continue
for k in range(dets.shape[0]):
f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(\
dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))
def evaluate_proposals_msr(self, all_boxes, output_dir):
# for each image
for im_ind, index in enumerate(self.image_index):
filename = os.path.join(output_dir, index + '.txt')
print('Writing PASCAL results to file ' + filename) # clw modify: for py3
with open(filename, 'wt') as f:
dets = all_boxes[im_ind]
if dets == []:
continue
for k in range(dets.shape[0]):
f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))
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
if __name__ == '__main__':
d = datasets.pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed; embed()
class pascal_voc(datasets.imdb):
def __init__(self, image_set, year, pascal_path=None):
datasets.imdb.__init__(self, 'voc_' + year + '_' + image_set)
self._year = year
self._image_set = image_set
self._pascal_path = self._get_default_path() if pascal_path is None \
else pascal_path
self._data_path = os.path.join(self._pascal_path,
'VOCdevkit' + self._year,
'VOC' + self._year)
self._classes = (
'__background__', # always index 0
'aeroplane',
'bicycle',
'bird',
'boat',
'bottle',
'bus',
'car',
'cat',
'chair',
'cow',
'diningtable',
'dog',
'horse',
'motorbike',
'person',
'pottedplant',
'sheep',
'sofa',
'train',
'tvmonitor')
self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
if cfg.IS_RPN:
self._roidb_handler = self.gt_roidb
else:
self._roidb_handler = self.region_proposal_roidb
# num of subclasses
self._num_subclasses = 240 + 1
# load the mapping for subcalss to class
filename = os.path.join(self._pascal_path, 'subcategory_exemplars',
'mapping.txt')
assert os.path.exists(filename), 'Path does not exist: {}'.format(
filename)
mapping = np.zeros(self._num_subclasses, dtype=np.int)
with open(filename) as f:
for line in f:
words = line.split()
subcls = int(words[0])
mapping[subcls] = self._class_to_ind[words[1]]
self._subclass_mapping = mapping
# PASCAL specific config options
self.config = {'cleanup': True, 'use_salt': True, 'top_k': 2000}
# statistics for computing recall
self._num_boxes_all = np.zeros(self.num_classes, dtype=np.int)
self._num_boxes_covered = np.zeros(self.num_classes, dtype=np.int)
self._num_boxes_proposal = 0
assert os.path.exists(self._pascal_path), \
'PASCAL path does not exist: {}'.format(self._pascal_path)
assert os.path.exists(self._data_path), \
'Path does not exist: {}'.format(self._data_path)
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.
"""
image_path = os.path.join(self._data_path, 'JPEGImages',
index + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path
def _load_image_set_index(self):
"""
Load the indexes listed in this dataset's image set file.
"""
# Example path to image set file:
# self._pascal_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txt
image_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',
self._image_set + '.txt')
assert os.path.exists(image_set_file), \
'Path does not exist: {}'.format(image_set_file)
with open(image_set_file) as f:
image_index = [x.strip() for x in f.readlines()]
return image_index
def _get_default_path(self):
"""
Return the default path where PASCAL VOC is expected to be installed.
"""
return os.path.join(datasets.ROOT_DIR, 'data', 'PASCAL')
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 = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')
if os.path.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_pascal_subcategory_exemplar_annotation(index)
for index in self.image_index
]
if cfg.IS_RPN:
# print(out recall)
for i in range(1, self.num_classes):
print('{}: Total number of boxes {:d}'.format(
self.classes[i], self._num_boxes_all[i]))
print('{}: Number of boxes covered {:d}'.format(
self.classes[i], self._num_boxes_covered[i]))
print('{}: Recall {:f}'.format(
self.classes[i],
float(self._num_boxes_covered[i]) /
float(self._num_boxes_all[i])))
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_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format.
