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dota-v1.5_evaluation_task1.py
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dota-v1.5_evaluation_task1.py
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# --------------------------------------------------------
# dota_evaluation_task1
# Licensed under The MIT License [see LICENSE for details]
# Written by Jian Ding, based on code from Bharath Hariharan
# --------------------------------------------------------
"""
To use the code, users should to config detpath, annopath and imagesetfile
detpath is the path for 15 result files, for the format, you can refer to "http://captain.whu.edu.cn/DOTAweb/tasks.html"
search for PATH_TO_BE_CONFIGURED to config the paths
Note, the evaluation is on the large scale images
"""
import xml.etree.ElementTree as ET
import os
#import cPickle
import numpy as np
import matplotlib.pyplot as plt
import polyiou
from functools import partial
import pdb
def parse_gt(filename):
"""
:param filename: ground truth file to parse
:return: all instances in a picture
"""
objects = []
with open(filename, 'r') as f:
while True:
line = f.readline()
if line:
splitlines = line.strip().split(' ')
object_struct = {}
if (len(splitlines) < 9):
continue
object_struct['name'] = splitlines[8]
# if (len(splitlines) == 9):
# object_struct['difficult'] = 0
# elif (len(splitlines) == 10):
# object_struct['difficult'] = int(splitlines[9])
object_struct['difficult'] = 0
object_struct['bbox'] = [float(splitlines[0]),
float(splitlines[1]),
float(splitlines[2]),
float(splitlines[3]),
float(splitlines[4]),
float(splitlines[5]),
float(splitlines[6]),
float(splitlines[7])]
objects.append(object_struct)
else:
break
return objects
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point 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 = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(detpath,
annopath,
imagesetfile,
classname,
# cachedir,
ovthresh=0.5,
use_07_metric=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in detpath.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt
#if not os.path.isdir(cachedir):
# os.mkdir(cachedir)
#cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
#print('imagenames: ', imagenames)
#if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
#print('parse_files name: ', annopath.format(imagename))
recs[imagename] = parse_gt(annopath.format(imagename))
# extract gt objects for this class
class_recs = {}
npos = 0
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets from Task1* files
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])
#print('check confidence: ', confidence)
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
#print('check sorted_scores: ', sorted_scores)
#print('check sorted_ind: ', sorted_ind)
## note the usage only in numpy not for list
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
#print('check imge_ids: ', image_ids)
#print('imge_ids len:', len(image_ids))
# go down dets and mark TPs and FPs
nd = len(image_ids)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
R = class_recs[image_ids[d]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
## compute det bb with each BBGT
if BBGT.size > 0:
# compute overlaps
# intersection
# 1. calculate the overlaps between hbbs, if the iou between hbbs are 0, the iou between obbs are 0, too.
# pdb.set_trace()
BBGT_xmin = np.min(BBGT[:, 0::2], axis=1)
BBGT_ymin = np.min(BBGT[:, 1::2], axis=1)
BBGT_xmax = np.max(BBGT[:, 0::2], axis=1)
BBGT_ymax = np.max(BBGT[:, 1::2], axis=1)
bb_xmin = np.min(bb[0::2])
bb_ymin = np.min(bb[1::2])
bb_xmax = np.max(bb[0::2])
bb_ymax = np.max(bb[1::2])
ixmin = np.maximum(BBGT_xmin, bb_xmin)
iymin = np.maximum(BBGT_ymin, bb_ymin)
ixmax = np.minimum(BBGT_xmax, bb_xmax)
iymax = np.minimum(BBGT_ymax, bb_ymax)
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb_xmax - bb_xmin + 1.) * (bb_ymax - bb_ymin + 1.) +
(BBGT_xmax - BBGT_xmin + 1.) *
(BBGT_ymax - BBGT_ymin + 1.) - inters)
overlaps = inters / uni
BBGT_keep_mask = overlaps > 0
BBGT_keep = BBGT[BBGT_keep_mask, :]
BBGT_keep_index = np.where(overlaps > 0)[0]
# pdb.set_trace()
def calcoverlaps(BBGT_keep, bb):
overlaps = []
for index, GT in enumerate(BBGT_keep):
overlap = polyiou.iou_poly(polyiou.VectorDouble(BBGT_keep[index]), polyiou.VectorDouble(bb))
overlaps.append(overlap)
return overlaps
if len(BBGT_keep) > 0:
overlaps = calcoverlaps(BBGT_keep, bb)
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
# pdb.set_trace()
jmax = BBGT_keep_index[jmax]
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
print('check fp:', fp)
print('check tp', tp)
print('npos num:', npos)
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap
def main():
detpath = r'/home/dingjian/data/DOTA-v1.5/example/RoITrans/Task1_{:s}.txt'
annopath = r'/home/dingjian/code/DOAI_server2/media/DOTA15_Task1_gt/{:s}.txt'
imagesetfile = r'/home/dingjian/code/DOAI_server2/media/testset.txt'
# detpath = r'PATH_TO_BE_CONFIGURED/Task1_{:s}.txt'
# annopath = r'PATH_TO_BE_CONFIGURED/{:s}.txt' # change the directory to the path of val/labelTxt, if you want to do evaluation on the valset
# imagesetfile = r'PATH_TO_BE_CONFIGURED/valset.txt'
# For DOTA-v1.5
classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', 'container-crane']
# For DOTA-v1.0
# classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
# 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', ']
classaps = []
map = 0
for classname in classnames:
print('classname:', classname)
rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
ovthresh=0.5,
use_07_metric=True)
map = map + ap
#print('rec: ', rec, 'prec: ', prec, 'ap: ', ap)
print('ap: ', ap)
classaps.append(ap)
# umcomment to show p-r curve of each category
# plt.figure(figsize=(8,4))
# plt.xlabel('recall')
# plt.ylabel('precision')
# plt.plot(rec, prec)
# plt.show()
map = map/len(classnames)
print('map:', map)
classaps = 100*np.array(classaps)
print('classaps: ', classaps)
if __name__ == '__main__':
main()