import numpy as np
import ubelt as ub
import networkx as nx
# from .assignment import _assign_confusion_vectors
from kwcoco.metrics.confusion_vectors import ConfusionVectors
from kwcoco.metrics.assignment import _assign_confusion_vectors
[docs]class DetectionMetrics(ub.NiceRepr):
"""
Attributes:
gid_to_true_dets (Dict): maps image ids to truth
gid_to_pred_dets (Dict): maps image ids to predictions
classes (CategoryTree): category coder
Example:
>>> dmet = DetectionMetrics.demo(
>>> nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8, score_noise=0.9, hacked=False)
>>> print(dmet.score_kwcoco(bias=0, compat='mutex', prioritize='iou')['mAP'])
...
>>> # NOTE: IN GENERAL NETHARN AND VOC ARE NOT THE SAME
>>> print(dmet.score_voc(bias=0)['mAP'])
0.8582...
>>> #print(dmet.score_coco()['mAP'])
"""
def __init__(dmet, classes=None):
dmet.classes = classes
dmet.gid_to_true_dets = {}
dmet.gid_to_pred_dets = {}
dmet._imgname_to_gid = {}
[docs] def clear(dmet):
dmet.gid_to_true_dets = {}
dmet.gid_to_pred_dets = {}
dmet._imgname_to_gid = {}
def __nice__(dmet):
info = {
'n_true_imgs': len(dmet.gid_to_true_dets),
'n_pred_imgs': len(dmet.gid_to_pred_dets),
'n_true_anns': sum(map(len, dmet.gid_to_true_dets.values())),
'n_pred_anns': sum(map(len, dmet.gid_to_pred_dets.values())),
'classes': dmet.classes,
}
return ub.repr2(info)
[docs] @classmethod
def from_coco(DetectionMetrics, true_coco, pred_coco, gids=None, verbose=0):
"""
Create detection metrics from two coco files representing the truth and
predictions.
Args:
true_coco (kwcoco.CocoDataset):
pred_coco (kwcoco.CocoDataset):
Example:
>>> import kwcoco
>>> true_coco = kwcoco.CocoDataset.demo('shapes')
>>> pred_coco = true_coco
>>> self = DetectionMetrics.from_coco(true_coco, pred_coco)
>>> self.score_voc()
"""
import kwimage
classes = true_coco.object_categories()
self = DetectionMetrics(classes)
if gids is None:
gids = sorted(set(true_coco.imgs.keys()) & set(pred_coco.imgs.keys()))
def _coco_to_dets(coco_dset, desc=''):
for gid in ub.ProgIter(gids, desc=desc, verbose=verbose):
img = coco_dset.imgs[gid]
gid = img['id']
imgname = img['file_name']
aids = coco_dset.gid_to_aids[gid]
annots = [coco_dset.anns[aid] for aid in aids]
dets = kwimage.Detections.from_coco_annots(
annots, dset=coco_dset, classes=classes)
yield dets, imgname, gid
for dets, imgname, gid in _coco_to_dets(true_coco, desc='add truth'):
self.add_truth(dets, imgname, gid=gid)
for dets, imgname, gid in _coco_to_dets(pred_coco, desc='add pred'):
self.add_predictions(dets, imgname, gid=gid)
return self
def _register_imagename(dmet, imgname, gid=None):
if gid is not None:
if imgname is None:
imgname = 'gid_{}'.format(str(gid))
dmet._imgname_to_gid[imgname] = gid
else:
if imgname is None:
raise ValueError('must specify imgname or gid')
try:
gid = dmet._imgname_to_gid[imgname]
except KeyError:
gid = len(dmet._imgname_to_gid) + 1
dmet._imgname_to_gid[imgname] = gid
return gid
[docs] def add_predictions(dmet, pred_dets, imgname=None, gid=None):
"""
Register/Add predicted detections for an image
Args:
pred_dets (Detections): predicted detections
imgname (str): a unique string to identify the image
gid (int, optional): the integer image id if known
"""
gid = dmet._register_imagename(imgname, gid)
dmet.gid_to_pred_dets[gid] = pred_dets
[docs] def add_truth(dmet, true_dets, imgname=None, gid=None):
"""
Register/Add groundtruth detections for an image
Args:
true_dets (Detections): groundtruth
imgname (str): a unique string to identify the image
gid (int, optional): the integer image id if known
"""
gid = dmet._register_imagename(imgname, gid)
dmet.gid_to_true_dets[gid] = true_dets
[docs] def true_detections(dmet, gid):
""" gets Detections representation for groundtruth in an image """
return dmet.gid_to_true_dets[gid]
[docs] def pred_detections(dmet, gid):
""" gets Detections representation for predictions in an image """
return dmet.gid_to_pred_dets[gid]
[docs] def confusion_vectors(dmet, ovthresh=0.5, bias=0, gids=None, compat='all',
prioritize='iou', ignore_classes='ignore',
background_class=ub.NoParam, verbose='auto',
workers=0, track_probs='try'):
"""
Assigns predicted boxes to the true boxes so we can transform the
detection problem into a classification problem for scoring.
