def single_image_histograms():
p = dtu.require_resource('frame0002.jpg')
image_cv = dtu.image_cv_from_jpg_fn(p)
res = go(image_cv)
outd = dtu.get_output_dir_for_test()
dtu.write_bgr_images_as_jpgs(res, outd)
def compare_faster2():
variables = collections.OrderedDict()
variables['alpha'] = dict(min=-180,
max=180,
description="angle",
resolution=5,
units='deg',
units_display='deg')
variables['r'] = dict(min=3,
max=5,
description="distance",
resolution=0.2,
units='m',
units_display='cm')
# this will fail if precision is float32
gh = GridHelper(variables, precision='float64')
val_fast = gh.create_new()
val_fast.fill(0)
val_slow = gh.create_new()
val_slow.fill(0)
od = dtu.get_output_dir_for_test()
F = 1
alpha0 = 177
# r0 = 4
r0 = 4.1
w0 = 1
value = dict(alpha=alpha0, r=r0)
gh.add_vote(val_slow, value, w0, F)
values = np.zeros((2, 1))
values[0, 0] = alpha0
values[1, 0] = r0
weights = np.zeros(1)
weights[0] = w0
gh.add_vote_faster(val_fast, values, weights, F)
print('sum slow: %s' % np.sum(val_slow))
print('sum fast: %s' % np.sum(val_fast))
d = grid_helper_plot(gh, val_slow)
fn = os.path.join(od, 'compare_faster_slow.jpg')
dtu.write_data_to_file(d.get_png(), fn)
d = grid_helper_plot(gh, val_fast)
fn = os.path.join(od, 'compare_faster_fast.jpg')
dtu.write_data_to_file(d.get_png(), fn)
f = lambda x: ' %5f' % x if x else ' '
print(dtu.indent(array_as_string(val_fast, f), 'val_fast '))
print(dtu.indent(array_as_string(val_slow, f), 'val_slow '))
assert_almost_equal(val_fast, val_slow)
def run_one(cmd):
v = False
cwd = dtu.get_output_dir_for_test()
if not os.path.exists(cwd):
dtu.mkdirs_thread_safe(cwd)
dtu.write_data_to_file('config echo 1', os.path.join(cwd, '.compmake.rc'))
try:
dtu.system_cmd_result(cwd,
cmd,
display_stdout=v,
display_stderr=v,
raise_on_error=True)
finally:
pass
def run(which, expect):
v = False
cwd = dtu.get_output_dir_for_test()
if not os.path.exists(cwd):
dtu.mkdirs_thread_safe(cwd)
try:
cmd = ['rosrun', 'easy_regression', 'run',
'--expect', expect,
'--test', which,
'-o', '.',
'-c', 'rmake']
dtu.system_cmd_result(cwd, cmd,
display_stdout=v,
display_stderr=v,
raise_on_error=True)
finally:
if False:
shutil.rmtree(cwd)
def single_image1():
p = dtu.require_resource('frame0002.jpg')
image_cv = dtu.image_cv_from_jpg_fn(p)
line_detector_name = 'baseline'
image_prep_name = 'baseline'
lane_filter_name = 'baseline'
anti_instagram_name = 'baseline'
robot_name = dtu.DuckietownConstants.ROBOT_NAME_FOR_TESTS
gp = GroundProjection(robot_name)
res, _stats = run_pipeline(image_cv, gp,
line_detector_name=line_detector_name,
image_prep_name=image_prep_name,
lane_filter_name=lane_filter_name,
anti_instagram_name=anti_instagram_name,
all_details=False, ground_truth=None)
outd = dtu.get_output_dir_for_test()
dtu.write_jpgs_to_dir(res, outd)
def anti_instagram_annotations_test():
out_base = dtu.get_output_dir_for_test()
# out_base = 'anti_instagram_annotations_test'
zipname = dtu.require_resource('ii-datasets.zip')
dirname = os.path.dirname(zipname)
base = os.path.join(dirname, 'ii-datasets')
cmd = ['unzip', '-o', zipname]
cwd = dirname
dtu.system_cmd_result(cwd,
cmd,
display_stdout=False,
display_stderr=False,
raise_on_error=True)
if not os.path.exists(base):
msg = 'Could not find expected unzipped directory:\n %s' % base
raise Exception(msg)
anti_instagram_annotations_test(base, out_base)
def grid_visualization():
variables = collections.OrderedDict()
variables['alpha'] = dict(min=-np.pi / 2,
max=np.pi / 2,
description="angle",
resolution=np.deg2rad(10),
units='rad',
units_display='deg')
variables['r'] = dict(min=3,
max=5,
description="distance",
resolution=0.1,
units='m',
units_display='cm')
gh = GridHelper(variables)
val = gh.create_new()
val.fill(0)
val += np.random.randn(*val.shape)
val[1, 2] = 0
d = grid_helper_plot(gh, val)
od = dtu.get_output_dir_for_test()
fn = os.path.join(od, 'grid_visualization.jpg')
dtu.write_data_to_file(d.get_png(), fn)
def compare_faster():
variables = collections.OrderedDict()
variables['alpha'] = dict(min=-180,
max=180,
description="angle",
resolution=5,
units='deg',
units_display='deg')
variables['r'] = dict(min=3,
max=5,
description="distance",
resolution=0.1,
units='m',
units_display='cm')
# this will fail if precision is float32
gh = GridHelper(variables, precision='float64')
val_fast = gh.create_new()
val_fast.fill(0)
val_slow = gh.create_new()
val_slow.fill(0)
od = dtu.get_output_dir_for_test()
F = 1
alpha0 = 7
# r0 = 4
r0 = 4.1
w0 = 1.
