def coco_stats():
"""
http://stattrek.com/online-calculator/hypergeometric.aspx
CommandLine:
python -m mtgmonte.stats --exec-coco_stats --show
Example:
>>> # DISABLE_DOCTEST
>>> from mtgmonte.stats import * # NOQA
>>> result = coco_stats()
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
from scipy.stats import hypergeom
N = pop_size = 60 # cards in deck # NOQA
K = num_success = 21 # number of creatures in deck # NOQA
n = sample_size = 6 # cards seen by coco # NOQA
# prob of at least that many hits
hypergeom
prb = hypergeom(N, K, n)
k = number_of_success = 1 # number of hits you want # NOQA
prb.pmf(k) # P(X = k)
#
prb.cdf(k) # P(X <= k)
1 - prb.cdf(k) # P(X > k)
(1 - prb.cdf(k)) + prb.pmf(k) # P(X >= k)
def prob_ge(k, prb=prb):
return (1 - prb.cdf(k)) + prb.pmf(k) # P(X >= k)
pt.ensure_pylab_qt4()
import numpy as np
k = np.arange(1, 3)
K_list = np.arange(15, 30)
label_list = [str(K_) + " creatures in deck" for K_ in K_list]
ydata_list = [prob_ge(k, prb=hypergeom(N, K_, n)) for K_ in K_list]
pt.multi_plot(
k,
ydata_list,
label_list=label_list,
title="probability of at least k hits with coco",
xlabel="k",
ylabel="prob",
num_xticks=len(k),
use_darkbackground=True,
)
def land_stats():
"""
http://stattrek.com/online-calculator/hypergeometric.aspx
CommandLine:
python -m mtgmonte.stats --exec-land_stats --show
Example:
>>> # DISABLE_DOCTEST
>>> from mtgmonte.stats import * # NOQA
>>> result = land_stats()
>>> print(result)
>>> ut.show_if_requested()
"""
import plottool as pt
from scipy.stats import hypergeom
N = pop_size = 60 # cards in deck # NOQA
# K = num_success = 25 # lands in deck # NOQA
n = sample_size = 6 # cards seen by coco # NOQA
# prob of at least that many hits
def prob_ge(k, prb):
return (1 - prb.cdf(k)) + prb.pmf(k) # P(X >= k)
pt.ensure_pylab_qt4()
N = deck_size = 60 # NOQA
land_range = (24, 27 + 1)
# N = deck_size = 40 # NOQA
# land_range = (15, 18 + 1)
xdata = range(0, 15) # turn
ydata_list = [[hypergeom(N, K, x + 7).expect() for x in xdata] for K in range(*land_range)]
spread_list = [[hypergeom(N, K, x + 7).std() for x in xdata] for K in range(*land_range)]
# spread_list = None
import numpy as np
label_list = ["%d lands" % (K,) for K in range(*land_range)]
pt.multi_plot(
xdata, ydata_list, spread_list=spread_list, label_list=label_list, num_xticks=15, num_yticks=13, fnum=1
)
min_lands_acceptable = np.minimum(np.array(xdata), [1, 2, 3, 4, 5, 6] + [6] * (len(xdata) - 6))
pt.multi_plot(
xdata,
[min_lands_acceptable, (np.array(xdata) ** 0.9) * 0.5 + 4],
label_list=["minimum ok", "maximum ok"],
num_xticks=15,
num_yticks=13,
fnum=1,
marker="o",
)
def show_toy_distributions(toy_params):
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 8, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'],
prob_colors=[pt.TRUE_BLUE, pt.FALSE_RED],
xdata=xdata,
figtitle='Toy Distributions')
def show_toy_distributions(toy_params):
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 8, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities(
[tp_pdf, fp_pdf], ['TP', 'TF'],
prob_colors=[pt.TRUE_BLUE, pt.FALSE_RED],
xdata=xdata,
figtitle='Toy Distributions')
def multidb_montage():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw multidb_montage --save montage.jpg --dpath ~/slides --diskshow --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> multidb_montage()
"""
import ibeis
import plottool as pt
import vtool as vt
import numpy as np
pt.ensure_pylab_qt4()
ibs1 = ibeis.opendb('PZ_MTEST')
ibs2 = ibeis.opendb('GZ_ALL')
ibs3 = ibeis.opendb('GIRM_Master1')
chip_lists = []
aids_list = []
for ibs in [ibs1, ibs2, ibs3]:
aids = ibs.sample_annots_general(minqual='good', sample_size=400)
aids_list.append(aids)
print(ut.depth_profile(aids_list))
for ibs, aids in zip([ibs1, ibs2, ibs3], aids_list):
chips = ibs.get_annot_chips(aids)
chip_lists.append(chips)
chip_list = ut.flatten(chip_lists)
np.random.shuffle(chip_list)
widescreen_ratio = 16 / 9
ratio = ut.PHI
ratio = widescreen_ratio
fpath = pt.get_save_directions()
#height = 6000
width = 6000
#width = int(height * ratio)
height = int(width / ratio)
dsize = (width, height)
dst = vt.montage(chip_list, dsize)
vt.imwrite(fpath, dst)
if ut.get_argflag('--show'):
pt.imshow(dst)
def multidb_montage():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw multidb_montage --save montage.jpg --dpath ~/slides --diskshow --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> multidb_montage()
"""
import ibeis
import plottool as pt
import vtool as vt
import numpy as np
pt.ensure_pylab_qt4()
ibs1 = ibeis.opendb('PZ_MTEST')
ibs2 = ibeis.opendb('GZ_ALL')
ibs3 = ibeis.opendb('GIRM_Master1')
chip_lists = []
aids_list = []
for ibs in [ibs1, ibs2, ibs3]:
aids = ibs.sample_annots_general(minqual='good', sample_size=400)
aids_list.append(aids)
print(ut.depth_profile(aids_list))
for ibs, aids in zip([ibs1, ibs2, ibs3], aids_list):
chips = ibs.get_annot_chips(aids)
chip_lists.append(chips)
chip_list = ut.flatten(chip_lists)
np.random.shuffle(chip_list)
widescreen_ratio = 16 / 9
ratio = ut.PHI
ratio = widescreen_ratio
fpath = pt.get_save_directions()
#height = 6000
width = 6000
#width = int(height * ratio)
height = int(width / ratio)
dsize = (width, height)
dst = vt.montage(chip_list, dsize)
vt.imwrite(fpath, dst)
if ut.get_argflag('--show'):
pt.imshow(dst)
def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(ut.hz_str('prob_annots = ', ut.array2string2(prob_annots, precision=2, max_line_width=140, suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2 ]
candset = {point: thresh_points[np.abs(thresh_points - point) < quant] for point in thresh_points}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {ax: others for ax, others in enumerate(covering_sets)}
soln_cover = ut.setcover_ilp(candidate_sets_dict, max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2]) for ax, other_xs in enumerate(current_covers) for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids], allow_directed=True, ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth', {aid: 4.0 for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep' : 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
def show_top_featmatches(qreq_, cm_list):
"""
Args:
qreq_ (ibeis.QueryRequest): query request object with hyper-parameters
cm_list (list):
SeeAlso:
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True,lnbnn_normalizer=normlnbnn-test -a default --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 -t best:lnbnn_on=True -a timectrl --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True -a default:size=30 --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:K=1,Knorm=5,lnbnn_on=True -a default:size=30 --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:K=1,Knorm=3,lnbnn_on=True -a default --sephack
CommandLine:
python -m ibeis.viz.viz_nearest_descriptors --exec-show_top_featmatches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_nearest_descriptors import * # NOQA
>>> import ibeis
>>> cm_list, qreq_ = ibeis.testdata_cmlist(defaultdb='PZ_MTEST',
>>> a=['default:has_none=mother,size=30'])
>>> show_top_featmatches(qreq_, cm_list)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
# for cm in cm_list:
# cm.score_csum(qreq_)
import numpy as np
import vtool as vt
from functools import partial
# Stack chipmatches
ibs = qreq_.ibs
infos = [cm.get_flat_fm_info() for cm in cm_list]
flat_metadata = dict([(k, np.concatenate(v))
for k, v in ut.dict_stack2(infos).items()])
fsv_flat = flat_metadata['fsv']
flat_metadata['fs'] = fsv_flat.prod(axis=1)
aids1 = flat_metadata['aid1'][:, None]
aids2 = flat_metadata['aid2'][:, None]
flat_metadata['aid_pairs'] = np.concatenate([aids1, aids2], axis=1)
# Take sample of metadata
sortx = flat_metadata['fs'].argsort()[::-1]
num = len(cm_list) * 3
# num = 10
taker = partial(np.take, indices=sortx[:num], axis=0)
flat_metadata_top = ut.map_dict_vals(taker, flat_metadata)
aid1s, aid2s, fms = ut.dict_take(flat_metadata_top, ['aid1', 'aid2', 'fm'])
annots = {}
aids = np.unique(np.hstack((aid1s, aid2s)))
annots = {aid: ibs.get_annot_lazy_dict(aid, config2_=qreq_.qparams)
for aid in aids}
label_lists = ibs.get_aidpair_truths(aid1s, aid2s) == ibs.const.TRUTH_MATCH
patch_size = 64
def extract_patches(annots, aid, fxs):
""" custom_func(lazydict, key, subkeys) for multigroup_lookup """
annot = annots[aid]
kpts = annot['kpts']
rchip = annot['rchip']
kpts_m = kpts.take(fxs, axis=0)
warped_patches, warped_subkpts = vt.get_warped_patches(rchip, kpts_m,
patch_size=patch_size)
return warped_patches
data_lists = vt.multigroup_lookup(annots, [aid1s, aid2s], fms.T, extract_patches)
import plottool as pt
pt.ensure_pylab_qt4()
import ibeis_cnn
inter = ibeis_cnn.draw_results.interact_patches(
label_lists, data_lists, flat_metadata_top, chunck_sizes=(2, 4),
ibs=ibs, hack_one_per_aid=False, sortby='fs', qreq_=qreq_)
