def iou_hi_hp(metric_folder):
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:
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'
])
# detection_filename = 'metric_train_90_0.0_0.0_1.0E-4_jumping_5.0_10_0.txt'
fp = os.path.join(metric_folder, detection_filename)
f = open(fp)
total_gr = 0
cnt = 0
for line in f:
cnt += 1
score, yp, yi, hp, hi, filename_root = line.strip(
).split(',')
width, height = 1, 1
phxmin, phymin, phxmax, phymax = metric_calculate.h2Coor(
width, height, hp, int(scale))
ghxmin, ghymin, ghxmax, ghymax = metric_calculate.h2Coor(
width, height, hi, int(scale))
total_IoU += metric_calculate.getIoU(
phxmin, phymin, phxmax, phymax, ghxmin, ghymin,
ghxmax, ghymax)
print total_IoU / cnt
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(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_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 vis_region_proposals(fixation_path, lsvm_region_dict, glsvm_region_dict):
for root, dirs, files in os.walk(fixation_path):
for file in files:
cls, year, id = file.split('_')
if cls != 'dog':
continue
# year='2011'
# id='001642.ggg'
scale = 6
id = id[:-4]
filename_root = '_'.join([year, id])
fixation = []
f = open(root + cls + '_' + year + '_' + id + '.txt')
for line in f:
x, y = line.strip().split(',')
fixation.append([int(x), int(y)])
f.close()
img = VOC2012_TRAIN_IMAGES + year + '_' + id + '.jpg'
img = cv2.imread(img, -1)
rows = img.shape[0]
cols = img.shape[1]
out = np.zeros((rows, cols, 3), dtype='uint8')
out = np.dstack([img])
for x, y in fixation:
cv2.circle(out, (x, y), 3, [255, 255, 0], 2)
try:
lhxmin, lhymin, lhxmax, lhymax = metric_calculate.h2Coor(
cols, rows, lsvm_region_dict[filename_root], 30)
ghxmin, ghymin, ghxmax, ghymax = metric_calculate.h2Coor(
cols, rows, glsvm_region_dict[filename_root], 30)
except KeyError:
continue
lstart = (int(lhxmin), int(lhymin))
lend = (int(lhxmax), int(lhymax))
gstart = (int(ghxmin), int(ghymin))
gend = (int(ghxmax), int(ghymax))
cv2.rectangle(out, lstart, lend, (0, 0, 255), thickness=3)
# cv2.putText(out, 'lsvm', lstart, cv2.FONT_HERSHEY_SIMPLEX,0.5,(100,0,100),1)
# cv2.putText(out, 'glsvm', gstart, cv2.FONT_HERSHEY_SIMPLEX,0.5,(100,0,100),1)
cv2.rectangle(out, gstart, gend, (255, 0, 0), thickness=3)
bbs = ground_truth_bb(VOC2012_TRAIN_ANNOTATIONS + filename_root,
cls)
for xmin, ymin, xmax, ymax in bbs:
cv2.rectangle(out, (xmin, ymin), (xmax, ymax), (0, 255, 255),
thickness=3)
# if scale ==1:
# gaze_ratio_file = open(VOC2012_OBJECT_ETLOSS+cls+'/'+str(scale*scale)+'/'+cls+'_'+filename_root+'.txt')
# else:
# gaze_ratio_file = open(VOC2012_OBJECT_ETLOSS+cls+'/'+str(scale*scale)+'/'+cls+'_'+filename_root+'_'+str(sy)+'_'+str(sx)+'.txt')
#
#
# gaze_ratio=gaze_ratio_file.readline().strip()
# gaze_ratio_file.close()
#
# cv2.putText(out, str(gaze_ratio[:4])+','+str(sy*scale+sx),\
# (int(0.5*(start[0]+end[0])),int(0.5*(start[1]+end[1]))),\
# cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,0,255),2
# )
cv2.imshow(cls + ' ' + filename_root, out)
k = cv2.waitKey(0)
#space to next image
cv2.destroyAllWindows()
if k == 1048608:
continue
else:
continue
def vis_region_proposals(fixation_path, lsvm_region_dict, glsvm_region_dict):
for root, dirs, files in os.walk(fixation_path):
for file in files:
class_index = -1
filename_root = file[:-5] #-5 because of .json
fixations = json.load(open(fixation_path + file)).values()
xmin, ymin, xmax, ymax, cls = ground_truth_bb(
VOC2012_TRAIN_ANNOTATIONS + filename_root)
image_path = (VOC2012_TRAIN_IMAGES + filename_root) + '.jpg'
image_res_x, image_res_y = Image.open(image_path).size
if cls != 'ridinghorse':
continue
# year='2011'
# id='001642.ggg'
scale = 8
img = VOC2012_TRAIN_IMAGES + filename_root + '.jpg'
img = cv2.imread(img, -1)
rows = img.shape[0]
cols = img.shape[1]
out = np.zeros((rows, cols, 3), dtype='uint8')
out = np.dstack([img])
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
# for cnt, subj in enumerate(fixations):
# for fix in subj:
# cv2.circle(out, (fix[0],fix[1]), 3, [255,255,0], 2)
for fix in fixations[0]:
cv2.circle(out, (fix[0], fix[1]), 1, [255, 255, 0], 2)
try:
lhxmin, lhymin, lhxmax, lhymax = metric_calculate.h2Coor(
cols, rows, lsvm_region_dict[filename_root], 30)
ghxmin, ghymin, ghxmax, ghymax = metric_calculate.h2Coor(
cols, rows, glsvm_region_dict[filename_root], 30)
except KeyError:
continue
lstart = (int(lhxmin), int(lhymin))
lend = (int(lhxmax), int(lhymax))
gstart = (int(ghxmin), int(ghymin))
gend = (int(ghxmax), int(ghymax))
cv2.rectangle(out, lstart, lend, (0, 0, 255), thickness=3)
# cv2.putText(out, 'lsvm', lstart, cv2.FONT_HERSHEY_SIMPLEX,0.5,(100,0,100),1)
# cv2.putText(out, 'glsvm', gstart, cv2.FONT_HERSHEY_SIMPLEX,0.5,(100,0,100),1)
cv2.rectangle(out, gstart, gend, (255, 0, 0), thickness=3)
bbs = ground_truth_bb(VOC2012_TRAIN_ANNOTATIONS + filename_root)
print bbs
xmin, ymin, xmax, ymax, cls = bbs
cv2.rectangle(out, (xmin, ymin), (xmax, ymax), (0, 255, 255),
thickness=3)
# if scale ==1:
# gaze_ratio_file = open(VOC2012_OBJECT_ETLOSS+cls+'/'+str(scale*scale)+'/'+cls+'_'+filename_root+'.txt')
# else:
# gaze_ratio_file = open(VOC2012_OBJECT_ETLOSS+cls+'/'+str(scale*scale)+'/'+cls+'_'+filename_root+'_'+str(sy)+'_'+str(sx)+'.txt')
#
#
# gaze_ratio=gaze_ratio_file.readline().strip()
# gaze_ratio_file.close()
#
# cv2.putText(out, str(gaze_ratio[:4])+','+str(sy*scale+sx),\
# (int(0.5*(start[0]+end[0])),int(0.5*(start[1]+end[1]))),\
# cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,0,255),2
# )
cv2.imshow(cls + ' ' + filename_root, out)
k = cv2.waitKey(0)
#space to next image
cv2.destroyAllWindows()
if k == 1048608:
continue
else:
continue
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)