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)]