inter.show()
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
superpixelDiameter = 15 # super-pixel size
slicWeight = 15.0 # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels) - 1
# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(
clusterProb.astype(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1,))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
regularizer = opengm.pottsFunction([nCluster] * 2, 0.0, beta)
fid = gm.addFunction(regularizer)
# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
uvIds = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
def intraoccurrence_connected():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = intraoccurrence_connected()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='PZ_Master1')
nid2_aid = {
#4880: [3690, 3696, 3703, 3706, 3712, 3721],
4880: [3690, 3696, 3703],
6537: [3739],
6653: [7671],
6610: [7566, 7408],
#6612: [7664, 7462, 7522],
#6624: [7465, 7360],
#6625: [7746, 7383, 7390, 7477, 7376, 7579],
6630: [7586, 7377, 7464, 7478],
#6677: [7500]
}
nid2_dbaids = {4880: [33, 6120, 7164], 6537: [7017, 7206], 6653: [7660]}
if ut.get_argflag('--small') or ut.get_argflag('--smaller'):
del nid2_aid[6630]
del nid2_aid[6537]
del nid2_dbaids[6537]
if ut.get_argflag('--smaller'):
nid2_dbaids[4880].remove(33)
nid2_aid[4880].remove(3690)
nid2_aid[6610].remove(7408)
#del nid2_aid[4880]
#del nid2_dbaids[4880]
aids = ut.flatten(nid2_aid.values())
temp_nids = [1] * len(aids)
postcut = ut.get_argflag('--postcut')
aids_list = ibs.group_annots_by_name(aids)[0]
ensure_edges = 'all' if True or not postcut else None
unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups(
ibs,
aids_list,
#invis_edges=invis_edges,
ensure_edges=ensure_edges,
temp_nids=temp_nids)
viz_graph.color_by_nids(unlabeled_graph,
unique_nids=[1] *
len(list(unlabeled_graph.nodes())))
viz_graph.ensure_node_images(ibs, unlabeled_graph)
nx.set_node_attributes(unlabeled_graph, 'shape', 'rect')
#unlabeled_graph = unlabeled_graph.to_undirected()
# Find the "database exemplars for these annots"
if False:
gt_aids = ibs.get_annot_groundtruth(aids)
gt_aids = [ut.setdiff(s, aids) for s in gt_aids]
dbaids = ut.unique(ut.flatten(gt_aids))
dbaids = ibs.filter_annots_general(dbaids, minqual='good')
ibs.get_annot_quality_texts(dbaids)
else:
dbaids = ut.flatten(nid2_dbaids.values())
exemplars = nx.DiGraph()
#graph = exemplars # NOQA
exemplars.add_nodes_from(dbaids)
def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}):
edge_list = ut.upper_diag_self_prodx(nodes)
graph.add_edges_from(edge_list, **edgeattrs)
return edge_list
for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)):
add_clique(exemplars, aids_)
viz_graph.ensure_node_images(ibs, exemplars)
viz_graph.color_by_nids(exemplars, ibs=ibs)
nx.set_node_attributes(unlabeled_graph, 'framewidth', False)
nx.set_node_attributes(exemplars, 'framewidth', 4.0)
nx.set_node_attributes(unlabeled_graph, 'group', 'unlab')
nx.set_node_attributes(exemplars, 'group', 'exemp')
#big_graph = nx.compose_all([unlabeled_graph])
big_graph = nx.compose_all([exemplars, unlabeled_graph])
# add sparse connections from unlabeled to exemplars
import numpy as np
rng = np.random.RandomState(0)
if True or not postcut:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes())))
flags = np.logical_or(exnids == nid_, flags)
exmatches = ut.compress(list(exemplars.nodes()), flags)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)),
color=pt.ORANGE,
implicit=True)
else:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
exmatches = ut.compress(list(exemplars.nodes()), flags)
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(exmatches))
exmatches = ut.compress(exmatches, exnids == nid_)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)))
pass
nx.set_node_attributes(big_graph, 'shape', 'rect')
#if False and postcut:
# ut.nx_delete_node_attr(big_graph, 'nid')
# ut.nx_delete_edge_attr(big_graph, 'color')
# viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs)
# viz_graph.color_by_nids(big_graph, ibs=ibs)
# big_graph = big_graph.to_undirected()
layoutkw = {
'sep': 1 / 5,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
as_directed = False
#as_directed = True
#hacknode = True
hacknode = 0
graph = big_graph
ut.nx_ensure_agraph_color(graph)
if hacknode:
nx.set_edge_attributes(graph, 'taillabel',
{e: str(e[0])
for e in graph.edges()})
nx.set_edge_attributes(graph, 'headlabel',
{e: str(e[1])
for e in graph.edges()})
explicit_graph = pt.get_explicit_graph(graph)
_, layout_info = pt.nx_agraph_layout(explicit_graph,
orig_graph=graph,
inplace=True,
**layoutkw)
if ut.get_argflag('--smaller'):
graph.node[7660]['pos'] = np.array([550, 350])
graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw)
elif ut.get_argflag('--small'):
graph.node[7660]['pos'] = np.array([750, 350])
graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400])
graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph, inplace=True, **layoutkw)
if not postcut:
#pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw,
# as_directed=False)
#pt.show_nx(graph, layout='agraph', layoutkw=layoutkw,
# as_directed=as_directed, hacknode=hacknode)
pt.show_nx(graph,
layout='custom',
layoutkw=layoutkw,
as_directed=as_directed,
hacknode=hacknode)
else:
#explicit_graph = pt.get_explicit_graph(graph)
#_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
# **layoutkw)
#layout_info['edge']['alpha'] = .8
#pt.apply_graph_layout_attrs(graph, layout_info)
#graph_layout_attrs = layout_info['graph']
##edge_layout_attrs = layout_info['edge']
##node_layout_attrs = layout_info['node']
#for key, vals in layout_info['node'].items():
# #print('[special] key = %r' % (key,))
# nx.set_node_attributes(graph, key, vals)
#for key, vals in layout_info['edge'].items():
# #print('[special] key = %r' % (key,))
# nx.set_edge_attributes(graph, key, vals)
#nx.set_edge_attributes(graph, 'alpha', .8)
#graph.graph['splines'] = graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'line'
cut_graph = graph.copy()
edge_list = list(cut_graph.edges())
edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list))
cut_flags = edge_nids.T[0] != edge_nids.T[1]
cut_edges = ut.compress(edge_list, cut_flags)
cut_graph.remove_edges_from(cut_edges)
ut.nx_delete_node_attr(cut_graph, 'nid')
viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs)
#ut.nx_get_default_node_attributes(exemplars, 'color', None)
ut.nx_delete_node_attr(cut_graph,
'color',
nodes=unlabeled_graph.nodes())
aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color',
None)
nid2_colors = ut.group_items(aid2_color.values(),
ibs.get_annot_nids(aid2_color.keys()))
nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors)
nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors)
#for val in nid2_colors.values():
# assert len(val) <= 1
# Get initial colors
nid2_color_ = {
nid: colors_[0]
for nid, colors_ in nid2_colors.items() if len(colors_) == 1
}
graph = cut_graph
viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_)
nx.set_node_attributes(cut_graph, 'framewidth', 4)
pt.show_nx(cut_graph,
layout='custom',
layoutkw=layoutkw,
as_directed=as_directed,
hacknode=hacknode)
pt.zoom_factory()
def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x
for x in to_rename},
copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x
for x in to_rename},
copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(
graph,
'User decision',
shape='rect',
#width=100, height=100,
color=pt.YELLOW,
style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections',
userdecision,
constraint=longcat,
color=pt.PINK)
graph.add_edge(userdecision,
'annotations',
constraint=longcat,
color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(
node_alias, ut.setdiff(node_alias.keys(), graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def general_identify_flow():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw general_identify_flow --show --save pairsim.png --dpi=100 --diskshow --clipwhite
python -m ibeis.scripts.specialdraw general_identify_flow --dpi=200 --diskshow --clipwhite --dpath ~/latex/cand/ --figsize=20,10 --save figures4/pairprob.png --arrow-width=2.0
Example:
>>> # SCRIPT
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> general_identify_flow()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
ns = 512
ut.inject_func_as_method(graph, ut.nx_makenode)
annot1_color = p_shade2
annot2_color = s1_shade2
#annot1_color2 = pt.color_funcs.lighten_rgb(colors.hex2color(annot1_color), .01)
annot1 = graph.nx_makenode('Annotation X',
width=ns,
height=ns,
groupid='annot',
color=annot1_color)
annot2 = graph.nx_makenode('Annotation Y',
width=ns,
height=ns,
groupid='annot',
color=annot2_color)
featX = graph.nx_makenode('Features X',
size=(ns / 1.2, ns / 2),
groupid='feats',
