bbe_2dtensor=bbe_2dtensor)
_, ax = plt.subplots(figsize=(15, 7.35))
ax.imshow(x / 255)
# iterate over rois
for k in range(pred_bbox2d.shape[0]):
print(np.array(clz_2dtensor[k]))
# print('{} -> {}'.format(k, classes[clz]))
clz = np.argmax(clz_2dtensor[k])
roi = roi_2dtensor[k]
bbox = pred_bbox2d[k]
rframe = box2frame(box=roi, apoint=[0, 0])
ax.add_patch(
Rectangle((rframe[0], rframe[1]),
rframe[2],
rframe[3],
linewidth=1,
edgecolor='r',
facecolor='none',
linestyle='-'))
tframe = box2frame(box=bbox, apoint=[0.5, 0.5])
ax.annotate(classes[clz], (tframe[0], tframe[1]),
color='green',
weight='bold',
fg_3dtensor = clz_4dtensor[:, :, :, 0]
bg_3dtensor = clz_4dtensor[:, :, :, 1]
fg_indices = tf.where(condition=tf.math.equal(x=fg_3dtensor, y=1.0))
fg_anchor_2dtensor = tf.gather_nd(params=anchor_4dtensor, indices=fg_indices)
fg_2dtensor = tf.gather_nd(params=bbox_4dtensor, indices=fg_indices)
bg_indices = tf.where(condition=tf.math.equal(x=bg_3dtensor, y=1.0))
bg_anchor_2dtensor = tf.gather_nd(params=anchor_4dtensor, indices=bg_indices)
ax.imshow(x/255)
ax.set_xlabel('Order: {}, Image ID: {}'.format(sample_order, img_id))
for i in range(fg_anchor_2dtensor.shape[0]):
box = np.array(fg_anchor_2dtensor[i], dtype='float32')
frame = box2frame(box=box, apoint=[0, 0])
ax.add_patch(Rectangle(
(frame[0], frame[1]), frame[2], frame[3],
linewidth=0.8,
edgecolor='cyan',
facecolor='none',
linestyle='-'))
box = np.array(fg_2dtensor[i], dtype='float32')
frame = box2frame(box=box, apoint=[0, 0])
ax.add_patch(Rectangle(
(frame[0], frame[1]), frame[2], frame[3],
linewidth=1.0,
edgecolor='yellow',
facecolor='none',
linestyle='-'))
axis=0) # (h, w, k, 1)
sum_neg_4dtensor = tf.reduce_sum(input_tensor=neg_5dtensor,
axis=0) # (h, w, k, 1)
# Shows
sum_pos_4dtensor = np.array(sum_pos_4dtensor)
sum_neg_4dtensor = np.array(sum_neg_4dtensor)
ax.imshow(x / 255)
ax.set_xlabel('Order: {}, Image ID: {}'.format(sample_order, img_id))
for h in range(H):
for w in range(W):
for k in range(K):
frame = box2frame(box=abox4d[h, w, k], apoint=[0, 0])
# positive anchors
if sum_pos_4dtensor[h, w, k, 0] > 0.0:
if True:
ax.add_patch(
Rectangle((frame[0], frame[1]),
frame[2],
frame[3],
linewidth=1,
edgecolor='cyan',
facecolor='none',
linestyle='-'))
# negative anchors
if sum_neg_4dtensor[h, w, k, 0] == -num_of_bboxes:
def update(routes, boxes, clean_after, min_frame_of_pos_route):
'''
To do:
- Add boxes to the suitable routes
- Remmove invalid routes
Arguments
routes: list of integers and tuples, tuples are points
boxes:
clean_after:
min_frame_of_pos_route:
Note
16777216 = 256*256*256 which is r*g*b
'''
if len(boxes) == 0:
return
if len(routes) == 0:
for box in boxes:
routes.append([random.randint(0, 16777216), box2frame(box)])
return
added_route_indices = []
for box in boxes:
frame = box2frame(box)
