def gr(metric_folder):
# categories=["jumping", "phoning", "playinginstrument", "reading", "ridingbike", "ridinghorse", "running", "takingphoto", "usingcomputer", "walking"]
epsilon = '0.001'
lbd = '1.0E-4'
detection_types = ["train"]
categories = ["jumping"]
gr_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
for category in categories:
# for category in ["dog"]:
for tradeoff in ['0.0']:
# for scale in ["50"]:
for scale in ['90']:
for typ in detection_types:
# detection_filename = '_'.join(["metric", typ,scale, str(tradeoff),str(epsilon), str(lbd), category+'_0.txt'])
# detection_filename = 'metric_val_90_0.0_0.0_1.0E-4_jumping_0.txt'
detection_filename = 'metric_train_90_0.0_0.0_1.0E-4_jumping_2.0_10_0.txt' #learning_rate, neuron
fp = os.path.join(metric_folder, detection_filename)
f = open(fp)
total_gr = 0
cnt = 0
for line in f:
score, yp, yi, hp, hi, filename_root = line.strip(
).split(',')
filename_root = filename_root.split('/')[-1].strip()
if True:
# if yi=='1':
cnt += 1
print cnt
grid_1, grid_2 = metric_calculate.h2GridCoor(
hp, int(scale))
gaze_file_root = filename_root
ratio_file = VOC2012_OBJECT_ETLOSS + category + '/' + str(
metric_calculate.convert_scale(int(scale))
) + '/' + gaze_file_root + '_' + str(
grid_1) + '_' + str(grid_2) + '.txt'
ratio_file = "/local/wangxin/Data/gaze_voc_actions_stefan/ETLoss_ratio/" + str(
metric_calculate.convert_scale(int(scale))
) + '/' + gaze_file_root + '_' + str(
grid_1) + '_' + str(grid_2) + '.txt'
#
with open(ratio_file) as ratio_f:
ratio = float(ratio_f.readline().strip())
total_gr += ratio
total_gr /= cnt
print total_gr
return gr_res
def metric_file_analyse(metric_folder):
# categories=["jumping", "phoning", "playinginstrument", "reading", "ridingbike", "ridinghorse", "running", "takingphoto", "usingcomputer", "walking"]
categories = ["jumping"]
epsilon = '0.0'
lbd = '1.0E-4'
detection_types = ["train"]
# categories = ["aeroplane" ,"cow", "dog" ,"cat" ,"motorbike" ,"boat" , "horse" , "sofa" ,"diningtable" ,"bicycle"]
detection_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
gr_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
all_positive = True
all_instance = True
for scale in ['90']:
# for tradeoff in ['0.0','0.1','0.2','0.3','0.4','0.5','0.6','0.7','0.8','0.9','1.0']:
for tradeoff in ['0.0']:
total_IoU = 0
for category in categories:
# for category in ["dog"]:
# for tradeoff in ['0.2']:
# for scale in ["50"]:
detection_res_3_tuple = [0] * 3
gr_res_3_tuple = [0] * 3
for typ in detection_types:
detection_filename = '_'.join([
"metric", typ, scale,
str(tradeoff),
str(epsilon),
str(lbd), category + '_0.txt'
])
fp = os.path.join(metric_folder, detection_filename)
f = open(fp)
# f=open("/local/wangxin/results/full_stefan_gaze/glsvm_pos_neg/symil_cccpgaze_posneg_cv_single_split_test/metric/metric_val_50_1.0_0.0_0.0_1.0E-6_jumping.txt")
total_gr = 0
cnt = 0
for line in f:
score, yp, yi, hp, hi, filename_root = line.strip(
).split(',')
# print score, yp, yi, hp, filename_root
filename_root = filename_root.split('/')[-1].strip()
# if True:
if yi == '1':
cnt += 1
grid_1, grid_2 = metric_calculate.h2GridCoor(
hp, int(scale))
gaze_file_root = filename_root
# ratio_file = VOC2012_OBJECT_ETLOSS+category+'/'+str(metric_calculate.convert_scale(int(scale)))+'/'+gaze_file_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
# ratio_file = "/local/wangxin/Data/gaze_voc_actions_stefan/ETLoss_ratio/"+str(metric_calculate.convert_scale(int(scale))) + '/'+gaze_file_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
#
# with open(ratio_file) as ratio_f:
# ratio = float(ratio_f.readline().strip())
# total_gr+=ratio
xml_filename_root = filename_root
#Obj#
# bbs = ground_truth_bb(VOC2012_TRAIN_ANNOTATIONS+xml_filename_root,category)
# bbs = ground_truth_bb_all(VOC2012_TRAIN_ANNOTATIONS+xml_filename_root)
#action
# bbs = ground_truth_bb_all_stefan_given_action(VOC2012_TRAIN_ANNOTATIONS+xml_filename_root,category)
bbs = ground_truth_bb_all_stefan(