color=lighten_hex(annot1_color, .1))
featY = graph.nx_makenode('Features Y',
size=(ns / 1.2, ns / 2),
groupid='feats',
color=lighten_hex(annot2_color, .1))
#'#4771B3')
global_pairvec = graph.nx_makenode(
'Global similarity\n(viewpoint, quality, ...)',
width=ns * ut.PHI * 1.2,
color=s2_shade2)
findnn = graph.nx_makenode('Find correspondences\n(nearest neighbors)',
shape='ellipse',
color=c_shade2)
local_pairvec = graph.nx_makenode(
'Local similarities\n(LNBNN, spatial error, ...)',
size=(ns * 2.2, ns),
color=lighten_hex(c_shade2, .1))
agglocal = graph.nx_makenode('Aggregate',
size=(ns / 1.1, ns / 2),
shape='ellipse',
color=lighten_hex(c_shade2, .2))
catvecs = graph.nx_makenode('Concatenate',
size=(ns / 1.1, ns / 2),
shape='ellipse',
color=lighten_hex(s2_shade2, .1))
pairvec = graph.nx_makenode('Vector of\npairwise similarities',
color=lighten_hex(s2_shade2, .2))
classifier = graph.nx_makenode('Classifier\n(SVM/RF/DNN)',
color=lighten_hex(s2_shade2, .3))
prob = graph.nx_makenode(
'Matching Probability\n(same individual given\nsimilar viewpoint)',
color=lighten_hex(s2_shade2, .4))
graph.add_edge(annot1, global_pairvec)
graph.add_edge(annot2, global_pairvec)
add_edge2(annot1, featX)
add_edge2(annot2, featY)
add_edge2(featX, findnn)
add_edge2(featY, findnn)
add_edge2(findnn, local_pairvec)
graph.add_edge(local_pairvec, agglocal, constraint=True)
graph.add_edge(agglocal, catvecs, constraint=False)
graph.add_edge(global_pairvec, catvecs)
graph.add_edge(catvecs, pairvec)
# graph.add_edge(annot1, classifier, style='invis')
# graph.add_edge(pairvec, classifier , constraint=False)
graph.add_edge(pairvec, classifier)
graph.add_edge(classifier, prob)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
#ut.nx_set_default_node_attributes(graph, 'fillcolor', nx.get_node_attributes(graph, 'color'))
#ut.nx_set_default_node_attributes(graph, 'style', 'rounded')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'xlabel',
nx.get_node_attributes(graph, 'label'))
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns)
ut.nx_set_default_node_attributes(graph, 'regular', False)
#font = 'MonoDyslexic'
#font = 'Mono_Dyslexic'
font = 'Ubuntu'
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', font)
#ut.nx_delete_node_attr(graph, 'width')
#ut.nx_delete_node_attr(graph, 'height')
#ut.nx_delete_node_attr(graph, 'fixedsize')
#ut.nx_delete_node_attr(graph, 'style')
#ut.nx_delete_node_attr(graph, 'regular')
#ut.nx_delete_node_attr(graph, 'shape')
#graph.node[annot1]['label'] = "<f0> left|<f1> mid\ dle|<f2> right"
#graph.node[annot2]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['shape'] = 'none'
#graph.node[annot1]['margin'] = '0'
layoutkw = {
'forcelabels': True,
'prog': 'dot',
'rankdir': 'LR',
# 'splines': 'curved',
'splines': 'line',
'samplepoints': 20,
'showboxes': 1,
# 'splines': 'polyline',
#'splines': 'spline',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
#'inputscale': 72,
# 'inputscale': 1,
# 'dpi': 72,
# 'concentrate': 'true', # merges edge lines
# 'splines': 'ortho',
# 'aspect': 1,
# 'ratio': 'compress',
# 'size': '5,4000',
# 'rank': 'max',
}
#fontkw = dict(fontfamilty='sans-serif', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=20)
fontkw = dict(fontname=font, fontweight='light', fontsize=12)
#prop = fm.FontProperties(fname='/usr/share/fonts/truetype/groovygh.ttf')
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def merge_viewpoint_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw merge_viewpoint_graph --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = merge_viewpoint_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
import networkx as nx
defaultdb = 'PZ_Master1'
ibs = ibeis.opendb(defaultdb=defaultdb)
#nids = None
aids = ibs.get_name_aids(4875)
ibs.print_annot_stats(aids)
left_aids = ibs.filter_annots_general(aids, view='left')[0:3]
right_aids = ibs.filter_annots_general(aids, view='right')
right_aids = list(set(right_aids) - {14517})[0:3]
back = ibs.filter_annots_general(aids, view='back')[0:4]
backleft = ibs.filter_annots_general(aids, view='backleft')[0:4]
backright = ibs.filter_annots_general(aids, view='backright')[0:4]
right_graph = nx.DiGraph(ut.upper_diag_self_prodx(right_aids))
left_graph = nx.DiGraph(ut.upper_diag_self_prodx(left_aids))
back_edges = [
tuple([back[0], backright[0]][::1]),
tuple([back[0], backleft[0]][::1]),
]
back_graph = nx.DiGraph(back_edges)
# Let the graph be a bit smaller
right_graph.edge[right_aids[1]][right_aids[2]]['constraint'] = ut.get_argflag('--constraint')
left_graph.edge[left_aids[1]][left_aids[2]]['constraint'] = ut.get_argflag('--constraint')
#right_graph = right_graph.to_undirected().to_directed()
#left_graph = left_graph.to_undirected().to_directed()
nx.set_node_attributes(right_graph, 'groupid', 'right')
nx.set_node_attributes(left_graph, 'groupid', 'left')
#nx.set_node_attributes(right_graph, 'scale', .2)
#nx.set_node_attributes(left_graph, 'scale', .2)
#back_graph.node[back[0]]['scale'] = 2.3
nx.set_node_attributes(back_graph, 'groupid', 'back')
view_graph = nx.compose_all([left_graph, back_graph, right_graph])
view_graph.add_edges_from([
[backright[0], right_aids[0]][::-1],
[backleft[0], left_aids[0]][::-1],
])
pt.ensure_pylab_qt4()
graph = graph = view_graph # NOQA
#graph = graph.to_undirected()
nx.set_edge_attributes(graph, 'color', pt.DARK_ORANGE[0:3])
#nx.set_edge_attributes(graph, 'color', pt.BLACK)
nx.set_edge_attributes(graph, 'color', {edge: pt.LIGHT_BLUE[0:3] for edge in back_edges})
#pt.close_all_figures();
from ibeis.viz import viz_graph
layoutkw = {
'nodesep': 1,
}
viz_graph.viz_netx_chipgraph(ibs, graph, with_images=1, prog='dot',
augment_graph=False, layoutkw=layoutkw)
if False:
"""
#view_graph = left_graph
pt.close_all_figures(); viz_netx_chipgraph(ibs, view_graph, with_images=0, prog='neato')
#viz_netx_chipgraph(ibs, view_graph, layout='pydot', with_images=False)
#back_graph = make_name_graph_interaction(ibs, aids=back, with_all=False)
aids = left_aids + back + backleft + backright + right_aids
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aid_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
ut.copy_files_to(, )
aids = ibs.filterannots_by_tags(ibs.get_valid_aids(),
dict(has_any_annotmatch='splitcase'))
aid1 = ibs.group_annots_by_name_dict(aids)[252]
aid2 = ibs.group_annots_by_name_dict(aids)[6791]
aids1 = ibs.get_annot_groundtruth(aid1)[0][0:4]
aids2 = ibs.get_annot_groundtruth(aid2)[0]
make_name_graph_interaction(ibs, aids=aids1 + aids2, with_all=False)
ut.ensuredir(ut.truthpath('~/slides/split/))
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aidA_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
"""
pass
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph, 'Unlabeled\nannotations\n(query)', width=ns, height=ns,
groupid='annot', color=p_shade2)
annot2 = ut.nx_makenode(graph, 'Labeled\nannotations\n(database)', width=ns, height=ns,
groupid='annot', color=s1_shade2)
occurprob = ut.nx_makenode(graph, 'Dense \nprobabilities', color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph, 'Cached \nprobabilities', color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph, 'Sparse\nprobabilities', color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph, 'Graph of\npotential matches', color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph, 'Graph cut algorithm', color=lighten_hex(s2_shade2, .2), shape='ellipse')
cc_names = ut.nx_makenode(graph, 'Identifications,\n splits, and merges are\nconnected compoments', color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def general_identify_flow():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw general_identify_flow --show --save pairsim.png --dpi=100 --diskshow --clipwhite
python -m ibeis.scripts.specialdraw general_identify_flow --dpi=200 --diskshow --clipwhite --dpath ~/latex/cand/ --figsize=20,10 --save figures4/pairprob.png --arrow-width=2.0
Example:
>>> # SCRIPT
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> general_identify_flow()
>>> ut.quit_if_noshow()
>>> ut.show_if_requested()
"""
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [ut.nx_makenode(name + str(n), **attrkw) for n in range(num)]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
ns = 512
ut.inject_func_as_method(graph, ut.nx_makenode)
annot1_color = p_shade2
annot2_color = s1_shade2
#annot1_color2 = pt.color_funcs.lighten_rgb(colors.hex2color(annot1_color), .01)
annot1 = graph.nx_makenode('Annotation X', width=ns, height=ns, groupid='annot', color=annot1_color)
annot2 = graph.nx_makenode('Annotation Y', width=ns, height=ns, groupid='annot', color=annot2_color)
featX = graph.nx_makenode('Features X', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot1_color, .1))
featY = graph.nx_makenode('Features Y', size=(ns / 1.2, ns / 2), groupid='feats', color=lighten_hex(annot2_color, .1))
