blob: e584ed75030403d7fd77218db006091d7475a95e [file] [log] [blame]
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
given a pascal voc imdb, compute mAP
"""
from ..logger import logger
import numpy as np
import os
import cPickle
def parse_voc_rec(filename):
"""
parse pascal voc record into a dictionary
:param filename: xml file path
:return: list of dict
"""
import xml.etree.ElementTree as ET
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_dict = dict()
obj_dict['name'] = obj.find('name').text
obj_dict['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_dict['bbox'] = [int(float(bbox.find('xmin').text)),
int(float(bbox.find('ymin').text)),
int(float(bbox.find('xmax').text)),
int(float(bbox.find('ymax').text))]
objects.append(obj_dict)
return objects
def voc_ap(rec, prec, use_07_metric=False):
"""
average precision calculations
[precision integrated to recall]
:param rec: recall
:param prec: precision
:param use_07_metric: 2007 metric is 11-recall-point based AP
:return: average precision
"""
if use_07_metric:
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap += p / 11.
else:
# append sentinel values at both ends
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute precision integration ladder
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# look for recall value changes
i = np.where(mrec[1:] != mrec[:-1])[0]
# sum (\delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(detpath, annopath, imageset_file, classname, annocache, ovthresh=0.5, use_07_metric=False):
"""
pascal voc evaluation
:param detpath: detection results detpath.format(classname)
:param annopath: annotations annopath.format(classname)
:param imageset_file: text file containing list of images
:param classname: category name
:param annocache: caching annotations
:param ovthresh: overlap threshold
:param use_07_metric: whether to use voc07's 11 point ap computation
:return: rec, prec, ap
"""
with open(imageset_file, 'r') as f:
lines = f.readlines()
image_filenames = [x.strip() for x in lines]
# load annotations from cache
if not os.path.isfile(annocache):
recs = {}
for ind, image_filename in enumerate(image_filenames):
recs[image_filename] = parse_voc_rec(annopath.format(image_filename))
if ind % 100 == 0:
logger.info('reading annotations for %d/%d' % (ind + 1, len(image_filenames)))
logger.info('saving annotations cache to %s' % annocache)
with open(annocache, 'wb') as f:
cPickle.dump(recs, f, protocol=cPickle.HIGHEST_PROTOCOL)
else:
with open(annocache, 'rb') as f:
recs = cPickle.load(f)
# extract objects in :param classname:
class_recs = {}
npos = 0
for image_filename in image_filenames:
objects = [obj for obj in recs[image_filename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in objects])
difficult = np.array([x['difficult'] for x in objects]).astype(np.bool)
det = [False] * len(objects) # stand for detected
npos = npos + sum(~difficult)
class_recs[image_filename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read detections
detfile = detpath.format(classname)
with open(detfile, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
bbox = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
if bbox.shape[0] > 0:
sorted_inds = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
bbox = bbox[sorted_inds, :]
image_ids = [image_ids[x] for x in sorted_inds]
# go down detections and mark true positives and false positives
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
r = class_recs[image_ids[d]]
bb = bbox[d, :].astype(float)
ovmax = -np.inf
bbgt = r['bbox'].astype(float)
if bbgt.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(bbgt[:, 0], bb[0])
iymin = np.maximum(bbgt[:, 1], bb[1])
ixmax = np.minimum(bbgt[:, 2], bb[2])
iymax = np.minimum(bbgt[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(bbgt[:, 2] - bbgt[:, 0] + 1.) *
(bbgt[:, 3] - bbgt[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not r['difficult'][jmax]:
if not r['det'][jmax]:
tp[d] = 1.
r['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid division by zero in case first detection matches a difficult ground ruth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap