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/
feature_manager_adaptors.py
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/
feature_manager_adaptors.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 math
from enum import Enum
import numpy as np
import cv2
from concurrent.futures import ThreadPoolExecutor, as_completed, wait
from utils_img import img_blocks, img_mask_blocks
from utils_features import sat_num_features
from utils_sys import Printer
from pyramid import Pyramid, PyramidType
kVerbose = True
kAdaptorNumRowDivs = 5 #2, 3
kAdaptorNumColDivs = 5 #2, 3
kNumLevelsInitSigma = 20
kBlockAdaptorUseParallelComputations = True
kBlockAdaptorMaxNumWorkers = 4
kPyramidAdaptorUseParallelComputations = True
kPyramidAdaptorMaxNumWorkers = 4
if not kVerbose:
def print(*args, **kwargs):
pass
# 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,
descriptor = None,
row_divs = kAdaptorNumRowDivs,
col_divs = kAdaptorNumColDivs,
do_parallel = kBlockAdaptorUseParallelComputations):
self.detector = detector
self.descriptor = descriptor
self.row_divs = row_divs
self.col_divs = col_divs
self.do_parallel = do_parallel # do parallel computations
self.is_detector_equal_to_descriptor = (self.detector == self.descriptor)
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 ', self.row_divs, 'x', self.col_divs)
block_generator = img_mask_blocks(frame, mask, self.row_divs, self.col_divs)
kps_all = [] # list are thread-safe
def detect_block(b_m_i_j):
b, m, i, j = b_m_i_j
if kVerbose and False:
print('BlockAdaptor in block (',i,',',j,')')
kps = self.detector.detect(b, mask=m)
#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_all.extend(kps)
if not self.do_parallel:
# process the blocks sequentially
for b, m, i, j in block_generator:
detect_block((b,m,i,j))
else:
with ThreadPoolExecutor(max_workers = 4) as executor:
executor.map(detect_block, block_generator) # automatic join() at the end of the `width` block
return np.array(kps_all)
def detectAndCompute(self, frame, mask=None):
if self.row_divs == 1 and self.col_divs == 1:
return self.detector.detectAndCompute(frame, mask)
else:
if kVerbose:
print('BlockAdaptor ', self.row_divs, 'x', self.col_divs)
block_generator = img_mask_blocks(frame, mask, self.row_divs, self.col_divs)
kps_all = []
des_all = []
kps_des_map = {} # (i,j) -> (kps,des)
def detect_and_compute_block(b_m_i_j):
b, m, i, j = b_m_i_j
if kVerbose and False:
print('BlockAdaptor in block (',i,',',j,')')
if self.is_detector_equal_to_descriptor:
kps, des = self.detector.detectAndCompute(b, mask=m)
else:
kps = self.detector.detect(b, mask=m)
kps, des = self.descriptor.compute(b, kps)
#print('adaptor: detected #features: ', len(kps), ' in block (',i,',',j,')')
# transform the points
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_des_map[(i,j)] = (kps,des)
if not self.do_parallel:
# process the blocks sequentially
for b, m, i, j in block_generator:
detect_and_compute_block((b, m, i, j))
else:
with ThreadPoolExecutor(max_workers = kBlockAdaptorMaxNumWorkers) as executor:
executor.map(detect_and_compute_block, block_generator) # automatic join() at the end of the `width` block
# now merge the computed results
for ij,(kps,des) in kps_des_map.items():
kps_all.extend(kps)
if des is not None and len(des)>0:
if len(des_all)>0:
des_all = np.vstack([des_all, des])
else:
des_all = des
return np.array(kps_all), np.array(des_all)
# 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 or add such option (DONE by FeatureManager.kdt_nms() )
class PyramidAdaptor(object):
def __init__(self,
detector,
descriptor=None,
num_features=2000,
num_levels=4,
scale_factor=1.2,
sigma0=1.0, # N.B.: SIFT use 1.6 for this value
first_level=0,
pyramid_type=PyramidType.RESIZE,
use_block_adaptor=False,
do_parallel = kPyramidAdaptorUseParallelComputations,
do_sat_features_per_level = False):
self.detector = detector
self.descriptor = descriptor
self.num_features = num_features
self.is_detector_equal_to_descriptor = (self.detector == self.descriptor)
self.num_levels = num_levels
self.scale_factor = scale_factor
self.inv_scale_factor = 1./scale_factor
self.sigma0 = sigma0
self.first_level = first_level
self.pyramid_type = pyramid_type
self.use_block_adaptor = use_block_adaptor
self.do_parallel = do_parallel # do parallel computations
self.do_sat_features_per_level = do_sat_features_per_level # saturate number of features for each level
self.pyramid = Pyramid(num_levels=num_levels,
scale_factor=scale_factor,
sigma0=sigma0,
first_level=first_level,
pyramid_type=pyramid_type)
self.initSigmaLevels()
self.block_adaptor = None
if self.use_block_adaptor:
self.block_adaptor = BlockAdaptor(self.detector, self.descriptor, row_divs = kAdaptorNumRowDivs, col_divs = kAdaptorNumColDivs, do_parallel=False)
def initSigmaLevels(self):
num_levels = max(kNumLevelsInitSigma, self.num_levels)
self.scale_factors = np.zeros(num_levels)
self.inv_scale_factors = np.zeros(num_levels)
self.scale_factors[0]=1.0