#'#4771B3')
global_pairvec = graph.nx_makenode('Global similarity\n(viewpoint, quality, ...)', width=ns * ut.PHI * 1.2, color=s2_shade2)
findnn = graph.nx_makenode('Find correspondences\n(nearest neighbors)', shape='ellipse', color=c_shade2)
local_pairvec = graph.nx_makenode('Local similarities\n(LNBNN, spatial error, ...)',
size=(ns * 2.2, ns), color=lighten_hex(c_shade2, .1))
agglocal = graph.nx_makenode('Aggregate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(c_shade2, .2))
catvecs = graph.nx_makenode('Concatenate', size=(ns / 1.1, ns / 2), shape='ellipse', color=lighten_hex(s2_shade2, .1))
pairvec = graph.nx_makenode('Vector of\npairwise similarities', color=lighten_hex(s2_shade2, .2))
classifier = graph.nx_makenode('Classifier\n(SVM/RF/DNN)', color=lighten_hex(s2_shade2, .3))
prob = graph.nx_makenode('Matching Probability\n(same individual given\nsimilar viewpoint)', color=lighten_hex(s2_shade2, .4))
graph.add_edge(annot1, global_pairvec)
graph.add_edge(annot2, global_pairvec)
add_edge2(annot1, featX)
add_edge2(annot2, featY)
add_edge2(featX, findnn)
add_edge2(featY, findnn)
add_edge2(findnn, local_pairvec)
graph.add_edge(local_pairvec, agglocal, constraint=True)
graph.add_edge(agglocal, catvecs, constraint=False)
graph.add_edge(global_pairvec, catvecs)
graph.add_edge(catvecs, pairvec)
# graph.add_edge(annot1, classifier, style='invis')
# graph.add_edge(pairvec, classifier , constraint=False)
graph.add_edge(pairvec, classifier)
graph.add_edge(classifier, prob)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
#ut.nx_set_default_node_attributes(graph, 'fillcolor', nx.get_node_attributes(graph, 'color'))
#ut.nx_set_default_node_attributes(graph, 'style', 'rounded')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'xlabel', nx.get_node_attributes(graph, 'label'))
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns)
ut.nx_set_default_node_attributes(graph, 'regular', False)
#font = 'MonoDyslexic'
#font = 'Mono_Dyslexic'
font = 'Ubuntu'
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', font)
#ut.nx_delete_node_attr(graph, 'width')
#ut.nx_delete_node_attr(graph, 'height')
#ut.nx_delete_node_attr(graph, 'fixedsize')
#ut.nx_delete_node_attr(graph, 'style')
#ut.nx_delete_node_attr(graph, 'regular')
#ut.nx_delete_node_attr(graph, 'shape')
#graph.node[annot1]['label'] = "<f0> left|<f1> mid\ dle|<f2> right"
#graph.node[annot2]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['label'] = ut.codeblock(
# '''
# <<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
# <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR>
# </TABLE>>
# ''')
#graph.node[annot1]['shape'] = 'none'
#graph.node[annot1]['margin'] = '0'
layoutkw = {
'forcelabels': True,
'prog': 'dot',
'rankdir': 'LR',
# 'splines': 'curved',
'splines': 'line',
'samplepoints': 20,
'showboxes': 1,
# 'splines': 'polyline',
#'splines': 'spline',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
#'inputscale': 72,
# 'inputscale': 1,
# 'dpi': 72,
# 'concentrate': 'true', # merges edge lines
# 'splines': 'ortho',
# 'aspect': 1,
# 'ratio': 'compress',
# 'size': '5,4000',
# 'rank': 'max',
}
#fontkw = dict(fontfamilty='sans-serif', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=20)
fontkw = dict(fontname=font, fontweight='light', fontsize=12)
#prop = fm.FontProperties(fname='/usr/share/fonts/truetype/groovygh.ttf')
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def event_space():
"""
pip install matplotlib-venn
"""
from matplotlib import pyplot as plt
# import numpy as np
from matplotlib_venn import venn3, venn2, venn3_circles
plt.figure(figsize=(4, 4))
v = venn3(subsets=(1, 1, 1, 1, 1, 1, 1), set_labels=('A', 'B', 'C'))
v.get_patch_by_id('100').set_alpha(1.0)
v.get_patch_by_id('100').set_color('white')
v.get_label_by_id('100').set_text('Unknown')
v.get_label_by_id('A').set_text('Set "A"')
c = venn3_circles(subsets=(1, 1, 1, 1, 1, 1, 1), linestyle='dashed')
c[0].set_lw(1.0)
c[0].set_ls('dotted')
plt.show()
same = set(['comparable', 'incomparable', 'same'])
diff = set(['comparable', 'incomparable', 'diff'])
# comparable = set(['comparable', 'same', 'diff'])
# incomparable = set(['incomparable', 'same', 'diff'])
subsets = [same, diff] # , comparable, incomparable]
set_labels = ('same', 'diff') # , 'comparable', 'incomparable')
venn3(subsets=subsets, set_labels=set_labels)
plt.show()
import plottool as pt
pt.ensure_pylab_qt4()
from matplotlib_subsets import treesets_rectangles
tree = (
(120, 'Same', None), [
((50, 'comparable', None), []),
((50, 'incomparable', None), [])
]
(120, 'Diff', None), [
((50, 'comparable', None), []),
((50, 'incomparable', None), [])
]
)
treesets_rectangles(tree)
plt.show()
from matplotlib import pyplot as plt
from matplotlib_venn import venn2, venn2_circles # NOQA
# Subset sizes
s = (
2, # Ab
3, # aB
1, # AB
)
v = venn2(subsets=s, set_labels=('A', 'B'))
# Subset labels
v.get_label_by_id('10').set_text('A but not B')
v.get_label_by_id('01').set_text('B but not A')
v.get_label_by_id('11').set_text('A and B')
# Subset colors
v.get_patch_by_id('10').set_color('c')
v.get_patch_by_id('01').set_color('#993333')
v.get_patch_by_id('11').set_color('blue')
# Subset alphas
v.get_patch_by_id('10').set_alpha(0.4)
v.get_patch_by_id('01').set_alpha(1.0)
v.get_patch_by_id('11').set_alpha(0.7)
# Border styles
c = venn2_circles(subsets=s, linestyle='solid')
c[0].set_ls('dashed') # Line style
c[0].set_lw(2.0) # Line width
plt.show()
def limited_power_toughness_histogram():
r"""
CommandLine:
python -m mtgmonte.stats --exec-limited_power_toughness_histogram --show
Example:
>>> # DISABLE_DOCTEST
>>> from mtgmonte.stats import * # NOQA
>>> result = limited_power_toughness_histogram()
>>> print(result)
>>> ut.show_if_requested()
"""
from mtgmonte import mtgobjs
from mtglib.gatherer_request import SearchRequest
from mtglib.card_extractor import CardExtractor
# from mtglib.card_renderer import CardList
request = SearchRequest({"set": "Oath of the Gatewatch"})
def add_page(url, page):
parts = url.split("/")
part1 = "/".join(parts[:-1])
part2 = "/Default.aspx?page=%d&" % (page,)
part3 = parts[-1].replace("Default.aspx?", "")
url2 = part1 + part2 + part3
return url2
card_list = []
for page in range(0, 10):
url = request.url
url2 = add_page(url, page)
extract = CardExtractor(url2)
card_list0 = extract.cards
for card in card_list0:
card2 = mtgobjs.Card2()
card2.__dict__.update(card.__dict__)
card_list.append(card2)
if len(card_list0) != 100:
break
for c in card_list:
c.nice_attrs += ["rarity"]
creats = [_card2 for _card2 in card_list if "Creature" in card2.types]
creats = [_card2 for _card2 in creats if _card2.rarity in ["Common", "Uncommon"]]
powtough = []
for c in creats:
try:
powtough.append((int(c.power), int(c.toughness)))
except ValueError:
pass
import plottool as pt
pt.ensure_pylab_qt4()
import numpy as np
scores_list = np.array(list(zip(*powtough)))
xdata = np.arange(0, np.max(scores_list) + 1)
powhist = np.histogram(scores_list[0], bins=xdata)[0]
toughist = np.histogram(scores_list[1], bins=xdata)[0]
pt.multi_plot(xdata, [powhist, toughist], label_list=["power", "toughness"], kind="bar")
bothhist = ut.dict_hist(powtough)
xdata = np.arange(len(bothhist))
dat = sorted(bothhist.items())
xticklabels = ut.take_column(dat, 0)
ydata = ut.take_column(dat, 1)
pt.multi_plot(xdata, [ydata], xticklabels=xticklabels, kind="bar")
def graphcut_flow():
r"""
Returns:
?: name
CommandLine:
python -m ibeis.scripts.specialdraw graphcut_flow --show --save cutflow.png --diskshow --clipwhite
python -m ibeis.scripts.specialdraw graphcut_flow --save figures4/cutiden.png --diskshow --clipwhite --dpath ~/latex/crall-candidacy-2015/ --figsize=24,10 --arrow-width=2.0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> graphcut_flow()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
pt.ensure_pylab_qt4()
import networkx as nx
# pt.plt.xkcd()
graph = nx.DiGraph()
def makecluster(name, num, **attrkw):
return [
ut.nx_makenode(graph, name + str(n), **attrkw) for n in range(num)
]
def add_edge2(u, v, *args, **kwargs):
v = ut.ensure_iterable(v)
u = ut.ensure_iterable(u)
for _u, _v in ut.product(u, v):
graph.add_edge(_u, _v, *args, **kwargs)
ns = 512
# *** Primary color:
p_shade2 = '#41629A'
# *** Secondary color
s1_shade2 = '#E88B53'
# *** Secondary color
s2_shade2 = '#36977F'
# *** Complement color
c_shade2 = '#E8B353'
annot1 = ut.nx_makenode(graph,
'Unlabeled\nannotations\n(query)',
width=ns,
height=ns,
groupid='annot',
color=p_shade2)
annot2 = ut.nx_makenode(graph,
'Labeled\nannotations\n(database)',
width=ns,
height=ns,
groupid='annot',
color=s1_shade2)
occurprob = ut.nx_makenode(graph,
'Dense \nprobabilities',
color=lighten_hex(p_shade2, .1))
cacheprob = ut.nx_makenode(graph,
'Cached \nprobabilities',
