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feature_detector.py
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feature_detector.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
import numpy as np
import cv2
from enum import Enum
from geom_helpers import imgBlocks, unpackSiftOctaveKps
kVerbose = True
kMinNumFeatureDefault = 2000
kAdaptorNumRowDivs = 4
kAdaptorNumColDivs = 4
kNumLevels = 4
kNumLevelsInitSigma = 12
kScaleFactor = 1.2
kSigmaLevel0 = 1.
kDrawOriginalExtractedFeatures = False # for debugging
class FeatureDetectorTypes(Enum):
SHI_TOMASI = 1
FAST = 2
SIFT = 3
SURF = 4
ORB = 5
BRISK = 6
AKAZE = 7
FREAK = 8 # DOES NOT WORK!
class FeatureDescriptorTypes(Enum):
NONE = 0 # used for LK tracker
SIFT = 1
SURF = 2
ORB = 3
BRISK = 4
AKAZE = 5
FREAK = 6 # DOES NOT WORK!
def feature_detector_factory(min_num_features=kMinNumFeatureDefault,
num_levels = kNumLevels,
detector_type = FeatureDetectorTypes.FAST,
descriptor_type = FeatureDescriptorTypes.ORB):
return FeatureDetector(min_num_features, num_levels, detector_type, descriptor_type)
# BlockAdaptor divides the image in row_divs x col_divs cells and extracts features in each of these cells
class BlockAdaptor(object):
def __init__(self, detector, row_divs = kAdaptorNumRowDivs, col_divs = kAdaptorNumColDivs):
self.detector = detector
self.row_divs = row_divs
self.col_divs = col_divs
def detect(self, frame, mask=None):
if self.row_divs == 1 and self.col_divs == 1:
return self.detector.detect(frame, mask)
else:
if kVerbose:
print('BlockAdaptor')
block_generator = imgBlocks(frame, self.row_divs, self.col_divs)
kps_global = []
for b, i, j in block_generator:
if kVerbose and False:
print('BlockAdaptor in block (',i,',',j,')')
kps = self.detector.detect(b)
#print('adaptor: detected #features: ', len(kps), ' in block (',i,',',j,')')
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0] + j, kp.pt[1] + i)
#print('kp.pt after: ', kp.pt)
kps_global.append(kp)
return kps_global
# PyramidAdaptor generate a pyramid of num_levels images and extracts features in each of these images
# TODO: check if a point on one level 'overlaps' with a point on other levels
class PyramidAdaptor(object):
def __init__(self, detector, num_levels = 4, scale_factor = 1.2, use_block_adaptor = False):
self.detector = detector
self.num_levels = num_levels
self.scale_factor = scale_factor
self.cur_pyr = []
self.scale_factors = None
self.inv_scale_factors = None
self.use_block_adaptor = use_block_adaptor
self.block_adaptor = None
if self.use_block_adaptor:
self.block_adaptor = BlockAdaptor(self.detector, row_divs = kAdaptorNumRowDivs, col_divs = kAdaptorNumColDivs)
self.initSigmaLevels()
def initSigmaLevels(self):
num_levels = max(kNumLevelsInitSigma, self.num_levels)
self.scale_factors = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.scale_factors[0]=1.0
for i in range(1,num_levels):
self.scale_factors[i]=self.scale_factors[i-1]*self.scale_factor
#print('self.scale_factors: ', self.scale_factors)
for i in range(num_levels):
self.inv_scale_factors[i]=1.0/self.scale_factors[i]
#print('self.inv_scale_factors: ', self.inv_scale_factors)
def detect(self, frame, mask=None):
if self.num_levels == 1:
return self.detector.detect(frame, mask)
else:
if kVerbose:
print('PyramidAdaptor')
self.computerPyramid(frame)
kps_global = []
for i in range(0,self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.cur_pyr[i]
kps = None
if self.block_adaptor is None:
kps = self.detector.detect(pyr_cur)
else:
kps = self.block_adaptor.detect(pyr_cur)
if kVerbose and False:
print("PyramidAdaptor - level", i, ", shape: ", pyr_cur.shape)
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0]*scale, kp.pt[1]*scale)
kp.size = kp.size*scale
kp.octave = i
#print('kp: ', kp.pt, kp.octave)
kps_global.append(kp)
return kps_global
def computerPyramid(self, frame):
self.cur_pyr = []
self.cur_pyr.append(frame)
inv_scale = 1./self.scale_factor
for i in range(1,self.num_levels):
pyr_cur = self.cur_pyr[-1]