Args:
ovthresh (float, default=0.5):
bounding box overlap iou threshold required for assignment
bias (float, default=0.0):
for computing bounding box overlap, either 1 or 0
gids (List[int], default=None):
which subset of images ids to compute confusion metrics on. If
not specified all images are used.
compat (str, default='all'):
can be ('ancestors' | 'mutex' | 'all'). determines which pred
boxes are allowed to match which true boxes. If 'mutex', then
pred boxes can only match true boxes of the same class. If
'ancestors', then pred boxes can match true boxes that match or
have a coarser label. If 'all', then any pred can match any
true, regardless of its category label.
prioritize (str, default='iou'):
can be ('iou' | 'class' | 'correct') determines which box to
assign to if mutiple true boxes overlap a predicted box. if
prioritize is iou, then the true box with maximum iou (above
ovthresh) will be chosen. If prioritize is class, then it will
prefer matching a compatible class above a higher iou. If
prioritize is correct, then ancestors of the true class are
preferred over descendents of the true class, over unreleated
classes.
ignore_classes (set, default={'ignore'}):
class names indicating ignore regions
background_class (str, default=ub.NoParam):
Name of the background class. If unspecified we try to
determine it with heuristics. A value of None means there is no
background class.
verbose (int, default='auto'): verbosity flag. In auto mode,
verbose=1 if len(gids) > 1000.
workers (int, default=0):
number of parallel assignment processes
track_probs (str, default='try'): can be 'try', 'force', or False.
if truthy, we assume probabilities for multiple classes are
available.
Ignore:
globals().update(xdev.get_func_kwargs(dmet.confusion_vectors))
"""
import kwarray
y_accum = ub.ddict(list)
_tracking_probs = bool(track_probs)
if _tracking_probs:
prob_accum = []
if gids is None:
gids = sorted(dmet._imgname_to_gid.values())
if verbose == 'auto':
verbose = 1 if len(gids) > 10 else 0
if background_class is ub.NoParam:
# Try to autodetermine background class name,
# otherwise fallback to None
background_class = None
if dmet.classes is not None:
lower_classes = [c.lower() for c in dmet.classes]
try:
idx = lower_classes.index('background')
background_class = dmet.classes[idx]
# TODO: if we know the background class name should we
# change bg_cidx in assignment?
except ValueError:
pass
from kwcoco.util import util_futures
workers = 0
jobs = util_futures.JobPool(mode='process', max_workers=workers)
for gid in ub.ProgIter(gids, desc='submit assign jobs',
verbose=verbose):
true_dets = dmet.true_detections(gid)
pred_dets = dmet.pred_detections(gid)
job = jobs.submit(
_assign_confusion_vectors, true_dets, pred_dets,
bg_weight=1, ovthresh=ovthresh, bg_cidx=-1, bias=bias,
classes=dmet.classes, compat=compat, prioritize=prioritize,
ignore_classes=ignore_classes)
job.gid = gid
for job in ub.ProgIter(jobs.jobs, desc='assign detections',
verbose=verbose):
y = job.result()
gid = job.gid
if _tracking_probs:
# Keep track of per-class probs
pred_dets = dmet.pred_detections(gid)
try:
pred_probs = pred_dets.probs
if pred_probs is None:
raise KeyError
except KeyError:
_tracking_probs = False
if track_probs == 'force':
raise Exception('unable to track probs')
elif track_probs == 'try':
pass
else:
raise KeyError(track_probs)
else:
pxs = np.array(y['pxs'], dtype=np.int)