color=lighten_hex(s1_shade2, .1))
sparseprob = ut.nx_makenode(graph,
'Sparse\nprobabilities',
color=lighten_hex(c_shade2, .1))
graph.add_edge(annot1, occurprob)
graph.add_edge(annot1, sparseprob)
graph.add_edge(annot2, sparseprob)
graph.add_edge(annot2, cacheprob)
matchgraph = ut.nx_makenode(graph,
'Graph of\npotential matches',
color=lighten_hex(s2_shade2, .1))
cutalgo = ut.nx_makenode(graph,
'Graph cut algorithm',
color=lighten_hex(s2_shade2, .2),
shape='ellipse')
cc_names = ut.nx_makenode(
graph,
'Identifications,\n splits, and merges are\nconnected compoments',
color=lighten_hex(s2_shade2, .3))
graph.add_edge(occurprob, matchgraph)
graph.add_edge(sparseprob, matchgraph)
graph.add_edge(cacheprob, matchgraph)
graph.add_edge(matchgraph, cutalgo)
graph.add_edge(cutalgo, cc_names)
ut.nx_set_default_node_attributes(graph, 'shape', 'rect')
ut.nx_set_default_node_attributes(graph, 'style', 'filled,rounded')
ut.nx_set_default_node_attributes(graph, 'fixedsize', 'true')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
ut.nx_set_default_node_attributes(graph, 'regular', False)
layoutkw = {
'prog': 'dot',
'rankdir': 'LR',
'splines': 'line',
'sep': 100 / 72,
'nodesep': 300 / 72,
'ranksep': 300 / 72,
}
fontkw = dict(fontname='Ubuntu', fontweight='light', fontsize=14)
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw, **fontkw)
pt.zoom_factory()
def double_depcache_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw double_depcache_graph --show --testmode
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=.5
python -m ibeis.scripts.specialdraw double_depcache_graph --save=figures5/doubledepc.png --dpath ~/latex/cand/ --diskshow --figsize=8,20 --dpi=220 --testmode --show --clipwhite --arrow-width=5
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = double_depcache_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import networkx as nx
import plottool as pt
pt.ensure_pylab_qt4()
# pt.plt.xkcd()
ibs = ibeis.opendb('testdb1')
reduced = True
implicit = True
annot_graph = ibs.depc_annot.make_graph(reduced=reduced, implicit=implicit)
image_graph = ibs.depc_image.make_graph(reduced=reduced, implicit=implicit)
to_rename = ut.isect(image_graph.nodes(), annot_graph.nodes())
nx.relabel_nodes(annot_graph, {x: 'annot_' + x for x in to_rename}, copy=False)
nx.relabel_nodes(image_graph, {x: 'image_' + x for x in to_rename}, copy=False)
graph = nx.compose_all([image_graph, annot_graph])
#graph = nx.union_all([image_graph, annot_graph], rename=('image', 'annot'))
# userdecision = ut.nx_makenode(graph, 'user decision', shape='rect', color=pt.DARK_YELLOW, style='diagonals')
# userdecision = ut.nx_makenode(graph, 'user decision', shape='circle', color=pt.DARK_YELLOW)
userdecision = ut.nx_makenode(graph, 'User decision', shape='rect',
#width=100, height=100,
color=pt.YELLOW, style='diagonals')
#longcat = True
longcat = False
#edge = ('feat', 'neighbor_index')
#data = graph.get_edge_data(*edge)[0]
#print('data = %r' % (data,))
#graph.remove_edge(*edge)
## hack
#graph.add_edge('featweight', 'neighbor_index', **data)
graph.add_edge('detections', userdecision, constraint=longcat, color=pt.PINK)
graph.add_edge(userdecision, 'annotations', constraint=longcat, color=pt.PINK)
# graph.add_edge(userdecision, 'annotations', implicit=True, color=[0, 0, 0])
if not longcat:
pass
#graph.add_edge('images', 'annotations', style='invis')
#graph.add_edge('thumbnails', 'annotations', style='invis')
#graph.add_edge('thumbnails', userdecision, style='invis')
graph.remove_node('Has_Notch')
graph.remove_node('annotmask')
layoutkw = {
'ranksep': 5,
'nodesep': 5,
'dpi': 96,
# 'nodesep': 1,
}
ns = 1000
ut.nx_set_default_node_attributes(graph, 'fontsize', 72)
ut.nx_set_default_node_attributes(graph, 'fontname', 'Ubuntu')
ut.nx_set_default_node_attributes(graph, 'style', 'filled')
ut.nx_set_default_node_attributes(graph, 'width', ns * ut.PHI)
ut.nx_set_default_node_attributes(graph, 'height', ns * (1 / ut.PHI))
#for u, v, d in graph.edge(data=True):
for u, vkd in graph.edge.items():
for v, dk in vkd.items():
for k, d in dk.items():
localid = d.get('local_input_id')
if localid:
# d['headlabel'] = localid
if localid not in ['1']:
d['taillabel'] = localid
#d['label'] = localid
if d.get('taillabel') in {'1'}:
del d['taillabel']
node_alias = {
'chips': 'Chip',
'images': 'Image',
'feat': 'Feat',
'featweight': 'Feat Weights',
'thumbnails': 'Thumbnail',
'detections': 'Detections',
'annotations': 'Annotation',
'Notch_Tips': 'Notch Tips',
'probchip': 'Prob Chip',
'Cropped_Chips': 'Croped Chip',
'Trailing_Edge': 'Trailing\nEdge',
'Block_Curvature': 'Block\nCurvature',
# 'BC_DTW': 'block curvature /\n dynamic time warp',
'BC_DTW': 'DTW Distance',
'vsone': 'Hots vsone',
'feat_neighbs': 'Nearest\nNeighbors',
'neighbor_index': 'Neighbor\nIndex',
'vsmany': 'Hots vsmany',
'annot_labeler': 'Annot Labeler',
'labeler': 'Labeler',
'localizations': 'Localizations',
'classifier': 'Classifier',
'sver': 'Spatial\nVerification',
'Classifier': 'Existence',
'image_labeler': 'Image Labeler',
}
node_alias = {
'Classifier': 'existence',
'feat_neighbs': 'neighbors',
'sver': 'spatial_verification',
'Cropped_Chips': 'cropped_chip',
'BC_DTW': 'dtw_distance',
'Block_Curvature': 'curvature',
'Trailing_Edge': 'trailing_edge',
'Notch_Tips': 'notch_tips',
'thumbnails': 'thumbnail',
'images': 'image',
'annotations': 'annotation',
'chips': 'chip',
#userdecision: 'User de'
}
node_alias = ut.delete_dict_keys(node_alias, ut.setdiff(node_alias.keys(),
graph.nodes()))
nx.relabel_nodes(graph, node_alias, copy=False)
fontkw = dict(fontname='Ubuntu', fontweight='normal', fontsize=12)
#pt.gca().set_aspect('equal')
#pt.figure()
pt.show_nx(graph, layoutkw=layoutkw, fontkw=fontkw)
pt.zoom_factory()
def merge_viewpoint_graph():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw merge_viewpoint_graph --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = merge_viewpoint_graph()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import plottool as pt
import ibeis
import networkx as nx
defaultdb = 'PZ_Master1'
ibs = ibeis.opendb(defaultdb=defaultdb)
#nids = None
aids = ibs.get_name_aids(4875)
ibs.print_annot_stats(aids)
left_aids = ibs.filter_annots_general(aids, view='left')[0:3]
right_aids = ibs.filter_annots_general(aids, view='right')
right_aids = list(set(right_aids) - {14517})[0:3]
back = ibs.filter_annots_general(aids, view='back')[0:4]
backleft = ibs.filter_annots_general(aids, view='backleft')[0:4]
backright = ibs.filter_annots_general(aids, view='backright')[0:4]
right_graph = nx.DiGraph(ut.upper_diag_self_prodx(right_aids))
left_graph = nx.DiGraph(ut.upper_diag_self_prodx(left_aids))
back_edges = [
tuple([back[0], backright[0]][::1]),
tuple([back[0], backleft[0]][::1]),
]
back_graph = nx.DiGraph(back_edges)
# Let the graph be a bit smaller
right_graph.edge[right_aids[1]][
right_aids[2]]['constraint'] = ut.get_argflag('--constraint')
left_graph.edge[left_aids[1]][left_aids[2]]['constraint'] = ut.get_argflag(
'--constraint')
#right_graph = right_graph.to_undirected().to_directed()
#left_graph = left_graph.to_undirected().to_directed()
nx.set_node_attributes(right_graph, 'groupid', 'right')
nx.set_node_attributes(left_graph, 'groupid', 'left')
#nx.set_node_attributes(right_graph, 'scale', .2)
#nx.set_node_attributes(left_graph, 'scale', .2)
#back_graph.node[back[0]]['scale'] = 2.3
nx.set_node_attributes(back_graph, 'groupid', 'back')
view_graph = nx.compose_all([left_graph, back_graph, right_graph])
view_graph.add_edges_from([
[backright[0], right_aids[0]][::-1],
[backleft[0], left_aids[0]][::-1],
])
pt.ensure_pylab_qt4()
graph = graph = view_graph # NOQA
#graph = graph.to_undirected()
nx.set_edge_attributes(graph, 'color', pt.DARK_ORANGE[0:3])
#nx.set_edge_attributes(graph, 'color', pt.BLACK)
nx.set_edge_attributes(graph, 'color',
{edge: pt.LIGHT_BLUE[0:3]
for edge in back_edges})
#pt.close_all_figures();
from ibeis.viz import viz_graph
layoutkw = {
'nodesep': 1,
}
viz_graph.viz_netx_chipgraph(ibs,
graph,
with_images=1,
prog='dot',
augment_graph=False,
layoutkw=layoutkw)
if False:
"""
#view_graph = left_graph
pt.close_all_figures(); viz_netx_chipgraph(ibs, view_graph, with_images=0, prog='neato')
#viz_netx_chipgraph(ibs, view_graph, layout='pydot', with_images=False)
#back_graph = make_name_graph_interaction(ibs, aids=back, with_all=False)
aids = left_aids + back + backleft + backright + right_aids
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aid_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
ut.copy_files_to(, )
aids = ibs.filterannots_by_tags(ibs.get_valid_aids(),
dict(has_any_annotmatch='splitcase'))
aid1 = ibs.group_annots_by_name_dict(aids)[252]
aid2 = ibs.group_annots_by_name_dict(aids)[6791]
aids1 = ibs.get_annot_groundtruth(aid1)[0][0:4]
aids2 = ibs.get_annot_groundtruth(aid2)[0]
