class PyramidAdaptor(object):
def __init__(
self,
detector,
descriptor=None,
num_features=2000,
num_levels=4,
scale_factor=1.2,
sigma0=1.0, # N.B.: SIFT use 1.6 for this value
first_level=0,
pyramid_type=PyramidType.RESIZE,
use_block_adaptor=False,
do_parallel=kPyramidAdaptorUseParallelComputations,
do_sat_features_per_level=False):
self.detector = detector
self.descriptor = descriptor
self.num_features = num_features
self.is_detector_equal_to_descriptor = (
self.detector == self.descriptor)
self.num_levels = num_levels
self.scale_factor = scale_factor
self.inv_scale_factor = 1. / scale_factor
self.sigma0 = sigma0
self.first_level = first_level
self.pyramid_type = pyramid_type
self.use_block_adaptor = use_block_adaptor
self.do_parallel = do_parallel # do parallel computations
self.do_sat_features_per_level = do_sat_features_per_level # saturate number of features for each level
self.pyramid = Pyramid(num_levels=num_levels,
scale_factor=scale_factor,
sigma0=sigma0,
first_level=first_level,
pyramid_type=pyramid_type)
self.initSigmaLevels()
self.block_adaptor = None
if self.use_block_adaptor:
self.block_adaptor = BlockAdaptor(self.detector,
self.descriptor,
row_divs=kAdaptorNumRowDivs,
col_divs=kAdaptorNumColDivs,
do_parallel=False)
def initSigmaLevels(self):
num_levels = max(kNumLevelsInitSigma, self.num_levels)
self.scale_factors = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.scale_factors[0] = 1.0
# compute desired number of features per level (by using the scale factor)
self.num_features_per_level = np.zeros(num_levels, dtype=np.int)
num_desired_features_per_level = self.num_features * (
1 - self.inv_scale_factor) / (
1 - math.pow(self.inv_scale_factor, self.num_levels))
sum_num_features = 0
for level in range(self.num_levels - 1):
self.num_features_per_level[level] = int(
round(num_desired_features_per_level))
sum_num_features += self.num_features_per_level[level]
num_desired_features_per_level *= self.inv_scale_factor
self.num_features_per_level[self.num_levels - 1] = max(
self.num_features - sum_num_features, 0)
#print('num_features_per_level:',self.num_features_per_level)
if self.first_level == -1:
self.scale_factors[0] = 1.0 / self.scale_factor
self.inv_scale_factors[0] = 1.0 / self.scale_factors[0]
for i in range(1, num_levels):
self.scale_factors[i] = self.scale_factors[i -
1] * self.scale_factor
self.inv_scale_factors[i] = 1.0 / self.scale_factors[i]
#print('self.inv_scale_factors: ', self.inv_scale_factors)
# detect on 'unfiltered' pyramid images ('unfiltered' meanining depends on the selected pyramid type)
def detect(self, frame, mask=None):
if self.num_levels == 1:
return self.detector.detect(frame, mask)
else:
#TODO: manage mask
if kVerbose:
print('PyramidAdaptor #levels:', self.num_levels, '(from',
self.first_level, '), scale_factor:', self.scale_factor,
', sigma0:', self.sigma0, ', type:',
self.pyramid_type.name)
self.pyramid.compute(frame)
kps_all = [] # list are thread-safe
def detect_level(scale, pyr_cur, i):
kps = []
if self.block_adaptor is None:
kps = self.detector.detect(pyr_cur)
else:
kps = self.block_adaptor.detect(pyr_cur)
if kVerbose and False:
print("PyramidAdaptor - level", i, ", shape: ",
pyr_cur.shape)
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0] * scale, kp.pt[1] * scale)
kp.size = kp.size * scale
kp.octave = i
#print('kp: ', kp.pt, kp.octave)
if self.do_sat_features_per_level:
kps, _ = sat_num_features(
kps, None,
self.num_features_per_level[i]) # experimental
kps_all.extend(kps)
if not self.do_parallel:
#print('sequential computations')
# process the blocks sequentially
for i in range(0, self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