# For unassigned truths, we need to create dummy probs
# where a background class has probability 1.
flags = pxs > -1
probs = np.zeros((len(pxs), pred_probs.shape[1]),
dtype=np.float32)
if background_class is not None:
bg_idx = dmet.classes.index(background_class)
probs[:, bg_idx] = 1
probs[flags] = pred_probs[pxs[flags]]
prob_accum.append(probs)
y['gid'] = [gid] * len(y['pred'])
for k, v in y.items():
y_accum[k].extend(v)
# else:
# for gid in ub.ProgIter(gids, desc='assign detections', verbose=verbose):
# true_dets = dmet.true_detections(gid)
# pred_dets = dmet.pred_detections(gid)
# y = _assign_confusion_vectors(true_dets, pred_dets, bg_weight=1,
# ovthresh=ovthresh, bg_cidx=-1,
# bias=bias, classes=dmet.classes,
# compat=compat, prioritize=prioritize,
# ignore_classes=ignore_classes)
# if _tracking_probs:
# # Keep track of per-class probs
# try:
# pred_probs = pred_dets.probs
# except KeyError:
# _tracking_probs = False
# else:
# pxs = np.array(y['pxs'], dtype=np.int)
# flags = pxs > -1
# probs = np.zeros((len(pxs), pred_probs.shape[1]),
# dtype=np.float32)
# bg_idx = dmet.classes.node_to_idx['background']
# probs[:, bg_idx] = 1
# probs[flags] = pred_probs[pxs[flags]]
# prob_accum.append(probs)
# y['gid'] = [gid] * len(y['pred'])
# for k, v in y.items():
# y_accum[k].extend(v)
_data = {}
for k, v in ub.ProgIter(list(y_accum.items()), desc='ndarray convert', verbose=verbose):
# Try to use 32 bit types for large evaluation problems
kw = dict()
if k in {'iou', 'score', 'weight'}:
kw['dtype'] = np.float32
if k in {'pxs', 'txs', 'gid', 'pred', 'true', 'pred_raw'}:
kw['dtype'] = np.int32
try:
_data[k] = np.asarray(v, **kw)
except TypeError:
_data[k] = np.asarray(v)
# Avoid pandas when possible
cfsn_data = kwarray.DataFrameArray(_data)
if 0:
import xdev
nbytes = 0
for k, v in _data.items():
nbytes += v.size * v.dtype.itemsize
print(xdev.byte_str(nbytes))
if _tracking_probs:
y_prob = np.vstack(prob_accum)
else:
y_prob = None
cfsn_vecs = ConfusionVectors(cfsn_data, classes=dmet.classes,
probs=y_prob)
return cfsn_vecs
[docs] def score_kwant(dmet, ovthresh=0.5):
"""
Scores the detections using kwant
"""
try:
from kwil.misc import kwant
if not kwant.is_available():
raise ImportError
except ImportError:
raise RuntimeError('kwant is not available')
import kw18
gids = list(dmet.gid_to_true_dets.keys())
true_kw18s = []
pred_kw18s = []
for gid in ub.ProgIter(gids, desc='convert to kw18'):
true_dets = dmet.gid_to_true_dets[gid]
pred_dets = dmet.gid_to_pred_dets[gid]
if len(true_dets) == 0:
print('foo')
if len(pred_dets) == 0:
# kwant breaks on 0 predictions, hack in a bad prediction
import kwimage
hack_ = kwimage.Detections.random(1)
hack_.scores[:] = 0
pred_dets = hack_
true_kw18 = kw18.make_kw18_from_detections(true_dets,
frame_number=gid,
timestamp=gid)
pred_kw18 = kw18.make_kw18_from_detections(pred_dets,
frame_number=gid,
timestamp=gid)
true_kw18s.append(true_kw18)
pred_kw18s.append(pred_kw18)
true_kw18 = true_kw18s
pred_kw18 = pred_kw18s
roc_info = kwant.score_events(true_kw18s, pred_kw18s,
ovthresh=ovthresh, prefiltered=True,
verbose=3)
fp = roc_info['fp'].values
tp = roc_info['tp'].values
ppv = tp / (tp + fp)
ppv[np.isnan(ppv)] = 1
tpr = roc_info['pd'].values
fpr = fp / fp[0]
import sklearn
roc_auc = sklearn.metrics.auc(fpr, tpr)
from kwcoco.metrics.functional import _average_precision
ap = _average_precision(tpr, ppv)
roc_info['fpr'] = fpr
roc_info['ppv'] = ppv
info = {
'roc_info': roc_info,
'ap': ap,
'roc_auc': roc_auc,
}
if False:
import kwil
kwil.autompl()
kwil.multi_plot(roc_info['fa'], roc_info['pd'],
xlabel='fa (fp count)',
ylabel='pd (tpr)', fnum=1,
title='kwant roc_auc={:.4f}'.format(roc_auc))
import kwil
kwil.autompl()
kwil.multi_plot(tpr, ppv,
xlabel='recall (fpr)',
ylabel='precision (tpr)',
fnum=2,
title='kwant ap={:.4f}'.format(ap))
return info
[docs] def score_kwcoco(dmet, ovthresh=0.5, bias=0, gids=None,
compat='all', prioritize='iou'):
""" our scoring method """
cfsn_vecs = dmet.confusion_vectors(ovthresh=ovthresh, bias=bias,
gids=gids,
compat=compat,
prioritize=prioritize)
info = {}
try:
cfsn_perclass = cfsn_vecs.binarize_ovr(mode=1)
perclass = cfsn_perclass.measures()
except Exception as ex:
print('warning: ex = {!r}'.format(ex))
else:
info['perclass'] = perclass['perclass']
info['mAP'] = perclass['mAP']
return info
[docs] def score_voc(dmet, ovthresh=0.5, bias=1, method='voc2012', gids=None,
ignore_classes='ignore'):
"""
score using voc method
Example:
>>> dmet = DetectionMetrics.demo(
>>> nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8,
>>> score_noise=.5)
>>> print(dmet.score_voc()['mAP'])
0.9399...
