feature_manager.py

"""
* 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 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')
LfNetFeature2D = import_from('feature_lfnet', 'LfNetFeature2D')
R2d2Feature2D = import_from('feature_r2d2', 'R2d2Feature2D')
KeyNetDescFeature2D = import_from('feature_keynet', 'KeyNetDescFeature2D')


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 
        if cv2.__version__.split('.')[0] == '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')                     
        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')                

        # 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     

        # --------------------------------------------- #
        # 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.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
            #    
            #                                                                                                                                                
        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.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.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])))            
        if False: 
            # count points for each octave => generate an octave histogram 
            kps_octaves = [k.octave for k in kps]
            kps_octaves = Counter(kps_octaves)
            print('levels-histogram: ', kps_octaves.most_common(12))