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#!/usr/bin/python
# EE4 Selected Topics From Computer Vision Coursework
# Vasil Zlatanov, Nunzio Pucci
DATA_FILE = 'data.npz'
CLUSTER_CNT = 256
KMEAN_PART = 33
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
data = np.load(DATA_FILE)
train = data['desc_tr']
# Train part will contain 15 000 descriptors to generate KMeans
part_idx = np.random.randint(train.shape[1])
parts = []
for i in train[:, part_idx]:
parts.append(i.T[300:1300])
train_part = np.vstack(parts)
print(train_part.shape)
kmeans = KMeans(n_clusters=CLUSTER_CNT, random_state=0).fit(train_part)
print("Generating histograms")
histogram = np.zeros((train.shape[0], train.shape[1],CLUSTER_CNT))
for i in range(train.shape[0]):
for j in range(train.shape[1]):
histogram[i][j] = np.bincount(kmeans.predict(train[i][j].T),minlength=CLUSTER_CNT)
print(histogram.shape)
plt.hist(histogram[1][5])
plt.show()
plt.hist(histogram[3][2])
plt.show()
plt.hist(histogram[7][8])
plt.show()
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