file)
return roidb
gt_roidb = [
self._load_coco_annotation(index) for index in self._image_index
]
with open(cache_file, 'wb') as fid:
cPickle.dump(gt_roidb, fid, cPickle.HIGHEST_PROTOCOL)
print 'wrote gt roidb to {}'.format(cache_file)
return gt_roidb
def _load_coco_annotation(self, index):
"""
Loads COCO bounding-box instance annotations. Crowd instances are
handled by marking their overlaps (with all categories) to -1. This
overlap value means that crowd "instances" are excluded from training.
"""
im_ann = self._COCO.loadImgs(index)[0]
width = im_ann['width']
height = im_ann['height']
annIds = self._COCO.getAnnIds(imgIds=index, iscrowd=None)
objs = self._COCO.loadAnns(annIds)
# Sanitize bboxes -- some are invalid
valid_objs = []
for obj in objs:
x1 = np.max((0, obj['bbox'][0]))
y1 = np.max((0, obj['bbox'][1]))
x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
objs = valid_objs
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
seg_areas = np.zeros((num_objs), dtype=np.float32)
# Lookup table to map from COCO category ids to our internal class
# indices
coco_cat_id_to_class_ind = dict([(self._class_to_coco_cat_id[cls],
self._class_to_ind[cls])
for cls in self._classes[1:]])
for ix, obj in enumerate(objs):
cls = coco_cat_id_to_class_ind[obj['category_id']]
boxes[ix, :] = obj['clean_bbox']
gt_classes[ix] = cls
seg_areas[ix] = obj['area']
if obj['iscrowd']:
# Set overlap to -1 for all classes for crowd objects
# so they will be excluded during training
overlaps[ix, :] = -1.0
else:
overlaps[ix, cls] = 1.0
ds_utils.validate_boxes(boxes, width=width, height=height)
overlaps = scipy.sparse.csr_matrix(overlaps)
return {
'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'seg_areas': seg_areas
}
def _get_box_file(self, index):
# first 14 chars / first 22 chars / all chars + .mat
# COCO_val2014_0/COCO_val2014_000000447/COCO_val2014_000000447991.mat
file_name = ('COCO_' + self._data_name + '_' + str(index).zfill(12) +
'.mat')
return osp.join(file_name[:14], file_name[:22], file_name)
def _print_detection_eval_metrics(self, coco_eval):
IoU_lo_thresh = 0.5
IoU_hi_thresh = 0.95
def _get_thr_ind(coco_eval, thr):
ind = np.where((coco_eval.params.iouThrs > thr - 1e-5)
& (coco_eval.params.iouThrs < thr + 1e-5))[0][0]
iou_thr = coco_eval.params.iouThrs[ind]
assert np.isclose(iou_thr, thr)
return ind
ind_lo = _get_thr_ind(coco_eval, IoU_lo_thresh)
ind_hi = _get_thr_ind(coco_eval, IoU_hi_thresh)
# precision has dims (iou, recall, cls, area range, max dets)
# area range index 0: all area ranges
# max dets index 2: 100 per image
precision = \
coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :, :, 0, 2]
ap_default = np.mean(precision[precision > -1])
print('~~~~ Mean and per-category AP @ IoU=[{:.2f},{:.2f}] '
'~~~~').format(IoU_lo_thresh, IoU_hi_thresh)
print '{:.1f}'.format(100 * ap_default)
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
# minus 1 because of __background__
precision = coco_eval.eval['precision'][ind_lo:(ind_hi + 1), :,
cls_ind - 1, 0, 2]
ap = np.mean(precision[precision > -1])
print '{:.1f}'.format(100 * ap)
print '~~~~ Summary metrics ~~~~'
coco_eval.summarize()
def _do_detection_eval(self, res_file, output_dir):
ann_type = 'bbox'
coco_dt = self._COCO.loadRes(res_file)
coco_eval = COCOeval(self._COCO, coco_dt)
coco_eval.params.useSegm = (ann_type == 'segm')
coco_eval.evaluate()
coco_eval.accumulate()
self._print_detection_eval_metrics(coco_eval)
eval_file = osp.join(output_dir, 'detection_results.pkl')
with open(eval_file, 'wb') as fid:
cPickle.dump(coco_eval, fid, cPickle.HIGHEST_PROTOCOL)
print 'Wrote COCO eval results to: {}'.format(eval_file)
def _coco_results_one_category(self, boxes, cat_id):
results = []
for im_ind, index in enumerate(self.image_index):
dets = boxes[im_ind].astype(np.float)
if dets == []:
continue
scores = dets[:, -1]
xs = dets[:, 0]
ys = dets[:, 1]
ws = dets[:, 2] - xs + 1
hs = dets[:, 3] - ys + 1
results.extend([{
'image_id': index,
'category_id': cat_id,
'bbox': [xs[k], ys[k], ws[k], hs[k]],
'score': scores[k]
} for k in xrange(dets.shape[0])])
return results
def _write_coco_results_file(self, all_boxes, res_file):
# [{"image_id": 42,
# "category_id": 18,
# "bbox": [258.15,41.29,348.26,243.78],
# "score": 0.236}, ...]
results = []
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print 'Collecting {} results ({:d}/{:d})'.format(
cls, cls_ind, self.num_classes - 1)
coco_cat_id = self._class_to_coco_cat_id[cls]
results.extend(
self._coco_results_one_category(all_boxes[cls_ind],
coco_cat_id))
print 'Writing results json to {}'.format(res_file)
with open(res_file, 'w') as fid:
json.dump(results, fid)
def evaluate_detections(self, all_boxes, output_dir):
res_file = osp.join(
output_dir,
('detections_' + self._image_set + self._year + '_results'))
if self.config['use_salt']:
res_file += '_{}'.format(str(uuid.uuid4()))
res_file += '.json'
self._write_coco_results_file(all_boxes, res_file)
# Only do evaluation on non-test sets
if self._image_set.find('test') == -1:
self._do_detection_eval(res_file, output_dir)
# Optionally cleanup results json file
if self.config['cleanup']:
os.remove(res_file)
def competition_mode(self, on):
if on:
self.config['use_salt'] = False
self.config['cleanup'] = False
else:
self.config['use_salt'] = True
self.config['cleanup'] = True