"""
# from . import voc_metrics
from kwcoco.metrics.assignment import _filter_ignore_regions
from kwcoco.metrics import voc_metrics
if gids is None:
gids = sorted(dmet._imgname_to_gid.values())
# Convert true/pred detections into VOC format
vmet = voc_metrics.VOC_Metrics(classes=dmet.classes)
for gid in gids:
true_dets = dmet.true_detections(gid)
pred_dets = dmet.pred_detections(gid)
if ignore_classes is not None:
true_ignore_flags, pred_ignore_flags = _filter_ignore_regions(
true_dets, pred_dets, ovthresh=ovthresh,
ignore_classes=ignore_classes)
true_dets = true_dets.compress(~true_ignore_flags)
pred_dets = pred_dets.compress(~pred_ignore_flags)
vmet.add_truth(true_dets, gid=gid)
vmet.add_predictions(pred_dets, gid=gid)
voc_scores = vmet.score(ovthresh, bias=bias, method=method)
return voc_scores
def _to_coco(dmet):
"""
Convert to a coco representation of truth and predictions
"""
import kwcoco
true = kwcoco.CocoDataset()
pred = kwcoco.CocoDataset()
for node in dmet.classes:
# cid = dmet.classes.graph.node[node]['id']
cid = dmet.classes.index(node)
supercategory = list(dmet.classes.graph.pred[node])
if len(supercategory) == 0:
supercategory = None
else:
assert len(supercategory) == 1
supercategory = supercategory[0]
true.add_category(node, id=cid, supercategory=supercategory)
pred.add_category(node, id=cid, supercategory=supercategory)
for imgname, gid in dmet._imgname_to_gid.items():
true.add_image(imgname, id=gid)
pred.add_image(imgname, id=gid)
idx_to_id = {
idx: dmet.classes.index(node)
for idx, node in enumerate(dmet.classes.idx_to_node)
}
for gid, pred_dets in dmet.gid_to_pred_dets.items():
pred_boxes = pred_dets.boxes
if 'scores' in pred_dets.data:
pred_scores = pred_dets.scores
else:
pred_scores = np.ones(len(pred_dets))
pred_cids = list(ub.take(idx_to_id, pred_dets.class_idxs))
pred_xywh = pred_boxes.to_xywh().data.tolist()
for bbox, cid, score in zip(pred_xywh, pred_cids, pred_scores):
pred.add_annotation(gid, cid, bbox=bbox, score=score)
for gid, true_dets in dmet.gid_to_true_dets.items():
true_boxes = true_dets.boxes
if 'weights' in true_dets.data:
true_weights = true_dets.weights
else:
true_weights = np.ones(len(true_boxes))
true_cids = list(ub.take(idx_to_id, true_dets.class_idxs))
true_xywh = true_boxes.to_xywh().data.tolist()
for bbox, cid, weight in zip(true_xywh, true_cids, true_weights):
true.add_annotation(gid, cid, bbox=bbox, weight=weight)
return pred, true
[docs] def score_coco(dmet, verbose=0):
"""
score using ms-coco method
Example:
>>> # xdoctest: +REQUIRES(--pycocotools)
>>> dmet = DetectionMetrics.demo(
>>> nimgs=100, nboxes=(0, 3), n_fp=(0, 1), nclasses=8)
>>> print(dmet.score_coco()['mAP'])
0.711016...
