def plot_rank_histogram(allres, orgres_type):
print('[viz] plotting %r rank histogram' % orgres_type)
ranks = allres.__dict__[orgres_type].ranks
label = 'P(rank | ' + orgres_type + ' match)'
title = orgres_type + ' match rankings histogram\n' + allres.title_suffix
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_histpdf(ranks, label=label) # FIXME
df2.set_xlabel('ground truth ranks')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.ibs, 'rankviz')
def plot_rank_histogram(allres, orgres_type):
print('[viz] plotting %r rank histogram' % orgres_type)
ranks = allres.__dict__[orgres_type].ranks
label = 'P(rank | ' + orgres_type + ' match)'
title = orgres_type + ' match rankings histogram\n' + allres.title_suffix
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_histpdf(ranks, label=label) # FIXME
df2.set_xlabel('ground truth ranks')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.ibs, 'rankviz')
def plot_score_pdf(allres, orgres_type, colorx=0.0, variation_truncate=False):
print('[viz] plotting ' + orgres_type + ' score pdf')
title = orgres_type + ' match score frequencies\n' + allres.title_suffix
scores = allres.__dict__[orgres_type].scores
print('[viz] len(scores) = %r ' % (len(scores),))
label = 'P(score | %r)' % orgres_type
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_pdf(scores, label=label, colorx=colorx)
if variation_truncate:
df2.variation_trunctate(scores)
#df2.variation_trunctate(false.scores)
df2.set_xlabel('score')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.ibs, 'scoreviz')
def plot_score_pdf(allres, orgres_type, colorx=0.0, variation_truncate=False):
print('[viz] plotting ' + orgres_type + ' score pdf')
title = orgres_type + ' match score frequencies\n' + allres.title_suffix
scores = allres.__dict__[orgres_type].scores
print('[viz] len(scores) = %r ' % (len(scores), ))
label = 'P(score | %r)' % orgres_type
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_pdf(scores, label=label, colorx=colorx)
if variation_truncate:
df2.variation_trunctate(scores)
#df2.variation_trunctate(false.scores)
df2.set_xlabel('score')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.ibs, 'scoreviz')
def plot_rank_stem(allres, orgres_type='true'):
print('[viz] plotting rank stem')
# Visualize rankings with the stem plot
ibs = allres.ibs
title = orgres_type + 'rankings stem plot\n' + allres.title_suffix
orgres = allres.__dict__[orgres_type]
df2.figure(fnum=FIGNUM, doclf=True, title=title)
x_data = orgres.qcxs
y_data = orgres.ranks
df2.draw_stems(x_data, y_data)
slice_num = int(np.ceil(np.log10(len(orgres.qcxs))))
df2.set_xticks(ibs.test_sample_cx[::slice_num])
df2.set_xlabel('query chip indeX (qcx)')
df2.set_ylabel('groundtruth chip ranks')
#df2.set_yticks(list(seen_ranks))
__dump_or_browse(allres.ibs, 'rankviz')
def plot_rank_stem(allres, orgres_type='true'):
print('[viz] plotting rank stem')
# Visualize rankings with the stem plot
ibs = allres.ibs
title = orgres_type + 'rankings stem plot\n' + allres.title_suffix
orgres = allres.__dict__[orgres_type]
df2.figure(fnum=FIGNUM, doclf=True, title=title)
x_data = orgres.qcxs
y_data = orgres.ranks
df2.draw_stems(x_data, y_data)
slice_num = int(np.ceil(np.log10(len(orgres.qcxs))))
df2.set_xticks(ibs.test_sample_cx[::slice_num])
df2.set_xlabel('query chip indeX (qcx)')
df2.set_ylabel('groundtruth chip ranks')
#df2.set_yticks(list(seen_ranks))
__dump_or_browse(allres.ibs, 'rankviz')
def TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers):
from plottool import draw_func2 as df2
import plottool
import vtool.patch as ptool
print('[rotinvar] 4) Draw histogram with interpolation annotations')
fnum = 1
gorimag = plottool.color_orimag(gori, gmag, True)
nRow, nCol = (2, 7)
df2.figure(fnum=1, pnum=(nRow, 1, nRow), doclf=True, docla=True)
plottool.draw_hist_subbin_maxima(hist, centers)
df2.set_xlabel('grad orientation (radians)')
df2.set_ylabel('grad magnitude')
df2.set_title('dominant orientations')
print('[rotinvar] 5) Show patch, gradients, magintude, and orientation')
df2.imshow(wpatch, pnum=(nRow, nCol, 1), fnum=fnum, title='patch')
df2.draw_vector_field(gradx,
grady,
pnum=(nRow, nCol, 2),
fnum=fnum,
title='gori (vec)')
df2.imshow(gorimag, pnum=(nRow, nCol, 3), fnum=fnum, title='gori (col)')
df2.imshow(np.abs(gradx), pnum=(nRow, nCol, 4), fnum=fnum, title='gradx')
df2.imshow(np.abs(grady), pnum=(nRow, nCol, 5), fnum=fnum, title='grady')
df2.imshow(gmag, pnum=(nRow, nCol, 6), fnum=fnum, title='gmag')
gpatch = ptool.gaussian_patch(shape=gori.shape)
df2.imshow(gpatch * 255,
pnum=(nRow, nCol, 7),
fnum=fnum,
title='gauss weights',
cmap_='hot')
#gpatch3 = np.dstack((gpatch, gpatch, gpatch))
#df2.draw_vector_field(gradx * gpatch, grady * gpatch, pnum=(nRow, nCol, 8), fnum=fnum, title='gori (vec)')
#df2.imshow(gorimag * gpatch3, pnum=(nRow, nCol, 9), fnum=fnum, title='gori (col)')
#df2.imshow(gradx * gpatch, pnum=(nRow, nCol, 10), fnum=fnum, title='gradx')
#df2.imshow(grady * gpatch, pnum=(nRow, nCol, 11), fnum=fnum, title='grady')
#df2.imshow(gmag * gpatch, pnum=(nRow, nCol, 12), fnum=fnum, title='gmag')
return locals()
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = util.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = util.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = util.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = util.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
