def test_load_dpm_detections(self):
conf = dict(self.config)
conf['detectors'] = ['dpm']
policy = DatasetPolicy(self.dataset, self.train_dataset, **conf)
assert (policy.detectors == ['dpm'])
dets = policy.load_ext_detections(self.dataset,
'dpm_may25',
force=True)
dets = dets.with_column_omitted('time')
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, 'val_dets.mat')
dets_correct = Table(
scipy.io.loadmat(filename)['dets'], [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'score', 'cls_ind', 'img_ind'
], 'dets_correct')
dets_correct = dets_correct.subset(
['x1', 'y1', 'x2', 'y2', 'score', 'cls_ind', 'img_ind'])
dets_correct.arr[:, :4] -= 1
dets_correct.arr[:, :4] = BoundingBox.convert_arr_from_corners(
dets_correct.arr[:, :4])
dets_correct.cols = ['x', 'y', 'w', 'h', 'score', 'cls_ind', 'img_ind']
print('----mine:')
print(dets)
print('----correct:')
print(dets_correct)
assert (dets_correct == dets)
def test_load_dpm_detections(self):
conf = dict(self.config)
conf["detectors"] = ["dpm"]
policy = DatasetPolicy(self.dataset, self.train_dataset, **conf)
assert policy.detectors == ["dpm"]
dets = policy.load_ext_detections(self.dataset, "dpm_may25", force=True)
dets = dets.with_column_omitted("time")
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, "val_dets.mat")
dets_correct = Table(
scipy.io.loadmat(filename)["dets"],
["x1", "y1", "x2", "y2", "dummy", "dummy", "dummy", "dummy", "score", "cls_ind", "img_ind"],
"dets_correct",
)
dets_correct = dets_correct.subset(["x1", "y1", "x2", "y2", "score", "cls_ind", "img_ind"])
dets_correct.arr[:, :4] -= 1
dets_correct.arr[:, :4] = BoundingBox.convert_arr_from_corners(dets_correct.arr[:, :4])
dets_correct.cols = ["x", "y", "w", "h", "score", "cls_ind", "img_ind"]
print ("----mine:")
print (dets)
print ("----correct:")
print (dets_correct)
assert dets_correct == dets
def load_dpm_dets_for_image(cls,
image,
dataset,
suffix='dets_all_may25_DP'):
"""
Loads multi-class array of detections for an image from .mat format.
"""
t = time.time()
name = os.path.splitext(image.name)[0]
# TODO: figure out how to deal with different types of detections
dets_dir = '/u/vis/x1/sergeyk/rl_detection/voc-release4/2007/tmp/dets_may25_DP'
filename = os.path.join(dets_dir, '%s_dets_all_may25_DP.mat' % name)
if not os.path.exists(filename):
dets_dir = '/u/vis/x1/sergeyk/rl_detection/voc-release4/2007/tmp/dets_jun1_DP_trainval'
filename = os.path.join(dets_dir,
'%s_dets_all_jun1_DP_trainval.mat' % name)
if not os.path.exists(filename):
filename = os.path.join(config.test_support_dir,
'dets/%s_dets_all_may25_DP.mat' % name)
if not os.path.exists(filename):
print("File does not exist!")
return None
mat = scipy.io.loadmat(filename)
dets = mat['dets_mc']
times = mat['times_mc']
# feat_time = times[0,0]
dets_seq = []
cols = [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'score', 'time'
]
for cls_ind, cls in enumerate(config.pascal_classes):
cls_dets = dets[cls_ind][0]
if cls_dets.shape[0] > 0:
det_time = times[cls_ind, 1]
# all detections get the final time
cls_dets = ut.append_index_column(cls_dets, det_time)
cls_dets = ut.append_index_column(cls_dets, cls_ind)
# subtract 1 pixel and convert from corners!
cls_dets[:, :4] -= 1
cls_dets[:, :4] = BoundingBox.convert_arr_from_corners(
cls_dets[:, :4])
dets_seq.append(cls_dets)
cols = [
'x', 'y', 'w', 'h', 'dummy', 'dummy', 'dummy', 'dummy', 'score',
'time', 'cls_ind'
]
# NMS detections per class individually
dets_mc = ut.collect(dets_seq, Detector.nms_detections, {'cols': cols})
dets_mc[:, :4] = BoundingBox.clipboxes_arr(
dets_mc[:, :4], (0, 0, image.size[0] - 1, image.size[1] - 1))
time_elapsed = time.time() - t
print("On image %s, took %.3f s" % (image.name, time_elapsed))
return dets_mc
def load_csc_dpm_dets_for_image(cls, image, dataset):
"""
Loads HOS's cascaded dets.