pyr_down = cv2.resize(pyr_cur,(0,0),fx=inv_scale,fy=inv_scale)
self.cur_pyr.append(pyr_down)
class ShiTomasiDetector(object):
def __init__(self, min_num_features=kMinNumFeatureDefault, quality_level = 0.01, min_coner_distance = 7):
self.min_num_features = min_num_features
self.quality_level = quality_level
self.min_coner_distance = min_coner_distance
def detect(self, frame, mask=None):
pts = cv2.goodFeaturesToTrack(frame, self.min_num_features, self.quality_level, self.min_coner_distance, mask=mask)
# convert matrix of pts into list of keypoints
if pts is not None:
kps = [ cv2.KeyPoint(p[0][0], p[0][1], 1) for p in pts ]
else:
kps = []
if kVerbose:
print('detector: Shi-Tomasi, #features: ', len(kps), ', #ref: ', self.min_num_features)
return kps
class FeatureDetector(object):
def __init__(self, min_num_features=kMinNumFeatureDefault,
num_levels = kNumLevels,
detector_type = FeatureDetectorTypes.SHI_TOMASI,
descriptor_type = FeatureDescriptorTypes.ORB):
self.detector_type = detector_type
self.descriptor_type = descriptor_type
self.num_levels = num_levels
self.scale_factor = kScaleFactor # scale factor bewteen two octaves
self.sigma_level0 = kSigmaLevel0 # sigma on first octave
self.initSigmaLevels()
self.min_num_features = min_num_features
# at present time pyramid adaptor has the priority and can combine a block adaptor withint itself
self.use_bock_adaptor = False
self.block_adaptor = None
self.use_pyramid_adaptor = False
self.pyramid_adaptor = None
print("using opencv ", cv2.__version__)
# check opencv version in order to use the right modules
if cv2.__version__.split('.')[0] == '3':
from cv2.xfeatures2d import SIFT_create, SURF_create, FREAK_create
from cv2 import ORB_create, BRISK_create, AKAZE_create
else:
SIFT_create = cv2.SIFT
SURF_create = cv2.SURF
ORB_create = cv2.ORB
BRISK_create = cv2.BRISK
AKAZE_create = cv2.AKAZE
FREAK_create = cv2.FREAK # TODO: to be checked
self.FAST_create = cv2.FastFeatureDetector_create
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.FREAK_create = FREAK_create # DOES NOT WORK!
self.orb_params = dict(nfeatures=min_num_features,
scaleFactor=self.scale_factor,
nlevels=self.num_levels,
patchSize=31,
edgeThreshold = 19,
fastThreshold = 20,
scoreType=cv2.ORB_HARRIS_SCORE) #scoreType=cv2.ORB_HARRIS_SCORE, scoreType=cv2.ORB_FAST_SCORE
self.detector_name = ''
self.decriptor_name = ''
# init detector
if self.detector_type == FeatureDetectorTypes.SIFT:
self._feature_detector = self.SIFT_create() # N.B.: The number of octaves is computed automatically from the image resolution
# from https://docs.opencv.org/3.4/d5/d3c/classcv_1_1xfeatures2d_1_1SIFT.html
self.scale_factor = 2 # from https://docs.opencv.org/3.1.0/da/df5/tutorial_py_sift_intro.html
# self.layer_scale_factor = math.sqrt(2) # with SIFT, 3 layers per octave are generated with a intra-layer scale factor = sqrt(2)
self.sigma_level0 = 1.6
self.initSigmaLevels()
self.detector_name = 'SIFT'
elif self.detector_type == FeatureDetectorTypes.SURF:
self._feature_detector = self.SURF_create(nOctaveLayers=self.num_levels)
self.detector_name = 'SURF'
elif self.detector_type == FeatureDetectorTypes.ORB:
self._feature_detector = self.ORB_create(**self.orb_params)
self.detector_name = 'ORB'
self.use_bock_adaptor = True
elif self.detector_type == FeatureDetectorTypes.BRISK:
self._feature_detector = self.BRISK_create(octaves=self.num_levels)
self.detector_name = 'BRISK'
self.scale_factor = 1.3 # from the BRISK opencv code this seems to be the used scale factor between intra-octave frames
self.initSigmaLevels()
elif self.detector_type == FeatureDetectorTypes.AKAZE:
self._feature_detector = self.AKAZE_create(nOctaves=self.num_levels)
self.detector_name = 'AKAZE'
elif self.detector_type == FeatureDetectorTypes.FREAK:
self._feature_detector = self.FREAK_create(nOctaves=self.num_levels)
self.detector_name = 'FREAK'
elif self.detector_type == FeatureDetectorTypes.FAST:
self._feature_detector = self.FAST_create(threshold=25, nonmaxSuppression=True)
self.detector_name = 'FAST'
