def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel, fnum=1, figtitle=''):
from plottool import draw_func2 as df2
from plottool.viz_keypoints import _annotate_kpts, show_keypoints
from plottool.viz_featrow import draw_feat_row
import plottool
import vtool.patch as ptool
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
gori_weights = ptool.gaussian_weight_patch(gmag)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch,
gradx,
grady,
gmag,
gori,
hist,
centers,
fnum=fnum)
df2.set_figtitle(figtitle + 'Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=fnum + 1)
df2.set_figtitle(figtitle)
# TEST_figure2(imgBGR, kpts2, Desc2, sel, fnum=fnum + 2)
# df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel):
import pyhesaff
import vtool.patch as ptool
from plottool import draw_func2 as df2
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch, gaussian_weighted=False)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
gori_weights = ptool.gaussian_weight_patch(gmag)
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
# Get dominant direction in radians
kpts2 = TEST_ptool_find_kpts_direction(imgBGR, kpts)
kpts2, desc2 = pyhesaff.vtool_adapt_rotation(img_fpath, kpts)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers)
df2.set_figtitle('Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=2)
df2.set_figtitle('Gravity Vector')
TEST_figure2(imgBGR, kpts2, desc2, sel, fnum=3)
df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel, fnum=1, figtitle=''):
from plottool import draw_func2 as df2
from plottool.viz_keypoints import _annotate_kpts, show_keypoints
from plottool.viz_featrow import draw_feat_row
import plottool
import vtool.patch as ptool
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
gori_weights = ptool.gaussian_weight_patch(gmag)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers, fnum=fnum)
df2.set_figtitle(figtitle + 'Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=fnum + 1)
df2.set_figtitle(figtitle)
# TEST_figure2(imgBGR, kpts2, Desc2, sel, fnum=fnum + 2)
# df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel):
import pyhesaff
#----------------------#
# --- Extract Data --- #
#----------------------#
kp = kpts[sel]
# Extract patches, gradients, and orientations
print('[rotinvar] 1) Extract patch, gradients, and orientations')
wpatch, wkp = ptool.get_warped_patch(imgBGR, kp, gray=True)
gradx, grady = ptool.patch_gradient(wpatch, gaussian_weighted=False)
gmag = ptool.patch_mag(gradx, grady)
gori = ptool.patch_ori(gradx, grady)
# Get orientation histogram
print('[rotinvar] 2) Get orientation histogram')
gori_weights = ptool.gaussian_weight_patch(gmag)
hist, centers = ptool.get_orientation_histogram(gori, gori_weights)
# Get dominant direction in radians
kpts2 = TEST_ptool_find_kpts_direction(imgBGR, kpts)
kpts2, desc2 = pyhesaff.vtool_adapt_rotation(img_fpath, kpts)
#----------------------#
# --- Draw Results --- #
#----------------------#
f1_loc = TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers)
df2.set_figtitle('Dominant Orienation Extraction')
TEST_figure2(imgBGR, kpts, desc, sel, fnum=2)
df2.set_figtitle('Gravity Vector')
TEST_figure2(imgBGR, kpts2, desc2, sel, fnum=3)
df2.set_figtitle('Rotation Invariant')
#df2.draw_keypoint_gradient_orientations(imgBGR, kp=kpts2[sel],
# sift=desc[sel], mode='vec',
# fnum=4)
#df2.draw_vector_field(gradx, grady, pnum=(1, 1, 1), fnum=4)
#df2.draw_kpts2(np.array([wkp]), sifts=desc[sel:sel + 1], ori=True)
return locals()
def gen_featweight_worker(tup):
"""
Function to be parallelized by multiprocessing / joblib / whatever.
Must take in one argument to be used by multiprocessing.map_async
Args:
tup (aid, tuple(kpts(ndarray), probchip_fpath )): keypoints and probability chip file path
aid, kpts, probchip_fpath
CommandLine:
python -m ibeis.algo.preproc.preproc_featweight --test-gen_featweight_worker --show
python -m ibeis.algo.preproc.preproc_featweight --test-gen_featweight_worker --show --dpath figures --save ~/latex/crall-candidacy-2015/figures/gen_featweight.jpg
python -m ibeis.algo.preproc.preproc_featweight --test-gen_featweight_worker --show --db PZ_MTEST --qaid_list=1,2,3,4,5,6,7,8,9
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_featweight import * # NOQA
>>> test_featweight_worker()
Ignore::
import plottool as pt
pt.imshow(probchip_list[0])
patch_list = [vtpatch.get_warped_patch(probchip, kp)[0].astype(np.float32) / 255.0 for kp in kpts[0:1]]
patch_ = patch_list[0].copy()
patch = patch_
patch = patch_[-20:, :20, 0]
import vtool as vt
gaussian_patch = vt.gaussian_patch(patch.shape[1], patch.shape[0], shape=patch.shape[0:2], norm_01=False)
import cv2
sigma = 1/10
xkernel = (cv2.getGaussianKernel(patch.shape[1], sigma))
ykernel = (cv2.getGaussianKernel(patch.shape[0], sigma))
#ykernel = ykernel / ykernel.max()
#xkernel = ykernel / xkernel.max()
gaussian_kern2 = ykernel.dot(xkernel.T)
print(gaussian_kern2.sum())
patch2 = patch.copy()
patch2 = np.multiply(patch2, ykernel)
patch2 = np.multiply(patch2.T, xkernel).T
if len(patch3.shape) == 2:
patch3 = patch.copy() * gaussian_patch[:,:]
else:
patch3 = patch.copy() * gaussian_patch[:,:, None]
sum2 = patch2.sum() / (patch2.size)
sum3 = patch3.sum() / (patch3.size)
print(sum2)
print(sum3)
fig = pt.figure(fnum=1, pnum=(1, 3, 1), doclf=True, docla=True)
pt.imshow(patch * 255)
fig = pt.figure(fnum=1, pnum=(1, 3, 2))
pt.imshow(gaussian_kern2 * 255.0)
fig = pt.figure(fnum=1, pnum=(1, 3, 3))
pt.imshow(patch2 * 255.0)
pt.update()
"""
(aid, kpts, probchip, chipsize) = tup
if probchip is None:
# hack for undetected chips. SETS ALL FEATWEIGHTS TO .25 = 1/4
weights = np.full(len(kpts), .25, dtype=np.float32)
else:
sfx, sfy = (probchip.shape[1] / chipsize[0], probchip.shape[0] / chipsize[1])
kpts_ = vt.offset_kpts(kpts, (0, 0), (sfx, sfy))
#vtpatch.get_warped_patches()
patch_list = [vtpatch.get_warped_patch(probchip, kp)[0].astype(np.float32) / 255.0
for kp in kpts_]
weight_list = [vtpatch.gaussian_average_patch(patch) for patch in patch_list]
#weight_list = [patch.sum() / (patch.size) for patch in patch_list]
weights = np.array(weight_list, dtype=np.float32)
return (aid, weights)