"""
filename = os.path.join(self._data_path, 'Annotations',
index + '.xml')
# print('Loading: {}'.format(filename)))
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
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)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
# Make pixel indexes 0-based
x1 = float(get_data_from_tag(obj, 'xmin')) - 1
y1 = float(get_data_from_tag(obj, 'ymin')) - 1
x2 = float(get_data_from_tag(obj, 'xmax')) - 1
y2 = float(get_data_from_tag(obj, 'ymax')) - 1
cls = self._class_to_ind[str(get_data_from_tag(
obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
gt_subclasses = np.zeros((num_objs), dtype=np.int32)
gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)
subindexes = np.zeros((num_objs, self.num_classes), dtype=np.int32)
subindexes_flipped = np.zeros((num_objs, self.num_classes),
dtype=np.int32)
if cfg.IS_RPN:
if cfg.IS_MULTISCALE:
# compute overlaps between grid boxes and gt boxes in multi-scales
# rescale the gt boxes
boxes_all = np.zeros((0, 4), dtype=np.float32)
for scale in cfg.TRAIN.SCALES:
boxes_all = np.vstack((boxes_all, boxes * scale))
gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))
# compute grid boxes
s = PIL.Image.open(self.image_path_from_index(index)).size
image_height = s[1]
image_width = s[0]
boxes_grid, _, _ = get_boxes_grid(image_height,
image_width)
# compute overlap
overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float),
boxes_all.astype(np.float))
# check how many gt boxes are covered by grids
if num_objs != 0:
index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))
max_overlaps = overlaps_grid.max(axis=0)
fg_inds = []
for k in range(1, self.num_classes):
fg_inds.extend(
np.where((gt_classes_all == k)
& (max_overlaps >=
cfg.TRAIN.FG_THRESH[k - 1]))[0])
index_covered = np.unique(index[fg_inds])
for i in range(self.num_classes):
self._num_boxes_all[i] += len(
np.where(gt_classes == i)[0])
self._num_boxes_covered[i] += len(
np.where(gt_classes[index_covered] == i)[0])
else:
assert len(cfg.TRAIN.SCALES_BASE) == 1
scale = cfg.TRAIN.SCALES_BASE[0]
feat_stride = 16
# faster rcnn region proposal
anchors = generate_anchors()
num_anchors = anchors.shape[0]
# image size
s = PIL.Image.open(self.image_path_from_index(index)).size
image_height = s[1]
image_width = s[0]
# height and width of the heatmap
height = np.round((image_height * scale - 1) / 4.0 + 1)
height = np.floor((height - 1) / 2 + 1 + 0.5)
height = np.floor((height - 1) / 2 + 1 + 0.5)
width = np.round((image_width * scale - 1) / 4.0 + 1)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
# gt boxes
gt_boxes = boxes * scale
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = num_anchors
K = shifts.shape[0]
all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2)))
all_anchors = all_anchors.reshape((K * A, 4))
# compute overlap
overlaps_grid = bbox_overlaps(all_anchors.astype(np.float),
gt_boxes.astype(np.float))
# check how many gt boxes are covered by anchors
if num_objs != 0:
max_overlaps = overlaps_grid.max(axis=0)
fg_inds = []
for k in range(1, self.num_classes):
fg_inds.extend(
np.where((gt_classes == k)
& (max_overlaps >=
cfg.TRAIN.FG_THRESH[k - 1]))[0])
for i in range(self.num_classes):
self._num_boxes_all[i] += len(
np.where(gt_classes == i)[0])
self._num_boxes_covered[i] += len(
np.where(gt_classes[fg_inds] == i)[0])
return {
'boxes': boxes,
'gt_classes': gt_classes,
'gt_subclasses': gt_subclasses,
'gt_subclasses_flipped': gt_subclasses_flipped,
'gt_overlaps': overlaps,
'gt_subindexes': subindexes,
'gt_subindexes_flipped': subindexes_flipped,
'flipped': False
}
def _load_pascal_subcategory_exemplar_annotation(self, index):
"""
Load image and bounding boxes info from txt file in the pascal subcategory exemplar format.
"""
if self._image_set == 'test':
return self._load_pascal_annotation(index)
filename = os.path.join(self._pascal_path, 'subcategory_exemplars',
index + '.txt')
assert os.path.exists(filename), \
'Path does not exist: {}'.format(filename)
# the annotation file contains flipped objects
lines = []
lines_flipped = []
with open(filename) as f:
for line in f:
words = line.split()
subcls = int(words[1])
is_flip = int(words[2])
if subcls != -1:
if is_flip == 0:
lines.append(line)
else:
lines_flipped.append(line)
num_objs = len(lines)
# store information of flipped objects
assert (num_objs == len(lines_flipped)
), 'The number of flipped objects is not the same!'
