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feature_manager.py
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"""
* This file is part of PYSLAM
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
import sys
import math
from enum import Enum
import numpy as np
import cv2
from collections import Counter
from parameters import Parameters
from feature_types import FeatureDetectorTypes, FeatureDescriptorTypes, FeatureInfo
from utils_sys import Printer, import_from
from utils_features import unpackSiftOctaveKps, UnpackOctaveMethod, sat_num_features, kdt_nms, ssc_nms, octree_nms, grid_nms
from utils_geom import hamming_distance, hamming_distances, l2_distance, l2_distances
from feature_manager_adaptors import BlockAdaptor, PyramidAdaptor
from pyramid import Pyramid, PyramidType
from feature_root_sift import RootSIFTFeature2D
from feature_shitomasi import ShiTomasiDetector
# import and check
SuperPointFeature2D = import_from('feature_superpoint', 'SuperPointFeature2D')
TfeatFeature2D = import_from('feature_tfeat', 'TfeatFeature2D')
Orbslam2Feature2D = import_from('feature_orbslam2', 'Orbslam2Feature2D')
HardnetFeature2D = import_from('feature_hardnet', 'HardnetFeature2D')
GeodescFeature2D = import_from('feature_geodesc', 'GeodescFeature2D')
SosnetFeature2D = import_from('feature_sosnet', 'SosnetFeature2D')
if False:
L2NetKerasFeature2D = import_from('feature_l2net_keras', 'L2NetKerasFeature2D') # not used at present time
L2NetFeature2D = import_from('feature_l2net', 'L2NetFeature2D')
LogpolarFeature2D = import_from('feature_logpolar', 'LogpolarFeature2D')
D2NetFeature2D = import_from('feature_d2net', 'D2NetFeature2D')
DelfFeature2D = import_from('feature_delf', 'DelfFeature2D')
ContextDescFeature2D = import_from('feature_contextdesc', 'ContextDescFeature2D')
ASLFeature2D = import_from('feature_aslfeat', 'ASLFeature2D')
LfNetFeature2D = import_from('feature_lfnet', 'LfNetFeature2D')
R2d2Feature2D = import_from('feature_r2d2', 'R2d2Feature2D')
KeyNetDescFeature2D = import_from('feature_keynet', 'KeyNetDescFeature2D')
DiskFeature2D = import_from('feature_disk', 'DiskFeature2D')
kVerbose = True
kNumFeatureDefault = Parameters.kNumFeatures
kNumLevelsDefault = 4
kScaleFactorDefault = 1.2
kNumLevelsInitSigma = 40
kSigmaLevel0 = Parameters.kSigmaLevel0
kDrawOriginalExtractedFeatures = False # for debugging
kFASTKeyPointSizeRescaleFactor = 4 # 7 is the standard keypoint size on layer 0 => actual size = 7*kFASTKeyPointSizeRescaleFactor
kAGASTKeyPointSizeRescaleFactor = 4 # 7 is the standard keypoint size on layer 0 => actual size = 7*kAGASTKeyPointSizeRescaleFactor
kShiTomasiKeyPointSizeRescaleFactor = 5 # 5 is the selected keypoint size on layer 0 (see below) => actual size = 5*kShiTomasiKeyPointSizeRescaleFactor
if not kVerbose:
def print(*args, **kwargs):
pass
class KeyPointFilterTypes(Enum):
NONE = 0
SAT = 1 # sat the number of features (keep the best N features: 'best' on the basis of the keypoint.response)
KDT_NMS = 2 # Non-Maxima Suppression based on kd-tree
SSC_NMS = 3 # Non-Maxima Suppression based on https://github.com/BAILOOL/ANMS-Codes
OCTREE_NMS = 4 # Distribute keypoints by using a octree (as a matter of fact, a quadtree): from ORBSLAM2
GRID_NMS = 5 # NMS by using a grid
def feature_manager_factory(num_features=kNumFeatureDefault,
num_levels = kNumLevelsDefault, # number of pyramid levels or octaves for detector and descriptor
scale_factor = kScaleFactorDefault, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB):
return FeatureManager(num_features, num_levels, scale_factor, detector_type, descriptor_type)
# Manager of both detector and descriptor
# This exposes an interface that is similar to OpenCV::Feature2D, i.e. detect(), compute() and detectAndCompute()
class FeatureManager(object):
def __init__(self, num_features=kNumFeatureDefault,
num_levels = kNumLevelsDefault, # number of pyramid levels or octaves for detector and descriptor
scale_factor = kScaleFactorDefault, # detection scale factor (if it can be set, otherwise it is automatically computed)
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB):
self.detector_type = detector_type
self._feature_detector = None
self.descriptor_type = descriptor_type
self._feature_descriptor = None
# main feature manager properties
self.num_features = num_features
self.num_levels = num_levels
self.first_level = 0 # not always applicable = > 0: start pyramid from input image;
# -1: start pyramid from up-scaled image*scale_factor (as in SIFT)
self.scale_factor = scale_factor # scale factor bewteen two octaves
self.sigma_level0 = kSigmaLevel0 # sigma on first octave
self.layers_per_octave = 3 # for methods that uses octaves (SIFT, SURF, etc)
# feature norm options
self.norm_type = None # descriptor norm type
self.descriptor_distance = None # pointer to a function for computing the distance between two points
self.descriptor_distances = None # pointer to a function for computing the distances between two array of corresponding points
# block adaptor options
self.use_bock_adaptor = False
self.block_adaptor = None
# pyramid adaptor options: at present time pyramid adaptor has the priority and can combine a block adaptor withint itself
self.use_pyramid_adaptor = False
