def renderMatching(pair, full_fps, matched_fps, inliers):
renderings = [full_fps[i].render(fallback_im=pair.im[i]) for i in [0, 1]]
ims = [cv2.cvtColor(i, cv2.COLOR_GRAY2BGR) for i in pair.im]
roffsets = [i * ims[0].shape[0] for i in [1, 2]]
full_im = np.concatenate([ims[0], renderings[0], renderings[1], ims[1]],
axis=0)
matched_ips = [matched_fp.ips_rc for matched_fp in matched_fps]
inl_ips = [i[:, inliers] for i in matched_ips]
endpoints = [inl_ips[i] + np.array([[roffsets[i], 0]]).T for i in [0, 1]]
for i in range(endpoints[0].shape[1]):
cv2.line(full_im, tuple(endpoints[0][[1, 0], i]),
tuple(endpoints[1][[1, 0], i]), (0, 255, 0), 1, cv2.LINE_AA)
if False:
outl_ips = [i[:, np.logical_not(inliers)] for i in matched_ips]
endpoints = [
outl_ips[i] + np.array([[roffsets[i], 0]]).T for i in [0, 1]
]
for i in range(endpoints[0].shape[1]):
cv2.line(full_im, tuple(endpoints[0][[1, 0], i]),
tuple(endpoints[1][[1, 0], i]), (0, 0, 255), 1,
cv2.LINE_AA)
outdir = os.path.join('results', 'match_render',
hyperparams.methodEvalString())
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, pair.name() + '.png')
cv2.imwrite(outfile, full_im)
def renderTrainSample(pair, fps, corr_rc, inl):
ims = [cv2.cvtColor(i, cv2.COLOR_GRAY2BGR) for i in pair.im]
rc = system.renderColors()
for fp, corr, im in zip(fps, corr_rc, ims):
for i in range(128):
cv2.circle(im, tuple(fp.ips_rc[[1, 0], i]), 6, tuple(rc[i]), 1,
cv2.LINE_AA)
if inl[i]:
thck = -1
else:
thck = 1
cv2.circle(im, tuple(corr[[1, 0], i]), 2, tuple(rc[i]), thck,
cv2.LINE_AA)
renderings = [i.render(with_points=False) for i in fps]
gray_ims = np.concatenate(ims, axis=0)
rend = np.concatenate(renderings, axis=0)
full_im = np.concatenate([gray_ims, rend], axis=1)
outdir = os.path.join('results', 'train_samples',
hyperparams.methodEvalString())
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, pair.name() + '.png')
cv2.imwrite(outfile, full_im)
def renderMatching(pair, fps):
""" returns true inlier count, R_err and t_err """
_, true_count, mask, R_err, t_err = evaluatePair(pair, fps)
renderings = [i.render(fallback_im=im) for i, im in zip(fps, pair.im)]
ims = [cv2.cvtColor(i, cv2.COLOR_GRAY2BGR) for i in pair.im]
roffsets = [i * ims[0].shape[0] for i in [1, 2]]
full_im = np.concatenate([ims[0], renderings[0], renderings[1], ims[1]],
axis=0)
inl_ips = [fp.ips_rc[:, mask] for fp in fps]
endpoints = [inl_ips[i] + np.array([[roffsets[i], 0]]).T for i in [0, 1]]
rc = system.renderColors()
inl_colors = rc[np.nonzero(mask)[0]]
for i in range(endpoints[0].shape[1]):
cv2.line(full_im, tuple(endpoints[0][[1, 0], i]),
tuple(endpoints[1][[1, 0], i]), tuple(inl_colors[i]), 2,
cv2.LINE_AA)
outdir = os.path.join('results', 'match_render',
hyperparams.methodEvalString())
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, pair.name() + '.png')
cv2.imwrite(outfile, full_im)
return true_count, R_err, t_err
def path():
cache_root = os.path.join('results', 'cache')
if not os.path.exists(cache_root):
os.makedirs(cache_root)
return os.path.join(cache_root, hyperparams.methodEvalString() + '.hkl')
import hyperparams
if __name__ == '__main__':
deploy_net, sess = hyperparams.bootstrapFromCheckpoint()
eval_set = hyperparams.getEvalSet()
fpasser = hyperparams.getForwardPasser(deploy_net, sess)
_, true_inl = hpatches.evaluate(fpasser, eval_set)
n = len(eval_set.folder_names)
assert len(true_inl) == 15 * n
w = 3
h = int(np.ceil(float(n) / w))
im = np.ones([8 * h, 8 * w]) * np.max(true_inl)
title = hyperparams.methodEvalString() + ':'
for i in range(n):
row = i / w
col = i % w
squ = scidist.squareform(true_inl[i * 15:(i + 1) * 15])
im[(row * 8 + 1):(row * 8 + 7), (col * 8 + 1):(col * 8 + 7)] = squ
if col == 0:
title = title + '\n'
title = title + eval_set.folder_names[i] + ', '
plt.imshow(im)
plt.colorbar()
plt.title(title)
plt.show()
pair_fps[0], pair_fps[1], 10, get_rt=True) for pair_fps in pairs_fps]
nmins = [i[0] for i in stats]
Rerrs = [i[1] for i in stats]
terrs = [i[2] for i in stats]
# Also save diffs of evaluation set
dR, dt = [], []
for pair in eval_pairs:
T = pair.T_0_1
dR.append(math.degrees(rpg_common_py.geometry.getRotationAngle(T.R)))
dt.append(np.linalg.norm(T.t))
# Save csv
csv_dir = os.path.join('results', 'match_render',
hyperparams.methodEvalString())
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
csv_path = os.path.join(csv_dir, '%s.csv' % hyperparams.methodEvalString())
csv_vals = zip([i.name() for i in eval_pairs], dR, dt, nmins, Rerrs, terrs)
with open(csv_path, 'w') as csv_file:
writer = csv.writer(csv_file, delimiter=' ', quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow(['name', 'dR', 'dt', 'nmin', 'eR', 'et'])
for val in csv_vals:
writer.writerow(val)
dR = np.array(dR)
dt = np.array(dt)
nmins = np.array(nmins)
mt100filt = nmins > 100