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Commit 4211d8fb authored by Roman Donchenko's avatar Roman Donchenko Committed by OpenCV Buildbot
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Merge pull request #2641 from SpecLad:merge-2.4

parents 6b986489 4db12032
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Showing with 197 additions and 73 deletions
modules/flann/include/opencv2/flann/dist.h
View file @ 4211d8fb
......@@ -777,6 +777,66 @@ struct ZeroIterator
};
/*
* Depending on processed distances, some of them are already squared (e.g. L2)
* and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
* we are working on ^2 distances, thus following templates to ensure that.
*/
template <typename Distance, typename ElementType>
struct squareDistance
{
typedef typename Distance::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist*dist; }
};
template <typename ElementType>
struct squareDistance<L2_Simple<ElementType>, ElementType>
{
typedef typename L2_Simple<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<L2<ElementType>, ElementType>
{
typedef typename L2<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
{
typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<HellingerDistance<ElementType>, ElementType>
{
typedef typename HellingerDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
{
typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename Distance>
typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
{
typedef typename Distance::ElementType ElementType;
squareDistance<Distance, ElementType> dummy;
return dummy( dist );
}
}
#endif //OPENCV_FLANN_DIST_H_
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
View file @ 4211d8fb
......@@ -209,8 +209,11 @@ private:
assert(index >=0 && index < n);
centers[0] = dsindices[index];
// Computing distance^2 will have the advantage of even higher probability further to pick new centers
// far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
......@@ -236,7 +239,10 @@ private:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
......@@ -248,7 +254,10 @@ private:
// Add the appropriate center
centers[centerCount] = dsindices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}
centers_length = centerCount;
......
modules/flann/include/opencv2/flann/kmeans_index.h
View file @ 4211d8fb
......@@ -210,6 +210,7 @@ public:
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
......@@ -235,7 +236,10 @@ public:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
......@@ -247,7 +251,10 @@ public:
// Add the appropriate center
centers[centerCount] = indices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}
centers_length = centerCount;
......
modules/ts/misc/summary.py
View file @ 4211d8fb
......@@ -40,6 +40,7 @@ if __name__ == "__main__":
parser.add_option("", "--with-cycles-reduction", action="store_true", dest="calc_cr", default=False, help="output cycle reduction percentages")
parser.add_option("", "--with-score", action="store_true", dest="calc_score", default=False, help="output automatic classification of speedups")
parser.add_option("", "--progress", action="store_true", dest="progress_mode", default=False, help="enable progress mode")
parser.add_option("", "--regressions", dest="regressions", default=None, metavar="LIST", help="comma-separated custom regressions map: \"[r][c]#current-#reference\" (indexes of columns are 0-based, \"r\" - reverse flag, \"c\" - color flag for base data)")
parser.add_option("", "--show-all", action="store_true", dest="showall", default=False, help="also include empty and \"notrun\" lines")
parser.add_option("", "--match", dest="match", default=None)
parser.add_option("", "--match-replace", dest="match_replace", default="")
......@@ -56,6 +57,24 @@ if __name__ == "__main__":
if options.columns:
options.columns = [s.strip().replace("\\n", "\n") for s in options.columns.split(",")]
if options.regressions:
assert not options.progress_mode, 'unsupported mode'
def parseRegressionColumn(s):
""" Format: '[r][c]<uint>-<uint>' """
reverse = s.startswith('r')
if reverse:
s = s[1:]
addColor = s.startswith('c')
if addColor:
s = s[1:]
parts = s.split('-', 1)
link = (int(parts[0]), int(parts[1]), reverse, addColor)
assert link[0] != link[1]
return link
options.regressions = [parseRegressionColumn(s) for s in options.regressions.split(',')]
# expand wildcards and filter duplicates
files = []
seen = set()
......@@ -90,8 +109,18 @@ if __name__ == "__main__":
sys.stderr.write("Error: no test data found" + os.linesep)
quit()
# find matches
setsCount = len(test_sets)
if options.regressions is None:
reference = -1 if options.progress_mode else 0
options.regressions = [(i, reference, False, True) for i in range(1, len(test_sets))]
for link in options.regressions:
(i, ref, reverse, addColor) = link
assert i >= 0 and i < setsCount
assert ref < setsCount
# find matches
test_cases = {}
name_extractor = lambda name: str(name)
......@@ -117,29 +146,29 @@ if __name__ == "__main__":
# header
tbl.newColumn("name", "Name of Test", align = "left", cssclass = "col_name")
i = 0
for set in test_sets:
tbl.newColumn(str(i), getSetName(set, i, options.columns, False), align = "center")
i += 1
metric_sets = test_sets[1:]
for i in range(setsCount):
tbl.newColumn(str(i), getSetName(test_sets[i], i, options.columns, False), align = "center")
def addHeaderColumns(suffix, description, cssclass):
for link in options.regressions:
(i, ref, reverse, addColor) = link
if reverse:
i, ref = ref, i
current_set = test_sets[i]
current = getSetName(current_set, i, options.columns)
if ref >= 0:
reference_set = test_sets[ref]
