def voc_eval(detpath, annopath, test_imgid_list, cls_name, ovthresh=0.5,
use_07_metric=False, use_diff=False):
'''
:param detpath:
:param annopath:
:param test_imgid_list: it 's a list that contains the img_name of test_imgs
:param cls_name:
:param ovthresh:
:param use_07_metric:
:param use_diff:
:return:
'''
# 1. parse xml to get gtboxes
# read list of images
imagenames = test_imgid_list
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(os.path.join(annopath, imagename+'.xml'))
# if i % 100 == 0:
# print('Reading annotation for {:d}/{:d}'.format(
# i + 1, len(imagenames)))
# 2. get gtboxes for this class.
class_recs = {}
num_pos = 0
# if cls_name == 'person':
# print ("aaa")
det_boxes = []
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == cls_name]
bbox = np.array([x['bbox'] for x in R])
if use_diff:
difficult = np.array([False for x in R]).astype(np.bool)
else:
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
num_pos = num_pos + sum(~difficult) # ignored the diffcult boxes
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det} # det means that gtboxes has already been detected
# 3. read the detection file
detfile = os.path.join(detpath, "det_"+cls_name+".txt")
with open(detfile, 'r') as f:
lines = f.readlines()
# for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
splitlines = [x.strip().split(' ') for x in lines if x.strip().split(' ')[0] in imagenames] # a list that include a list
image_ids = [x[0] for x in splitlines] # img_id is img_name
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
nd = len(image_ids) # num of detections. That, a line is a det_box.
tp = np.zeros(nd)
fp = np.zeros(nd)
tp_angle_offsets = []
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind] #reorder the img_name
# go down dets and mark TPs and FPs
for d in range(nd):
R = class_recs[image_ids[d]] # img_id is img_name
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
overlaps = []
angle_offsets = []
for i in range(len(BBGT)):
# print('bb', bb)
# print('BBGT', BBGT[i])
overlap = iou_rotate.iou_rotate_calculate1(np.array([bb]),
BBGT[i],
use_gpu=False)[0]
overlaps.append(overlap)
angle_offset = abs(bb[-1] - BBGT[i][0][-1])
angle_offsets.append(angle_offset)
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
# print('angle offset', angle_offsets[jmax])
if angle_offsets[jmax] < ANGLE_TRESHOLD:
tp[d] = 1.
R['det'][jmax] = 1
tp_angle_offsets.append(angle_offsets[jmax])
else:
fp[d] = 1.
# print(ovmax, angle_offsets[jmax])
else:
fp[d] = 1.
# print(ovmax, angle_offsets[jmax])
# 4. get recall, precison and AP
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(num_pos)
# 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)
gt_cls_num = num_pos
tp_angle_offsets = np.array(tp_angle_offsets)
angle_offsets_mse = np.mean(tp_angle_offsets**2)
angle_offsets_rmse = np.sqrt(angle_offsets_mse)
print('angle_offsets_mse:', angle_offsets_mse)
print('angle_offsets_rmse:', angle_offsets_rmse)
return rec, prec, ap, gt_cls_num, tp, fp
def voc_eval(detpath,
gtboxes_horizontal_dict,
gtboxes_rotate_dict,
cls_name,
mode,
ovthresh=0.5,
use_07_metric=False):
'''
:param detpath:
:param annopath:
:param test_imgid_list: it 's a list that contains the img_name of test_imgs
:param cls_name:
:param ovthresh:
:param use_07_metric:
:param use_diff:
:return:
'''
