1. Import Python libraries
A honey bee (Apis).
Can a machine 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.
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. Identifying different species of bees in the wild means that we can better understand the prevalence and growth of these important insects.
A bumble bee (Bombus).
After loading and pre-processing images, this notebook walks through building a model that can automatically detect honey bees and bumble bees.
# used to change filepaths
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import display
%matplotlib inline
import pandas as pd
import numpy as np
# import Image from PIL
from PIL import Image
from skimage.feature import hog
from skimage.color import rgb2grey
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
# import train_test_split from sklearn's model selection module
from sklearn.model_selection import train_test_split
# import SVC from sklearn's svm module
from sklearn.svm import SVC
# import accuracy_score from sklearn's metrics module
from sklearn.metrics import roc_curve, auc, accuracy_score2. Display image of each bee type
Now that we have all of our imports ready, it is time to look at some images. We will load our labels.csv file into a dataframe called labels, where the index is the image name (e.g. an index of 1036 refers to an image named 1036.jpg) and the genus column tells us the bee type. genus takes the value of either 0.0 (Apis or honey bee) or 1.0 (Bombus or bumble bee).
The function get_image converts an index value from the dataframe into a file path where the image is located, opens the image using the Image object in Pillow, and then returns the image as a numpy array.
We'll use this function to load the sixth Apis image and then the sixth Bombus image in the dataframe.
# load the labels using pandas
labels = pd.read_csv("datasets/labels.csv", index_col=0)
# show the first five rows of the dataframe using head
display(labels.head())
def get_image(row_id, root="datasets/"):
"""
Converts an image number into the file path where the image is located,
opens the image, and returns the image as a numpy array.
"""
filename = "{}.jpg".format(row_id)
file_path = os.path.join(root, filename)
img = Image.open(file_path)
return np.array(img)
# subset the dataframe to just Apis (genus is 0.0) get the value of the sixth item in the index
apis_row = labels[labels.genus == 0.0].index[5]
# show the corresponding image of an Apis
plt.imshow(get_image(apis_row))
plt.show()
# subset the dataframe to just Bombus (genus is 1.0) get the value of the sixth item in the index
bombus_row = labels[labels.genus == 1.0].index[5]
# show the corresponding image of a Bombus
plt.imshow(get_image(bombus_row))
plt.show()
3. Image manipulation with rgb2grey
scikit-image has a number of image processing functions built into the library, for example, converting an image to greyscale. The rgb2grey function computes the luminance of an RGB image using the following formula Y = 0.2125 R + 0.7154 G + 0.0721 B.
Image data is represented as a matrix, where the depth is the number of channels. An RGB image has three channels (red, green, and blue) whereas the returned greyscale image has only one channel. Accordingly, the original color image has the dimensions 100x100x3 but after calling rgb2grey, the resulting greyscale image has only one channel, making the dimensions 100x100x1.