self.pyramid_adaptor = None
self.pyramid_type = PyramidType.RESIZE
self.pyramid_do_parallel = True
self.do_sat_features_per_level = False # if pyramid adaptor is active, one can require to compute a certain number of features per level (see PyramidAdaptor)
self.force_multiscale_detect_and_compute = False # automatically managed below depending on features
self.oriented_features = True # automatically managed below depending on selected features
self.do_keypoints_size_rescaling = False # automatically managed below depending on selected features
self.need_color_image = False # automatically managed below depending on selected features
self.keypoint_filter_type = KeyPointFilterTypes.SAT # default keypoint-filter type
self.need_nms = False # need or not non-maximum suppression of keypoints
self.keypoint_nms_filter_type = KeyPointFilterTypes.KDT_NMS # default keypoint-filter type if NMS is needed
# initialize sigmas for keypoint levels (used for SLAM)
self.init_sigma_levels()
# --------------------------------------------- #
# manage different opencv versions
# --------------------------------------------- #
print("using opencv ", cv2.__version__)
# check opencv version in order to use the right modules
opencv_major = int(cv2.__version__.split('.')[0])
opencv_minor = int(cv2.__version__.split('.')[1])
if opencv_major == 3:
SIFT_create = import_from('cv2.xfeatures2d','SIFT_create')
SURF_create = import_from('cv2.xfeatures2d','SURF_create')
FREAK_create = import_from('cv2.xfeatures2d','FREAK_create')
ORB_create = import_from('cv2','ORB_create')
BRISK_create = import_from('cv2','BRISK_create')
KAZE_create = import_from('cv2','KAZE_create')
AKAZE_create = import_from('cv2','AKAZE_create')
BoostDesc_create = import_from('cv2','xfeatures2d_BoostDesc','create')
MSD_create = import_from('cv2','xfeatures2d_MSDDetector') # found but it does not work! (it does not find the .create() method)
#Affine_create = import_from('cv2','xfeatures2d_AffineFeature2D') # not found
DAISY_create = import_from('cv2','xfeatures2d_DAISY','create')
STAR_create = import_from('cv2','xfeatures2d_StarDetector','create')
HL_create = import_from('cv2','xfeatures2d_HarrisLaplaceFeatureDetector','create')
LATCH_create = import_from('cv2','xfeatures2d_LATCH','create')
LUCID_create = import_from('cv2','xfeatures2d_LUCID','create')
VGG_create = import_from('cv2','xfeatures2d_VGG','create')
BEBLID_create = import_from('cv2','xfeatures2d','BEBLID_create')
elif opencv_major == 4 and opencv_minor >= 5:
SIFT_create = import_from('cv2','SIFT_create')
SURF_create = import_from('cv2.xfeatures2d','SURF_create')
FREAK_create = import_from('cv2.xfeatures2d','FREAK_create')
ORB_create = import_from('cv2','ORB_create')
BRISK_create = import_from('cv2','BRISK_create')
KAZE_create = import_from('cv2','KAZE_create')
AKAZE_create = import_from('cv2','AKAZE_create')
BoostDesc_create = import_from('cv2','xfeatures2d_BoostDesc','create')
MSD_create = import_from('cv2','xfeatures2d_MSDDetector')
DAISY_create = import_from('cv2','xfeatures2d_DAISY','create')
STAR_create = import_from('cv2','xfeatures2d_StarDetector','create')
HL_create = import_from('cv2','xfeatures2d_HarrisLaplaceFeatureDetector','create')
LATCH_create = import_from('cv2','xfeatures2d_LATCH','create')
LUCID_create = import_from('cv2','xfeatures2d_LUCID','create')
VGG_create = import_from('cv2','xfeatures2d_VGG','create')
BEBLID_create = import_from('cv2','xfeatures2d','BEBLID_create')
else:
SIFT_create = import_from('cv2.xfeatures2d','SIFT_create')
SURF_create = import_from('cv2.xfeatures2d','SURF_create')
FREAK_create = import_from('cv2.xfeatures2d','FREAK_create')
ORB_create = import_from('cv2','ORB')
BRISK_create = import_from('cv2','BRISK')
KAZE_create = import_from('cv2','KAZE')
AKAZE_create = import_from('cv2','AKAZE')
BoostDesc_create = import_from('cv2','xfeatures2d_BoostDesc','create')
MSD_create = import_from('cv2','xfeatures2d_MSDDetector')
DAISY_create = import_from('cv2','xfeatures2d_DAISY','create')
STAR_create = import_from('cv2','xfeatures2d_StarDetector','create')
HL_create = import_from('cv2','xfeatures2d_HarrisLaplaceFeatureDetector','create')
LATCH_create = import_from('cv2','xfeatures2d_LATCH','create')
LUCID_create = import_from('cv2','xfeatures2d_LUCID','create')
VGG_create = import_from('cv2','xfeatures2d_VGG','create')
BEBLID_create = import_from('cv2','xfeatures2d','BEBLID_create')
# pure detectors
self.FAST_create = import_from('cv2','FastFeatureDetector_create')
self.AGAST_create = import_from('cv2','AgastFeatureDetector_create')
self.GFTT_create = import_from('cv2','GFTTDetector_create')
self.MSER_create = import_from('cv2','MSER_create')
self.MSD_create = MSD_create
self.STAR_create = STAR_create
self.HL_create = HL_create
# detectors and descriptors
self.SIFT_create = SIFT_create
self.SURF_create = SURF_create
self.ORB_create = ORB_create
self.BRISK_create = BRISK_create
self.AKAZE_create = AKAZE_create
self.KAZE_create = KAZE_create
# pure descriptors
self.FREAK_create = FREAK_create # only descriptor
self.BoostDesc_create = BoostDesc_create