added = False
removed = []
for i in range(len(routes)):
if i in added_route_indices:
continue
route = routes[i]
last_item = route[-1]
if type(last_item) is int:
if last_item == -1:
continue
if last_item > clean_after:
removed.append(i)
continue
routes[i][-1] += 1
last_item = route[-2]
if in_box(point=last_item[:2], box=box):
routes[i].append(frame)
added_route_indices.append(i)
added = True
else:
if type(route[-1]) is int:
routes[i][-1] += 1
else:
routes[i].append(1)
# Remove invalid route after some frames
for idx in sorted(removed, reverse=True):
if len(routes[idx]) < min_frame_of_pos_route:
del routes[idx]
else:
routes[idx].append(-1) #no more adding for this route
if added is not True:
routes.append([random.randint(0, 16777216), frame])
_, ax = plt.subplots(figsize=(15, 7.35))
num_of_rois = y_pred.shape[0]
ax.imshow(x / 255)
ax.set_xlabel('Order: {}, Image ID: {}'.format(sample_order, img_id))
for k in range(num_of_rois):
pclz = int(y_pred[k, 8])
# if pclz == len(classes)-1:
# continue
rbox = np.array(y_pred[k, :4])
pbox = np.array(y_pred[k, 4:8])
tframe = box2frame(box=pbox, apoint=[0.5, 0.5])
ax.annotate(classes[pclz], (tframe[0], tframe[1]),
color='green',
weight='bold',
fontsize=8,
ha='center',
va='center')
rframe = box2frame(box=rbox, apoint=[0, 0])
tframe = box2frame(box=pbox, apoint=[0, 0])
ax.add_patch(
Rectangle((rframe[0], rframe[1]),
rframe[2],
rframe[3],
sum_neg_4dtensor = tf.reduce_sum(input_tensor=neg_5dtensor,
axis=0) # (h, w, k, 1)
# Shows
sum_pos_4dtensor = np.array(sum_pos_4dtensor)
sum_neg_4dtensor = np.array(sum_neg_4dtensor)
ax[int(lvl / 2), lvl % 2].imshow(x / 255)
ax[int(lvl / 2), lvl % 2].set_xlim([0, x.shape[1]])
ax[int(lvl / 2), lvl % 2].set_ylim([x.shape[0], 0])
for h in range(H):
for w in range(W):
for k in range(K):
frame = box2frame(box=anchor4d[h, w, k], apoint=[0, 0])
# positive anchors
if sum_pos_4dtensor[h, w, k, 0] > 0.0:
if True:
ax[int(lvl / 2), lvl % 2].add_patch(
Rectangle((frame[0], frame[1]),
frame[2],
frame[3],
linewidth=1,
edgecolor='cyan',
facecolor='none',
linestyle='-'))
# negative anchors
if sum_neg_4dtensor[h, w, k, 0] == -num_of_bboxes:
bbe_2dtensor = clzbbe_2dtensor[:, len(classes):]
pred_bbox2d = bbe2box2d(box_2dtensor=roi_2dtensor,
bbe_2dtensor=bbe_2dtensor)
fig, ax = plt.subplots(figsize=(15, 7.35))
ax.imshow(x / 255)
for k in range(roi_2dtensor.shape[0]):
clz1d = np.array(clz_2dtensor[k]) # onehot
if np.sum(clz1d) == 0:
continue
clz = np.argmax(clz1d)
pred_bbox = pred_bbox2d[k]
tframe = box2frame(box=pred_bbox, apoint=[0.5, 0.5])
ax.annotate(classes[clz], (tframe[0], tframe[1]),
color='green',
weight='bold',
fontsize=8,
ha='center',
va='center')
tframe = box2frame(box=pred_bbox, apoint=[0.0, 0.0])
ax.add_patch(
Rectangle((tframe[0], tframe[1]),
tframe[2],
tframe[3],
linewidth=1,