make_name_graph_interaction(ibs, aids=aids1 + aids2, with_all=False)
ut.ensuredir(ut.truthpath('~/slides/split/))
for aid, chip in zip(aids, ibs.get_annot_chips(aids)):
fpath = ut.truepath('~/slides/merge/aidA_%d.jpg' % (aid,))
vt.imwrite(fpath, vt.resize_to_maxdims(chip, (400, 400)))
"""
pass
def intraoccurrence_connected():
r"""
CommandLine:
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --postcut
python -m ibeis.scripts.specialdraw intraoccurrence_connected --show --smaller
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = intraoccurrence_connected()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='PZ_Master1')
nid2_aid = {
#4880: [3690, 3696, 3703, 3706, 3712, 3721],
4880: [3690, 3696, 3703],
6537: [3739],
6653: [7671],
6610: [7566, 7408],
#6612: [7664, 7462, 7522],
#6624: [7465, 7360],
#6625: [7746, 7383, 7390, 7477, 7376, 7579],
6630: [7586, 7377, 7464, 7478],
#6677: [7500]
}
nid2_dbaids = {
4880: [33, 6120, 7164],
6537: [7017, 7206],
6653: [7660]
}
if ut.get_argflag('--small') or ut.get_argflag('--smaller'):
del nid2_aid[6630]
del nid2_aid[6537]
del nid2_dbaids[6537]
if ut.get_argflag('--smaller'):
nid2_dbaids[4880].remove(33)
nid2_aid[4880].remove(3690)
nid2_aid[6610].remove(7408)
#del nid2_aid[4880]
#del nid2_dbaids[4880]
aids = ut.flatten(nid2_aid.values())
temp_nids = [1] * len(aids)
postcut = ut.get_argflag('--postcut')
aids_list = ibs.group_annots_by_name(aids)[0]
ensure_edges = 'all' if True or not postcut else None
unlabeled_graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, aids_list,
#invis_edges=invis_edges,
ensure_edges=ensure_edges, temp_nids=temp_nids)
viz_graph.color_by_nids(unlabeled_graph, unique_nids=[1] *
len(list(unlabeled_graph.nodes())))
viz_graph.ensure_node_images(ibs, unlabeled_graph)
nx.set_node_attributes(unlabeled_graph, 'shape', 'rect')
#unlabeled_graph = unlabeled_graph.to_undirected()
# Find the "database exemplars for these annots"
if False:
gt_aids = ibs.get_annot_groundtruth(aids)
gt_aids = [ut.setdiff(s, aids) for s in gt_aids]
dbaids = ut.unique(ut.flatten(gt_aids))
dbaids = ibs.filter_annots_general(dbaids, minqual='good')
ibs.get_annot_quality_texts(dbaids)
else:
dbaids = ut.flatten(nid2_dbaids.values())
exemplars = nx.DiGraph()
#graph = exemplars # NOQA
exemplars.add_nodes_from(dbaids)
def add_clique(graph, nodes, edgeattrs={}, nodeattrs={}):
edge_list = ut.upper_diag_self_prodx(nodes)
graph.add_edges_from(edge_list, **edgeattrs)
return edge_list
for aids_, nid in zip(*ibs.group_annots_by_name(dbaids)):
add_clique(exemplars, aids_)
viz_graph.ensure_node_images(ibs, exemplars)
viz_graph.color_by_nids(exemplars, ibs=ibs)
nx.set_node_attributes(unlabeled_graph, 'framewidth', False)
nx.set_node_attributes(exemplars, 'framewidth', 4.0)
nx.set_node_attributes(unlabeled_graph, 'group', 'unlab')
nx.set_node_attributes(exemplars, 'group', 'exemp')
#big_graph = nx.compose_all([unlabeled_graph])
big_graph = nx.compose_all([exemplars, unlabeled_graph])
# add sparse connections from unlabeled to exemplars
import numpy as np
rng = np.random.RandomState(0)
if True or not postcut:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(list(exemplars.nodes())))
flags = np.logical_or(exnids == nid_, flags)
exmatches = ut.compress(list(exemplars.nodes()), flags)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)),
color=pt.ORANGE, implicit=True)
else:
for aid_ in unlabeled_graph.nodes():
flags = rng.rand(len(exemplars)) > .5
exmatches = ut.compress(list(exemplars.nodes()), flags)
nid_ = ibs.get_annot_nids(aid_)
exnids = np.array(ibs.get_annot_nids(exmatches))
exmatches = ut.compress(exmatches, exnids == nid_)
big_graph.add_edges_from(list(ut.product([aid_], exmatches)))
pass
nx.set_node_attributes(big_graph, 'shape', 'rect')
#if False and postcut:
# ut.nx_delete_node_attr(big_graph, 'nid')
# ut.nx_delete_edge_attr(big_graph, 'color')
# viz_graph.ensure_graph_nid_labels(big_graph, ibs=ibs)
# viz_graph.color_by_nids(big_graph, ibs=ibs)
# big_graph = big_graph.to_undirected()
layoutkw = {
'sep' : 1 / 5,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
as_directed = False
#as_directed = True
#hacknode = True
hacknode = 0
graph = big_graph
ut.nx_ensure_agraph_color(graph)
if hacknode:
nx.set_edge_attributes(graph, 'taillabel', {e: str(e[0]) for e in graph.edges()})
nx.set_edge_attributes(graph, 'headlabel', {e: str(e[1]) for e in graph.edges()})
explicit_graph = pt.get_explicit_graph(graph)
_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
inplace=True, **layoutkw)
if ut.get_argflag('--smaller'):
graph.node[7660]['pos'] = np.array([550, 350])
graph.node[6120]['pos'] = np.array([200, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([200, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph,
inplace=True, **layoutkw)
elif ut.get_argflag('--small'):
graph.node[7660]['pos'] = np.array([750, 350])
graph.node[33]['pos'] = np.array([300, 600]) + np.array([350, -400])
graph.node[6120]['pos'] = np.array([500, 600]) + np.array([350, -400])
graph.node[7164]['pos'] = np.array([410, 480]) + np.array([350, -400])
nx.set_node_attributes(graph, 'pin', 'true')
_, layout_info = pt.nx_agraph_layout(graph,
inplace=True, **layoutkw)
if not postcut:
#pt.show_nx(graph.to_undirected(), layout='agraph', layoutkw=layoutkw,
# as_directed=False)
#pt.show_nx(graph, layout='agraph', layoutkw=layoutkw,
# as_directed=as_directed, hacknode=hacknode)
pt.show_nx(graph, layout='custom', layoutkw=layoutkw,
as_directed=as_directed, hacknode=hacknode)
else:
#explicit_graph = pt.get_explicit_graph(graph)
#_, layout_info = pt.nx_agraph_layout(explicit_graph, orig_graph=graph,
# **layoutkw)
#layout_info['edge']['alpha'] = .8
#pt.apply_graph_layout_attrs(graph, layout_info)
#graph_layout_attrs = layout_info['graph']
##edge_layout_attrs = layout_info['edge']
##node_layout_attrs = layout_info['node']
#for key, vals in layout_info['node'].items():
# #print('[special] key = %r' % (key,))
# nx.set_node_attributes(graph, key, vals)
#for key, vals in layout_info['edge'].items():
# #print('[special] key = %r' % (key,))
# nx.set_edge_attributes(graph, key, vals)
#nx.set_edge_attributes(graph, 'alpha', .8)
#graph.graph['splines'] = graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'polyline' # graph_layout_attrs.get('splines', 'line')
#graph.graph['splines'] = 'line'
cut_graph = graph.copy()
edge_list = list(cut_graph.edges())
edge_nids = np.array(ibs.unflat_map(ibs.get_annot_nids, edge_list))
cut_flags = edge_nids.T[0] != edge_nids.T[1]
cut_edges = ut.compress(edge_list, cut_flags)
cut_graph.remove_edges_from(cut_edges)
ut.nx_delete_node_attr(cut_graph, 'nid')
viz_graph.ensure_graph_nid_labels(cut_graph, ibs=ibs)
#ut.nx_get_default_node_attributes(exemplars, 'color', None)
ut.nx_delete_node_attr(cut_graph, 'color', nodes=unlabeled_graph.nodes())
aid2_color = ut.nx_get_default_node_attributes(cut_graph, 'color', None)
nid2_colors = ut.group_items(aid2_color.values(), ibs.get_annot_nids(aid2_color.keys()))
nid2_colors = ut.map_dict_vals(ut.filter_Nones, nid2_colors)
nid2_colors = ut.map_dict_vals(ut.unique, nid2_colors)
#for val in nid2_colors.values():
# assert len(val) <= 1
# Get initial colors
nid2_color_ = {nid: colors_[0] for nid, colors_ in nid2_colors.items()
if len(colors_) == 1}
graph = cut_graph
viz_graph.color_by_nids(cut_graph, ibs=ibs, nid2_color_=nid2_color_)
nx.set_node_attributes(cut_graph, 'framewidth', 4)
pt.show_nx(cut_graph, layout='custom', layoutkw=layoutkw,
as_directed=as_directed, hacknode=hacknode)
pt.zoom_factory()
def setcover_example():
"""
CommandLine:
python -m ibeis.scripts.specialdraw setcover_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.scripts.specialdraw import * # NOQA
>>> result = setcover_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import ibeis
import plottool as pt
from ibeis.viz import viz_graph
import networkx as nx
pt.ensure_pylab_qt4()
ibs = ibeis.opendb(defaultdb='testdb2')
if False:
# Select a good set
aids = ibs.get_name_aids(ibs.get_valid_nids())
# ibeis.testdata_aids('testdb2', a='default:mingt=2')
aids = [a for a in aids if len(a) > 1]
for a in aids:
print(ut.repr3(ibs.get_annot_stats_dict(a)))
print(aids[-2])
#aids = [78, 79, 80, 81, 88, 91]
aids = [78, 79, 81, 88, 91]
qreq_ = ibs.depc.new_request('vsone', aids, aids, cfgdict={})
cm_list = qreq_.execute()
from ibeis.algo.hots import graph_iden
infr = graph_iden.AnnotInference(cm_list)
unique_aids, prob_annots = infr.make_prob_annots()
import numpy as np
print(
ut.hz_str(
'prob_annots = ',
ut.array2string2(prob_annots,
precision=2,
max_line_width=140,
suppress_small=True)))
# ut.setcover_greedy(candidate_sets_dict)
max_weight = 3
prob_annots[np.diag_indices(len(prob_annots))] = np.inf
prob_annots = prob_annots
thresh_points = np.sort(prob_annots[np.isfinite(prob_annots)])