self.use_bock_adaptor = True
self.use_pyramid_adaptor = self.num_levels > 1
elif self.detector_type == FeatureDetectorTypes.SHI_TOMASI:
self._feature_detector = ShiTomasiDetector(self.min_num_features)
self.detector_name = 'Shi-Tomasi'
self.use_bock_adaptor = False
self.use_pyramid_adaptor = self.num_levels > 1
else:
raise ValueError("Unknown feature extractor %s" % self.detector_type)
if self.use_bock_adaptor is True:
self.block_adaptor = BlockAdaptor(self._feature_detector, row_divs = kAdaptorNumRowDivs, col_divs = kAdaptorNumColDivs)
if self.use_pyramid_adaptor is True:
self.pyramid_adaptor = PyramidAdaptor(self._feature_detector, self.num_levels, self.scale_factor, use_block_adaptor=self.use_bock_adaptor)
# init descriptor
if self.descriptor_type == FeatureDescriptorTypes.SIFT:
self._feature_descriptor = self.SIFT_create()
self.decriptor_name = 'SIFT'
elif self.descriptor_type == FeatureDescriptorTypes.SURF:
self._feature_descriptor = self.SURF_create(nOctaveLayers=self.num_levels)
self.decriptor_name = 'SURF'
elif self.descriptor_type == FeatureDescriptorTypes.ORB:
self._feature_descriptor = self.ORB_create(**self.orb_params)
self.decriptor_name = 'ORB'
elif self.descriptor_type == FeatureDescriptorTypes.BRISK:
self._feature_descriptor = self.BRISK_create(octaves=self.num_levels)
self.decriptor_name = 'BRISK'
elif self.descriptor_type == FeatureDescriptorTypes.AKAZE:
self._feature_descriptor = self.AKAZE_create(nOctaves=self.num_levels)
self.decriptor_name = 'AKAZE'
elif self.descriptor_type == FeatureDescriptorTypes.FREAK:
self._feature_descriptor = self.FREAK_create(nOctaves=self.num_levels)
self.decriptor_name = 'FREAK'
elif self.descriptor_type == FeatureDescriptorTypes.NONE:
self._feature_descriptor = None
self.decriptor_name = 'None'
else:
raise ValueError("Unknown feature extractor %s" % self.detector_type)
def initSigmaLevels(self):
num_levels = max(kNumLevelsInitSigma, self.num_levels)
self.scale_factors = np.zeros(num_levels)
self.level_sigmas2 = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.inv_level_sigmas2 = np.zeros(num_levels)
# TODO: in the SIFT case, this sigma management could be refined.
# SIFT method has layers with intra-layer scale factor = math.sqrt(2)
self.scale_factors[0]=1.0
self.level_sigmas2[0]=self.sigma_level0*self.sigma_level0
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[i-1]
#print('self.scale_factors: ', self.scale_factors)
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.inv_scale_factors: ', self.inv_scale_factors)
# detect keypoints without computing their descriptors
# out: kps
def detect(self, frame, mask=None):
if frame.ndim>2:
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
if self.use_pyramid_adaptor:
kps = self.pyramid_adaptor.detect(frame, mask)
elif self.use_bock_adaptor:
kps = self.block_adaptor.detect(frame, mask)
else:
kps = self._feature_detector.detect(frame, mask)
kps = self.satNumberOfFeatures(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_name, ', #features: ', len(kps))
return kps
# detect keypoints and their descriptors
# out: kps, des
def detectAndCompute(self, frame, mask=None):
if frame.ndim>2:
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
kps = self.detect(frame, mask)
kps, des = self._feature_descriptor.compute(frame, kps)
if self.detector_type == FeatureDetectorTypes.SIFT:
unpackSiftOctaveKps(kps)
if kVerbose:
#print('detector: ', self.detector_name, ', #features: ', len(kps))
print('descriptor: ', self.decriptor_name, ', #features: ', len(kps))
return kps, des
# keep the first 'self.min_num_features' best features
def satNumberOfFeatures(self, kps):
if kVerbose:
print('sat: ', self.detector_name, ', #features: ', len(kps),', #max: ', self.min_num_features)
if len(kps) > self.min_num_features:
# keep the features with the best response
kps = sorted(kps, key=lambda x:x.response, reverse=True)[:self.min_num_features]
if False:
for k in kps:
print("response: ", k.response)
print("size: ", k.size)
print("octave: ", k.octave)
return kps