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test.py
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test.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ssh.py
@Contact : [email protected]
@License : (C)Copyright 2018-2019
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
2/18/19 7:08 AM gxrao 1.0 None
'''
# test code:
echo "pedestrian_detect"
python yolo_eval.py \
--detpath=./data/pedestrian_train/pedestrian_detect_tiny_yolov3person.txt \
--imagesetfile=/datasets/pedestrian_dataset/yolo_style/shuffle_test_in.txt\
--classname='0'
#code
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ssh.py
@Contact : [email protected]
@License : (C)Copyright 2018-2019
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
2/18/19 7:08 AM gxrao 1.0 None
'''
import numpy as np
import os
import cPickle
from argparse import ArgumentParser
def get_file_name(in_path):
label_name = os.path.basename(in_path).split('.jpg')[0]
return label_name
def get_file_name_suffix(in_path):
label_name = os.path.basename(in_path)
return label_name
def parser():
parser = ArgumentParser("Map calculator")
parser.add_argument("--detpath", dest="in_detpath", help='Path to detections',
default='./data/detect_new_poolperson.txt', type=str)
parser.add_argument("--imagesetfile", dest="in_imageset", help='text file containing the list of images, one image per line',
default='/datasets/shoulder_data/test_ordinary/test_in.txt', type=str)
parser.add_argument("--classname", dest="in_classname", help='Category name (duh)',
default='person', type=str)
parser.add_argument("--cachedir", dest="in_cachedir", help='Directory for caching the annotations',
default='./data', type=str)
parser.add_argument("--ovthresh", dest="th", help='Overlap threshold (default = 0.5)',
default='0.5', type=float)
return parser.parse_args()
def parse_rec(filepath):
"""
Parse a ssh xml file
style:
person x1 y1 x2 y2
person x1 y1 x2 y2
"""
object = []
with open(filepath) as fd:
lines = fd.readlines()
bbox = [line.strip().split(' ')[1:] for line in lines]
class_name = [line.strip().split(' ')[0] for line in lines]
for i, bbox in enumerate(bbox):
obj_struct = {}
obj_struct['name'] = class_name[i]
obj_struct['bbox'] = list(map(float, bbox))
object.append(obj_struct)
return object
def parse_rec(filepath, imgpath):
"""
Parse a yolo xml file
style:
person x1 y1 x2 y2
person x1 y1 x2 y2
"""
object = []
import cv2
img = cv2.imread(imgpath)
im_height, im_width = img.shape[0], img.shape[1]
with open(filepath) as fd:
lines = fd.readlines()
ssh_bbox = [line.strip().split(' ')[1:] for line in lines]
bbox = []
for line in ssh_bbox:
line = [float(val) for val in line]
x_l = int((line[0] - line[2] / 2.0) * im_width + 1)
x_r = int((line[0] + line[2] / 2.0) * im_width + 1)
y_l = int((line[1] - line[3] / 2.0) * im_height + 1)
y_h = int((line[1] + line[3] / 2.0) * im_height + 1)
bbox.append([x_l, y_l, x_r, y_h])
class_name = [line.strip().split(' ')[0] for line in lines]
for i, bbox in enumerate(bbox):
obj_struct = {}
obj_struct['name'] = class_name[i]
obj_struct['bbox'] = list(map(float, bbox))
object.append(obj_struct)
return object
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,
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)
usage:
heardshoulder detection ap calculation
'''
darknet :AP for person = 0.8805 with_longgang= 0.8756
'''
save_txt = r'/home/hupeng/data/ordinary/ssh_test_in.txt'
detpath = r'./data/detect_result.txt'
annopath = r'/home/hupeng/data/ordinary/annotations/{}.txt'
classname = 'person'
cachedir = './data'
voc_eval(detpath,
annopath,
save_txt,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False)
save_txt can get from the from following code
set_path = r'/home/hupeng/data/with_test/image_set/test_in.txt'
image_path = r'/home/hupeng/data/ordinary/image'
save_txt = r'/home/hupeng/data/ordinary/ssh_test_in.txt'
'''
# 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')
if os.path.exists(cachefile):
os.remove(cachefile)
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(imagename.replace('.jpg', '.txt').replace('images', 'annotations').replace('JPEGImages','labels'),
imagename)
if i % 100 == 0:
print 'Reading annotation for {:d}/{:d}'.format(
i + 1, len(imagenames))
# save
print 'Saving cached annotations to {:s}'.format(cachefile)
with open(cachefile, 'w') as f:
cPickle.dump(recs, f)
else:
# load
with open(cachefile, 'r') as f:
recs = cPickle.load(f)
# 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])
num_np = np.array([True for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(num_np)
class_recs[imagename] = {'bbox': bbox,
'det': det}
# read dets
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])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
# sort by confidence
sorted_ind = np.argsort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# 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)
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['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 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)
print('AP for {} = {:.4f}'.format('person', ap))
return rec, prec, ap
if __name__ == "__main__":
'''
darknet :AP for person = 0.8805
'''
args = parser()
detpath = args.in_detpath
save_txt = args.in_imageset
classname = args.in_classname
cachedir = args.in_cachedir
th = args.th
voc_eval(detpath,
save_txt,
classname,
cachedir,
ovthresh=th,
use_07_metric=False)