self.DAISY_create = DAISY_create
self.LATCH_create = LATCH_create
self.LUCID_create = LUCID_create
self.VGG_create = VGG_create
self.BEBLID_create = BEBLID_create
# --------------------------------------------- #
# check if we want descriptor == detector
# --------------------------------------------- #
self.is_detector_equal_to_descriptor = (self.detector_type.name == self.descriptor_type.name)
# N.B.: the following descriptors assume keypoint.octave extacly represents an octave with a scale_factor=2
# and not a generic level with scale_factor < 2
if self.descriptor_type in [
FeatureDescriptorTypes.SIFT, # [NOK] SIFT seems to assume the use of octaves (https://github.com/opencv/opencv_contrib/blob/master/modules/xfeatures2d/src/sift.cpp#L1128)
FeatureDescriptorTypes.ROOT_SIFT, # [NOK] same as SIFT
#FeatureDescriptorTypes.SURF, # [OK] SURF computes the descriptor by considering the keypoint.size (https://github.com/opencv/opencv_contrib/blob/master/modules/xfeatures2d/src/surf.cpp#L600)
FeatureDescriptorTypes.AKAZE, # [NOK] AKAZE does NOT seem to compute the right scale index for each keypoint.size (https://github.com/opencv/opencv/blob/master/modules/features2d/src/kaze/AKAZEFeatures.cpp#L1508)
FeatureDescriptorTypes.KAZE, # [NOK] similar to KAZE
#FeatureDescriptorTypes.FREAK, # [OK] FREAK computes the right scale index for each keypoint.size (https://github.com/opencv/opencv_contrib/blob/master/modules/xfeatures2d/src/freak.cpp#L468)
#FeatureDescriptorTypes.BRISK # [OK] BRISK computes the right scale index for each keypoint.size (https://github.com/opencv/opencv/blob/master/modules/features2d/src/brisk.cpp#L697)
#FeatureDescriptorTypes.BOOST_DESC # [OK] BOOST_DESC seems to properly rectify each keypoint patch size (https://github.com/opencv/opencv_contrib/blob/master/modules/xfeatures2d/src/boostdesc.cpp#L346)
]:
self.scale_factor = 2 # the above descriptors work on octave layers with a scale_factor=2!
Printer.orange('forcing scale factor=2 for detector', self.descriptor_type.name)
self.orb_params = dict(nfeatures=num_features,
scaleFactor=self.scale_factor,
nlevels=self.num_levels,
patchSize=31,
edgeThreshold = 10, #31, #19, #10, # margin from the frame border
fastThreshold = 20,
firstLevel = self.first_level,
WTA_K = 2,
scoreType=cv2.ORB_FAST_SCORE) #scoreType=cv2.ORB_HARRIS_SCORE, scoreType=cv2.ORB_FAST_SCORE
# --------------------------------------------- #
# init detector
# --------------------------------------------- #
if self.detector_type == FeatureDetectorTypes.SIFT or self.detector_type == FeatureDetectorTypes.ROOT_SIFT:
sift = self.SIFT_create(nOctaveLayers=self.layers_per_octave)
self.set_sift_parameters()
if self.detector_type == FeatureDetectorTypes.ROOT_SIFT:
self._feature_detector = RootSIFTFeature2D(sift)
else:
self._feature_detector = sift
#
#
elif self.detector_type == FeatureDetectorTypes.SURF:
self._feature_detector = self.SURF_create(nOctaves = self.num_levels, nOctaveLayers=self.layers_per_octave)
#self.intra_layer_factor = 1.2599 # num layers = nOctaves*nOctaveLayers scale=2^(1/nOctaveLayers) = 1.2599
self.scale_factor = 2 # force scale factor = 2 between octaves
#
#
elif self.detector_type == FeatureDetectorTypes.ORB:
self._feature_detector = self.ORB_create(**self.orb_params)
self.use_bock_adaptor = True # add a block adaptor?
self.need_nms = self.num_levels > 1 # ORB tends to generate overlapping keypoint on different levels <= KDT NMS seems to be very useful here!
#
#
elif self.detector_type == FeatureDetectorTypes.ORB2:
orb2_num_levels = self.num_levels
self._feature_detector = Orbslam2Feature2D(self.num_features, self.scale_factor, orb2_num_levels)
self.keypoint_filter_type = KeyPointFilterTypes.NONE # ORB2 cpp implementation already includes the algorithm OCTREE_NMS
#
#
elif self.detector_type == FeatureDetectorTypes.BRISK:
self._feature_detector = self.BRISK_create(octaves=self.num_levels)
#self.intra_layer_factor = 1.3 # from the BRISK opencv code this seems to be the used scale factor between intra-octave frames
#self.intra_layer_factor = math.sqrt(2) # approx, num layers = nOctaves*nOctaveLayers, from the BRISK paper there are octave ci and intra-octave di layers, t(ci)=2^i, t(di)=2^i * 1.5
self.scale_factor = 2 # force scale factor = 2 between octaves
#self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.KAZE:
self._feature_detector = self.KAZE_create(nOctaves=self.num_levels, threshold=0.0005) # default: threshold = 0.001f
self.scale_factor = 2 # force scale factor = 2 between octaves
#
#
elif self.detector_type == FeatureDetectorTypes.AKAZE:
self._feature_detector = self.AKAZE_create(nOctaves=self.num_levels, threshold=0.0005) # default: threshold = 0.001f
self.scale_factor = 2 # force scale factor = 2 between octaves
#
#
elif self.detector_type == FeatureDetectorTypes.SUPERPOINT:
self.oriented_features = False
self._feature_detector = SuperPointFeature2D()
if self.descriptor_type != FeatureDescriptorTypes.NONE:
self.use_pyramid_adaptor = self.num_levels > 1
self.need_nms = self.num_levels > 1
self.pyramid_type = PyramidType.GAUSS_PYRAMID
self.pyramid_do_parallel = False # N.B.: SUPERPOINT interface class is not thread-safe!