"""
from pycocotools import coco
from pycocotools import cocoeval
# The original pycoco-api prints to much, supress it
import kwcoco as nh
pred, true = dmet._to_coco()
quiet = verbose == 0
with nh.util.SupressPrint(coco, cocoeval, enabled=quiet):
cocoGt = true._aspycoco()
cocoDt = pred._aspycoco()
for ann in cocoGt.dataset['annotations']:
w, h = ann['bbox'][-2:]
ann['ignore'] = ann['weight'] < .5
ann['area'] = w * h
ann['iscrowd'] = False
for ann in cocoDt.dataset['annotations']:
w, h = ann['bbox'][-2:]
ann['area'] = w * h
evaler = cocoeval.COCOeval(cocoGt, cocoDt, iouType='bbox')
evaler.evaluate()
evaler.accumulate()
evaler.summarize()
coco_ap = evaler.stats[1]
coco_scores = {
'mAP': coco_ap,
'evalar_stats': evaler.stats
}
return coco_scores
[docs] @classmethod
def demo(cls, **kwargs):
"""
Creates random true boxes and predicted boxes that have some noisy
offset from the truth.
Kwargs:
nclasses (int, default=1): number of foreground classes.
nimgs (int, default=1): number of images in the coco datasts.
nboxes (int, default=1): boxes per image.
n_fp (int, default=0): number of false positives.
n_fn (int, default=0): number of false negatives.
box_noise (float, default=0): std of a normal distribution used to
perterb both box location and box size.
cls_noise (float, default=0): probability that a class label will
change. Must be within 0 and 1.
anchors (ndarray, default=None): used to create random boxes
null_pred (bool, default=0):
if True, predicted classes are returned as null, which means
only localization scoring is suitable.
with_probs (bool, default=1):
if True, includes per-class probabilities with predictions
Example:
>>> kwargs = {}
>>> # Seed the RNG
>>> kwargs['rng'] = 0
>>> # Size parameters determine how big the data is
>>> kwargs['nimgs'] = 5
>>> kwargs['nboxes'] = 7
>>> kwargs['nclasses'] = 11
>>> # Noise parameters perterb predictions further from the truth
>>> kwargs['n_fp'] = 3
>>> kwargs['box_noise'] = 0.1
>>> kwargs['cls_noise'] = 0.5
>>> dmet = DetectionMetrics.demo(**kwargs)
>>> print('dmet.classes = {}'.format(dmet.classes))
dmet.classes = <CategoryTree(nNodes=12, maxDepth=3, maxBreadth=4...)>
>>> # Can grab kwimage.Detection object for any image
>>> print(dmet.true_detections(gid=0))
<Detections(4)>
>>> print(dmet.pred_detections(gid=0))
<Detections(7)>
Example:
>>> # Test case with null predicted categories
>>> dmet = DetectionMetrics.demo(nimgs=30, null_pred=1, nclasses=3,
>>> nboxes=10, n_fp=10, box_noise=0.3,
>>> with_probs=False)
>>> dmet.gid_to_pred_dets[0].data
>>> dmet.gid_to_true_dets[0].data
>>> cfsn_vecs = dmet.confusion_vectors()
>>> binvecs_ovr = cfsn_vecs.binarize_ovr()
>>> binvecs_per = cfsn_vecs.binarize_peritem()
>>> measures_per = binvecs_per.measures()
>>> measures_ovr = binvecs_ovr.measures()
>>> print('measures_per = {!r}'.format(measures_per))
>>> print('measures_ovr = {!r}'.format(measures_ovr))
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> pr_per.draw(fnum=1)
>>> measures_ovr['perclass'].draw(key='pr', fnum=2)
"""
import kwimage
import kwarray
import kwcoco
# Parse kwargs
rng = kwarray.ensure_rng(kwargs.get('rng', 0))
# TODO: use kwcoco.demo.perterb instead of rolling the logic here
# todo: accept and coerce classes instead of nclasses
classes = kwargs.get('classes', None)
nclasses = kwargs.get('nclasses', None)
if classes is not None and nclasses is not None:
raise ValueError('cant specify both classes and nclasses')
elif classes is None and nclasses is None:
nclasses = 1
nimgs = kwargs.get('nimgs', 1)
box_noise = kwargs.get('box_noise', 0)
cls_noise = kwargs.get('cls_noise', 0)
null_pred = kwargs.get('null_pred', False)
with_probs = kwargs.get('with_probs', True)
# specify an amount of overlap between true and false scores
score_noise = kwargs.get('score_noise', 0.2)
anchors = kwargs.get('anchors', None)
scale = 100.0
# Build random variables
from kwarray import distributions
DiscreteUniform = distributions.DiscreteUniform.seeded(rng=rng)
def _parse_arg(key, default):
value = kwargs.get(key, default)
try:
low, high = value
return (low, high + 1)
except Exception:
return (0, value + 1)
nboxes_RV = DiscreteUniform(*_parse_arg('nboxes', 1))
n_fp_RV = DiscreteUniform(*_parse_arg('n_fp', 0))
n_fn_RV = DiscreteUniform(*_parse_arg('n_fn', 0))
box_noise_RV = distributions.Normal(0, box_noise, rng=rng)
cls_noise_RV = distributions.Bernoulli(cls_noise, rng=rng)