# get gtboxes for this class.
# **************************************
class_recs = {}
num_pos = 0
for imagename in gtboxes_horizontal_dict.keys():
if mode == 0:
R = [
obj for obj in gtboxes_horizontal_dict[imagename]
if obj['name'] == cls_name
]
else:
R = [
obj for obj in gtboxes_rotate_dict[imagename]
if obj['name'] == cls_name
]
bbox = np.array([x['bbox'] for x in R])
det = [False] * len(R)
num_pos = num_pos + len(R)
class_recs[imagename] = {
'bbox': bbox,
'det': det
} # det means that gtboxes has already been detected
# read the detection file
if mode == 0:
detfile = os.path.join(detpath, "det_h_" + cls_name + ".txt")
else:
detfile = os.path.join(detpath, "det_r_" + cls_name + ".txt")
with open(detfile, 'r') as f:
lines = f.readlines()
# for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
splitlines = [x.strip().split(' ')
for x in lines] # a list that include a list
image_ids = [x[0] for x in splitlines] # img_id is img_name
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
nd = len(image_ids) # num of detections. That, a line is a det_box.
tp = np.zeros(nd)
fp = np.zeros(nd)
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind] #reorder the img_name
# go down dets and mark TPs and FPs
for d in range(nd):
R = class_recs[image_ids[d]] # img_id is img_name
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
if mode == 0:
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
else:
overlaps = []
for i in range(len(BBGT)):
overlap = iou_rotate.iou_rotate_calculate1(
np.array([bb]),
np.array([BBGT[i]]),
# BBGT[i].reshape(1,-1),
use_gpu=False)[0]
overlaps.append(overlap)
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.
# 4. get recall, precison and AP
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(num_pos)
# 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)
if len(tp) > 0 and len(fp) > 0:
return rec, prec, ap
else:
return np.array([0], np.float64), np.array([0], np.float64), ap
def eval(rboxes, gboxes, iou_th, use_07_metric, mode):
rbox_images = rboxes.keys()
fp = np.zeros(len(rbox_images))
tp = np.zeros(len(rbox_images))
box_num = 0
for i in range(len(rbox_images)):
rbox_image = rbox_images[i]
if len(rboxes[rbox_image][0]['bbox']) > 0:
rbox_lists = np.array(rboxes[rbox_image][0]['bbox'])
if len(gboxes[rbox_image]) > 0:
gbox_list = np.array(
[obj['bbox'] for obj in gboxes[rbox_image]])
box_num = box_num + len(gbox_list)
gbox_list = np.concatenate(
(gbox_list, np.zeros((np.shape(gbox_list)[0], 1))), axis=1)
confidence = rbox_lists[:, -1]
box_index = np.argsort(-confidence)
rbox_lists = rbox_lists[box_index, :]
for rbox_list in rbox_lists:
if mode == 0:
ixmin = np.maximum(gbox_list[:, 0], rbox_list[0])
iymin = np.maximum(gbox_list[:, 1], rbox_list[1])
ixmax = np.minimum(gbox_list[:, 2], rbox_list[2])
iymax = np.minimum(gbox_list[:, 3], rbox_list[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((rbox_list[2] - rbox_list[0] + 1.) *
(rbox_list[3] - rbox_list[1] + 1.) +
(gbox_list[:, 2] - gbox_list[:, 0] + 1.) *
(gbox_list[:, 3] - gbox_list[:, 1] + 1.) -
inters)
overlaps = inters / uni
else:
overlaps = iou_rotate.iou_rotate_calculate1(
np.array([rbox_list[:-1]]),
gbox_list,
use_gpu=False)[0]
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > iou_th:
if gbox_list[jmax, -1] == 0:
tp[i] += 1
gbox_list[jmax, -1] = 1
else:
fp[i] += 1