"""
t = time.time()
name = os.path.splitext(image.name)[0]
# if uest dataset, use HOS's detections. if not, need to output my own
if re.search('test', dataset.name):
dirname = config.get_dets_test_wholeset_dir()
filename = os.path.join(
dirname,
'%s_dets_all_test_original_cascade_wholeset.mat' % name)
else:
dirname = config.get_dets_nov19()
filename = os.path.join(dirname, '%s_dets_all_nov19.mat' % name)
print filename
if not os.path.exists(filename):
raise RuntimeError("File %s does not exist!" % filename)
return None
mat = scipy.io.loadmat(filename)
dets = mat['dets_mc']
times = mat['times_mc']
# feat_time = times[0,0]
dets_seq = []
cols = [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'dummy', 'dummy', 'score'
]
for cls_ind, cls in enumerate(dataset.classes):
cls_dets = dets[cls_ind][0]
if cls_dets.shape[0] > 0:
good_ind = [0, 1, 2, 3, 10]
cls_dets = cls_dets[:, good_ind]
det_time = times[cls_ind, 1]
# all detections get the final time
cls_dets = ut.append_index_column(cls_dets, det_time)
cls_dets = ut.append_index_column(cls_dets, cls_ind)
# convert from corners!
cls_dets[:, :4] = BoundingBox.convert_arr_from_corners(
cls_dets[:, :4])
cls_dets[:, :4] = BoundingBox.clipboxes_arr(
cls_dets[:, :4], (0, 0, image.size[0], image.size[1]))
dets_seq.append(cls_dets)
cols = ['x', 'y', 'w', 'h', 'score', 'time', 'cls_ind']
dets_mc = ut.collect(dets_seq, Detector.nms_detections, {'cols': cols})
time_elapsed = time.time() - t
print("On image %s, took %.3f s" % (image.name, time_elapsed))
return dets_mc
def load_dpm_dets_for_image(cls, image, dataset, suffix='dets_all_may25_DP'):
"""
Loads multi-class array of detections for an image from .mat format.
"""
t = time.time()
name = os.path.splitext(image.name)[0]
# TODO: figure out how to deal with different types of detections
dets_dir = '/u/vis/x1/sergeyk/rl_detection/voc-release4/2007/tmp/dets_may25_DP'
filename = os.path.join(dets_dir, '%s_dets_all_may25_DP.mat' % name)
if not os.path.exists(filename):
dets_dir = '/u/vis/x1/sergeyk/rl_detection/voc-release4/2007/tmp/dets_jun1_DP_trainval'
filename = os.path.join(
dets_dir, '%s_dets_all_jun1_DP_trainval.mat' % name)
if not os.path.exists(filename):
filename = os.path.join(
config.test_support_dir, 'dets/%s_dets_all_may25_DP.mat' % name)
if not os.path.exists(filename):
print("File does not exist!")
return None
mat = scipy.io.loadmat(filename)
dets = mat['dets_mc']
times = mat['times_mc']
# feat_time = times[0,0]
dets_seq = []
cols = ['x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy',
'dummy', 'score', 'time']
for cls_ind, cls in enumerate(config.pascal_classes):
cls_dets = dets[cls_ind][0]
if cls_dets.shape[0] > 0:
det_time = times[cls_ind, 1]
# all detections get the final time
cls_dets = ut.append_index_column(cls_dets, det_time)
cls_dets = ut.append_index_column(cls_dets, cls_ind)
# subtract 1 pixel and convert from corners!
cls_dets[:, :4] -= 1
cls_dets[:, :
4] = BoundingBox.convert_arr_from_corners(cls_dets[:, :4])
dets_seq.append(cls_dets)
cols = ['x', 'y', 'w', 'h', 'dummy', 'dummy', 'dummy',
'dummy', 'score', 'time', 'cls_ind']
# NMS detections per class individually
dets_mc = ut.collect(dets_seq, Detector.nms_detections, {'cols': cols})
dets_mc[:, :4] = BoundingBox.clipboxes_arr(
dets_mc[:, :4], (0, 0, image.size[0] - 1, image.size[1] - 1))
time_elapsed = time.time() - t
print("On image %s, took %.3f s" % (image.name, time_elapsed))
return dets_mc
def load_csc_dpm_dets_for_image(cls, image, dataset):
"""
Loads HOS's cascaded dets.
"""
t = time.time()
name = os.path.splitext(image.name)[0]
# if uest dataset, use HOS's detections. if not, need to output my own
if re.search('test', dataset.name):
dirname = config.get_dets_test_wholeset_dir()
filename = os.path.join(
dirname, '%s_dets_all_test_original_cascade_wholeset.mat' % name)
else:
dirname = config.get_dets_nov19()
filename = os.path.join(dirname, '%s_dets_all_nov19.mat' % name)
print filename
if not os.path.exists(filename):
raise RuntimeError("File %s does not exist!" % filename)
return None
mat = scipy.io.loadmat(filename)
dets = mat['dets_mc']
times = mat['times_mc']
# feat_time = times[0,0]
dets_seq = []
cols = ['x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy',
'dummy', 'dummy', 'dummy', 'score']
for cls_ind, cls in enumerate(dataset.classes):
cls_dets = dets[cls_ind][0]
if cls_dets.shape[0] > 0:
good_ind = [0, 1, 2, 3, 10]
cls_dets = cls_dets[:, good_ind]
det_time = times[cls_ind, 1]
# all detections get the final time
cls_dets = ut.append_index_column(cls_dets, det_time)
cls_dets = ut.append_index_column(cls_dets, cls_ind)
# convert from corners!
cls_dets[:, :
4] = BoundingBox.convert_arr_from_corners(cls_dets[:, :4])
cls_dets[:, :4] = BoundingBox.clipboxes_arr(
cls_dets[:, :4], (0, 0, image.size[0], image.size[1]))
dets_seq.append(cls_dets)
cols = ['x', 'y', 'w', 'h', 'score', 'time', 'cls_ind']
dets_mc = ut.collect(dets_seq, Detector.nms_detections, {'cols': cols})
time_elapsed = time.time() - t
print("On image %s, took %.3f s" % (image.name, time_elapsed))
return dets_mc