reference = getSetName(reference_set, ref, options.columns)
else:
reference = 'previous'
tbl.newColumn(str(i) + '-' + str(ref) + suffix, '%s\nvs\n%s\n(%s)' % (current, reference, description), align='center', cssclass=cssclass)
if options.calc_cr:
i = 1
for set in metric_sets:
reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
tbl.newColumn(str(i) + "$", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(cycles reduction)", align = "center", cssclass = "col_cr")
i += 1
addHeaderColumns(suffix='$', description='cycles reduction', cssclass='col_cr')
if options.calc_relatives:
i = 1
for set in metric_sets:
reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
tbl.newColumn(str(i) + "%", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(x-factor)", align = "center", cssclass = "col_rel")
i += 1
addHeaderColumns(suffix='%', description='x-factor', cssclass='col_rel')
if options.calc_score:
i = 1
for set in metric_sets:
reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
tbl.newColumn(str(i) + "S", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(score)", align = "center", cssclass = "col_name")
i += 1
addHeaderColumns(suffix='S', description='score', cssclass='col_name')
# rows
prevGroupName = None
......@@ -166,68 +195,87 @@ if __name__ == "__main__":
if options.intersect_logs:
needNewRow = False
break
tbl.newCell(str(i), "-")
if options.calc_relatives and i > 0:
tbl.newCell(str(i) + "%", "-")
if options.calc_cr and i > 0:
tbl.newCell(str(i) + "$", "-")
if options.calc_score and i > 0:
tbl.newCell(str(i) + "$", "-")
else:
status = case.get("status")
if status != "run":
tbl.newCell(str(i), status, color = "red")
if status != "notrun":
needNewRow = True
if options.calc_relatives and i > 0:
tbl.newCell(str(i) + "%", "-", color = "red")
if options.calc_cr and i > 0:
tbl.newCell(str(i) + "$", "-", color = "red")
if options.calc_score and i > 0:
tbl.newCell(str(i) + "S", "-", color = "red")
tbl.newCell(str(i), status, color="red")
else:
val = getter(case, cases[0], options.units)
def getter_fn(fn):
if fn and i > 0 and val:
for j in reversed(range(i)) if options.progress_mode else [0]:
r = cases[j]
if r is not None and r.get("status") == 'run':
return fn(case, r, options.units)
return None
valp = getter_fn(getter_p) if options.calc_relatives or options.progress_mode else None
valcr = getter_fn(getter_cr) if options.calc_cr else None
val_score = getter_fn(getter_score) if options.calc_score else None
if not valp or i == 0:
color = None
elif valp > 1.05:
color = "green"
elif valp < 0.95:
color = "red"
else:
color = None
if val:
needNewRow = True
tbl.newCell(str(i), formatValue(val, options.metric, options.units), val, color = color)
if options.calc_relatives and i > 0:
tbl.newCell(str(i) + "%", formatValue(valp, "%"), valp, color = color, bold = color)
if options.calc_cr and i > 0:
tbl.newCell(str(i) + "$", formatValue(valcr, "$"), valcr, color = color, bold = color)
if options.calc_score and i > 0:
tbl.newCell(str(i) + "S", formatValue(val_score, "S"), val_score, color = color, bold = color)
tbl.newCell(str(i), formatValue(val, options.metric, options.units), val)
if needNewRow:
for link in options.regressions:
(i, reference, reverse, addColor) = link
if reverse:
i, reference = reference, i
tblCellID = str(i) + '-' + str(reference)
case = cases[i]
if case is None:
if options.calc_relatives:
tbl.newCell(tblCellID + "%", "-")
if options.calc_cr:
tbl.newCell(tblCellID + "$", "-")
if options.calc_score:
tbl.newCell(tblCellID + "$", "-")
else:
status = case.get("status")
if status != "run":
tbl.newCell(str(i), status, color="red")
if status != "notrun":
needNewRow = True
if options.calc_relatives:
tbl.newCell(tblCellID + "%", "-", color="red")
if options.calc_cr:
tbl.newCell(tblCellID + "$", "-", color="red")
if options.calc_score:
tbl.newCell(tblCellID + "S", "-", color="red")
else:
val = getter(case, cases[0], options.units)
def getRegression(fn):
if fn and val:
for j in reversed(range(i)) if reference < 0 else [reference]:
r = cases[j]
if r is not None and r.get("status") == 'run':
return fn(case, r, options.units)
valp = getRegression(getter_p) if options.calc_relatives or options.progress_mode else None
valcr = getRegression(getter_cr) if options.calc_cr else None
val_score = getRegression(getter_score) if options.calc_score else None
if not valp:
color = None
elif valp > 1.05:
color = 'green'
elif valp < 0.95:
color = 'red'
else:
color = None
if addColor:
if not reverse:
tbl.newCell(str(i), formatValue(val, options.metric, options.units), val, color=color)
else:
r = cases[reference]
if r is not None and r.get("status") == 'run':
val = getter(r, cases[0], options.units)
tbl.newCell(str(reference), formatValue(val, options.metric, options.units), val, color=color)
if options.calc_relatives:
tbl.newCell(tblCellID + "%", formatValue(valp, "%"), valp, color=color, bold=color)
if options.calc_cr:
tbl.newCell(tblCellID + "$", formatValue(valcr, "$"), valcr, color=color, bold=color)
if options.calc_score:
tbl.newCell(tblCellID + "S", formatValue(val_score, "S"), val_score, color = color, bold = color)
if not needNewRow:
tbl.trimLastRow()
if options.regressionsOnly:
for r in reversed(range(len(tbl.rows))):
delete = True
i = 1
for set in metric_sets:
val = tbl.rows[r].cells[len(tbl.rows[r].cells)-i].value
for i in range(1, len(options.regressions) + 1):
val = tbl.rows[r].cells[len(tbl.rows[r].cells) - i].value
if val is not None and val < float(options.regressionsOnly):
delete = False
i += 1
if (delete):
break
else:
tbl.rows.pop(r)
# output table
......
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