def visualize_proposals(self,
imgs,
proposals,
gt_bboxes,
img_meta,
slice_num,
isProposal=True):
if slice_num is None:
img = tensor2img3D(imgs, slice_num=45)
else:
img = tensor2img3D(imgs, slice_num=slice_num)
batch_num = 0
for cur_proposals, cur_gt_bboxes, cur_img_meta in zip(
proposals, gt_bboxes, img_meta):
bboxes = []
cur_proposals = cur_proposals.cpu().numpy()
for bbox in cur_proposals:
if slice_num is None:
bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3]])
elif slice_num is not None and slice_num >= math.floor(
bbox[4]) and slice_num <= math.ceil(bbox[5]):
# select bounding boxes on this slice
bboxes.append([bbox[0], bbox[1], bbox[2], bbox[3]])
part_filename = 'prop' if isProposal else 'anch'
filename = 'tests/iter_{}_img_id_{}_batch_{}_{}.png'.format(
self.iteration, cur_img_meta['image_id'], batch_num,
part_filename)
self.show_bboxes_gt_bboxes(img,
np.array(bboxes),
gt_bboxes=cur_gt_bboxes,
out_file=filename)
batch_num += 1
def visualize_gt_bboxes_masks(self, imgs, gt_bboxes, img_meta, gt_masks):
for num_img in range(len(gt_bboxes)):
gt_bboxes_np = gt_bboxes[num_img].cpu().numpy()
gt_masks_np = gt_masks[num_img].cpu().numpy()
bbox_num = 0
for bbox in gt_bboxes_np:
for slice_num in range(int(bbox[4]), int(bbox[5])):
img = tensor2img3D(imgs, slice_num=slice_num)
filename = 'tests/iter_{}_img_id_{}_bbox_num_{}_slice_{}.png'.format(
self.iteration, img_meta[num_img]['image_id'],
bbox_num, slice_num)
plt.figure()
plt.imshow(img)
plt.imshow(gt_masks_np[0, slice_num, :, :] * 255,
alpha=0.3)
ax = plt.gca()
rect = pts.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='red',
linewidth=2)
ax.add_patch(rect)
plt.savefig(filename)
plt.close()
bbox_num += 1
breakpoint()
def visualize_gt_bboxes(self, imgs, gt_bboxes, img_meta):
gt_bboxes_np = gt_bboxes[0].cpu().numpy()
for bbox in gt_bboxes_np:
for slice_num in range(int(bbox[4]), int(bbox[5])):
img = tensor2img3D(imgs, slice_num=slice_num)
filename = 'tests/iter_{}_img_id_{}_slice_{}.png'.format(self.iteration, img_meta[0]['image_id'], slice_num)
mmcv.imshow_bboxes(img, np.array([bbox]), show=False, out_file=filename)
def visualize_anchor_boxes(self,
imgs,
cls_scores,
img_metas,
slice_num=45,
top_k=None,
shuffle=False):
featmap_sizes = [featmap.size()[-3:] for featmap in cls_scores]
assert len(featmap_sizes) == len(self.anchor_generators)
anchor_list, valid_flag_list = self.get_anchors(
featmap_sizes, img_metas)
img = tensor2img3D(imgs, slice_num=slice_num)
anchors = []
unique_set = set()
for bboxes in anchor_list[0]:
bboxes = bboxes.cpu().numpy()
for bbox in bboxes:
# select each aspect ratio bounding box in the middle of an image
# if bbox[0] >= 100 and bbox[0] <= 400 and bbox[2] >= 100 and bbox[2] <= 400 and \
# bbox[1] >= 150 and bbox[1] <= 450 and bbox[3] >= 150 and bbox[3] <= 450 and \
# slice_num >= bbox[4] and slice_num <= bbox[5] and (bbox[5] - bbox[4]) not in unique_set:
# unique_set.add(bbox[5] - bbox[4])
# anchors.append([bbox[0], bbox[1], bbox[2], bbox[3]])
# Get all anchors in the middle of the image
if bbox[0] >= 100 and bbox[0] <= 400 and bbox[2] >= 100 and bbox[2] <= 400 and \
bbox[1] >= 150 and bbox[1] <= 450 and bbox[3] >= 150 and bbox[3] <= 450 and \
slice_num >= bbox[4] and slice_num <= bbox[5] and (bbox[2] - bbox[0]) not in unique_set:
anchors.append([bbox[0], bbox[1], bbox[2], bbox[3]])
print(unique_set)
breakpoint()
if shuffle is True:
while True:
random.shuffle(anchors)
mmcv.imshow_bboxes(img, np.array(anchors), top_k=20)
elif top_k is None:
mmcv.imshow_bboxes(img, np.array(anchors))
else:
mmcv.imshow_bboxes(img, np.array(anchors), top_k=top_k)
breakpoint()
