# Our Naïve Bees Classifier

We're excited to launch a new comeptition with our partner Metis. If you're not sure what we're talking about, head to:

### The Competition Homepage

The question at hand is: can you identify a bee as a honey bee or a bumble bee? These bees have different behaviors and appearances, but given the variety of backgrounds, positions, and image resolutions it can be a challenge for machines to tell them apart. You can win part of the \$5,000 prize pool by competing!

Being able to identify bee species from images is a task that ultimately would allow researchers to more quickly and effectively collect field data. Pollinating bees have critical roles in both ecology and agriculture, and diseases like colony collapse disorder threaten these species.

Here, we'll walk through what a first-pass approach to this question could look like. We'll use relatively simple features and models to show that even without GPU computing and neural nets, we can start to answer this question.

### This notebook contains three sections:

We'll get started by loading the python modules in our analysis toolkit.

In [1]:
import os
from tqdm import tqdm # smart progress bar

# ================  GRAPHICS
from PIL import Image

import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rc_params

# get our styles
mpl_default = rc_params()

import seaborn as sns
sns.set(rc=mpl_default)

%matplotlib inline
plt.rcParams = mpl_default

# ================  NUMBERS AND MATHS AND THINGS
import pandas as pd
import numpy as np

# BEES colormap! Black -> Yellow
CMAP = [(0,0,0),
(22,0,0),
(43,0,0),
(77,14,0),
(149,68,0),
(220,123,0),
(255,165,0),
(255,192,0),
(255,220,0),
(255,235,0),
(255,245,0),
(255,255,0)]

bees_cm = mpl.colors.ListedColormap(np.array(CMAP)/255.)


The first step is to look at the files we downloaded from the competition webpage and load the training labels and submission format. The data for this competition is very simple. It's a set of training and test images, and then a set of labels for the training images. The submission format has a placeholder for your predictions. As noted on the problem description page, a prediction of 1.0 means you are sure that the bee pictured is a Bombus. A prediction of 0.0 means that you are sure it's an Apis. A prediction of 0.5 means you don't know which it is.

In [2]:
# list the contents of the data directory
!tree --filelimit=10 data/

data/
├── SubmissionFormat.csv
├── images
│   ├── test [992 entries exceeds filelimit, not opening dir]
│   └── train [3969 entries exceeds filelimit, not opening dir]
└── train_labels.csv

3 directories, 2 files


In [3]:
# load the labels using pandas
index_col=0)

index_col=0)

print "Number of training examples is: ", labels.shape[0]
print "Predictions should be type:", labels.dtypes[0]

Number of training examples is:  3969
Predictions should be type: float64


Out[3]:
genus
id
520 1
3800 1
3289 1
2695 1
4922 1
In [4]:
# let's see what's in one of the image folders
!ls data/images/train/ | head -n 10

1.jpg
100.jpg
1000.jpg
1002.jpg
1003.jpg
1004.jpg
1007.jpg
1008.jpg
1009.jpg
1010.jpg



We can see that the image file names match the id in labels and submission_format. This is how we match an image to whether the bee is an Apis or Bombus.

Ok, finally, let's look at some bees. The PIL library (we recommend the Pillow fork) is a great way to interact with images in Python. We'll use PIL for accessing images, but do most of our processing by converting the images to Numpy arrays. Also, note that all of the images are the same size (200x200 px), which we've done to make your lives easier.

In [7]:
def get_image(row_or_str, root="data/images/"):
# if we have an instance from the data frame, pull out the id
# otherwise, it is a string for the image id
if isinstance(row_or_str, pd.core.series.Series):
row_id = row_or_str.name
else:
row_id = row_or_str

filename = "{}.jpg".format(row_id)

# test both of these so we don't have to specify. Image should be
# in one of these two. If not, we let Image.open raise an exception.
train_path = os.path.join(root, "train", filename)
test_path = os.path.join(root, "test", filename)

file_path = train_path if os.path.exists(train_path) else test_path

return np.array(Image.open(file_path), dtype=np.int32)

# make sure it works for strings and series
assert (get_image(labels.iloc[0]) == get_image("520")).all()

# confirm images are the same shape
print "Size of image 520:  ", get_image("520").shape
print "Size of image 1001: ", get_image("1001").shape

Size of image 520:   (200, 200, 3)
Size of image 1001:  (200, 200, 3)


In [8]:
# imshow expects floats between 0-1, but we have
# integers 0-255 from PIL so we need to rescale to view the image
plt.imshow((get_image("520") / 255.))

