import visualize from visualize import display_images import model as modellib from model import log import balloon get_ipython().magic(u'matplotlib inline') # ## Configurations # # Configurations are defined in balloon.py # In[2]: config = balloon.BalloonConfig() BALLOON_DIR = os.path.join(ROOT_DIR, "datasets/balloon") # ## Dataset # In[3]: # Load dataset # Get the dataset from the releases page # https://github.com/matterport/Mask_RCNN/releases dataset = balloon.BalloonDataset() dataset.load_balloon(BALLOON_DIR, "train") # Must call before using the dataset dataset.prepare()
sys.path.append(ROOT_DIR) # Specifies the path for looking the following packages from mrcnn import utils from mrcnn import visualize from mrcnn.visualize import display_images from mrcnn import model as modellib from mrcnn.model import log import balloon # Creating the deractory to save logs and weights of the model MODEL_DIR = os.path.join(ROOT_DIR,"logs") # Loading the configuration:Object name, No. of epochs and all hyperparameters config = balloon.BalloonConfig() # Configurations are defined in 'balloon.py' and 'config.py' # To modify (if needed) some setting in config. class InferenceConfig(config.__class__): GPU_COUNT = 1 IMAGES_PER_GPU = 1 import cv2 import numpy as np def random_colors(N): np.random.seed(1) colors = [tuple(255 * np.random.rand(3)) for _ in range(N)]