def test_sift_patchmatch_scores(data, labels):
"""
data = X_test
labels = y_test
"""
import pyhesaff
import numpy as np
if len(data.shape) == 4 and data.shape[-1] == 1:
data = data.reshape(data.shape[0:3])
elif len(data.shape) == 4 and data.shape[-1] == 3:
import vtool as vt
# TODO use dataset to infer data colorspace
data = vt.convert_image_list_colorspace(data, 'GRAY', src_colorspace='BGR')
patch_list = data
print('Extract SIFT descr')
vecs_list = pyhesaff.extract_desc_from_patches(patch_list)
print('Compute SIFT dist')
sqrddist = (
(vecs_list[0::2].astype(np.float32) - vecs_list[1::2].astype(np.float32)) ** 2).sum(axis=1)
sqrddist_ = sqrddist[None, :].T
VEC_PSEUDO_MAX_DISTANCE_SQRD = 2.0 * (512.0 ** 2.0)
#sift_scores = 1 - (sqrddist_.flatten() / VEC_PSEUDO_MAX_DISTANCE_SQRD)
sift_scores = (sqrddist_.flatten() / VEC_PSEUDO_MAX_DISTANCE_SQRD)
sift_list = vecs_list
return sift_scores, sift_list
def extract_patches(ibs, aid_list, fxs_list=None, patch_size=None, colorspace=None):
"""
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.ingest_ibeis import * # NOQA
>>> ut.show_if_requested()
"""
depc = ibs.depc
kpts_list = depc.d.get_feat_kpts(aid_list)
if fxs_list is None:
fxs_list = [slice(None)] * len(kpts_list)
kpts_list_ = ut.ziptake(kpts_list, fxs_list)
chip_list = depc.d.get_chips_img(aid_list)
# convert to approprate colorspace
if colorspace is not None:
chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, kpts_list_),
nTotal=len(aid_list),
lbl='warping patches')
]
return patches_list
def extract_patches(ibs,
aid_list,
fxs_list=None,
patch_size=None,
colorspace=None):
"""
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.ingest_ibeis import * # NOQA
>>> ut.show_if_requested()
"""
depc = ibs.depc
kpts_list = depc.d.get_feat_kpts(aid_list)
if fxs_list is None:
fxs_list = [slice(None)] * len(kpts_list)
kpts_list_ = ut.ziptake(kpts_list, fxs_list)
chip_list = depc.d.get_chips_img(aid_list)
# convert to approprate colorspace
if colorspace is not None:
chip_list = vt.convert_image_list_colorspace(chip_list, colorspace)
# ut.print_object_size(chip_list, 'chip_list')
patch_size = 64
patches_list = [
vt.get_warped_patches(chip, kpts, patch_size=patch_size)[0]
for chip, kpts in ut.ProgIter(zip(chip_list, kpts_list_),
nTotal=len(aid_list),
lbl='warping patches')
]
return patches_list
def test_cv2_flann():
"""
Ignore:
[name for name in dir(cv2) if 'create' in name.lower()]
[name for name in dir(cv2) if 'stereo' in name.lower()]
ut.grab_zipped_url('https://priithon.googlecode.com/archive/a6117f5e81ec00abcfb037f0f9da2937bb2ea47f.tar.gz', download_dir='.')
"""
import cv2
from vtool.tests import dummy
import plottool as pt
import vtool as vt
img1 = vt.imread(ut.grab_test_imgpath('easy1.png'))
img2 = vt.imread(ut.grab_test_imgpath('easy2.png'))
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(img1, img2)
pt.imshow(disparity)
pt.show()
#cv2.estima
flow = cv2.createOptFlow_DualTVL1()
img1, img2 = vt.convert_image_list_colorspace([img1, img2], 'gray', src_colorspace='bgr')
img2 = vt.resize(img2, img1.shape[0:2][::-1])
out = img1.copy()
flow.calc(img1, img2, out)
orb = cv2.ORB_create()
kp1, vecs1 = orb.detectAndCompute(img1, None)
kp2, vecs2 = orb.detectAndCompute(img2, None)
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img1)
skp, sd = descriptor.compute(img1, skp)
tkp = detector.detect(img2)
tkp, td = descriptor.compute(img2, tkp)
out = img1.copy()
cv2.drawKeypoints(img1, kp1, outImage=out)
pt.imshow(out)
vecs1 = dummy.testdata_dummy_sift(10)
vecs2 = dummy.testdata_dummy_sift(10) # NOQA
FLANN_INDEX_KDTREE = 0 # bug: flann enums are missing
#flann_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=4)
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params, search_params) # NOQA
cv2.flann.Index(vecs1, index_params)
#cv2.FlannBasedMatcher(flann_params)
cv2.flann.Index(vecs1, flann_params) # NOQA
def test_cv2_flann():
"""
Ignore:
[name for name in dir(cv2) if 'create' in name.lower()]
[name for name in dir(cv2) if 'stereo' in name.lower()]
ut.grab_zipped_url('https://priithon.googlecode.com/archive/a6117f5e81ec00abcfb037f0f9da2937bb2ea47f.tar.gz', download_dir='.')
"""
import cv2
from vtool.tests import dummy
import plottool as pt
import vtool as vt
img1 = vt.imread(ut.grab_test_imgpath('easy1.png'))
img2 = vt.imread(ut.grab_test_imgpath('easy2.png'))
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(img1, img2)
pt.imshow(disparity)
pt.show()
#cv2.estima
flow = cv2.createOptFlow_DualTVL1()
img1, img2 = vt.convert_image_list_colorspace([img1, img2],
'gray',
src_colorspace='bgr')
img2 = vt.resize(img2, img1.shape[0:2][::-1])
out = img1.copy()
flow.calc(img1, img2, out)
orb = cv2.ORB_create()
kp1, vecs1 = orb.detectAndCompute(img1, None)
kp2, vecs2 = orb.detectAndCompute(img2, None)
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img1)
skp, sd = descriptor.compute(img1, skp)
tkp = detector.detect(img2)
tkp, td = descriptor.compute(img2, tkp)
out = img1.copy()
cv2.drawKeypoints(img1, kp1, outImage=out)
pt.imshow(out)
vecs1 = dummy.testdata_dummy_sift(10)
vecs2 = dummy.testdata_dummy_sift(10) # NOQA
FLANN_INDEX_KDTREE = 0 # bug: flann enums are missing
#flann_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=4)
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params, search_params) # NOQA
cv2.flann.Index(vecs1, index_params)
#cv2.FlannBasedMatcher(flann_params)
cv2.flann.Index(vecs1, flann_params) # NOQA