Out[8]:
<matplotlib.image.AxesImage at 0x10c5d9950>


# Build features from the image

Now we need to turn these images into something that a machine learning algorithm can understand. We've got a matrix of pixel values, but those don't contain enough interesting information on their own for most algorithms. We need to help the algorithms along by picking out some of the salient features for them.

We'll be building three kinds of features:

First, we'll start by extracting the color information since both HOG and DAISY work on grayscale images.

In [22]:
def extract_rgb_info(rgb, ax=None):
"""Extract color statistics as features:
- pixel values (flattened)
- X, Y sums per channel
- percentiles per channel
- percentile diffs per channel

Plots if ax is passed
"""
# toss alpha if it exists
if rgb.shape[2] == 4:
rgb = rgb[:, :, :3]

features = [rgb.flatten()]

# add some basic statistics on the color channels (R, G, B)
for channel in range(3):
this_channel = rgb[:, :, channel].astype(np.float)
sums = np.hstack([this_channel.sum(),
this_channel.sum(axis=0),
this_channel.sum(axis=1)])

# percentiles
ps = [1, 3, 5, 10, 50, 90, 95, 97, 99]
percentiles = np.array(np.percentile(this_channel, ps))
diff = percentiles[-4:] - percentiles[:4]

# plot if we have been passed an axis
if ax is not None:
channel_name = ['r', 'g', 'b'][channel]
sns.kdeplot(this_channel.flatten(),
ax=ax,
label=channel_name,
color=channel_name)
ax.set_title("Color channels")

# store the features for this channel
features += [sums, percentiles, diff]

# return all the color features as a flat array
return np.hstack(features).flatten()

extract_rgb_info(get_image("1974"), ax=plt.gca())

Out[22]:
array([ 201.,  194.,  127., ...,  179.,  177.,  171.])


Now we can build a big matrix that includes the raw pixel values, our extracted color information, and the HOG and DAISY feature descriptors. Lucky for us, there are great implementations of HOG and DAISY in the skimage package. They're also available as part of the most robust free computer vision library, OpenCV. OpenCV also has some very good Python tutorials, if you want to get started. However, the setup is just a bit more arduous than pip install scikit-image.

We'll create a function, preprocess, that will load an image and generate a flat array for all of the features we care about. Also, completely for free, I've included a demo paramater that will plot a pretty image of all of the features that we're constructing.

In [23]:
from skimage.feature import daisy
from skimage.feature import hog
from skimage.color import rgb2gray
from skimage.exposure import equalize_hist

def preprocess(img, demo=False):
""" Turn raw pixel values into features.
"""

def _demo_plot(img, stage="", is_ints=False, axes_idx=0):
""" Utility to visualize the features we're building
"""
if demo:
axes[axes_idx].imshow(img / 255. if is_ints else img,
cmap=bees_cm)
axes[axes_idx].set_title(stage)
return axes_idx + 1

if demo:
fig, axes = plt.subplots(3, 2, figsize=(15, 20))
axes = axes.flatten()

# track which subplot we're plotting to
axes_idx = 0
axes_idx = _demo_plot(img, stage="Raw Image", is_ints=True, axes_idx=axes_idx)

# FEATURE 1: Raw image and color data
if demo:
color_info = extract_rgb_info(img, ax=axes[axes_idx])
axes_idx += 1
else:
color_info = extract_rgb_info(img)

# remove color information (hog and daisy only work on grayscale)
gray = rgb2gray(img)
axes_idx = _demo_plot(gray, stage="Convert to grayscale", axes_idx=axes_idx)

# equalize the image
gray = equalize_hist(gray)
axes_idx = _demo_plot(gray, stage="Equalized histogram", axes_idx=axes_idx)

# FEATURE 2: histogram of oriented gradients features
hog_features = hog(gray,
orientations=12,
pixels_per_cell=(8, 8),
cells_per_block=(1, 1),
visualise=demo)

# if demo, we actually got a tuple back; unpack it and plot
if demo:
hog_features, hog_image = hog_features
axes_idx = _demo_plot(hog_image, stage="HOG features", axes_idx=axes_idx)

# FEATURE 3: DAISY features - sparser for demo so can be visualized
params = {'step': 25, 'radius': 25, 'rings': 3} if demo \
else {'step': 10, 'radius': 15, 'rings': 4}
daisy_features = daisy(gray,
histograms=4,
orientations=8,
normalization='l1',
visualize=demo,
**params)

if demo:
daisy_features, daisy_image = daisy_features
axes_idx = _demo_plot(daisy_image, stage="DAISY features", axes_idx=axes_idx)

# return a flat array of the raw, hog and daisy features
return np.hstack([color_info, hog_features, daisy_features.flatten()])

preprocess_result = preprocess(get_image("520"), demo=True)