def test_patch_ori_main():
r"""
Returns:
?: locals_
CommandLine:
python -m pyhesaff.tests.test_patch_orientation --test-test_patch_ori_main
python -m pyhesaff.tests.test_patch_orientation --test-test_patch_ori_main --show
Example:
>>> # GUI_DOCTEST
>>> from pyhesaff.tests.test_patch_orientation import * # NOQA
>>> test_patch_ori_main()
>>> ut.show_if_requested()
"""
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
print('[rotinvar] loading test data')
import pyhesaff.tests.pyhestest as pyhestest
test_data = pyhestest.load_test_data(short=True, n=3)
img_fpath = test_data['img_fpath']
kpts = test_data['kpts']
desc = test_data['desc']
imgBGR = test_data['imgBGR']
sel = min(len(kpts) - 1, 3)
locals_ = TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel)
return locals_
def test_patch_ori_main():
r"""
Returns:
?: locals_
CommandLine:
python -m pyhesaff.tests.test_patch_orientation --test-test_patch_ori_main
python -m pyhesaff.tests.test_patch_orientation --test-test_patch_ori_main --show
Example:
>>> # xdoctest: +SKIP
>>> from pyhesaff.tests.test_patch_orientation import * # NOQA
>>> test_patch_ori_main()
>>> ut.show_if_requested()
"""
print('[rotinvar] loading test data')
import pyhesaff.tests.pyhestest as pyhestest
test_data = pyhestest.load_test_data(short=True, n=3)
img_fpath = test_data['img_fpath']
kpts = test_data['kpts']
desc = test_data['desc']
imgBGR = test_data['imgBGR']
sel = min(len(kpts) - 1, 3)
locals_ = TEST_keypoint(imgBGR, img_fpath, kpts, desc, sel)
return locals_
def test_adaptive_scale():
# Get relevant test data
"""
__name__ = 'IPython'
exec(open('test_pyhesaff.py').read())
exec(open('vtellipse.py').read())
"""
print('test_adaptive_scale()')
from pyhesaff.tests import pyhestest
test_data = pyhestest.load_test_data(short=True, n=4)
img_fpath = test_data['img_fpath']
imgL = test_data['imgL']
imgBGR = test_data['imgBGR']
kpts = test_data['kpts']
desc = test_data['desc']
# WHY ARENT THESE 1s?
#np.sqrt(((desc / 256.0) ** 2).sum(1))
nScales = 16
nSamples = 16
low, high = -1, 2
nKp = len(kpts)
df2.figure(fnum=1, doclf=True, docla=True)
def show_kpts(kpts_, px, title):
show_keypoints(imgBGR, kpts_, pnum=(2, 3, px + 3), fnum=1,
color=df2.BLUE, title=title)
def plot_line(vals, title):
df2.figure(fnum=1, pnum=(2, 1, 1))
df2.plot(vals, 'bo-')
def plot_marks(vals, marker, title):
df2.figure(fnum=1, pnum=(2, 1, 1))
df2.plot(vals[0], vals[1], marker)
fx = 0
# INPUT
kpts = np.array(kpts, dtype=np.float64)
show_kpts(kpts, 1, 'original keypoints')
# STEP1: EXPAND KEYPOINTS
print('step1: expand_keypoints()')
expanded_kpts = vtellipse.expand_scales(kpts, nScales, low, high)
show_kpts(expanded_kpts, 2, 'expanded keypoint')
# STEP2: UNIFORM SAMPLE / INTERPOLATE MAXIMA
print('step2: uniform_sample / interpolate maxima()')
border_vals_sum = vtellipse.sample_ell_border_vals(imgBGR, expanded_kpts, nKp, nScales, nSamples)
x_data_list, y_data_list = vtellipse.find_maxima_with_neighbors(border_vals_sum)
peak_list = vtellipse.interpolate_peaks(x_data_list, y_data_list)
plot_line(border_vals_sum[fx], 'gradient mag')
plot_marks([x_data_list[fx][0], y_data_list[fx][0]], 'go', 'left')
plot_marks([x_data_list[fx][1], y_data_list[fx][1]], 'ro', 'extreme')
plot_marks([x_data_list[fx][2], y_data_list[fx][2]], 'go', 'right')
plot_marks(peak_list[fx].T, 'rx', 'interpolated peaks')
parabolas_list = []
for x_data, y_data in zip(x_data_list, y_data_list):
parabola_points = []
for (x1, x2, x3), (y1, y2, y3) in zip(x_data.T, y_data.T):
coeff = np.polyfit((x1, x2, x3), (y1, y2, y3), 2)
xpoints = np.linspace(x1, x3, 50)
ypoints = np.polyval(coeff, xpoints)
parabola_points.append((xpoints, ypoints))
parabolas_list.append(parabola_points)
df2.set_ylabel('gradient magnitude')
df2.set_xlabel('scale index')
for xdat, ydat in parabolas_list[0]:
plot_marks([xdat, ydat], 'g--', '')
# STEP 3: INTERPOLATE SCALES
print('step3: interpolate scales')
subscale_list = vtellipse.interpolate_between(peak_list, nScales, high, low)
subscale_kpts = vtellipse.expand_subscales(kpts, subscale_list)
#show_kpts(subscale_kpts, 3, 'subscale keypoint')
# STEP 4: Check Image Bounds
print('step4: check image bounds')
# Make sure that the new shapes are in bounds
height, width = imgBGR.shape[0:2]
isvalid = vtellipse.check_kpts_in_bounds(subscale_kpts, width, height)
adapted_kpts = np.array(subscale_kpts[isvalid], dtype=np.float32)
show_kpts(adapted_kpts, 3, 'adapted keypoint')
#df2.update()
scales = 2 ** np.linspace(low, high, nScales)
adapted_kpts = vtellipse.adaptive_scale(img_fpath, kpts, nScales, low, high, nSamples)
#plot_vals(adapted_kpts, pnum=(3
#viz.show_keypoints(imgBGR, adapted_kpts, pnum=(3, 1, 3), fnum=1, color=df2.BLUE, title='adapted keypoints')
#adapted_desc = pyhesaff.extract_desc(img_fpath, adapted_kpts)
#desc = pyhesaff.extract_desc(img_fpath, kpts)
##interact.interact_keypoints(imgBGR, adapted_kpts, adapted_desc)
#df2.update()
return locals()
def test_adaptive_scale():
# Get relevant test data
"""
__name__ = 'IPython'
exec(open('test_pyhesaff.py').read())
exec(open('vtellipse.py').read())
"""
print('test_adaptive_scale()')
from pyhesaff.tests import pyhestest
test_data = pyhestest.load_test_data(short=True, n=4)
img_fpath = test_data['img_fpath']
imgL = test_data['imgL']
imgBGR = test_data['imgBGR']
kpts = test_data['kpts']
desc = test_data['desc']
