visualize_data.py

import os
import turicreate as tc

path = '../'  # The path to the root of your project directory.
sf_path = os.path.join(path, 'data.sframe')  # The path to load the SFrame from. See `gen_sframe.py` for more.
data = tc.SFrame(sf_path)  # The training data for our model.

# This takes the first 50 rows from the SFrame we just loaded from disk and drops the rest of it. We do this so the
# visualization doesn't take too long to load and run.
data = data.head(50)

# This removes the path and raw image from data. When visualizing the data, we only want to see our supplied annotations
# and the bounding boxes for detected objects. This change won't affect the SFrame located on disk at sf_path, just
# the in-memory representation of it.
del data['path']

# This draws bounding boxes on the "image" column of data, which makes it easy to visualize the raw data.
data['image'] = tc.object_detector.util.draw_bounding_boxes(data['image'], data['annotations'])

# This will open a new window on your computer so you can visualize the data.
data.explore()