def test(hs, qcx):
fx2_scale = sv2.keypoint_scale(hs.feats.cx2_kpts[qcx])
fx = fx2_scale.argsort()[::-1][40]
rchip = hs.get_chip(qcx)
kp = hs.feats.cx2_kpts[qcx][fx]
# Show full image and keypoint
df2.figure(fignum=9000, doclf=True)
df2.imshow(rchip, plotnum=(1,3,1))
df2.draw_kpts2([kp], ell_color=(1,0,0), pts=True)
# Show cropped image and keypoint
patch, subkp = get_patch(rchip, kp)
df2.imshow(patch, plotnum=(1,3,2))
df2.draw_kpts2([subkp], ell_color=(1,0,0), pts=True)
# Show warped image and keypoint
wpatch, wkp = get_warped_patch(rchip, kp)
df2.imshow(wpatch, plotnum=(1,3,3))
df2.draw_kpts2([wkp], ell_color=(1,0,0), pts=True)
#
df2.set_figtitle('warp test')
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = helpers.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qcx, cx, score, rank) in enumerate(orgres.iter()):
helpers.print_(fmt_str % (ix + 1,))
# Skip low ranks
if rank > 5:
rank_skips.append(qcx)
continue
other_cxs = hs.get_other_indexed_cxs(qcx)
# Skip no groundtruth
if len(other_cxs) == 0:
gt_skips.append(qcx)
continue
res = qcx2_res[qcx]
# Get matching feature indexes
fm = res.cx2_fm[cx]
# Get their scores
fs = res.cx2_fs[cx]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape,))
desc1 = cx2_desc[qcx][fm[:, 0]]
desc2 = cx2_desc[cx][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qcx][fm[:, 0]]
kpts2 = cx2_kpts[cx][fm[:, 1]]
# Get their scale
scale1_m = sv2.keypoint_scale(kpts1)
scale2_m = sv2.keypoint_scale(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips),))
print('Skipped %d for rank > 5, %d for no gt' % (len(rank_skips), len(gt_skips),))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
def measure_feat_pairs(allres, orgtype='top_true'):
print('Measure ' + orgtype + ' pairs')
orgres = allres.__dict__[orgtype]
entropy_list = []
scale_list = []
score_list = []
lbl = 'Measuring ' + orgtype + ' pair '
fmt_str = helpers.make_progress_fmt_str(len(orgres), lbl)
rank_skips = []
gt_skips = []
for ix, (qcx, cx, score, rank) in enumerate(orgres.iter()):
helpers.print_(fmt_str % (ix + 1, ))
# Skip low ranks
if rank > 5:
rank_skips.append(qcx)
continue
other_cxs = hs.get_other_indexed_cxs(qcx)
# Skip no groundtruth
if len(other_cxs) == 0:
gt_skips.append(qcx)
continue
res = qcx2_res[qcx]
# Get matching feature indexes
fm = res.cx2_fm[cx]
# Get their scores
fs = res.cx2_fs[cx]
# Get matching descriptors
printDBG('\nfm.shape=%r' % (fm.shape, ))
desc1 = cx2_desc[qcx][fm[:, 0]]
desc2 = cx2_desc[cx][fm[:, 1]]
# Get matching keypoints
kpts1 = cx2_kpts[qcx][fm[:, 0]]
kpts2 = cx2_kpts[cx][fm[:, 1]]
# Get their scale
scale1_m = sv2.keypoint_scale(kpts1)
scale2_m = sv2.keypoint_scale(kpts2)
# Get their entropy
entropy1 = descriptor_entropy(desc1, bw_factor=1)
entropy2 = descriptor_entropy(desc2, bw_factor=1)
# Append to results
entropy_tup = np.array(zip(entropy1, entropy2))
scale_tup = np.array(zip(scale1_m, scale2_m))
entropy_tup = entropy_tup.reshape(len(entropy_tup), 2)
scale_tup = scale_tup.reshape(len(scale_tup), 2)
entropy_list.append(entropy_tup)
scale_list.append(scale_tup)
score_list.append(fs)
print('Skipped %d total.' % (len(rank_skips) + len(gt_skips), ))
print('Skipped %d for rank > 5, %d for no gt' % (
len(rank_skips),
len(gt_skips),
))
print(np.unique(map(len, entropy_list)))
def evstack(tup):
return np.vstack(tup) if len(tup) > 0 else np.empty((0, 2))
def ehstack(tup):
return np.hstack(tup) if len(tup) > 0 else np.empty((0, 2))
entropy_pairs = evstack(entropy_list)
scale_pairs = evstack(scale_list)
scores = ehstack(score_list)
print('\n * Measured %d pairs' % len(entropy_pairs))
return entropy_pairs, scale_pairs, scores