# probably not the best way to go about searching for these thresholds
# but when you have a hammer...
if False:
quant = sorted(np.diff(thresh_points))[(len(thresh_points) - 1) // 2]
candset = {
point: thresh_points[np.abs(thresh_points - point) < quant]
for point in thresh_points
}
check_thresholds = len(aids) * 2
thresh_points2 = np.array(
ut.setcover_greedy(candset, max_weight=check_thresholds).keys())
thresh_points = thresh_points2
# pt.plot(sorted(thresh_points), 'rx')
# pt.plot(sorted(thresh_points2), 'o')
# prob_annots = prob_annots.T
# thresh_start = np.mean(thresh_points)
current_idxs = []
current_covers = []
current_val = np.inf
for thresh in thresh_points:
covering_sets = [np.where(row >= thresh)[0] for row in (prob_annots)]
candidate_sets_dict = {
ax: others
for ax, others in enumerate(covering_sets)
}
soln_cover = ut.setcover_ilp(candidate_sets_dict,
max_weight=max_weight)
exemplar_idxs = list(soln_cover.keys())
soln_weight = len(exemplar_idxs)
val = max_weight - soln_weight
# print('val = %r' % (val,))
# print('soln_weight = %r' % (soln_weight,))
if val < current_val:
current_val = val
current_covers = covering_sets
current_idxs = exemplar_idxs
exemplars = ut.take(aids, current_idxs)
ensure_edges = [(aids[ax], aids[ax2])
for ax, other_xs in enumerate(current_covers)
for ax2 in other_xs]
graph = viz_graph.make_netx_graph_from_aid_groups(
ibs, [aids],
allow_directed=True,
ensure_edges=ensure_edges,
temp_nids=[1] * len(aids))
viz_graph.ensure_node_images(ibs, graph)
nx.set_node_attributes(graph, 'framewidth', False)
nx.set_node_attributes(graph, 'framewidth',
{aid: 4.0
for aid in exemplars})
nx.set_edge_attributes(graph, 'color', pt.ORANGE)
nx.set_node_attributes(graph, 'color', pt.LIGHT_BLUE)
nx.set_node_attributes(graph, 'shape', 'rect')
layoutkw = {
'sep': 1 / 10,
'prog': 'neato',
'overlap': 'false',
#'splines': 'ortho',
'splines': 'spline',
}
pt.show_nx(graph, layout='agraph', layoutkw=layoutkw)
pt.zoom_factory()
def show_top_featmatches(qreq_, cm_list):
"""
Args:
qreq_ (ibeis.QueryRequest): query request object with hyper-parameters
cm_list (list):
SeeAlso:
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True,lnbnn_normalizer=normlnbnn-test -a default --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_Master1 -t best:lnbnn_on=True -a timectrl --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:lnbnn_on=True -a default:size=30 --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:K=1,Knorm=5,lnbnn_on=True -a default:size=30 --sephack
python -m ibeis --tf TestResult.draw_feat_scoresep --show --db PZ_MTEST -t best:K=1,Knorm=3,lnbnn_on=True -a default --sephack
CommandLine:
python -m ibeis.viz.viz_nearest_descriptors --exec-show_top_featmatches --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_nearest_descriptors import * # NOQA
>>> import ibeis
>>> cm_list, qreq_ = ibeis.testdata_cmlist(defaultdb='PZ_MTEST',
>>> a=['default:has_none=mother,size=30'])
>>> show_top_featmatches(qreq_, cm_list)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
# for cm in cm_list:
# cm.score_csum(qreq_)
import numpy as np
import vtool as vt
from functools import partial
# Stack chipmatches
ibs = qreq_.ibs
infos = [cm.get_flat_fm_info() for cm in cm_list]
flat_metadata = dict([(k, np.concatenate(v))
for k, v in ut.dict_stack2(infos).items()])
fsv_flat = flat_metadata['fsv']
flat_metadata['fs'] = fsv_flat.prod(axis=1)
aids1 = flat_metadata['aid1'][:, None]
aids2 = flat_metadata['aid2'][:, None]
flat_metadata['aid_pairs'] = np.concatenate([aids1, aids2], axis=1)
# Take sample of metadata
sortx = flat_metadata['fs'].argsort()[::-1]
num = len(cm_list) * 3
# num = 10
taker = partial(np.take, indices=sortx[:num], axis=0)
flat_metadata_top = ut.map_dict_vals(taker, flat_metadata)
aid1s, aid2s, fms = ut.dict_take(flat_metadata_top, ['aid1', 'aid2', 'fm'])
annots = {}
aids = np.unique(np.hstack((aid1s, aid2s)))
annots = {
aid: ibs.get_annot_lazy_dict(aid, config2_=qreq_.qparams)
for aid in aids
}
label_lists = ibs.get_aidpair_truths(aid1s, aid2s) == ibs.const.TRUTH_MATCH
patch_size = 64
def extract_patches(annots, aid, fxs):
""" custom_func(lazydict, key, subkeys) for multigroup_lookup """
annot = annots[aid]
kpts = annot['kpts']
rchip = annot['rchip']
kpts_m = kpts.take(fxs, axis=0)
warped_patches, warped_subkpts = vt.get_warped_patches(
rchip, kpts_m, patch_size=patch_size)
return warped_patches
data_lists = vt.multigroup_lookup(annots, [aid1s, aid2s], fms.T,
extract_patches)
import plottool as pt # NOQA
pt.ensure_pylab_qt4()
import ibeis_cnn
inter = ibeis_cnn.draw_results.interact_patches(label_lists,
data_lists,
flat_metadata_top,
chunck_sizes=(2, 4),
ibs=ibs,
hack_one_per_aid=False,
sortby='fs',
qreq_=qreq_)
inter.show()
def chip_tester():
import plottool as pt
pt.ensure_pylab_qt4()
from ibeis.core_annots import * # NOQA
import ibeis
defaultdb = 'GZ_ALL'
ibs = ibeis.opendb(defaultdb=defaultdb)
aid_list = ibs.get_valid_aids()
depc = ibs.depc_annot
chips_orig = depc.get_property('chips', aid_list, 'img', config={})
chips_aeq = depc.get_property('chips', aid_list, 'img', config={'adapteq': True})
chips_heq = depc.get_property('chips', aid_list, 'img', config={'histeq': True})
import pyhesaff
nkpts_list = np.array(list(ut.generate(pyhesaff.detect_num_kpts_in_image, chips_orig, force_serial=ibs.force_serial)))
nkpts_list = np.array(nkpts_list)
nfeats_orig = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats'))
nfeats_hteq = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats', config={'histeq': True}))
nfeats_ateq = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats', config={'adapteq': True}))
sortx = np.array(nfeats_orig).argsort()
sortx = np.array(nfeats_hteq).argsort()
sortx = np.array(nfeats_ateq).argsort()
aids = ut.take(aid_list, sortx)
chips = chips_orig
chips_bad = ut.take(chips, sortx)
chips_good = ut.take(chips, sortx[::-1])
import ibeis.viz.interact.interact_chip
ibeis.viz.interact.interact_chip.interact_multichips(ibs, aids)
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_bad, nPerPage=15)
iteract_obj.start()
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_good, nPerPage=15)
iteract_obj.start()
x = sklearn.cluster.KMeans(2)
x.fit(np.nan_to_num(measures))
import vtool.quality_classifier
from vtool.quality_classifier import contrast_measures
chips128 = depc.get_property('chips', aid_list, 'img', config={'dim_size': 256})
gray_chips = [vt.convert_colorspace(x, 'GRAY') for x in ut.ProgIter(chips128)]
measures = list(ut.generate(contrast_measures, gray_chips, force_serial=ibs.force_serial))
measures = np.array(measures)
measures = np.nan_to_num(measures)
y = measures.T[3]
sortx = y.argsort()
ys = y.take(sortx)
pca = sklearn.decomposition.PCA(1)
pca.fit(measures)
pca_measure = pca.transform(measures)
nfeats_white = (nfeats_orig - nfeats_orig.mean()) / nfeats_orig.std()
pca_white = (pca_measure - pca_measure.mean()) / pca_measure.std()
sortx = nfeats_white.argsort()
pt.plt.plot(pca_white[sortx], 'x')
pt.plt.plot(nfeats_white[sortx], '.')
pyhesaff.detect_feats_in_image
svc = sklearn.svm.LinearSVC()
svc.fit(measures, nfeats_orig > 500)
svc.predict(measures) == (nfeats_orig > 500)
svr = sklearn.svm.LinearSVR()
svr.fit(measures, nfeats_orig)
svr.predict(measures)
depc['feat'].get_config_history(z1)
depc['feat'].get_config_history(z2)
pt.plt.plot(nfeats_hteq[sortx], 'x')
pt.plt.plot(nfeats_orig[sortx], '.')
pt.plt.plot(nfeats_ateq[sortx], 'o')
z1 = ibs.depc_annot.get_rowids('feat', aid_list, config={'histeq': True})
z2 = ibs.depc_annot.get_rowids('feat', aid_list)
assert len(set(z1).intersection(z2)) == 0
def chip_tester():
import plottool as pt
pt.ensure_pylab_qt4()
from ibeis.core_annots import * # NOQA
import ibeis
defaultdb = 'GZ_ALL'
ibs = ibeis.opendb(defaultdb=defaultdb)
aid_list = ibs.get_valid_aids()
depc = ibs.depc_annot
chips_orig = depc.get_property('chips', aid_list, 'img', config={})
chips_aeq = depc.get_property('chips',
aid_list,
'img',
config={'adapteq': True})
chips_heq = depc.get_property('chips',
aid_list,
'img',
config={'histeq': True})
import pyhesaff
nkpts_list = np.array(
list(
ut.generate(pyhesaff.detect_num_kpts_in_image,
chips_orig,
force_serial=ibs.force_serial)))
nkpts_list = np.array(nkpts_list)
nfeats_orig = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats'))
nfeats_hteq = np.array(
ibs.depc_annot.get('feat',
aid_list,
'num_feats',
config={'histeq': True}))
nfeats_ateq = np.array(
ibs.depc_annot.get('feat',
aid_list,
'num_feats',
config={'adapteq': True}))
sortx = np.array(nfeats_orig).argsort()
sortx = np.array(nfeats_hteq).argsort()
sortx = np.array(nfeats_ateq).argsort()
aids = ut.take(aid_list, sortx)
chips = chips_orig
chips_bad = ut.take(chips, sortx)
chips_good = ut.take(chips, sortx[::-1])
import ibeis.viz.interact.interact_chip
ibeis.viz.interact.interact_chip.interact_multichips(ibs, aids)
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_bad,
nPerPage=15)
iteract_obj.start()
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_good,
nPerPage=15)
iteract_obj.start()
x = sklearn.cluster.KMeans(2)
x.fit(np.nan_to_num(measures))
import vtool.quality_classifier
from vtool.quality_classifier import contrast_measures
chips128 = depc.get_property('chips',
aid_list,
'img',
config={'dim_size': 256})
gray_chips = [
vt.convert_colorspace(x, 'GRAY') for x in ut.ProgIter(chips128)
]
measures = list(
ut.generate(contrast_measures,
gray_chips,
force_serial=ibs.force_serial))
measures = np.array(measures)
measures = np.nan_to_num(measures)
y = measures.T[3]
sortx = y.argsort()
ys = y.take(sortx)
pca = sklearn.decomposition.PCA(1)
pca.fit(measures)
pca_measure = pca.transform(measures)
nfeats_white = (nfeats_orig - nfeats_orig.mean()) / nfeats_orig.std()
pca_white = (pca_measure - pca_measure.mean()) / pca_measure.std()
sortx = nfeats_white.argsort()
pt.plt.plot(pca_white[sortx], 'x')
pt.plt.plot(nfeats_white[sortx], '.')
pyhesaff.detect_feats_in_image
svc = sklearn.svm.LinearSVC()
svc.fit(measures, nfeats_orig > 500)
svc.predict(measures) == (nfeats_orig > 500)
svr = sklearn.svm.LinearSVR()
svr.fit(measures, nfeats_orig)
svr.predict(measures)
depc['feat'].get_config_history(z1)
depc['feat'].get_config_history(z2)
pt.plt.plot(nfeats_hteq[sortx], 'x')
pt.plt.plot(nfeats_orig[sortx], '.')