# compute desired number of features per level (by using the scale factor)
self.num_features_per_level = np.zeros(num_levels,dtype=np.int)
num_desired_features_per_level = self.num_features*(1 - self.inv_scale_factor)/(1 - math.pow(self.inv_scale_factor, self.num_levels))
sum_num_features = 0
for level in range(self.num_levels-1):
self.num_features_per_level[level] = int(round(num_desired_features_per_level))
sum_num_features += self.num_features_per_level[level];
num_desired_features_per_level *= self.inv_scale_factor
self.num_features_per_level[self.num_levels-1] = max(self.num_features - sum_num_features, 0)
#print('num_features_per_level:',self.num_features_per_level)
if self.first_level==-1:
self.scale_factors[0]=1.0/self.scale_factor
self.inv_scale_factors[0]=1.0/self.scale_factors[0]
for i in range(1,num_levels):
self.scale_factors[i]=self.scale_factors[i-1]*self.scale_factor
self.inv_scale_factors[i]=1.0/self.scale_factors[i]
#print('self.inv_scale_factors: ', self.inv_scale_factors)
# detect on 'unfiltered' pyramid images ('unfiltered' meanining depends on the selected pyramid type)
def detect(self, frame, mask=None):
if self.num_levels == 1:
return self.detector.detect(frame, mask)
else:
#TODO: manage mask
if kVerbose:
print('PyramidAdaptor #levels:', self.num_levels,'(from',self.first_level,'), scale_factor:', self.scale_factor,', sigma0:', self.sigma0,', type:', self.pyramid_type.name)
self.pyramid.compute(frame)
kps_all = [] # list are thread-safe
def detect_level(scale,pyr_cur,i):
kps = []
if self.block_adaptor is None:
kps = self.detector.detect(pyr_cur)
else:
kps = self.block_adaptor.detect(pyr_cur)
if kVerbose and False:
print("PyramidAdaptor - level", i, ", shape: ", pyr_cur.shape)
for kp in kps:
#print('kp.pt before: ', kp.pt)
kp.pt = (kp.pt[0]*scale, kp.pt[1]*scale)
kp.size = kp.size*scale
kp.octave = i
#print('kp: ', kp.pt, kp.octave)
if self.do_sat_features_per_level:
kps, _ = sat_num_features(kps, None, self.num_features_per_level[i]) # experimental
kps_all.extend(kps)
if not self.do_parallel:
#print('sequential computations')
# process the blocks sequentially
for i in range(0,self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
detect_level(scale,pyr_cur,i)
else:
#print('parallel computations')
futures = []
with ThreadPoolExecutor(max_workers = 4) as executor:
for i in range(0,self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
futures.append(executor.submit(detect_level, scale, pyr_cur, i))
wait(futures) # wait all the task are completed
return np.array(kps_all)
# detect on 'unfiltered' pyramid images ('unfiltered' meanining depends on the selected pyramid type)
# compute descriptors on 'filtered' pyramid images ('filtered' meanining depends on the selected pyramid type)
def detectAndCompute(self, frame, mask=None):
if self.num_levels == 1:
return self.detector.detectAndCompute(frame, mask)
else:
if kVerbose:
print('PyramidAdaptor [dc] #levels:', self.num_levels,'(from',self.first_level,'), scale_factor:', self.scale_factor,', sigma0:', self.sigma0,', type:', self.pyramid_type.name)
self.pyramid.compute(frame)
kps_all = []
des_all = []
kps_des_map = {} # i -> (kps,des)
def detect_and_compute_level(scale, pyr_cur, pyr_cur_filtered, N, i):
kps = []
if self.block_adaptor is None:
#kps, des = self.detector.detectAndCompute(pyr_cur)
if self.is_detector_equal_to_descriptor:
kps, des = self.detector.detectAndCompute(pyr_cur)
else:
kps = self.detector.detect(pyr_cur)
#print('description of filtered')
kps, des = self.descriptor.compute(pyr_cur_filtered, kps)
else:
kps, des = self.block_adaptor.detectAndCompute(pyr_cur)
if kVerbose and False:
print("PyramidAdaptor - level", i, ", shape: ", pyr_cur.shape)
for kp in kps:
#print('before: kp.pt:', kp.pt,', size:',kp.size,', octave:',kp.octave,', angle:',kp.angle)
kp.pt = (kp.pt[0]*scale, kp.pt[1]*scale)
kp.size = kp.size*scale
kp.octave = i
#print('after: kp.pt:', kp.pt,', size:',kp.size,', octave:',kp.octave,', angle:',kp.angle)
if self.do_sat_features_per_level:
kps, des = sat_num_features(kps, des, N) # experimental
kps_des_map[i] = (kps,des)
if not self.do_parallel:
#print('sequential computations')
# process the blocks sequentially
for i in range(0,self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
pyr_cur_filtered = self.pyramid.imgs_filtered[i]
detect_and_compute_level(scale, pyr_cur, pyr_cur_filtered, self.num_features_per_level[i], i)
else:
#print('parallel computations')
futures = []
with ThreadPoolExecutor(max_workers = 4) as executor:
for i in range(0,self.num_levels):
scale = self.scale_factors[i]
pyr_cur = self.pyramid.imgs[i]
pyr_cur_filtered = self.pyramid.imgs_filtered[i]
futures.append(executor.submit(detect_and_compute_level, scale, pyr_cur, pyr_cur_filtered, self.num_features_per_level[i], i))
wait(futures) # wait all the task are completed
# now merge the computed results
for i,(kps,des) in kps_des_map.items():
kps_all.extend(kps)
if des is not None and len(des)>0:
if len(des_all)>0:
des_all = np.vstack([des_all, des])
else:
des_all = des
return np.array(kps_all), np.array(des_all)