detect_level(scale, pyr_cur, i)
else:
#print('parallel computations')
futures = []
with ThreadPoolExecutor(max_workers=4) as executor:
for i in range(0, self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
futures.append(
executor.submit(detect_level, scale, pyr_cur, i))
wait(futures) # wait all the task are completed
return np.array(kps_all)
# detect on 'unfiltered' pyramid images ('unfiltered' meanining depends on the selected pyramid type)
# compute descriptors on 'filtered' pyramid images ('filtered' meanining depends on the selected pyramid type)
def detectAndCompute(self, frame, mask=None):
if self.num_levels == 1:
return self.detector.detectAndCompute(frame, mask)
else:
if kVerbose:
print('PyramidAdaptor [dc] #levels:', self.num_levels, '(from',
self.first_level, '), scale_factor:', self.scale_factor,
', sigma0:', self.sigma0, ', type:',
self.pyramid_type.name)
self.pyramid.compute(frame)
kps_all = []
des_all = []
kps_des_map = {} # i -> (kps,des)
def detect_and_compute_level(scale, pyr_cur, pyr_cur_filtered, N,
i):
kps = []
if self.block_adaptor is None:
#kps, des = self.detector.detectAndCompute(pyr_cur)
if self.is_detector_equal_to_descriptor:
kps, des = self.detector.detectAndCompute(pyr_cur)
else:
kps = self.detector.detect(pyr_cur)
#print('description of filtered')
kps, des = self.descriptor.compute(
pyr_cur_filtered, kps)
else:
kps, des = self.block_adaptor.detectAndCompute(pyr_cur)
if kVerbose and False:
print("PyramidAdaptor - level", i, ", shape: ",
pyr_cur.shape)
for kp in kps:
#print('before: kp.pt:', kp.pt,', size:',kp.size,', octave:',kp.octave,', angle:',kp.angle)
kp.pt = (kp.pt[0] * scale, kp.pt[1] * scale)
kp.size = kp.size * scale
kp.octave = i
#print('after: kp.pt:', kp.pt,', size:',kp.size,', octave:',kp.octave,', angle:',kp.angle)
if self.do_sat_features_per_level:
kps, des = sat_num_features(kps, des, N) # experimental
kps_des_map[i] = (kps, des)
if not self.do_parallel:
#print('sequential computations')
# process the blocks sequentially
for i in range(0, self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
pyr_cur_filtered = self.pyramid.imgs_filtered[i]
detect_and_compute_level(scale, pyr_cur, pyr_cur_filtered,
self.num_features_per_level[i], i)
else:
#print('parallel computations')
futures = []
with ThreadPoolExecutor(max_workers=4) as executor:
for i in range(0, self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
pyr_cur_filtered = self.pyramid.imgs_filtered[i]
futures.append(
executor.submit(detect_and_compute_level, scale,
pyr_cur, pyr_cur_filtered,
self.num_features_per_level[i], i))
wait(futures) # wait all the task are completed
# now merge the computed results
for i, (kps, des) in kps_des_map.items():
kps_all.extend(kps)
if des is not None and len(des) > 0:
if len(des_all) > 0:
des_all = np.vstack([des_all, des])
else:
des_all = des
return np.array(kps_all), np.array(des_all)
from utils_img import combine_images_horizontally
img = cv2.imread('../data/kitti06-12.png', cv2.IMREAD_COLOR)
pyramid_params = dict(
num_levels=8,
scale_factor=1.2,
sigma0=1.,
first_level=-1, # 0: start from image; -1: start from image*scale_factor
pyramid_type=PyramidType.RESIZE)
pyramid = Pyramid(**pyramid_params)
print('pyramid_type: ', pyramid.pyramid_type.name)
print('first_level: ', pyramid.first_level)
time_start = time.time()
pyramid.compute(img)
duration = time.time() - time_start
print('duration: ', duration)
for i in range(0, pyramid.num_levels):
name = 'level ' + str(i) + ': img - img_filtered'
img_pyr = combine_images_horizontally(pyramid.imgs[i],
pyramid.imgs_filtered[i])
cv2.imshow(name, img_pyr)
print(name, ' size: ', pyramid.imgs[i].shape)
k = cv2.waitKey(0)
cv2.destroyAllWindows()