# WHY ARENT THESE 1s?
#np.sqrt(((desc / 256.0) ** 2).sum(1))
nScales = 16
nSamples = 16
low, high = -1, 2
nKp = len(kpts)
df2.figure(fnum=1, doclf=True, docla=True)
def show_kpts(kpts_, px, title):
show_keypoints(imgBGR, kpts_, pnum=(2, 3, px + 3), fnum=1,
color=df2.BLUE, title=title)
def plot_line(vals, title):
df2.figure(fnum=1, pnum=(2, 1, 1))
df2.plot(vals, 'bo-')
def plot_marks(vals, marker, title):
df2.figure(fnum=1, pnum=(2, 1, 1))
df2.plot(vals[0], vals[1], marker)
fx = 0
# INPUT
kpts = np.array(kpts, dtype=np.float64)
show_kpts(kpts, 1, 'original keypoints')
# STEP1: EXPAND KEYPOINTS
print('step1: expand_keypoints()')
expanded_kpts = vtellipse.expand_scales(kpts, nScales, low, high)
show_kpts(expanded_kpts, 2, 'expanded keypoint')
# STEP2: UNIFORM SAMPLE / INTERPOLATE MAXIMA
print('step2: uniform_sample / interpolate maxima()')
border_vals_sum = vtellipse.sample_ell_border_vals(imgBGR, expanded_kpts, nKp, nScales, nSamples)
x_data_list, y_data_list = vtellipse.find_maxima_with_neighbors(border_vals_sum)
peak_list = vtellipse.interpolate_peaks(x_data_list, y_data_list)
plot_line(border_vals_sum[fx], 'gradient mag')
plot_marks([x_data_list[fx][0], y_data_list[fx][0]], 'go', 'left')
plot_marks([x_data_list[fx][1], y_data_list[fx][1]], 'ro', 'extreme')
plot_marks([x_data_list[fx][2], y_data_list[fx][2]], 'go', 'right')
plot_marks(peak_list[fx].T, 'rx', 'interpolated peaks')
parabolas_list = []
for x_data, y_data in zip(x_data_list, y_data_list):
parabola_points = []
for (x1, x2, x3), (y1, y2, y3) in zip(x_data.T, y_data.T):
coeff = np.polyfit((x1, x2, x3), (y1, y2, y3), 2)
xpoints = np.linspace(x1, x3, 50)
ypoints = np.polyval(coeff, xpoints)
parabola_points.append((xpoints, ypoints))
parabolas_list.append(parabola_points)
df2.set_ylabel('gradient magnitude')
df2.set_xlabel('scale index')
for xdat, ydat in parabolas_list[0]:
plot_marks([xdat, ydat], 'g--', '')
# STEP 3: INTERPOLATE SCALES
print('step3: interpolate scales')
subscale_list = vtellipse.interpolate_between(peak_list, nScales, high, low)
subscale_kpts = vtellipse.expand_subscales(kpts, subscale_list)
#show_kpts(subscale_kpts, 3, 'subscale keypoint')
# STEP 4: Check Image Bounds
print('step4: check image bounds')
# Make sure that the new shapes are in bounds
height, width = imgBGR.shape[0:2]
isvalid = vtellipse.check_kpts_in_bounds(subscale_kpts, width, height)
adapted_kpts = np.array(subscale_kpts[isvalid], dtype=np.float32)
show_kpts(adapted_kpts, 3, 'adapted keypoint')
#df2.update()
scales = 2 ** np.linspace(low, high, nScales)
adapted_kpts = vtellipse.adaptive_scale(img_fpath, kpts, nScales, low, high, nSamples)
#plot_vals(adapted_kpts, pnum=(3
#viz.show_keypoints(imgBGR, adapted_kpts, pnum=(3, 1, 3), fnum=1, color=df2.BLUE, title='adapted keypoints')
#adapted_desc = pyhesaff.extract_desc(img_fpath, adapted_kpts)
#desc = pyhesaff.extract_desc(img_fpath, kpts)
##interact.interact_keypoints(imgBGR, adapted_kpts, adapted_desc)
#df2.update()
return locals()