value = dict(alpha=alpha0, r=r0)
gh.add_vote(val_slow, value, w0, F)
assert_equal(np.sum(val_slow > 0), 9)
values = np.zeros((2, 1))
values[0, 0] = alpha0
values[1, 0] = r0
weights = np.zeros(1)
weights[0] = w0
gh.add_vote_faster(val_fast, values, weights, F)
assert_equal(np.sum(val_fast > 0), 9)
d = grid_helper_plot(gh, val_slow)
fn = os.path.join(od, 'compare_faster_slow.jpg')
dtu.write_data_to_file(d.get_png(), fn)
d = grid_helper_plot(gh, val_fast)
fn = os.path.join(od, 'compare_faster_fast.jpg')
dtu.write_data_to_file(d.get_png(), fn)
D = val_fast - val_slow
diff = np.max(np.abs(D))
print('diff: %r' % diff)
if diff > 1e-8:
print(dtu.indent(array_as_string_sign(val_fast), 'val_fast '))
print(dtu.indent(array_as_string_sign(val_slow), 'val_slow '))
print(dtu.indent(array_as_string_sign(D), 'Diff '))
print('non zero val_fast: %s' % val_fast[val_fast > 0])
print('non zero val_slow: %s' % val_slow[val_slow > 0])
assert_almost_equal(val_fast, val_slow)
def _cwd():
cwd = dtu.get_output_dir_for_test()
if not os.path.exists(cwd):
dtu.mkdirs_thread_safe(cwd)
return cwd
def dirn(misc):
outd = dtu.get_output_dir_for_test()
return os.path.join(outd, misc)
def voting_kernel1():
resolution = 10.0
variables = collections.OrderedDict()
variables['x'] = dict(min=100,
max=500,
description="x",
resolution=resolution,
units='cm',
units_display='cm')
variables['y'] = dict(min=100,
max=500,
description="y",
resolution=resolution,
units='cm',
units_display='cm')
gh = GridHelper(variables)
votes = gh.create_new()
votes.fill(0)
points = []
estimated = []
estimated_weighted = []
F = 1
N = 25
errors_x = []
errors_x_w = []
for i in range(N):
dx = resolution / N
Dy = 70
Dx = 70
u = (i / 5)
v = i % 5
x = 125 + dx * i + Dx * u
y = 127 + Dy * v
p = dict(x=x, y=y)
points.append(p)
weight = 1
gh.add_vote(votes, p, weight=weight, F=F)
tmp = gh.create_new()
tmp.fill(0)
gh.add_vote(tmp, p, weight=weight, F=F)
assert_almost_equal(tmp.sum(), weight)
estimate = gh.get_max(tmp)
estimated.append(estimate)
estimate_weigthed = gh.get_max_weighted(tmp, F=F)
estimated_weighted.append(estimate_weigthed)
errors_x.append(p['x'] - estimate['x'])
errors_x_w.append(p['x'] - estimate_weigthed['x'])
errors_x = np.array(errors_x)
errors_x_w = np.array(errors_x_w)
dtu.logger.debug('errors_x: %s' % errors_x)
dtu.logger.debug('mean: %s' % np.abs(errors_x).mean())
dtu.logger.debug('errors_x_w: %s' % errors_x_w)
dtu.logger.debug('mean: %s' % np.abs(errors_x_w).mean())
assert (errors_x.max() <= +resolution / 2)
assert (errors_x.min() >= -resolution / 2)
assert (np.abs(errors_x_w).max() <= resolution / 10)
a = dtu.CreateImageFromPylab(dpi=1000)
with a as pylab:
grid_helper_plot_field(gh, votes, pylab)
pylab.axis('equal')
grid_helper_annotate_axes(gh, pylab)
for p in points:
grid_helper_mark_point(gh, pylab, p, color='blue', markersize=4)
for e in estimated:
grid_helper_mark_point(gh, pylab, e, color='red', markersize=3)
for e in estimated_weighted:
grid_helper_mark_point(gh, pylab, e, color='green', markersize=3)
b = dtu.CreateImageFromPylab(dpi=1000)
with b as pylab:
x = np.array([_['x'] for _ in points])
xe = np.array([_['x'] for _ in estimated])
xew = np.array([_['x'] for _ in estimated_weighted])
xe = xe - x
xew = xew - x
x = x * 0
pylab.plot(x, '.', label='x')
pylab.plot(xe, '.', label='x estimated')
pylab.plot(xew, '.', label='x estimated weighted')
pylab.legend()
od = dtu.get_output_dir_for_test()
fn = os.path.join(od, 'voting_kernel1.jpg')
dtu.write_data_to_file(a.get_png(), fn)
fn = os.path.join(od, 'errors.jpg')
dtu.write_data_to_file(b.get_png(), fn)