self.force_multiscale_detect_and_compute = True # force it since SUPERPOINT cannot compute descriptors separately from keypoints
#
#
elif self.detector_type == FeatureDetectorTypes.FAST:
self.oriented_features = False
self._feature_detector = self.FAST_create(threshold=20, nonmaxSuppression=True)
if self.descriptor_type != FeatureDescriptorTypes.NONE:
#self.use_bock_adaptor = True # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
#self.do_sat_features_per_level = True
self.need_nms = self.num_levels > 1
self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
self.do_keypoints_size_rescaling = True
#
#
elif self.detector_type == FeatureDetectorTypes.SHI_TOMASI:
self.oriented_features = False
self._feature_detector = ShiTomasiDetector(self.num_features)
if self.descriptor_type != FeatureDescriptorTypes.NONE:
#self.use_bock_adaptor = False # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
self.need_nms = self.num_levels > 1
self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
self.do_keypoints_size_rescaling = True
#
#
elif self.detector_type == FeatureDetectorTypes.AGAST:
self.oriented_features = False
self._feature_detector = self.AGAST_create(threshold=10, nonmaxSuppression=True)
if self.descriptor_type != FeatureDescriptorTypes.NONE:
#self.use_bock_adaptor = True # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
self.need_nms = self.num_levels > 1
self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
self.do_keypoints_size_rescaling = True
#
#
elif self.detector_type == FeatureDetectorTypes.GFTT:
self.oriented_features = False
self._feature_detector = self.GFTT_create(self.num_features, qualityLevel=0.01, minDistance=3, blockSize=5, useHarrisDetector=False, k=0.04)
if self.descriptor_type != FeatureDescriptorTypes.NONE:
#self.use_bock_adaptor = True # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
self.need_nms = self.num_levels > 1
self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
self.do_keypoints_size_rescaling = True
#
#
elif self.detector_type == FeatureDetectorTypes.MSER:
self._feature_detector = self.MSER_create()
#self.use_bock_adaptor = True # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
self.pyramid_do_parallel = False # parallel computations generate segmentation fault (is MSER thread-safe?)
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
self.need_nms = self.num_levels > 1
#self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
#
#
elif self.detector_type == FeatureDetectorTypes.MSD:
#detector = ShiTomasiDetector(self.num_features)
#self._feature_detector = self.MSD_create(detector)
self._feature_detector = self.MSD_create()
print('MSD detector info:',dir(self._feature_detector))
#self.use_bock_adaptor = True # override a block adaptor?
#self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
#self.need_nms = self.num_levels > 1
#self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
#
#
elif self.detector_type == FeatureDetectorTypes.STAR:
self.oriented_features = False
self._feature_detector = self.STAR_create(maxSize=45,
responseThreshold=10, # =30
lineThresholdProjected=10,
lineThresholdBinarized=8,
suppressNonmaxSize=5)
if self.descriptor_type != FeatureDescriptorTypes.NONE:
#self.use_bock_adaptor = True # override a block adaptor?
self.use_pyramid_adaptor = self.num_levels > 1 # override a pyramid adaptor?
#self.pyramid_type = PyramidType.GAUSS_PYRAMID
#self.first_level = 0
#self.need_nms = self.num_levels > 1
#self.keypoint_nms_filter_type = KeyPointFilterTypes.OCTREE_NMS
#
#
elif self.detector_type == FeatureDetectorTypes.HL:
self.oriented_features = False
self._feature_detector = self.HL_create(numOctaves=self.num_levels,
corn_thresh=0.005, # = 0.01
DOG_thresh=0.01, # = 0.01
maxCorners=self.num_features,
num_layers=4) #
self.scale_factor = 2 # force scale factor = 2 between octaves
#
#
elif self.detector_type == FeatureDetectorTypes.D2NET:
self.need_color_image = True
self.num_levels = 1 # force unless you have 12GB of VRAM
multiscale=self.num_levels>1
self._feature_detector = D2NetFeature2D(multiscale=multiscale)
#self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.DELF:
self.need_color_image = True
#self.num_levels = 1 # force #scales are computed internally
self._feature_detector = DelfFeature2D(num_features=self.num_features,score_threshold=20)
self.scale_factor = self._feature_detector.scale_factor
#self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.CONTEXTDESC:
self.set_sift_parameters()
self.need_color_image = True
#self.num_levels = 1 # force # computed internally by SIFT method
self._feature_detector = ContextDescFeature2D(num_features=self.num_features)
#self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.LFNET:
self.need_color_image = True
#self.num_levels = 1 # force
self._feature_detector = LfNetFeature2D(num_features=self.num_features)
#self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.R2D2:
self.need_color_image = True
#self.num_levels = - # internally recomputed
self._feature_detector = R2d2Feature2D(num_features=self.num_features)
self.scale_factor = self._feature_detector.scale_f
self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.ASLFEAT:
self.need_color_image = True
#self.num_levels = - # internally recomputed
self._feature_detector = ASLFeature2D(num_features=self.num_features)
self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.KEYNET:
#self.num_levels = - # internally recomputed
self._feature_detector = KeyNetDescFeature2D(num_features=self.num_features)
self.num_features = self._feature_detector.num_features
