def test_vis_instance_segmentation_invalid_inputs(self):
with self.assertRaises(ValueError):
vis_instance_segmentation(self.img,
self.mask,
self.label,
self.score,
label_names=self.label_names)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='sbd')
parser.add_argument('image')
args = parser.parse_args()
model = FCISPSROIAlignResNet101(n_fg_class=20,
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = read_image(args.image, color=True)
masks, labels, scores = model.predict([img])
mask, label, score = masks[0], labels[0], scores[0]
bbox = mask_to_bbox(mask)
colors = voc_colormap(list(range(1, len(mask) + 1)))
ax = vis_bbox(img, bbox, instance_colors=colors, alpha=0.5, linewidth=1.5)
vis_instance_segmentation(
None,
mask,
label,
score,
label_names=sbd_instance_segmentation_label_names,
instance_colors=colors,
alpha=0.7,
ax=ax)
plt.show()
def test_vis_instance_segmentation_invalid_inputs(self):
if not optional_modules:
return
with self.assertRaises(ValueError):
vis_instance_segmentation(
self.img, self.bbox, self.mask, self.label, self.score,
label_names=self.label_names)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model',
choices=('faster_rcnn_fpn_resnet50', 'faster_rcnn_fpn_resnet101',
'mask_rcnn_fpn_resnet50', 'mask_rcnn_fpn_resnet101'),
default='faster_rcnn_fpn_resnet50')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='coco')
parser.add_argument('image')
args = parser.parse_args()
if args.model == 'faster_rcnn_fpn_resnet50':
mode = 'bbox'
model = FasterRCNNFPNResNet50(n_fg_class=len(coco_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'faster_rcnn_fpn_resnet101':
mode = 'bbox'
model = FasterRCNNFPNResNet101(n_fg_class=len(coco_bbox_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'mask_rcnn_fpn_resnet50':
mode = 'instance_segmentation'
model = MaskRCNNFPNResNet50(
n_fg_class=len(coco_instance_segmentation_label_names),
pretrained_model=args.pretrained_model)
elif args.model == 'mask_rcnn_fpn_resnet101':
mode = 'instance_segmentation'
model = MaskRCNNFPNResNet101(
n_fg_class=len(coco_instance_segmentation_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image)
if mode == 'bbox':
bboxes, labels, scores = model.predict([img])
bbox = bboxes[0]
label = labels[0]
score = scores[0]
vis_bbox(img, bbox, label, score, label_names=coco_bbox_label_names)
elif mode == 'instance_segmentation':
masks, labels, scores = model.predict([img])
mask = masks[0]
label = labels[0]
score = scores[0]
vis_instance_segmentation(
img,
mask,
label,
score,
label_names=coco_instance_segmentation_label_names)
plt.show()
def save_instance_image(image,
mask,
label=None,
label_names=None,
filename=None):
vis_instance_segmentation(image, mask, label, label_names=label_names)
plt.tick_params(labelbottom=False,
labelleft=False,
labelright=False,
labeltop=False)
plt.tick_params(bottom=False, left=False, right=False, top=False)
plt.savefig(filename, bbox_inches='tight', pad_inches=0)
plt.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default=None)
parser.add_argument('--dataset', choices=('sbd', 'coco'), default='sbd')
parser.add_argument('image')
args = parser.parse_args()
if args.dataset == 'sbd':
if args.pretrained_model is None:
args.pretrained_model = 'sbd'
label_names = sbd_instance_segmentation_label_names
model = FCISResNet101(n_fg_class=len(label_names),
pretrained_model=args.pretrained_model)
elif args.dataset == 'coco':
if args.pretrained_model is None:
args.pretrained_model = 'coco'
label_names = coco_instance_segmentation_label_names
proposal_creator_params = FCISResNet101.proposal_creator_params
proposal_creator_params['min_size'] = 2
model = FCISResNet101(n_fg_class=len(label_names),
anchor_scales=(4, 8, 16, 32),
pretrained_model=args.pretrained_model,
proposal_creator_params=proposal_creator_params)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = read_image(args.image, color=True)
masks, labels, scores = model.predict([img])
mask, label, score = masks[0], labels[0], scores[0]
bbox = mask_to_bbox(mask)
colors = voc_colormap(list(range(1, len(mask) + 1)))
ax = vis_bbox(img, bbox, instance_colors=colors, alpha=0.5, linewidth=1.5)
vis_instance_segmentation(None,
mask,
label,
score,
label_names=label_names,
instance_colors=colors,
alpha=0.7,
ax=ax)
plt.show()
def test_vis_instance_segmentation(self):
ax = vis_instance_segmentation(self.img,
self.mask,
self.label,
self.score,
label_names=self.label_names,
instance_colors=self.instance_colors)
self.assertIsInstance(ax, matplotlib.axes.Axes)
def test_vis_instance_segmentation(self):
if not optional_modules:
return
ax = vis_instance_segmentation(
self.img, self.bbox, self.mask, self.label, self.score,
label_names=self.label_names)
self.assertIsInstance(ax, matplotlib.axes.Axes)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model', default='coco')
parser.add_argument('image')
args = parser.parse_args()
proposal_creator_params = {
'nms_thresh': 0.7,
'n_train_pre_nms': 12000,
'n_train_post_nms': 2000,
'n_test_pre_nms': 6000,
'n_test_post_nms': 1000,
'force_cpu_nms': False,
'min_size': 0
}
model = FCISPSROIAlignResNet101(
n_fg_class=len(coco_instance_segmentation_label_names),
min_size=800, max_size=1333,
anchor_scales=(2, 4, 8, 16, 32),
pretrained_model=args.pretrained_model,
proposal_creator_params=proposal_creator_params)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = read_image(args.image, color=True)
masks, labels, scores = model.predict([img])
mask, label, score = masks[0], labels[0], scores[0]
bbox = mask_to_bbox(mask)
colors = voc_colormap(list(range(1, len(mask) + 1)))
ax = vis_bbox(
img, bbox, instance_colors=colors, alpha=0.5, linewidth=1.5)
vis_instance_segmentation(
None, mask, label, score,
label_names=coco_instance_segmentation_label_names,
instance_colors=colors, alpha=0.7, ax=ax)
plt.show()
