def __init__(self, gpu_id=None):
self.gpu_id = gpu_id
self.model = "fm-res39"
self.model_path = fast_mask_root + "/models/" + self.model + ".test.prototxt"
self.weight_path = fast_mask_root + "/params/" + self.model + "_final.caffemodel"
self.threshold = 0.85
self.COLORS = COLORS
self.br = cv_bridge.CvBridge()
self.net = None
self.save_and_display = rospy.get_param("~save_and_display", True)
# load config
if os.path.exists(fast_mask_root +
"/configs/%szoom.json" % self.model):
load_config(fast_mask_root + "/configs/%szoom.json" % self.model)
else:
print "Specified config does not exists, use the default config..."
s = rospy.Service("fast_mask_segmentation_server",
FastMaskSegmentation,
self.handle_fast_mask_segmentation)
rospy.spin()
parser.add_argument('--restore', dest='restore', type=str)
parser.add_argument('--debug', dest='debug', type=bool, default=False)
parser.add_argument('--init_weights', dest='init_weights', type=str,
default='ResNet-50-model.caffemodel')
parser.add_argument('--step', dest='step', type=int, default=int(1e5))
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
config.model = args.model
config.solver_prototxt = args.model + '.solver.prototxt'
if os.path.exists("configs/%s.json" % config.model):
load_config("configs/%s.json" % config.model)
else:
print "The config file does not exist, use the default config..."
caffe.set_mode_gpu()
caffe.set_device(int(args.gpu_id))
setproctitle.setproctitle('spider ' + args.model)
print 'models/%s' % config.solver_prototxt
solver = caffe.SGDSolver('models/%s' % config.solver_prototxt)
setproctitle.setproctitle(args.model)
# restore
if getattr(args, 'restore', None) is not None:
solver.restore("params/%s" % args.restore)
# finetune
else:
return args
if __name__ == '__main__':
args = parse_args()
# caffe setup
caffe.set_mode_gpu()
caffe.set_device(int(args.gpu_id))
net = caffe.Net('models/' + args.model + '.test.prototxt',
'params/' + args.init_weights, caffe.TEST)
# load config
if os.path.exists("configs/%s.json" % args.model):
load_config("configs/%s.json" % args.model)
else:
print("Specified config does not exists, use the default config...")
image = load_image(args.input_image)
oh, ow = image.shape[:2]
im_scale = config.TEST_SCALE * 1.0 / max(oh, ow)
input_blob = image - config.RGB_MEAN
input_blob = input_blob.transpose((2, 0, 1))
ih, iw = int(oh * im_scale), int(ow * im_scale)
ih, iw = ih - ih % 4, iw - iw % 4
input_blob = resize_blob(input_blob, dest_shape=(ih, iw))
input_blob = input_blob[np.newaxis, ...]
ret_masks, ret_scores = gen_masks(net,
input_blob,
# caffe setup
caffe.set_mode_gpu()
gpu_id = 0
caffe.set_device(gpu_id)
model = "fm-res39"
init_weights = "fm-res39_final.caffemodel"
input_image = fast_mask_root + "/images/frame0000.jpg"
threshold = 0.85
net = caffe.Net(fast_mask_root + 'models/' + model + '.test.prototxt',
fast_mask_root + 'params/' + init_weights, caffe.TEST)
# load config
if os.path.exists("configs/%s.json" % model):
load_config("configs/%szoom.json" % model)
else:
print "Specified config does not exists, use the default config..."
image = cv2.imread(input_image)
image = image.astype(np.float64)
oh, ow = image.shape[:2]
im_scale = config.TEST_SCALE * 1.0 / max(oh, ow)
input_blob = image - config.RGB_MEAN
input_blob = input_blob.transpose((2, 0, 1))
ih, iw = int(oh * im_scale), int(ow * im_scale)
ih, iw = ih - ih % 4, iw - iw % 4
input_blob = resize_blob(input_blob, dest_shape=(ih, iw))
input_blob = input_blob[np.newaxis, ...]
ret_masks, ret_scores = gen_masks(net,
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
caffe.set_mode_gpu()
caffe.set_device(int(args.gpu_id))
setproctitle.setproctitle(args.model_prototxt)
net = caffe.Net('models/' + args.model_prototxt + ".test.prototxt",
'params/' + args.init_weights, caffe.TEST)
if os.path.exists("configs/%s.json" % args.model_prototxt):
load_config("configs/%s.json" % args.model_prototxt)
else:
print "Specified config does not exists, use the default config..."
config.ANNOTATION_TYPE = "val2014"
config.IMAGE_SET = "val2014"
from spiders.coco_ssm_spider import COCOSSMDemoSpider
spider = COCOSSMDemoSpider()
ds = spider.dataset
i = 0
while i < args.start_from:
spider.next_idx()
i += 1
timer = Timer()
