boatClassification.py

import pandas as pd
import numpy as np
import glob
from sklearn import cluster
from scipy.misc import imread
import cv2
import skimage.measure as sm
# import progressbar
import multiprocessing
import random
import matplotlib.pyplot as plt
import seaborn as sns
new_style = {'grid': False}
plt.rc('axes', **new_style)

# Function to show 4 images
def show_four(imgs, title):
    #select_imgs = [np.random.choice(imgs) for _ in range(4)]
    select_imgs = [imgs[np.random.choice(len(imgs))] for _ in range(4)]
    _, ax = plt.subplots(1, 4, sharex='col', sharey='row', figsize=(20, 3))
    plt.suptitle(title, size=20)
    for i, img in enumerate(select_imgs):
        ax[i].imshow(img)

# Function to show 8 images
def show_eight(imgs, title):
    select_imgs = [imgs[np.random.choice(len(imgs))] for _ in range(8)]
    _, ax = plt.subplots(2, 4, sharex='col', sharey='row', figsize=(20, 6))
    plt.suptitle(title, size=20)
    for i, img in enumerate(select_imgs):
        ax[i // 4, i % 4].imshow(img)

select = 500 # Only load 500 images for speed
# Data loading
train_files = sorted(glob.glob('./train/*/*.jpg'), key=lambda x: random.random())[:select]
train = np.array([imread(img) for img in train_files])
print('Length of train {}'.format(len(train)))

print('Sizes in train:')
shapes = np.array([str(img.shape) for img in train])
pd.Series(shapes).value_counts()

# for uniq in pd.Series(shapes).unique():
#     show_four(train[shapes == uniq], 'Images with shape: {}'.format(uniq))
#     plt.show()

# Function for computing distance between images
def compare(args):
    img, img2 = args
    img = (img - img.mean()) / img.std()
    img2 = (img2 - img2.mean()) / img2.std()
    return np.mean(np.abs(img - img2))

# Resize the images to speed it up.
train = [cv2.resize(img, (224, 224), interpolation = cv2.INTER_LINEAR) for img in train]

# Create the distance matrix in a multithreaded fashion
pool = multiprocessing.Pool(8)
#bar = progressbar.ProgressBar(max=len(train))
distances = np.zeros((len(train), len(train)))
for i, img in enumerate(train): #enumerate(bar(train)):
    all_imgs = [(img, f) for f in train]
    dists = pool.map(compare, all_imgs)
    distances[i, :] = dists

print(distances)
plt.hist(distances.flatten(), bins=50)
plt.title('Histogram of distance matrix')
print('')

cls = cluster.DBSCAN(metric='precomputed', min_samples=5, eps=0.6)
y = cls.fit_predict(distances)
print(y)
print('Cluster sizes:')
print(pd.Series(y).value_counts())

for uniq in pd.Series(y).value_counts().index:
    if uniq != -1:
        size = len(np.array(train)[y == uniq])
        if size > 10:
            show_eight(np.array(train)[y == uniq], 'BoatID: {} - Image count {}'.format(uniq, size))
            plt.show()
        else:
            show_four(np.array(train)[y == uniq], 'BoatID: {} - Image count {}'.format(uniq, size))
            plt.show() 

size = len(np.array(train)[y == -1])
show_eight(np.array(train)[y == -1], 'BoatID: {} (Unclassified images) - Image count {}'.format(-1, size))