gt_subclasses_flipped = np.zeros((num_objs), dtype=np.int32)
for ix, line in enumerate(lines_flipped):
words = line.split()
subcls = int(words[1])
gt_subclasses_flipped[ix] = subcls
boxes = np.zeros((num_objs, 4), dtype=np.float32)
gt_classes = np.zeros((num_objs), dtype=np.int32)
gt_subclasses = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
subindexes = np.zeros((num_objs, self.num_classes), dtype=np.int32)
subindexes_flipped = np.zeros((num_objs, self.num_classes),
dtype=np.int32)
for ix, line in enumerate(lines):
words = line.split()
cls = self._class_to_ind[words[0]]
subcls = int(words[1])
# Make pixel indexes 0-based
boxes[ix, :] = [float(n) - 1 for n in words[3:7]]
gt_classes[ix] = cls
gt_subclasses[ix] = subcls
overlaps[ix, cls] = 1.0
subindexes[ix, cls] = subcls
subindexes_flipped[ix, cls] = gt_subclasses_flipped[ix]
overlaps = scipy.sparse.csr_matrix(overlaps)
if cfg.IS_RPN:
if cfg.IS_MULTISCALE:
# compute overlaps between grid boxes and gt boxes in multi-scales
# rescale the gt boxes
boxes_all = np.zeros((0, 4), dtype=np.float32)
for scale in cfg.TRAIN.SCALES:
boxes_all = np.vstack((boxes_all, boxes * scale))
gt_classes_all = np.tile(gt_classes, len(cfg.TRAIN.SCALES))
# compute grid boxes
s = PIL.Image.open(self.image_path_from_index(index)).size
image_height = s[1]
image_width = s[0]
boxes_grid, _, _ = get_boxes_grid(image_height,
image_width)
# compute overlap
overlaps_grid = bbox_overlaps(boxes_grid.astype(np.float),
boxes_all.astype(np.float))
# check how many gt boxes are covered by grids
if num_objs != 0:
index = np.tile(range(num_objs), len(cfg.TRAIN.SCALES))
max_overlaps = overlaps_grid.max(axis=0)
fg_inds = []
for k in range(1, self.num_classes):
fg_inds.extend(
np.where((gt_classes_all == k)
& (max_overlaps >=
cfg.TRAIN.FG_THRESH[k - 1]))[0])
index_covered = np.unique(index[fg_inds])
for i in range(self.num_classes):
self._num_boxes_all[i] += len(
np.where(gt_classes == i)[0])
self._num_boxes_covered[i] += len(
np.where(gt_classes[index_covered] == i)[0])
else:
assert len(cfg.TRAIN.SCALES_BASE) == 1
scale = cfg.TRAIN.SCALES_BASE[0]
feat_stride = 16
# faster rcnn region proposal
base_size = 16
ratios = [3.0, 2.0, 1.5, 1.0, 0.75, 0.5, 0.25]
scales = 2**np.arange(1, 6, 0.5)
anchors = generate_anchors(base_size, ratios, scales)
num_anchors = anchors.shape[0]
# image size
s = PIL.Image.open(self.image_path_from_index(index)).size
image_height = s[1]
image_width = s[0]
# height and width of the heatmap
height = np.round((image_height * scale - 1) / 4.0 + 1)
height = np.floor((height - 1) / 2 + 1 + 0.5)
height = np.floor((height - 1) / 2 + 1 + 0.5)
width = np.round((image_width * scale - 1) / 4.0 + 1)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
width = np.floor((width - 1) / 2.0 + 1 + 0.5)
# gt boxes
gt_boxes = boxes * scale
# 1. Generate proposals from bbox deltas and shifted anchors
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack(
(shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = num_anchors
K = shifts.shape[0]
all_anchors = (anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2)))
all_anchors = all_anchors.reshape((K * A, 4))
# compute overlap
overlaps_grid = bbox_overlaps(all_anchors.astype(np.float),
gt_boxes.astype(np.float))
# check how many gt boxes are covered by anchors
if num_objs != 0:
max_overlaps = overlaps_grid.max(axis=0)
fg_inds = []
for k in range(1, self.num_classes):
fg_inds.extend(
np.where((gt_classes == k)
& (max_overlaps >=
cfg.TRAIN.FG_THRESH[k - 1]))[0])
for i in range(self.num_classes):
self._num_boxes_all[i] += len(
np.where(gt_classes == i)[0])
self._num_boxes_covered[i] += len(
np.where(gt_classes[fg_inds] == i)[0])
return {
'boxes': boxes,
'gt_classes': gt_classes,
'gt_subclasses': gt_subclasses,
'gt_subclasses_flipped': gt_subclasses_flipped,
'gt_overlaps': overlaps,
'gt_subindexes': subindexes,
'gt_subindexes_flipped': subindexes_flipped,
'flipped': False
}
def region_proposal_roidb(self):
"""
Return the database of regions of interest.