else:
fp[i] += 1
else:
fp[i] += len(rboxes[rbox_image][0]['bbox'])
else:
continue
rec = np.zeros(len(rbox_images))
prec = np.zeros(len(rbox_images))
if box_num == 0:
for i in range(len(fp)):
if fp[i] != 0:
prec[i] = 0
else:
prec[i] = 1
else:
fp = np.cumsum(fp)
tp = np.cumsum(tp)
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
rec = tp / box_num
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap, box_num
def eval(rboxes, gboxes, iou_th, use_07_metric, mode):
rbox_images = rboxes.keys()
fp = np.zeros(len(rbox_images))
tp = np.zeros(len(rbox_images))
box_num = 0
for i in range(len(rbox_images)):
rbox_image = rbox_images[i]
if len(rboxes[rbox_image]) > 0:
rbox_lists = np.array(rboxes[rbox_image][0]['bbox'])
if len(gboxes[rbox_image]) > 0:
gbox_list = np.array(
[obj['bbox'] for obj in gboxes[rbox_image]])
box_num = box_num + len(gbox_list)
gbox_list = np.concatenate(
(gbox_list, np.zeros((np.shape(gbox_list)[0], 1))), axis=1)
confidence = rbox_lists[:, -1]
box_index = np.argsort(-confidence)
rbox_lists = rbox_lists[box_index, :]
for rbox_list in rbox_lists:
if mode == 0:
ixmin = np.maximum(gbox_list[:, 0], rbox_list[0])
iymin = np.maximum(gbox_list[:, 1], rbox_list[1])
ixmax = np.minimum(gbox_list[:, 2], rbox_list[2])
iymax = np.minimum(gbox_list[:, 3], rbox_list[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih # 重合的面积
# print(inters)
# union
uni = (
(rbox_list[2] - rbox_list[0] + 1.) *
(rbox_list[3] - rbox_list[1] + 1.) +
(gbox_list[:, 2] - gbox_list[:, 0] + 1.) *
(gbox_list[:, 3] - gbox_list[:, 1] + 1.) - inters
) # 并的面积
overlaps = inters / uni
else:
overlaps = iou_rotate.iou_rotate_calculate1(
np.array([rbox_list[:-1]]),
gbox_list,
use_gpu=False)[0]
ovmax = np.max(overlaps) # 取最大重合的面积
# print(ovmax)
jmax = np.argmax(overlaps) # 取最大重合面积的索引
if ovmax > iou_th: # 比阈值大
if gbox_list[jmax, -1] == 0:
tp[i] = tp[i] + 1
gbox_list[jmax, -1] = 1
else:
fp[i] = fp[i] + 1
else:
fp[i] = fp[i] + 1
else:
fp[i] = fp[i] + len(rboxes[rbox_image])
else:
continue
rec = np.zeros(len(rbox_images))
prec = np.zeros(len(rbox_images))
if box_num == 0:
for i in range(len(fp)):
if fp[i] != 0:
prec[i] = 0
else:
prec[i] = 1
else:
# print(fp)
# print(tp)
fp = np.cumsum(fp) # 累计求和
tp = np.cumsum(tp)
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
# print(box_num)
rec = tp / box_num # 标出来的对的占应该标出来的百分比
# avoid division by zero in case first detection matches a difficult ground ruth
# 标出来的对的占所有标出来的百分比
ap = voc_ap(rec, prec, use_07_metric)
# ap=voc_ap(rec, prec, use_07_metric)
# print(ap)
return rec, prec, ap
def eval(rboxes, gboxes, iou_th, use_07_metric, mode):
rbox_images = rboxes.keys()
fp = np.zeros(len(rbox_images))
tp = np.zeros(len(rbox_images))
box_num = 0
for i in range(len(rbox_images)):
rbox_image = rbox_images[i]
if len(rboxes[rbox_image]) > 0:
rbox_lists = np.array(rboxes[rbox_image][0]['bbox'])
if len(gboxes[rbox_image]) > 0:
gbox_list = np.array([obj['bbox'] for obj in gboxes[rbox_image]])
box_num = box_num + len(gbox_list)
gbox_list = np.concatenate((gbox_list, np.zeros((np.shape(gbox_list)[0], 1))), axis=1)
confidence = rbox_lists[:, -1]
box_index = np.argsort(-confidence)
rbox_lists = rbox_lists[box_index, :]
for rbox_list in rbox_lists:
if mode == 0:
ixmin = np.maximum(gbox_list[:, 0], rbox_list[0])
iymin = np.maximum(gbox_list[:, 1], rbox_list[1])