VOC2012_TRAIN_ANNOTATIONS + xml_filename_root)
im = Image.open(VOC2012_TRAIN_IMAGES +
xml_filename_root + '.jpg')
width, height = im.size
hxmin, hymin, hxmax, hymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
# print hxmin, hymin, hxmax, hymax
IoU = metric_calculate.getTopIoU(
hxmin, hymin, hxmax, hymax, bbs)
# print filename_root, IoU, hp
total_IoU += IoU
total_IoU /= cnt
print category, scale, total_IoU
# total_gr /= cnt
detection_res_3_tuple[detection_types.index(
typ)] = total_IoU
# gr_res_3_tuple[detection_types.index(typ)] = total_gr
detection_res[scale][tradeoff][
category] = detection_res_3_tuple
# gr_res[scale][tradeoff][category]= gr_res_3_tuple
return detection_res, gr_res
def metric_file_analyse_detection_positive(metric_folder, categories):
epsilon = '0.001'
lbd = '1.0E-4'
detection_types = ["train", "valval", "valtest"]
scale_cv = ["50"]
tradeoff_cv = [
'0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9',
'1.0'
]
only_tp = True
if data_typ == "ferrari":
etloss_root = VOC2012_OBJECT_ETLOSS
elif data_typ == "stefan:":
etloss_root = VOC2012_ACTION_ETLOSS
detection_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
gr_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
for category in categories:
# for category in ["dog"]:
for tradeoff in tradeoff_cv:
# for scale in ["50"]:
for scale in scale_cv:
detection_res_3_tuple = [0] * 3
gr_res_3_tuple = [0] * 3
for typ in detection_types:
detection_filename = '_'.join([
"metric", typ, tradeoff, scale,
str(epsilon),
str(lbd), category + '.txt'
])
fp = os.path.join(metric_folder, str(scale),
detection_filename)
f = open(fp)
total_gr = 0
total_IoU = 0
cnt = 0
for line in f:
yp, yi, hp, filename_root = line.strip().split(',')
filename_root = filename_root.split('/')[-1].strip()
if yi == '1' and (only_tp and yp == '1'):
cnt += 1
grid_1, grid_2 = metric_calculate.h2GridCoor(
hp, int(scale))
gaze_file_root = filename_root
ratio_file = etloss_root + category + '/' + str(
metric_calculate.convert_scale(int(scale))
) + '/' + gaze_file_root + '_' + str(
grid_1) + '_' + str(grid_2) + '.txt'
with open(ratio_file) as ratio_f:
ratio = float(ratio_f.readline().strip())
total_gr += ratio
xml_filename_root = filename_root
bbs = ground_truth_bb_all_stefan(
VOC2012_TRAIN_ANNOTATIONS + xml_filename_root)
# bbs = ground_truth_bb_all(VOC2012_TRAIN_ANNOTATIONS+xml_filename_root)
im = Image.open(VOC2012_TRAIN_IMAGES +
xml_filename_root + '.jpg')
width, height = im.size
hxmin, hymin, hxmax, hymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
IoU = metric_calculate.getTopIoU(
hxmin, hymin, hxmax, hymax, bbs)
total_IoU += IoU
total_IoU /= cnt
total_gr /= cnt
detection_res_3_tuple[detection_types.index(
typ)] = total_IoU
gr_res_3_tuple[detection_types.index(typ)] = total_gr
detection_res[scale][tradeoff][
category] = detection_res_3_tuple
gr_res[scale][tradeoff][category] = gr_res_3_tuple
return detection_res, gr_res
def metric_file_analyse(metric_folder, categories):
# categories=["jumping", "phoning", "playinginstrument", "reading", "ridingbike", "ridinghorse", "running", "takingphoto", "usingcomputer", "walking"]
epsilon = '0.001'
lbd = '1.0E-4'
detection_types = ["train", "valval", "valtest"]
detection_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
gr_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
all_positive = True
all_instance = True
for category in categories:
# for category in ["dog"]:
for tradeoff in [
'0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8',
'0.9', '1.0'
]:
# for scale in ["50"]:
for scale in ['50']:
detection_res_3_tuple = [0] * 3
gr_res_3_tuple = [0] * 3
for typ in detection_types:
detection_filename = '_'.join([
"metric", typ, tradeoff, scale,
str(epsilon),
str(lbd), category + '.txt'
])
fp = os.path.join(metric_folder, str(scale),
detection_filename)
f = open(fp)
total_gr = 0
total_IoU = 0
cnt = 0
for line in f:
yp, yi, hp, filename_root = line.strip().split(',')
filename_root = filename_root.split('/')[-1].strip()
if True:
# if yi=='1':
cnt += 1
grid_1, grid_2 = metric_calculate.h2GridCoor(
hp, int(scale))
gaze_file_root = filename_root
# ratio_file = VOC2012_OBJECT_ETLOSS+category+'/'+str(metric_calculate.convert_scale(int(scale)))+'/'+gaze_file_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
# ratio_file = "/local/wangxin/Data/gaze_voc_actions_stefan/ETLoss_ratio/"+str(metric_calculate.convert_scale(int(scale))) + '/'+gaze_file_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
#
# with open(ratio_file) as ratio_f:
# ratio = float(ratio_f.readline().strip())
# total_gr+=ratio
xml_filename_root = filename_root
bbs = ground_truth_bb_all_stefan(
VOC2012_TRAIN_ANNOTATIONS + xml_filename_root)
# bbs = ground_truth_bb_all(VOC2012_TRAIN_ANNOTATIONS+xml_filename_root)
im = Image.open(VOC2012_TRAIN_IMAGES +
xml_filename_root + '.jpg')
width, height = im.size
hxmin, hymin, hxmax, hymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
IoU = metric_calculate.getTopIoU(
hxmin, hymin, hxmax, hymax, bbs)
total_IoU += IoU
total_IoU /= cnt
# total_gr /= cnt
detection_res_3_tuple[detection_types.index(
typ)] = total_IoU
# gr_res_3_tuple[detection_types.index(typ)] = total_gr
detection_res[scale][tradeoff][
category] = detection_res_3_tuple
# gr_res[scale][tradeoff][category]= gr_res_3_tuple
return detection_res, gr_res
def metric_file_analyse(metric_folder, typ):
epsilon = '0.0'
lbd = '1.0E-4'
action_categories = [
"jumping", "phoning", "playinginstrument", "reading", "ridingbike",
"ridinghorse", "running", "takingphoto", "usingcomputer", "walking"
]
detection_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
gr_res = collections.defaultdict(lambda: collections.defaultdict(
lambda: collections.defaultdict(lambda: None)))
for category in action_categories:
for tradeoff in ['0.0', '0.1', '0.5', '1.0', '10.0', '100.0']:
for scale in ["50"]:
detection_filename = '_'.join([
"metric", typ, scale, tradeoff,
str(epsilon),
str(lbd), category + '_0.txt'
])
fp = os.path.join(metric_folder, detection_filename)
f = open(fp)
total_gr = 0
total_IoU_positive = 0
total_IoU_negative = 0
cnt = 0
tn = 0
tp = 1
for line in f:
score, yp, yi, hp, image_path = line.strip().split(',')
grid_1, grid_2 = metric_calculate.h2GridCoor(
hp, int(scale))
filename_root = image_path.split("/")[-1].split('.')[0]
if yi == '1' and yp == '1':
tp += 1
cnt += 1
bbs = ground_truth_bb_all_action(
VOC2012_TRAIN_ANNOTATIONS + filename_root)
# ratio_file = VOC2012_ACTION_ETLOSS_ACTION+category+'/'+str(metric_calculate.convert_scale(scale))+'/'+filename_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
# ratio_f = open(ratio_file)
# ratio = float(ratio_f.readline().strip())
# ratio_f.close()
# fixation_ratio+=ratio
im = Image.open(VOC2012_TRAIN_IMAGES + filename_root +
'.jpg')
width, height = im.size
hxmin, hymin, hxmax, hymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
IoU = metric_calculate.getTopIoU(
hxmin, hymin, hxmax, hymax, bbs)
total_IoU_positive += IoU
if yi == '-1' and yp == '-1':
tn += 1
cnt += 1
bbs = ground_truth_bb_all_action(
VOC2012_TRAIN_ANNOTATIONS + filename_root)
# ratio_file = VOC2012_ACTION_ETLOSS_ACTION+category+'/'+str(metric_calculate.convert_scale(scale))+'/'+filename_root+'_'+str(grid_1)+'_'+str(grid_2)+'.txt'
# ratio_f = open(ratio_file)
# ratio = float(ratio_f.readline().strip())
# ratio_f.close()
# fixation_ratio+=ratio
im = Image.open(VOC2012_TRAIN_IMAGES + filename_root +
'.jpg')
width, height = im.size
hxmin, hymin, hxmax, hymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
IoU = metric_calculate.getTopIoU(
hxmin, hymin, hxmax, hymax, bbs)
total_IoU_negative += IoU
print "tp:%d, scale:%s, tradeoff:%s, totalIOU:%s" % (
tp, scale, tradeoff, total_IoU_positive / tp)
print "tn:%d, scale:%s, tradeoff:%s, totalIOU:%s" % (
tn, scale, tradeoff, total_IoU_negative / tn)