-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
101 lines (81 loc) · 3.27 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
from sklearn.externals import joblib
from sklearn.svm import LinearSVC
from hog import HOG
from data_loader import Dataset
import argparse
from skimage.transform import rescale, resize, downscale_local_mean
from skimage.color import rgb2gray
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score
import matplotlib.pyplot as plt
import itertools
import numpy as np
#==========================================================================================================================================
#FILL THE FOLLOWING VARIABLES WITH YOUR DIRECTORY/INFO
myDirectory = '[FILL THIS IN]'
def plot_confusion_matrix(cm, classes=['inflamed aorta', 'negative'],
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", default="svm.pickle",
help="path to where the model will be stored")
args = vars(ap.parse_args())
print("Collecting annotations ...")
#CHANGE 'inflammed aorta' to the disease which you are working to diagnose
d = Dataset(myDirectory,
myDirectory, ['inflamed aorta'])
labels, images = d.load_data()
print("Gathered {} image slices".format(len(images)))
data = []
labels_new = []
hog = HOG(orientations=19, pixelsPerCell=(8, 8),
cellsPerBlock=(3, 3), transform=True)
for i, image in enumerate(images):
if i % 100 == 0:
print("Gathering features, {} of {}".format(i, len(images)))
if 0 not in image.shape:
image_resized = resize(image, (291, 218), anti_aliasing=True)
hist = hog.describe(rgb2gray(image_resized))
data.append(hist)
labels_new.append(labels[i])
X_train, X_test, y_train, y_test = train_test_split(data, labels_new, random_state=0)
print("Training on {} images".format(len(X_train)))
print("Testing on {} images".format(len(X_test)))
clf = LinearSVC()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix,
title='Confusion matrix, without normalization')
plt.show()
print("Accuracy Score: {:.2f}".format(accuracy_score(y_test, y_pred)))