# the values of true and false scores starts off with no overlap and
# the overlap increases as the score noise increases.
def _interp(v1, v2, alpha):
return v1 * alpha + (1 - alpha) * v2
mid = 0.5
# true_high = 2.0
true_high = 1.0
true_low = _interp(0, mid, score_noise)
false_high = _interp(true_high, mid - 1e-3, score_noise)
true_mean = _interp(0.5, .8, score_noise)
false_mean = _interp(0.5, .2, score_noise)
true_score_RV = distributions.TruncNormal(
mean=true_mean, std=.5, low=true_low, high=true_high, rng=rng)
false_score_RV = distributions.TruncNormal(
mean=false_mean, std=.5, low=0, high=false_high, rng=rng)
# Create the category hierarcy
if nclasses is not None:
graph = nx.DiGraph()
graph.add_node('background', id=0)
for cid in range(1, nclasses + 1):
# binary heap encoding of a tree
cx = cid - 1
parent_cx = (cx - 1) // 2
node = 'cat_{}'.format(cid)
graph.add_node(node, id=cid)
if parent_cx > 0:
supercategory = 'cat_{}'.format(parent_cx + 1)
graph.add_edge(supercategory, node)
classes = kwcoco.CategoryTree(graph)
frgnd_cx_RV = distributions.DiscreteUniform(1, len(classes), rng=rng)
else:
classes = kwcoco.CategoryTree.coerce(classes)
# TODO: remove background classes via rejection sampling
frgnd_cx_RV = distributions.DiscreteUniform(0, len(classes), rng=rng)
dmet = cls()
dmet.classes = classes
for gid in range(nimgs):
# Sample random variables
nboxes_ = nboxes_RV()
n_fp_ = n_fp_RV()
n_fn_ = n_fn_RV()
imgname = 'img_{}'.format(gid)
dmet._register_imagename(imgname, gid)
# Generate random ground truth detections
true_boxes = kwimage.Boxes.random(num=nboxes_, scale=scale,
anchors=anchors, rng=rng,
format='cxywh')
# Prevent 0 sized boxes: increase w/h by 1
true_boxes.data[..., 2:4] += 1
true_cxs = frgnd_cx_RV(len(true_boxes))
true_weights = np.ones(len(true_boxes), dtype=np.int32)
# Initialize predicted detections as a copy of truth
pred_boxes = true_boxes.copy()
pred_cxs = true_cxs.copy()
# Perterb box coordinates
pred_boxes.data = np.abs(pred_boxes.data.astype(np.float) +
box_noise_RV())
# Perterb class predictions
change = cls_noise_RV(len(pred_cxs))
pred_cxs_swap = frgnd_cx_RV(len(pred_cxs))
pred_cxs[change] = pred_cxs_swap[change]
# Drop true positive boxes
if n_fn_:
pred_boxes.data = pred_boxes.data[n_fn_:]
pred_cxs = pred_cxs[n_fn_:]
# pred_scores = np.linspace(true_min, true_max, len(pred_boxes))[::-1]
n_tp_ = len(pred_boxes)
pred_scores = true_score_RV(n_tp_)
# Add false positive boxes
if n_fp_:
false_boxes = kwimage.Boxes.random(num=n_fp_, scale=scale,
rng=rng, format='cxywh')
false_cxs = frgnd_cx_RV(n_fp_)
false_scores = false_score_RV(n_fp_)
pred_boxes.data = np.vstack([pred_boxes.data, false_boxes.data])
pred_cxs = np.hstack([pred_cxs, false_cxs])
pred_scores = np.hstack([pred_scores, false_scores])