self.num_levels = self._feature_detector.num_levels
self.scale_factor = self._feature_detector.scale_factor
self.keypoint_filter_type = KeyPointFilterTypes.NONE
#
#
elif self.detector_type == FeatureDetectorTypes.DISK:
self.num_levels = 1 # force
self.need_color_image = True
self._feature_detector = DiskFeature2D(num_features=self.num_features)
#
#
else:
raise ValueError("Unknown feature detector %s" % self.detector_type)
if self.need_nms:
self.keypoint_filter_type = self.keypoint_nms_filter_type
if self.use_bock_adaptor:
self.orb_params['edgeThreshold'] = 0
# --------------------------------------------- #
# init descriptor
# --------------------------------------------- #
if self.is_detector_equal_to_descriptor:
Printer.green('using same detector and descriptor object: ', self.detector_type.name)
self._feature_descriptor = self._feature_detector
else:
# detector and descriptors are different
self.num_levels_descriptor = self.num_levels
if self.use_pyramid_adaptor:
# NOT VALID ANYMORE -> if there is a pyramid adaptor, the descriptor does not need to rescale the images which are rescaled by the pyramid adaptor itself
#self.orb_params['nlevels'] = 1
#self.num_levels_descriptor = 1 #self.num_levels
pass
# actual descriptor initialization
if self.descriptor_type == FeatureDescriptorTypes.SIFT or self.descriptor_type == FeatureDescriptorTypes.ROOT_SIFT:
sift = self.SIFT_create(nOctaveLayers=3)
if self.descriptor_type == FeatureDescriptorTypes.ROOT_SIFT:
self._feature_descriptor = RootSIFTFeature2D(sift)
else:
self._feature_descriptor = sift
#
#
elif self.descriptor_type == FeatureDescriptorTypes.SURF:
self.oriented_features = True # SURF computes the keypoint orientation
self._feature_descriptor = self.SURF_create(nOctaves = self.num_levels_descriptor, nOctaveLayers=3)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.ORB:
self._feature_descriptor = self.ORB_create(**self.orb_params)
#self.oriented_features = False # N.B: ORB descriptor does not compute orientation on its own
#
#
elif self.descriptor_type == FeatureDescriptorTypes.ORB2:
self._feature_descriptor = self.ORB_create(**self.orb_params)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.BRISK:
self.oriented_features = True # BRISK computes the keypoint orientation
self._feature_descriptor = self.BRISK_create(octaves=self.num_levels_descriptor)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.KAZE:
if not self.is_detector_equal_to_descriptor:
Printer.red('WARNING: KAZE descriptors can only be used with KAZE or AKAZE keypoints.') # https://kyamagu.github.io/mexopencv/matlab/AKAZE.html
self._feature_descriptor = self.KAZE_create(nOctaves=self.num_levels_descriptor)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.AKAZE:
if not self.is_detector_equal_to_descriptor:
Printer.red('WARNING: AKAZE descriptors can only be used with KAZE or AKAZE keypoints.') # https://kyamagu.github.io/mexopencv/matlab/AKAZE.html
self._feature_descriptor = self.AKAZE_create(nOctaves=self.num_levels_descriptor)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.FREAK:
self.oriented_features = True # FREAK computes the keypoint orientation
self._feature_descriptor = self.FREAK_create(nOctaves=self.num_levels_descriptor)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.SUPERPOINT:
if self.detector_type != FeatureDetectorTypes.SUPERPOINT:
raise ValueError("You cannot use SUPERPOINT descriptor without SUPERPOINT detector!\nPlease, select SUPERPOINT as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse the same SuperPointDector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.TFEAT:
self._feature_descriptor = TfeatFeature2D()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.BOOST_DESC:
self.do_keypoints_size_rescaling = False # below a proper keypoint size scale factor is set depending on the used detector
boost_des_keypoint_size_scale_factor = 1.5
# from https://docs.opencv.org/3.4.2/d1/dfd/classcv_1_1xfeatures2d_1_1BoostDesc.html#details
#scale_factor: adjust the sampling window of detected keypoints 6.25f is default and fits for KAZE, SURF
# detected keypoints window ratio 6.75f should be the scale for SIFT
# detected keypoints window ratio 5.00f should be the scale for AKAZE, MSD, AGAST, FAST, BRISK
# keypoints window ratio 0.75f should be the scale for ORB
# keypoints ratio 1.50f was the default in original implementation
if self.detector_type in [FeatureDetectorTypes.KAZE, FeatureDetectorTypes.SURF]:
boost_des_keypoint_size_scale_factor = 6.25
elif self.detector_type == FeatureDetectorTypes.SIFT:
boost_des_keypoint_size_scale_factor = 6.75
elif self.detector_type in [FeatureDetectorTypes.AKAZE, FeatureDetectorTypes.AGAST, FeatureDetectorTypes.FAST, FeatureDetectorTypes.BRISK]:
boost_des_keypoint_size_scale_factor = 5.0
elif self.detector_type == FeatureDetectorTypes.ORB:
boost_des_keypoint_size_scale_factor = 0.75
self._feature_descriptor = self.BoostDesc_create(scale_factor=boost_des_keypoint_size_scale_factor)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.DAISY:
self._feature_descriptor = self.DAISY_create()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.LATCH:
self._feature_descriptor = self.LATCH_create()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.LUCID:
self._feature_descriptor = self.LUCID_create(lucid_kernel=1, # =1
blur_kernel=3 ) # =2
self.need_color_image = True
#
#
elif self.descriptor_type == FeatureDescriptorTypes.VGG:
self._feature_descriptor = self.VGG_create()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.HARDNET:
self._feature_descriptor = HardnetFeature2D(do_cuda=True)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.GEODESC:
self._feature_descriptor = GeodescFeature2D()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.SOSNET:
self._feature_descriptor = SosnetFeature2D()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.L2NET:
#self._feature_descriptor = L2NetKerasFeature2D() # keras-tf version
self._feature_descriptor = L2NetFeature2D()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.LOGPOLAR:
self._feature_descriptor = LogpolarFeature2D()
#
#
elif self.descriptor_type == FeatureDescriptorTypes.D2NET:
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.D2NET:
raise ValueError("You cannot use D2NET descriptor without D2NET detector!\nPlease, select D2NET as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.DELF:
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.DELF:
raise ValueError("You cannot use DELF descriptor without DELF detector!\nPlease, select DELF as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.CONTEXTDESC:
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.CONTEXTDESC:
raise ValueError("You cannot use CONTEXTDESC descriptor without CONTEXTDESC detector!\nPlease, select CONTEXTDESC as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.LFNET:
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.LFNET:
raise ValueError("You cannot use LFNET descriptor without LFNET detector!\nPlease, select LFNET as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.R2D2:
self.oriented_features = False
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.R2D2:
raise ValueError("You cannot use R2D2 descriptor without R2D2 detector!\nPlease, select R2D2 as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.ASLFEAT:
self.oriented_features = False
self.need_color_image = True
if self.detector_type != FeatureDetectorTypes.ASLFEAT:
raise ValueError("You cannot use ASLFEAT descriptor without ASLFEAT detector!\nPlease, select ASLFEAT as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.KEYNET:
self.oriented_features = False
if self.detector_type != FeatureDetectorTypes.KEYNET:
raise ValueError("You cannot use KEYNET internal descriptor without KEYNET detector!\nPlease, select KEYNET as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.BEBLID:
BEBLID_SIZE_256_BITS = 101 # https://docs.opencv.org/master/d7/d99/classcv_1_1xfeatures2d_1_1BEBLID.html
BEBLID_scale_factor = 1.0 # it depends on the used detector https://docs.opencv.org/master/d7/d99/classcv_1_1xfeatures2d_1_1BEBLID.html#a38997aa059977abf6a2d6bf462d50de0a7b2a1e106c93d76cdfe5cef053277a04
# TODO: adapt BEBLID scale factor to actual used detector
# 1.0 is OK for ORB2 detector
self._feature_descriptor = self.BEBLID_create(BEBLID_scale_factor, BEBLID_SIZE_256_BITS)
#
#
elif self.descriptor_type == FeatureDescriptorTypes.DISK:
self.oriented_features = False
if self.detector_type != FeatureDetectorTypes.DISK:
raise ValueError("You cannot use DISK internal descriptor without DISK detector!\nPlease, select DISK as both descriptor and detector!")
self._feature_descriptor = self._feature_detector # reuse detector object
#
#
elif self.descriptor_type == FeatureDescriptorTypes.NONE:
self._feature_descriptor = None
else:
raise ValueError("Unknown feature descriptor %s" % self.descriptor_type)
# --------------------------------------------- #
# init from FeatureInfo
# --------------------------------------------- #
# get and set norm type
try:
self.norm_type = FeatureInfo.norm_type[self.descriptor_type]
except:
Printer.red('You did not set the norm type for: ', self.descriptor_type.name)
raise ValueError("Unmanaged norm type for feature descriptor %s" % self.descriptor_type.name)
# set descriptor distance functions
if self.norm_type == cv2.NORM_HAMMING:
self.descriptor_distance = hamming_distance
self.descriptor_distances = hamming_distances
if self.norm_type == cv2.NORM_L2:
self.descriptor_distance = l2_distance
self.descriptor_distances = l2_distances
# get and set reference max descriptor distance
try:
Parameters.kMaxDescriptorDistance = FeatureInfo.max_descriptor_distance[self.descriptor_type]
except:
Printer.red('You did not set the reference max descriptor distance for: ', self.descriptor_type.name)
raise ValueError("Unmanaged max descriptor distance for feature descriptor %s" % self.descriptor_type.name)
Parameters.kMaxDescriptorDistanceSearchEpipolar = Parameters.kMaxDescriptorDistance
# --------------------------------------------- #
# other required initializations
# --------------------------------------------- #
if not self.oriented_features:
Printer.orange('WARNING: using NON-ORIENTED features: ', self.detector_type.name,'-',self.descriptor_type.name, ' (i.e. kp.angle=0)')
if self.is_detector_equal_to_descriptor and \
( self.detector_type == FeatureDetectorTypes.SIFT or
self.detector_type == FeatureDetectorTypes.ROOT_SIFT or
self.detector_type == FeatureDetectorTypes.CONTEXTDESC ):
self.init_sigma_levels_sift()
else:
self.init_sigma_levels()
if self.use_bock_adaptor:
self.block_adaptor = BlockAdaptor(self._feature_detector, self._feature_descriptor)
if self.use_pyramid_adaptor:
self.pyramid_params = dict(detector=self._feature_detector,
descriptor=self._feature_descriptor,
num_features = self.num_features,
num_levels=self.num_levels,
scale_factor=self.scale_factor,
sigma0=self.sigma_level0,
first_level=self.first_level,
pyramid_type=self.pyramid_type,
use_block_adaptor=self.use_bock_adaptor,
do_parallel = self.pyramid_do_parallel,
do_sat_features_per_level = self.do_sat_features_per_level)
self.pyramid_adaptor = PyramidAdaptor(**self.pyramid_params)
def set_sift_parameters(self):