pt.plt.plot(nfeats_ateq[sortx], 'o')
z1 = ibs.depc_annot.get_rowids('feat', aid_list, config={'histeq': True})
z2 = ibs.depc_annot.get_rowids('feat', aid_list)
assert len(set(z1).intersection(z2)) == 0
def dummy_cut_example():
r"""
CommandLine:
python -m ibeis.workflow --exec-dummy_cut_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.workflow import * # NOQA
>>> result = dummy_cut_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import opengm
import numpy as np
import plottool as pt
pt.ensure_pylab_qt4()
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4],
[0.0, 0.5, 0.2, 0.9],
[0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 4
num_annots = 4
#cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2) for a1, a2 in ut.iprod(
aids, aids) if a1 != a2], dtype=np.uint32)
aid_pairs.sort(axis=1)
# add a potts function
# penalizes neighbors for having different labels
# beta = 0 # 0.1 # strength of potts regularizer
#beta = 0.1 # 0.1 # strength of potts regularizer
# Places to look for the definition of this stupid class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
#shape = [number_of_labels] * 2
#regularizer = opengm.PottsGFunction(shape, 0.0, beta)
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
#regularizer = opengm.PottsGFunction(shape)
#reg_fid = gm.addFunction(regularizer)
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# regularizer1 = opengm.pottsFunction([number_of_labels] * 2, valueEqual=0.0, valueNotEqual=beta)
# gm.addFactors(reg_fid, aid_pairs)
# 2nd order function
pair_fid = gm.addFunction(cost_matrix)
gm.addFactors(pair_fid, aid_pairs)
if False:
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
else:
Inf = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
pass
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
superpixelDiameter = 15 # super-pixel size
slicWeight = 15.0 # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels) - 1
# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(
clusterProb.astype(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1, ))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
regularizer = opengm.pottsFunction([nCluster] * 2, 0.0, beta)
fid = gm.addFunction(regularizer)
# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
uvIds = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self, ):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
#graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
#)
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {
True: {
'mu': 1.0,
'sigma': 2.2
},
False: {
'mu': 7.0,
'sigma': .9
}
}
if False:
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array(
[hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs])
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots)
graph = pystruct.models.EdgeFeatureGraphCRF(
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'), )
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg()
def dummy_cut_example():
r"""
CommandLine:
python -m ibeis.workflow --exec-dummy_cut_example --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.workflow import * # NOQA
>>> result = dummy_cut_example()
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
import opengm
import numpy as np
import plottool as pt
pt.ensure_pylab_qt4()
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4],
[0.0, 0.5, 0.2, 0.9],
[0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 4
num_annots = 4
#cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2)
for a1, a2 in ut.iprod(aids, aids) if a1 != a2],
dtype=np.uint32)
aid_pairs.sort(axis=1)
# add a potts function
# penalizes neighbors for having different labels
# beta = 0 # 0.1 # strength of potts regularizer
#beta = 0.1 # 0.1 # strength of potts regularizer
# Places to look for the definition of this stupid class
# ~/code/opengm/src/interfaces/python/opengm/opengmcore/pyFunctionTypes.cxx
# /src/interfaces/python/opengm/opengmcore/function_injector.py
#shape = [number_of_labels] * 2
#regularizer = opengm.PottsGFunction(shape, 0.0, beta)
# __init__( (object)arg1, (object)shape [, (object)values=()]) -> object :
# values = np.arange(1, ut.num_partitions(num_annots) + 1)
#regularizer = opengm.PottsGFunction(shape)
#reg_fid = gm.addFunction(regularizer)
# A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
# http://arxiv.org/pdf/1404.0533.pdf
# regularizer1 = opengm.pottsFunction([number_of_labels] * 2, valueEqual=0.0, valueNotEqual=beta)
# gm.addFactors(reg_fid, aid_pairs)
# 2nd order function
pair_fid = gm.addFunction(cost_matrix)
gm.addFactors(pair_fid, aid_pairs)
if False:
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10,
damping=0.5,
convergenceBound=0.001)
else:
Inf = opengm.inference.Multicut
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self, ):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
print(callback.labels)
print(cost_matrix)
pt.imshow(cost_matrix, cmap='magma')
opengm.visualizeGm(gm=gm)
pass
def learn_prob_score(num_scores=5, pad=55, ret_enc=False, use_cache=None):
r"""
Args:
num_scores (int): (default = 5)
Returns:
tuple: (discr_domain, discr_p_same)
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-learn_prob_score --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> num_scores = 2
>>> (discr_domain, discr_p_same, encoder) = learn_prob_score(num_scores, ret_enc=True, use_cache=False)
>>> print('discr_p_same = %r' % (discr_p_same,))
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> encoder.visualize()
>>> ut.show_if_requested()
"""
num_annots_train = 200
num_names_train = 5
toy_data = get_toy_data_1v1(num_annots_train, num_names_train)
#pairwise_aidxs, pairwise_scores, pairwise_matches = ut.dict_take(
# toy_data, 'pairwise_aidxs, pairwise_scores, pairwise_matches'.split(', '))
diag_scores, diag_labels = ut.dict_take(
toy_data, 'diag_scores, diag_labels'.split(', '))
#is_diag = [r < c for r, c, in pairwise_aidxs]
#diag_scores = pairwise_scores.compress(is_diag)
#diag_labels = pairwise_matches.compress(is_diag)
# Learn P(S_{ij} | M_{ij})
import vtool as vt
encoder = vt.ScoreNormalizer(
reverse=True, monotonize=True,
adjust=4,
)
encoder.fit(X=diag_scores, y=diag_labels, verbose=True)
if False:
import plottool as pt
pt.ensure_pylab_qt4()
encoder.visualize()
#show_toy_distributions()
def discretize_probs(encoder):
p_tp_given_score = encoder.p_tp_given_score / encoder.p_tp_given_score.sum()
bins = len(p_tp_given_score) - (pad * 2)
stride = int(np.ceil(bins / num_scores))
idxs = np.arange(0, bins, stride) + pad
discr_p_same = p_tp_given_score.take(idxs)
discr_p_same = discr_p_same / discr_p_same.sum()
discr_domain = encoder.score_domain.take(idxs)
return discr_domain, discr_p_same
discr_domain, discr_p_same = discretize_probs(encoder)
if ret_enc:
return discr_domain, discr_p_same, encoder
return discr_domain, discr_p_same
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
#graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
#)
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {
True: {'mu': 1.0, 'sigma': 2.2},
False: {'mu': 7.0, 'sigma': .9}
}
if False:
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array([hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs])
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots)
graph = pystruct.models.EdgeFeatureGraphCRF(
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg()
def learn_prob_score(num_scores=5, pad=55, ret_enc=False, use_cache=None):
r"""
Args:
num_scores (int): (default = 5)
Returns:
tuple: (discr_domain, discr_p_same)
CommandLine:
python -m ibeis.algo.hots.demobayes --exec-learn_prob_score --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.hots.demobayes import * # NOQA
>>> num_scores = 2
>>> (discr_domain, discr_p_same, encoder) = learn_prob_score(num_scores, ret_enc=True, use_cache=False)
>>> print('discr_p_same = %r' % (discr_p_same,))
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> encoder.visualize()
>>> ut.show_if_requested()
"""
num_annots_train = 200
num_names_train = 5
toy_data = get_toy_data_1v1(num_annots_train, num_names_train)
#pairwise_aidxs, pairwise_scores, pairwise_matches = ut.dict_take(
# toy_data, 'pairwise_aidxs, pairwise_scores, pairwise_matches'.split(', '))
diag_scores, diag_labels = ut.dict_take(
toy_data, 'diag_scores, diag_labels'.split(', '))
#is_diag = [r < c for r, c, in pairwise_aidxs]
#diag_scores = pairwise_scores.compress(is_diag)
#diag_labels = pairwise_matches.compress(is_diag)
# Learn P(S_{ij} | M_{ij})
import vtool as vt
encoder = vt.ScoreNormalizer(
reverse=True,
monotonize=True,
adjust=4,
)
encoder.fit(X=diag_scores, y=diag_labels, verbose=True)
if False:
import plottool as pt
pt.ensure_pylab_qt4()
encoder.visualize()
#show_toy_distributions()
def discretize_probs(encoder):
p_tp_given_score = encoder.p_tp_given_score / encoder.p_tp_given_score.sum(
)
bins = len(p_tp_given_score) - (pad * 2)
stride = int(np.ceil(bins / num_scores))
idxs = np.arange(0, bins, stride) + pad
discr_p_same = p_tp_given_score.take(idxs)
discr_p_same = discr_p_same / discr_p_same.sum()
discr_domain = encoder.score_domain.take(idxs)
return discr_domain, discr_p_same
discr_domain, discr_p_same = discretize_probs(encoder)
if ret_enc:
return discr_domain, discr_p_same, encoder
return discr_domain, discr_p_same
def event_space():
"""
pip install matplotlib-venn
"""
from matplotlib import pyplot as plt
# import numpy as np
from matplotlib_venn import venn3, venn2, venn3_circles
plt.figure(figsize=(4, 4))
v = venn3(subsets=(1, 1, 1, 1, 1, 1, 1), set_labels=('A', 'B', 'C'))
v.get_patch_by_id('100').set_alpha(1.0)
v.get_patch_by_id('100').set_color('white')
v.get_label_by_id('100').set_text('Unknown')
v.get_label_by_id('A').set_text('Set "A"')
c = venn3_circles(subsets=(1, 1, 1, 1, 1, 1, 1), linestyle='dashed')
c[0].set_lw(1.0)
c[0].set_ls('dotted')
plt.show()
same = set(['comparable', 'incomparable', 'same'])
diff = set(['comparable', 'incomparable', 'diff'])
# comparable = set(['comparable', 'same', 'diff'])
# incomparable = set(['incomparable', 'same', 'diff'])
subsets = [same, diff] # , comparable, incomparable]
set_labels = ('same', 'diff') # , 'comparable', 'incomparable')
venn3(subsets=subsets, set_labels=set_labels)
plt.show()
import plottool as pt
pt.ensure_pylab_qt4()
from matplotlib_subsets import treesets_rectangles
tree = ((120, 'Same', None), [
((50, 'comparable', None), []), ((50, 'incomparable', None), [])
](120, 'Diff', None), [((50, 'comparable', None), []),
((50, 'incomparable', None), [])])
treesets_rectangles(tree)
plt.show()
from matplotlib import pyplot as plt
from matplotlib_venn import venn2, venn2_circles # NOQA
# Subset sizes
s = (
2, # Ab
3, # aB
1, # AB
)
v = venn2(subsets=s, set_labels=('A', 'B'))
# Subset labels
v.get_label_by_id('10').set_text('A but not B')
v.get_label_by_id('01').set_text('B but not A')
v.get_label_by_id('11').set_text('A and B')
# Subset colors
v.get_patch_by_id('10').set_color('c')
v.get_patch_by_id('01').set_color('#993333')
v.get_patch_by_id('11').set_color('blue')
# Subset alphas
v.get_patch_by_id('10').set_alpha(0.4)
v.get_patch_by_id('01').set_alpha(1.0)
v.get_patch_by_id('11').set_alpha(0.7)
# Border styles
c = venn2_circles(subsets=s, linestyle='solid')
c[0].set_ls('dashed') # Line style
c[0].set_lw(2.0) # Line width
plt.show()
