# [NA-Birds](http://dl.allaboutbirds.org/nabirds) dataset wrapper

For more deatils to see how to use this library take a look at [nabirds/display.py](nabirds/display.py).

## Annotation and Image Loading

Here is some example code how to load images and use the predefined train-test split.

```python
# replace NAB_Annotations with CUB_Annotations to load CUB200-2011 annotations
from nabirds import NAB_Annotations, Dataset

annot = NAB_Annotations("path/to/nab/folder")

train_data = Dataset(annot.train_uuids, annot)
test_data = Dataset(annot.test_uuids, annot)

print("Loaded {} training and {} test images".format(len(train_data), len(test_data)))
```

## Dataset Iteration
```python
import matplotlib.pyplot as plt

# either access the images directly
im, parts, label = test_data[100]
plt.imshow(im)
plt.show()

# or iterate over the dataset
for im, parts, label in train_data:
    plt.imshow(im)
    plt.show()

```

## Working with Part Annotations
Both datasets (NAB and CUB) have part annotations. Each annotation has for each of the predefined parts the location of this part and a boolean (`0` or `1`) value whether this part is visible. A [`Dataset`](nabirds/dataset/__init__.py#L5) instance returns besides the image and the class label this information:

```python

im, parts, label = train_data[100]

print(parts)
# array([[  0, 529, 304,   1],
#        [  1, 427, 277,   1],
#        [  2, 368, 323,   1],
#        [  3,   0,   0,   0],
#        [  4, 449, 292,   1],
#        [  5, 398, 502,   1],
#        [  6, 430, 398,   1],
#        [  7,   0,   0,   0],
#        [  8, 365, 751,   1],
#        [  9,   0,   0,   0],
#        [ 10,   0,   0,   0]])

```

In order to filter by only visible parts use the [`utils.visible_part_locs`](nabirds/dataset/utils.py#L28) function. It returns the indices and the x-y positions of the visible parts:

```python
idxs, xy = visible_part_locs(parts)
print(idxs)
# array([0, 1, 2, 4, 5, 6, 8])
print(xy)
# array([[529, 427, 368, 449, 398, 430, 365],
#        [304, 277, 323, 292, 502, 398, 751]])

x, y = xy
plt.imshow(im)
plt.scatter(x,y, marker="x", c=idxs)
plt.show()
```