# N.B.: The number of SIFT octaves is automatically computed from the image resolution,
# here we can set the number of layers in each octave.
# from https://docs.opencv.org/3.4/d5/d3c/classcv_1_1xfeatures2d_1_1SIFT.html
#self.intra_layer_factor = 1.2599 # num layers = nOctaves*nOctaveLayers scale=2^(1/nOctaveLayers) = 1.2599
self.scale_factor = 2 # force scale factor = 2 between octaves
self.sigma_level0 = 1.6 # https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L118
# from https://docs.opencv.org/3.1.0/da/df5/tutorial_py_sift_intro.html
self.first_level = -1 # https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L731
# initialize scale factors, sigmas for each octave level;
# these are used for managing image pyramids and weighting (information matrix) reprojection error terms in the optimization
def init_sigma_levels(self):
print('num_levels: ', self.num_levels)
num_levels = max(kNumLevelsInitSigma, self.num_levels)
self.inv_scale_factor = 1./self.scale_factor
self.scale_factors = np.zeros(num_levels)
self.level_sigmas2 = np.zeros(num_levels)
self.level_sigmas = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.inv_level_sigmas2 = np.zeros(num_levels)
self.log_scale_factor = math.log(self.scale_factor)
self.scale_factors[0] = 1.0
self.level_sigmas2[0] = self.sigma_level0*self.sigma_level0
self.level_sigmas[0] = math.sqrt(self.level_sigmas2[0])
for i in range(1,num_levels):
self.scale_factors[i] = self.scale_factors[i-1]*self.scale_factor
self.level_sigmas2[i] = self.scale_factors[i]*self.scale_factors[i]*self.level_sigmas2[0]
self.level_sigmas[i] = math.sqrt(self.level_sigmas2[i])
for i in range(num_levels):
self.inv_scale_factors[i] = 1.0/self.scale_factors[i]
self.inv_level_sigmas2[i] = 1.0/self.level_sigmas2[i]
#print('self.scale_factor: ', self.scale_factor)
#print('self.scale_factors: ', self.scale_factors)
#print('self.level_sigmas: ', self.level_sigmas)
#print('self.inv_scale_factors: ', self.inv_scale_factors)
# initialize scale factors, sigmas for each octave level;
# these are used for managing image pyramids and weighting (information matrix) reprojection error terms in the optimization;
# this method can be used only when the following mapping is adopted for SIFT:
# keypoint.octave = (unpacked_octave+1)*3+unpacked_layer where S=3 is the number of levels per octave
def init_sigma_levels_sift(self):
print('initializing SIFT sigma levels')
print('num_levels: ', self.num_levels)
self.num_levels = 3*self.num_levels + 3 # we map: level=keypoint.octave = (unpacked_octave+1)*3+unpacked_layer where S=3 is the number of scales per octave
num_levels = max(kNumLevelsInitSigma, self.num_levels)
#print('num_levels: ', num_levels)
# N.B: if we adopt the mapping: keypoint.octave = (unpacked_octave+1)*3+unpacked_layer
# then we can consider a new virtual scale_factor = 2^(1/3) (used between two contiguous layers of the same octave)
print('original scale factor: ', self.scale_factor)
self.scale_factor = math.pow(2,1./3)
self.inv_scale_factor = 1./self.scale_factor
self.scale_factors = np.zeros(num_levels)
self.level_sigmas2 = np.zeros(num_levels)
self.level_sigmas = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.inv_level_sigmas2 = np.zeros(num_levels)
self.log_scale_factor = math.log(self.scale_factor)
self.sigma_level0 = 1.6 # https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L118
# from https://docs.opencv.org/3.1.0/da/df5/tutorial_py_sift_intro.html
sigma_level02 = self.sigma_level0*self.sigma_level0
# N.B.: these are used only when recursive filtering is applied: see https://www.vlfeat.org/api/sift.html#sift-tech-ss
#sift_init_sigma = 0.5
#sift_init_sigma2 = 0.25
# see also https://www.vlfeat.org/api/sift.html
self.scale_factors[0] = 1.0
self.level_sigmas2[0] = sigma_level02 # -4*sift_init_sigma2 N.B.: this is an absolute sigma,
# not a delta_sigma used for incrementally filtering contiguos layers => we must not subtract (4*sift_init_sigma2)
# https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L197
self.level_sigmas[0] = math.sqrt(self.level_sigmas2[0])
for i in range(1,num_levels):
self.scale_factors[i] = self.scale_factors[i-1]*self.scale_factor
self.level_sigmas2[i] = self.scale_factors[i]*self.scale_factors[i]*sigma_level02 # https://github.com/opencv/opencv/blob/173442bb2ecd527f1884d96d7327bff293f0c65a/modules/nonfree/src/sift.cpp#L224
self.level_sigmas[i] = math.sqrt(self.level_sigmas2[i])
for i in range(num_levels):
self.inv_scale_factors[i] = 1.0/self.scale_factors[i]
self.inv_level_sigmas2[i] = 1.0/self.level_sigmas2[i]
#print('self.scale_factor: ', self.scale_factor)
#print('self.scale_factors: ', self.scale_factors)
#print('self.level_sigmas: ', self.level_sigmas)
#print('self.inv_scale_factors: ', self.inv_scale_factors)
# filter matches by using
# Non-Maxima Suppression (NMS) based on kd-trees
# or SSC NMS (https://github.com/BAILOOL/ANMS-Codes)
# or SAT (get features with best responses)
# or OCTREE_NMS (implemented in ORBSLAM2, distribution of features in a quad-tree)
def filter_keypoints(self, type, frame, kps, des=None):
filter_name = type.name
if type == KeyPointFilterTypes.NONE:
pass
elif type == KeyPointFilterTypes.KDT_NMS:
kps, des = kdt_nms(kps, des, self.num_features)
elif type == KeyPointFilterTypes.SSC_NMS:
kps, des = ssc_nms(kps, des, frame.shape[1], frame.shape[0], self.num_features)
elif type == KeyPointFilterTypes.OCTREE_NMS:
if des is not None:
raise ValueError('at the present time, you cannot use OCTREE_NMS with descriptors')
kps = octree_nms(frame, kps, self.num_features)
elif type == KeyPointFilterTypes.GRID_NMS:
kps, des, _ = grid_nms(kps, des, frame.shape[0], frame.shape[1], self.num_features, dist_thresh=4)
elif type == KeyPointFilterTypes.SAT:
if len(kps) > self.num_features:
kps, des = sat_num_features(kps, des, self.num_features)