Ground-truth ROIs are also included.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(
self.cache_path, self.name + '_' + cfg.REGION_PROPOSAL +
'_region_proposal_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = pickle.load(fid)
print('{} roidb loaded from {}'.format(self.name, cache_file))
return roidb
if self._image_set != 'test':
gt_roidb = self.gt_roidb()
print('Loading region proposal network boxes...')
model = cfg.REGION_PROPOSAL
rpn_roidb = self._load_rpn_roidb(gt_roidb, model)
print('Region proposal network boxes loaded')
roidb = datasets.imdb.merge_roidbs(rpn_roidb, gt_roidb)
else:
print('Loading region proposal network boxes...')
model = cfg.REGION_PROPOSAL
roidb = self._load_rpn_roidb(None, model)
print('Region proposal network boxes loaded')
print('{} region proposals per image'.format(
self._num_boxes_proposal / len(self.image_index)))
with open(cache_file, 'wb') as fid:
pickle.dump(roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote roidb to {}'.format(cache_file))
return roidb
def _load_rpn_roidb(self, gt_roidb, model):
# set the prefix
if self._image_set == 'test':
prefix = model + '/testing'
else:
prefix = model + '/training'
box_list = []
for index in self.image_index:
filename = os.path.join(self._pascal_path, 'region_proposals',
prefix, index + '.txt')
assert os.path.exists(filename), \
'RPN data not found at: {}'.format(filename)
raw_data = np.loadtxt(filename, dtype=float)
if len(raw_data.shape) == 1:
if raw_data.size == 0:
raw_data = raw_data.reshape((0, 5))
else:
raw_data = raw_data.reshape((1, 5))
x1 = raw_data[:, 0]
y1 = raw_data[:, 1]
x2 = raw_data[:, 2]
y2 = raw_data[:, 3]
score = raw_data[:, 4]
inds = np.where((x2 > x1) & (y2 > y1))[0]
raw_data = raw_data[inds, :4]
self._num_boxes_proposal += raw_data.shape[0]
box_list.append(raw_data)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def selective_search_roidb(self):
"""
Return the database of selective search regions of interest.
Ground-truth ROIs are also included.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(
self.cache_path, self.name + '_selective_search_roidb.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = pickle.load(fid)
print('{} ss roidb loaded from {}'.format(
self.name, cache_file))
return roidb
if int(self._year) == 2007 or self._image_set != 'test':
gt_roidb = self.gt_roidb()
ss_roidb = self._load_selective_search_roidb(gt_roidb)
roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
else:
roidb = self._load_selective_search_roidb(None)
with open(cache_file, 'wb') as fid:
pickle.dump(roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote ss roidb to {}'.format(cache_file))
return roidb
def _load_selective_search_roidb(self, gt_roidb):
filename = os.path.abspath(
os.path.join(self.cache_path, '..', 'selective_search_data',
self.name + '.mat'))
assert os.path.exists(filename), \
'Selective search data not found at: {}'.format(filename)
raw_data = sio.loadmat(filename)['boxes'].ravel()
box_list = []
for i in range(raw_data.shape[0]):
box_list.append(raw_data[i][:, (1, 0, 3, 2)] - 1)
return self.create_roidb_from_box_list(box_list, gt_roidb)
def selective_search_IJCV_roidb(self):
"""
Return the database of selective search regions of interest.
Ground-truth ROIs are also included.
This function loads/saves from/to a cache file to speed up future calls.