ixmax = np.minimum(gbox_list[:, 2], rbox_list[2])
iymax = np.minimum(gbox_list[:, 3], rbox_list[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih # 重合的面积
# print(inters)
# union
uni = ((rbox_list[2] - rbox_list[0] + 1.) * (rbox_list[3] - rbox_list[1] + 1.) +
(gbox_list[:, 2] - gbox_list[:, 0] + 1.) *
(gbox_list[:, 3] - gbox_list[:, 1] + 1.) - inters) # 并的面积
overlaps = inters / uni
else:
overlaps = iou_rotate.iou_rotate_calculate1(np.array([rbox_list[:-1]]), gbox_list, use_gpu=False)[0]
ovmax = np.max(overlaps) # 取最大重合的面积
# print(ovmax)
jmax = np.argmax(overlaps) # 取最大重合面积的索引
if ovmax > iou_th: # 比阈值大
if gbox_list[jmax, -1] == 0:
tp[i] = tp[i] + 1
gbox_list[jmax, -1] = 1
else:
fp[i] = fp[i] + 1
else:
fp[i] = fp[i] + 1
else:
fp[i] = fp[i] + len(rboxes[rbox_image])
else:
continue
rec = np.zeros(len(rbox_images))
prec = np.zeros(len(rbox_images))
if box_num == 0:
for i in range(len(fp)):
if fp[i] != 0:
prec[i] = 0
else:
prec[i] = 1
else:
# print(fp)
# print(tp)
fp = np.cumsum(fp) # 累计求和
tp = np.cumsum(tp)
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
# print(box_num)
rec = tp / box_num # 标出来的对的占应该标出来的百分比
# avoid division by zero in case first detection matches a difficult ground ruth
# 标出来的对的占所有标出来的百分比
ap = voc_ap(rec, prec, use_07_metric)
# ap=voc_ap(rec, prec, use_07_metric)
# print(ap)
return rec, prec, ap
def voc_eval(detpath,
annopath,
test_imgid_list,
cls_name,
ovthresh=0.5,
use_07_metric=False,
use_diff=False):
'''
:param detpath:
:param annopath:
:param test_imgid_list: it 's a list that contains the img_name of test_imgs
:param cls_name:
:param ovthresh:
:param use_07_metric:
:param use_diff:
:return:
'''
# 1. parse xml to get gtboxes
# read list of images
# read list of images
imagenames = test_imgid_list
recs = {}
class_recs = {}
npos = 0
for imagename in imagenames:
try:
recs[imagename] = parse_rec(
os.path.join(annopath, imagename + '.xml'))
except FileNotFoundError: # 이미지는 있지만 이미지에 해당하는 annotation 파일이 없는 경우 pass
continue
R = [obj for obj in recs[imagename] if obj['name'] == cls_name]
if len(
R
) == 0: # 같은 xml 파일이라도 읽어오지 못하는 문제가 있어 그럴경우, 일단 continue, 원인 찾는중
continue
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
}
# 3. read the detection file
detfile = os.path.join(detpath, "det_" + cls_name + ".txt")
with open(detfile, 'r') as f:
lines = f.readlines()
if len(lines) == 0:
return 0, 0, 0
else:
# for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
splitlines = [x.strip().split('__')
for x in lines] # a list that include a list
image_ids = [x[0] for x in splitlines] # img_id is img_name
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)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind] # reorder the img_name
nd = len(image_ids) # num of detections. That, a line is a det_box.
tp = np.zeros(nd)
fp = np.zeros(nd)
# go down dets and mark TPs and FPs
for d in range(nd):
try: # 위에서 특정 xml 을 못읽는 문제 때문에 Key Error 발생, 일단 exception 처리함
R = class_recs[image_ids[d]]
except KeyError:
continue
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
overlaps = []
for i in range(len(BBGT)):
overlap = iou_rotate.iou_rotate_calculate1(
np.array([bb]), BBGT[i], use_gpu=False)[0]
overlaps.append(overlap)
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.
# 4. get recall, precison and AP
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