t pushMerge branch 'master' of skozl.com:e4-vision

1 files changed, 23 insertions, 7 deletions

diff --git a/evaluate.py b/evaluate.py
index 309326e..6b8fc80 100644..100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -1,8 +1,9 @@
+#!/usr/bin/python
 # EE4 Selected Topics From Computer Vision Coursework
 # Vasil Zlatanov, Nunzio Pucci
 
 DATA_FILE = 'data.npz'
-CLUSTER_CNT = 1337
+CLUSTER_CNT = 256
 KMEAN_PART = 33
 
 import numpy as np
@@ -12,19 +13,34 @@ from sklearn.cluster import KMeans
 
 data = np.load(DATA_FILE)
 
-train = data['train']
+train = data['desc_tr']
 
 # Train part will contain 15 000 descriptors to generate KMeans
-part_idx = np.random.random_integers(train.shape[1])
-train_part = np.vstack(train[:][part_idx][300:1300])
+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])
+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()