def compile_props(self):
good_props = []
for i in range(len(self.annotations)):
img_path = self.annotations[i].path
#print(img_path)
hoi_list = self.annotations[i].hoi_list
humans = []
objects = []
hoi_ids = []
for p in hoi_list:
for c in p.connections:
humans.append(p.human_boxes[c[0] - 1])
objects.append(p.object_boxes[c[1] - 1])
hoi_ids.append(p.hoi_id)
# find distinct humans and objects
unique_p = []
n = len(humans)
for j in range(len(humans)):
h = humans[j]
o = objects[j]
idxs = []
idxs.append(j)
for k in range(len(humans)):
if k != j:
if min(tools.compute_iou2(h, humans[k]),
tools.compute_iou2(o, objects[k])) > 0.5:
idxs.append(k)
unique_p.append(idxs)
for p in unique_p:
p.sort()
up2 = []
for p in unique_p:
if p not in up2:
up2.append(p)
for p in up2:
hois = []
for idx in p:
hois.append(hoi_ids[idx])
gt_vector = tools.build_gt_vec(hois).astype(np.int32)
h_box = np.array(humans[p[0]]).astype(np.int32)
o_box = np.array(objects[p[0]]).astype(np.int32)
good_props.append(test_prop(img_path, h_box, o_box, gt_vector))
return good_props
def get_img_props(self, det_props, annots, prop_number):
img_name = det_props[0]
t1_neg_set = []
t2_neg_set = []
pos_set = []
if prop_number < 4 and prop_number > 1:
prop_number = 4
for proposal in det_props[1]:
obj_name = proposal[1][2].replace(' ', '_')
gt_hois = annots.hoi_list # Change this to be a dictionary
confirmed_hoi_list = []
t1 = False
for gt in gt_hois:
if gt.obj == obj_name:
for conn in gt.connections:
o_idx = conn[1]
h_idx = conn[0]
#Compute IOUs
iou_h = tools.compute_iou(proposal[0][0],
gt.human_boxes[h_idx - 1])
iou_o = tools.compute_iou(proposal[1][0],
gt.object_boxes[o_idx - 1])
min_iou = min(iou_o, iou_h)
if min_iou >= 0.5:
confirmed_hoi_list.append(
gt.hoi_id.astype(np.int32))
elif min_iou > 0.1:
t1 = True
if len(confirmed_hoi_list) == 0:
if t1 == True:
confirmed_hoi_list.append(
self.no_interaction_idxs[obj_name])
gt_vector = tools.build_gt_vec(confirmed_hoi_list).astype(
np.int32)
t1_neg_set.append([
img_name, proposal[0][0], proposal[1][0], gt_vector,
proposal[0][1], proposal[1][1]
])
else:
confirmed_hoi_list.append(
self.no_interaction_idxs[obj_name])
gt_vector = tools.build_gt_vec(confirmed_hoi_list).astype(
np.int32)
t2_neg_set.append([
img_name, proposal[0][0], proposal[1][0], gt_vector,
proposal[0][1], proposal[1][1]
])
else:
gt_vector = tools.build_gt_vec(confirmed_hoi_list).astype(
np.int32)
pos_set.append([
img_name, proposal[0][0], proposal[1][0], gt_vector,
proposal[0][1], proposal[1][1]
])
# Now we choose a random selection from each
batch_prop_list = []
if prop_number == 1:
if len(pos_set) != 0:
batch_prop_list.append(random.choice(pos_set))
elif len(t2_neg_set) != 0:
batch_prop_list.append(random.choice(t2_neg_set))
elif len(t1_neg_set) != 0:
batch_prop_list.append(random.choice(t1_neg_set))
else:
index = random.choice(range(len(self)))
batch_prop_list = self.get_img_props(self.proposals[index],
self.annotations[index],
self.proposal_count)
return batch_prop_list
n_pos = random.randrange(1, prop_number -
1) # Random number between 1 and max props
if len(pos_set) < n_pos:
for i in range(len(pos_set)):
batch_prop_list.append(pos_set[i])
n_pos = len(pos_set)
else:
picks = random.sample(pos_set, k=n_pos)
for i in range(len(picks)):
batch_prop_list.append(picks[i])
n_t1 = random.randrange(1, prop_number - n_pos) # random number
if len(t1_neg_set) < n_t1:
for i in range(len(t1_neg_set)):
batch_prop_list.append(t1_neg_set[i])
n_t1 = len(t1_neg_set)
else:
picks = random.sample(t1_neg_set, k=n_t1)
for i in range(len(picks)):
batch_prop_list.append(picks[i])
n_t2 = prop_number - n_pos - n_t1
if len(t2_neg_set) < n_t2:
for i in range(len(t2_neg_set)):
batch_prop_list.append(t2_neg_set[i])
else:
picks = random.sample(t2_neg_set, k=n_t2)
for i in range(len(picks)):
batch_prop_list.append(picks[i])
while len(batch_prop_list) < prop_number:
if len(t2_neg_set) != 0:
batch_prop_list.append(random.choice(t2_neg_set))
elif len(t1_neg_set) != 0:
batch_prop_list.append(random.choice(t1_neg_set))
elif len(pos_set) != 0:
batch_prop_list.append(random.choice(pos_set))
else:
index = random.choice(range(len(self)))
batch_prop_list = self.get_img_props(self.proposals[index],
self.annotations[index],
self.proposal_count)
return batch_prop_list