def __init__(self, name, project):
fname = "{}-{}.h5".format(name, project)
self.fname = os.path.join(DIR, fname)
# Uses default im_list
config = utils.get_config(project)
self.im_list = utils.open_im_list(config["im_list"])
self.n = len(self.im_list)
self.save_freq = 10
# Metrics
# im x NUMCLASS
self.load()
assert self.precision.shape[0] == self.n
assert self.recall.shape[0] == self.n
assert self.iou.shape[0] == self.n
gt_s = image_utils.scale_maxside(gt, maxside=maxside)
box = image_utils.random_crop(img_s)
data = image_utils.crop_array(img_s, box)
label = image_utils.crop_array(gt_s, box)
# Batch size of 1
data = data[np.newaxis, ...]
label = label[np.newaxis, ...]
yield (data, label)
if __name__ == "__main__":
project = "local"
config = utils.get_config(project)
im_list = utils.open_im_list(config["im_list"])
datasource = DataSource(config)
generator = DataGenerator(im_list, datasource, maxside=512)
data, label = generator.next()
print data.shape
print label.shape
data = data[0]
label = label[0]
unlabeled = np.max(label, axis=2) == 0
gt = np.argmax(label, axis=2) + 1
gt[unlabeled] = 0
gt = utils.add_color(gt)
config = utils.get_config(args.project)
if args.prediction is not None:
config["pspnet_prediction"] = args.prediction
datasource = DataSource(config)
evaluator = Evaluator(args.name, args.project,
datasource) # Evaluation results
vis = ProjectVisualizer(args.project,
datasource,
MAX=args.number,
evaluator=evaluator)
# Image List
im_list = None
if args.im_list:
# Open specific image list
im_list = utils.open_im_list(args.im_list)
elif args.category:
im_list = evaluator.get_im_list_by_category(args.category)
else:
# Open default image list
im_list = utils.open_im_list(config["im_list"])
if args.randomize:
# Shuffle image list
random.seed(3)
random.shuffle(im_list)
im_list = im_list[args.start:]
vis.visualize_images(im_list, category=args.category)
img_color = np.zeros((h,w,3))
for i in xrange(1,151):
img_color[img == i] = utils.to_color(i)
path = self.save(img_color)
return img_color, path
def save(self, img):
fname = "{}.jpg".format(uuid.uuid4().hex)
path = os.path.join(IMAGES_DIR, fname)
misc.imsave(path, img)
return path
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--project', required=True, help="Project name")
parser.add_argument('-i', '--image', help="Image name")
args = parser.parse_args()
im = args.image
if not args.image:
im_list = utils.open_im_list(args.project)
im = im_list[0]
print args.project, im
config = utils.get_config(args.project)
datasource = DataSource(config)
vis = ImageVisualizer(args.project, datasource)
paths = vis.visualize(im)
print paths