Python Toolkit for VEDAI dataset


In [1]:
from Tools import get_image_path, visualize_annotations, \
                visualize_prediction, load_prediction_txt, \
                evaluate, precision_recall_11points, recall_FPPI

In [2]:
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 15)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap

1. Visualize the annotations


In [3]:
image_id = 27
print get_image_path(image_id)
plt.subplot(2,1,1)
visualize_annotations(image_id, vehicle_shape='ellipse')
plt.subplot(2,1,2)
visualize_annotations(image_id, vehicle_shape='polygon', image_type='ir')


/media/guang/NTFS_Partition_small/VEDAI/Vehicules1024/00000027_co.png