# Transform the scores for the assigned class into a predicted
# probability for each class. (Currently a bit hacky).
class_probs = _demo_construct_probs(
pred_cxs, pred_scores, classes, rng,
hacked=kwargs.get('hacked', 1))
true_dets = kwimage.Detections(boxes=true_boxes,
class_idxs=true_cxs,
weights=true_weights)
pred_dets = kwimage.Detections(boxes=pred_boxes,
class_idxs=pred_cxs,
scores=pred_scores)
# Hack in the probs
if with_probs:
pred_dets.data['probs'] = class_probs
if null_pred:
pred_dets.data['class_idxs'] = np.array(
[None] * len(pred_dets), dtype=object)
dmet.add_truth(true_dets, imgname=imgname)
dmet.add_predictions(pred_dets, imgname=imgname)
return dmet
[docs] def summarize(dmet, out_dpath=None, plot=False, title=''):
"""
Example:
>>> from kwcoco.metrics.confusion_vectors import * # NOQA
>>> from kwcoco.metrics.detect_metrics import DetectionMetrics
>>> dmet = DetectionMetrics.demo(
>>> n_fp=(0, 128), n_fn=(0, 4), nimgs=512, nboxes=(0, 32),
>>> nclasses=3, rng=0)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> dmet.summarize(plot=True, title='DetectionMetrics summary demo')
>>> kwplot.show_if_requested()
"""
cfsn_vecs = dmet.confusion_vectors()
ovr_cfsn = cfsn_vecs.binarize_ovr(keyby='name')
bin_cfsn = cfsn_vecs.binarize_peritem()
ovr_measures = ovr_cfsn.measures()
bin_measures = bin_cfsn.measures()
summary = {
'ovr_measures': ovr_measures,
'bin_measures': bin_measures,
}
if plot:
print('summary = {}'.format(ub.repr2(summary, nl=1)))
print('out_dpath = {!r}'.format(out_dpath))
ovr_measures['perclass']
if out_dpath:
pass
perclass = ovr_measures['perclass']
ovr_measures['mAUC']
ovr_measures['mAP']
bin_measures.summary_plot(title=title, fnum=1)
perclass.summary_plot(title=title, fnum=2)
# # Is this micro-versus-macro average?
# bin_measures['ap']
# bin_measures['auc']
return summary
def _demo_construct_probs(pred_cxs, pred_scores, classes, rng, hacked=1):
"""
Constructs random probabilities for demo data
"""
# Setup probs such that the assigned class receives a probability
# equal-(ish) to the assigned score.
# Its a bit tricky to setup hierarchical probs such that we get the
# scores in the right place. We punt and just make probs
# conditional. The right thing to do would be to do this, and then
# perterb ancestor categories such that the probability evenetually
# converges on the right value at that specific classes depth.
# import torch
# Ensure probs
pred_scores2 = pred_scores.clip(0, 1.0)
class_energy = rng.rand(len(pred_scores2), len(classes)).astype(np.float32)
for p, x, s in zip(class_energy, pred_cxs, pred_scores2):
p[x] = s
if hacked:
# HACK! All that nice work we did is too slow for doctests
return class_energy
# class_energy = torch.Tensor(class_energy)
# cond_logprobs = classes.conditional_log_softmax(class_energy, dim=1)
# cond_probs = torch.exp(cond_logprobs).numpy()
# # I was having a difficult time getting this right, so an
# # inefficient per-item non-vectorized implementation it is.
# # Note: that this implementation takes 70% of the time in this function
# # and is a bottleneck for the doctests. A vectorized implementation would
# # be nice.
# idx_to_ancestor_idxs = classes.idx_to_ancestor_idxs()
# idx_to_groups = {idx: group for group in classes.idx_groups for idx in group}
# def set_conditional_score(row, cx, score, idx_to_groups):
# group_cxs = np.array(idx_to_groups[cx])
# flags = group_cxs == cx
# group_row = row[group_cxs]
# # Ensure that that heriarchical probs sum to 1
# current = group_row[~flags]
# other = current * (1 - score) / current.sum()
# other = np.nan_to_num(other)
# group_row[~flags] = other
# group_row[flags] = score
# row[group_cxs] = group_row
# for row, cx, score in zip(cond_probs, pred_cxs, pred_scores2):
# set_conditional_score(row, cx, score, idx_to_groups)
# for ancestor_cx in idx_to_ancestor_idxs[cx]:
# if ancestor_cx != cx:
# # Hack all parent probs to 1.0 so conditional probs
# # turn into real probs.
# set_conditional_score(row, ancestor_cx, 1.0, idx_to_groups)
# # TODO: could add a fudge factor here so the
# # conditional prob is higher than score, but parent
# # probs are less than 1.0
# # TODO: could also maximize entropy of descendant nodes
# # so classes.decision2 would stop at this node
# # For each level the conditional probs must sum to 1
# if cond_probs.size > 0:
# for idxs in classes.idx_groups:
# level = cond_probs[:, idxs]
# totals = level.sum(axis=1)
# assert level.shape[1] == 1 or np.allclose(totals, 1.0), str(level) + ' : ' + str(totals)
# cond_logprobs = torch.Tensor(cond_probs).log()
# class_probs = classes._apply_logprob_chain_rule(cond_logprobs, dim=1).exp().numpy()
# class_probs = class_probs.reshape(-1, len(classes))
# # print([p[x] for p, x in zip(class_probs, pred_cxs)])
# # print(pred_scores2)
# return class_probs
[docs]def eval_detections_cli(**kw):
"""
CommandLine:
xdoctest -m ~/code/kwcoco/kwcoco/metrics/detect_metrics.py eval_detections_cli
"""
import scriptconfig as scfg
import kwcoco
class EvalDetectionCLI(scfg.Config):
default = {
'true': scfg.Path(None, help='true coco dataset'),
'pred': scfg.Path(None, help='predicted coco dataset'),
'out_dpath': scfg.Path('./out', help='output directory')
}
pass
config = EvalDetectionCLI()
cmdline = kw.pop('cmdline', True)
config.load(kw, cmdline=cmdline)
true_coco = kwcoco.CocoDataset(config['true'])
pred_coco = kwcoco.CocoDataset(config['pred'])
from kwcoco.metrics.detect_metrics import DetectionMetrics
dmet = DetectionMetrics.from_coco(true_coco, pred_coco)
voc_info = dmet.score_voc()
cls_info = voc_info['perclass'][0]
tp = cls_info['tp']
fp = cls_info['fp']
fn = cls_info['fn']
tpr = cls_info['tpr']
ppv = cls_info['ppv']
fp = cls_info['fp']
# Compute the MCC as TN->inf
thresh = cls_info['thresholds']
# https://erotemic.wordpress.com/2019/10/23/closed-form-of-the-mcc-when-tn-inf/
mcc_lim = tp / (np.sqrt(fn + tp) * np.sqrt(fp + tp))
f1 = 2 * (ppv * tpr) / (ppv + tpr)
draw = False
if draw:
mcc_idx = mcc_lim.argmax()
f1_idx = f1.argmax()
import kwplot
plt = kwplot.autoplt()
kwplot.multi_plot(
xdata=thresh,
ydata=mcc_lim,
xlabel='threshold',
ylabel='mcc*',
fnum=1, pnum=(1, 4, 1),
title='MCC*',
color=['blue'],
)
plt.plot(thresh[mcc_idx], mcc_lim[mcc_idx], 'r*', markersize=20)
plt.plot(thresh[f1_idx], mcc_lim[f1_idx], 'k*', markersize=20)
kwplot.multi_plot(
xdata=fp,
ydata=tpr,
xlabel='fp (fa)',
ylabel='tpr (pd)',
fnum=1, pnum=(1, 4, 2),
title='ROC',
color=['blue'],
)
plt.plot(fp[mcc_idx], tpr[mcc_idx], 'r*', markersize=20)
plt.plot(fp[f1_idx], tpr[f1_idx], 'k*', markersize=20)
kwplot.multi_plot(
xdata=tpr,
ydata=ppv,
xlabel='tpr (recall)',
ylabel='ppv (precision)',
fnum=1, pnum=(1, 4, 3),
title='PR',
color=['blue'],
)
plt.plot(tpr[mcc_idx], ppv[mcc_idx], 'r*', markersize=20)
plt.plot(tpr[f1_idx], ppv[f1_idx], 'k*', markersize=20)
kwplot.multi_plot(
xdata=thresh,
ydata=f1,
xlabel='threshold',
ylabel='f1',
fnum=1, pnum=(1, 4, 4),
title='F1',
color=['blue'],
)
plt.plot(thresh[mcc_idx], f1[mcc_idx], 'r*', markersize=20)
plt.plot(thresh[f1_idx], f1[f1_idx], 'k*', markersize=20)
if __name__ == '__main__':
"""
CommandLine:
python ~/code/kwcoco/kwcoco/metrics/detect_metrics.py all
"""
import xdoctest
xdoctest.doctest_module(__file__)