else:
raise ValueError("Unknown match-filter type")
return kps, des, filter_name
def rescale_keypoint_size(self, kps):
# if keypoints are FAST, etc. then rescale their small sizes
# in order to let descriptors compute an encoded representation with a decent patch size
scale = 1
doit = False
if self.detector_type == FeatureDetectorTypes.FAST:
scale = kFASTKeyPointSizeRescaleFactor
doit = True
elif self.detector_type == FeatureDetectorTypes.AGAST:
scale = kAGASTKeyPointSizeRescaleFactor
doit = True
elif self.detector_type == FeatureDetectorTypes.SHI_TOMASI or self.detector_type == FeatureDetectorTypes.GFTT:
scale = kShiTomasiKeyPointSizeRescaleFactor
doit = True
if doit:
for kp in kps:
kp.size *= scale
# detect keypoints without computing their descriptors
# out: kps (array of cv2.KeyPoint)
def detect(self, frame, mask=None, filter=True):
if not self.need_color_image and frame.ndim>2: # check if we have to convert to gray image
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
if self.use_pyramid_adaptor:
# detection with pyramid adaptor (it can optionally include a block adaptor per level)
kps = self.pyramid_adaptor.detect(frame, mask)
elif self.use_bock_adaptor:
# detection with block adaptor
kps = self.block_adaptor.detect(frame, mask)
else:
# standard detection
kps = self._feature_detector.detect(frame, mask)
# filter keypoints
filter_name = 'NONE'
if filter:
kps, _, filter_name = self.filter_keypoints(self.keypoint_filter_type, frame, kps)
# if keypoints are FAST, etc. give them a decent size in order to properly compute the descriptors
if self.do_keypoints_size_rescaling:
self.rescale_keypoint_size(kps)
if kDrawOriginalExtractedFeatures: # draw the original features
imgDraw = cv2.drawKeypoints(frame, kps, None, color=(0,255,0), flags=0)
cv2.imshow('detected keypoints',imgDraw)
if kVerbose:
print('detector:',self.detector_type.name,', #features:', len(kps),', [kp-filter:',filter_name,']')
return kps
# compute the descriptors once given the keypoints
def compute(self, frame, kps, filter = True):
if not self.need_color_image and frame.ndim>2: # check if we have to convert to gray image
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
kps, des = self._feature_descriptor.compute(frame, kps) # then, compute descriptors
# filter keypoints
filter_name = 'NONE'
if filter:
kps, des, filter_name = self.filter_keypoints(self.keypoint_filter_type, frame, kps, des)
if kVerbose:
print('descriptor:',self.descriptor_type.name,', #features:', len(kps),', [kp-filter:',filter_name,']')
return kps, des
# detect keypoints and their descriptors
# out: kps, des
def detectAndCompute(self, frame, mask=None, filter = True):
if not self.need_color_image and frame.ndim>2: # check if we have to convert to gray image
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
if self.use_pyramid_adaptor:
# detectAndCompute with pyramid adaptor (it can optionally include a block adaptor per level)
if self.force_multiscale_detect_and_compute:
# force detectAndCompute on each level instead of first {detect() on each level} and then {compute() on resulting detected keypoints one time}
kps, des = self.pyramid_adaptor.detectAndCompute(frame, mask)
#
else:
kps = self.detect(frame, mask, filter=True) # first, detect by using adaptor on the different pyramid levels
kps, des = self.compute(frame, kps, filter=False) # then, separately compute the descriptors on detected keypoints (one time)
filter = False # disable keypoint filtering since we already applied it for detection
elif self.use_bock_adaptor:
# detectAndCompute with block adaptor (force detect/compute on each block)
#
#kps, des = self.block_adaptor.detectAndCompute(frame, mask)
#
kps = self.detect(frame, mask, filter=True) # first, detect by using adaptor
kps, des = self.compute(frame, kps, filter=False) # then, separately compute the descriptors
filter = False # disable keypoint filtering since we already applied it for detection
else:
# standard detectAndCompute
if self.is_detector_equal_to_descriptor:
# detector = descriptor => call them together with detectAndCompute() method
kps, des = self._feature_detector.detectAndCompute(frame, mask)
if kVerbose:
print('detector:', self.detector_type.name,', #features:',len(kps))
print('descriptor:', self.descriptor_type.name,', #features:',len(kps))
else:
# detector and descriptor are different => call them separately
# 1. first, detect keypoint locations
kps = self.detect(frame, mask, filter=False)
# 2. then, compute descriptors
kps, des = self._feature_descriptor.compute(frame, kps)
if kVerbose:
#print('detector: ', self.detector_type.name, ', #features: ', len(kps))
print('descriptor: ', self.descriptor_type.name, ', #features: ', len(kps))
# filter keypoints
filter_name = 'NONE'
if filter:
kps, des, filter_name = self.filter_keypoints(self.keypoint_filter_type, frame, kps, des)
if self.detector_type == FeatureDetectorTypes.SIFT or \
self.detector_type == FeatureDetectorTypes.ROOT_SIFT or \
self.detector_type == FeatureDetectorTypes.CONTEXTDESC :
unpackSiftOctaveKps(kps, method=UnpackOctaveMethod.INTRAL_LAYERS)
if kVerbose:
print('detector:',self.detector_type.name,', descriptor:', self.descriptor_type.name,', #features:', len(kps),' (#ref:', self.num_features, '), [kp-filter:',filter_name,']')
self.debug_print(kps)
return kps, des
def debug_print(self, kps):
if False:
# raw print of all keypoints
for k in kps:
print("response: ", k.response, "\t, size: ", k.size, "\t, octave: ", k.octave, "\t, angle: ", k.angle)
if False:
# generate a rough histogram for keypoint sizes
kps_sizes = [kp.size for kp in kps]
kps_sizes_histogram = np.histogram(kps_sizes, bins=10)
print('size-histogram: \n', list(zip(kps_sizes_histogram[1],kps_sizes_histogram[0])))
# generate histogram at level 0
kps_sizes = [kp.size for kp in kps if kp.octave==1]