"""
cache_file = os.path.join(
self.cache_path,
'{:s}_selective_search_IJCV_top_{:d}_roidb.pkl'.format(
self.name, self.config['top_k']))
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
roidb = pickle.load(fid)
print('{} ss roidb loaded from {}'.format(
self.name, cache_file))
return roidb
gt_roidb = self.gt_roidb()
ss_roidb = self._load_selective_search_IJCV_roidb(gt_roidb)
roidb = datasets.imdb.merge_roidbs(gt_roidb, ss_roidb)
with open(cache_file, 'wb') as fid:
pickle.dump(roidb, fid, pickle.HIGHEST_PROTOCOL)
print('wrote ss roidb to {}'.format(cache_file))
return roidb
def _load_selective_search_IJCV_roidb(self, gt_roidb):
IJCV_path = os.path.abspath(
os.path.join(self.cache_path, '..',
'selective_search_IJCV_data',
'voc_' + self._year))
assert os.path.exists(IJCV_path), \
'Selective search IJCV data not found at: {}'.format(IJCV_path)
top_k = self.config['top_k']
box_list = []
for i in range(self.num_images):
filename = os.path.join(IJCV_path,
self.image_index[i] + '.mat')
raw_data = sio.loadmat(filename)
box_list.append(
(raw_data['boxes'][:top_k, :] - 1).astype(np.uint16))
return self.create_roidb_from_box_list(box_list, gt_roidb)
def _write_voc_results_file(self, all_boxes):
use_salt = self.config['use_salt']
comp_id = 'comp4'
if use_salt:
comp_id += '-{}'.format(os.getpid())
# VOCdevkit/results/VOC2007/Main/comp4-44503_det_test_aeroplane.txt
path = os.path.join(self._pascal_path, 'VOCdevkit' + self._year,
'results', 'VOC' + self._year, 'Main',
comp_id + '_')
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print('Writing {} VOC results file'.format(cls))
filename = path + 'det_' + self._image_set + '_' + cls + '.txt'
print(filename)
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# the VOCdevkit expects 1-based indices
for k in range(dets.shape[0]):
f.write(
'{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, 4], dets[k, 0] + 1,
dets[k, 1] + 1, dets[k, 2] + 1,
dets[k, 3] + 1))
return comp_id
def _do_matlab_eval(self, comp_id, output_dir='output'):
rm_results = self.config['cleanup']
path = os.path.join(os.path.dirname(__file__),
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
.format(self._pascal_path + '/VOCdevkit' + self._year, comp_id,
self._image_set, output_dir, int(rm_results))
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
# evaluate detection results
def evaluate_detections(self, all_boxes, output_dir):
comp_id = self._write_voc_results_file(all_boxes)
self._do_matlab_eval(comp_id, output_dir)
def evaluate_proposals(self, all_boxes, output_dir):
# for each image
for im_ind, index in enumerate(self.image_index):
filename = os.path.join(output_dir, index + '.txt')
print('Writing PASCAL results to file ' + filename)
with open(filename, 'wt') as f:
# for each class
for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
for k in range(dets.shape[0]):
f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format( \
dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3], dets[k, 4]))
def evaluate_proposals_msr(self, all_boxes, output_dir):
# for each image
for im_ind, index in enumerate(self.image_index):
filename = os.path.join(output_dir, index + '.txt')
print('Writing PASCAL results to file ' + filename)
with open(filename, 'wt') as f:
dets = all_boxes[im_ind]
if dets == []:
continue
for k in range(dets.shape[0]):
f.write('{:f} {:f} {:f} {:f} {:.32f}\n'.format(
dets[k, 0], dets[k, 1], dets[k, 2], dets[k, 3],
dets[k, 4]))
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
if __name__ == '__main__':
d = datasets.pascal_voc('trainval', '2007')
res = d.roidb
status = subprocess.call(cmd, shell=True)
"""
process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
while True:
buff = process.stdout.readline()
if buff == '' and process.poll() != None:
break
sys.stdout.write(buff)
process.wait()
"""
def evaluate_detections(self, all_boxes, output_dir):
comp_id = self._write_voc_results_file(all_boxes)
self._do_matlab_eval(comp_id, output_dir)
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
if __name__ == '__main__':
d = datasets.pascal_voc('trainval', '2007')
res = d.roidb
from IPython import embed; embed()
path = os.path.join(os.path.dirname(__file__),
'VOCdevkit-matlab-wrapper')
cmd = 'cd {} && '.format(path)
cmd += '{:s} -nodisplay -nodesktop '.format(datasets.MATLAB)
cmd += '-r "dbstop if error; '
cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\',{:d}); quit;"' \
.format(self._devkit_path, comp_id,
self._image_set, output_dir, int(rm_results))
print('Running:\n{}'.format(cmd))
status = subprocess.call(cmd, shell=True)
def evaluate_detections(self, all_boxes, output_dir):
comp_id = self._write_voc_results_file(all_boxes)
self._do_matlab_eval(comp_id, output_dir)
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
if __name__ == '__main__':
d = datasets.pascal_voc('Train', '/home/ubuntu/grapes')
res = d.roidb
from IPython import embed
embed()