plot_matrix.py

#!/usr/bin/env python3

"""."""

# Import packages.
import cv2
import sys
import numpy as np
import matplotlib.pyplot as plt

# My imports.
from constants import EMOTIONS_5, EMOTIONS_8
from emotion_recognition import SVM

from os.path import join

# Build and train the classifier we're using.
SVM = SVM()
if (os.path.isfile('svm.pkl')):
    SVM.load()
else:
    SVM.train()
    SVM.save()

images = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_IMAGES_FILENAME))
labels = np.load(join(SAVE_DIRECTORY, SAVE_DATASET_LABELS_FILENAME))
images = images.reshape([-1, SIZE_FACE, SIZE_FACE, 1])
labels = labels.reshape([-1, len(EMOTIONS_5)])

data = np.zeros((len(EMOTIONS_5), len(EMOTIONS_5)))
for i in xrange(images.shape[0]):
	result = network.predict(images[i])
	data[np.argmax(labels[i]), result[0].index(max(result[0]))] += 1
	#print x[i], ' vs ', y[i]

# Take % by column
for i in range(len(data)):
	total = np.sum(data[i])
	for x in range(len(data[0])):
		data[i][x] = data[i][x] / total
print data

print '[+] Generating graph'
c = plt.pcolor(data, edgecolors='k', linewidths=4, cmap='Blues', vmin=0.0, vmax = 1.0)


def show_values(pc, fmt="%.2f", **kw):
    from itertools import izip
    pc.update_scalarmappable()
    ax = pc.get_axes()
    ax.set_yticks(np.arange(len(EMOTIONS)) + 0.5, minor = False)
    ax.set_xticks(np.arange(len(EMOTIONS)) + 0.5, minor = False)
    ax.set_xticklabels(EMOTIONS, minor = False)
    ax.set_yticklabels(EMOTIONS, minor = False)
    for p, color, value in izip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):
        x, y = p.vertices[:-2, :].mean(0)
        if np.all(color[:3] > 0.5):
            color = (0.0, 0.0, 0.0)
        else:
            color = (1.0, 1.0, 1.0)
        ax.text(x, y, fmt % value, ha="center", va="center", color=color, **kw)


show_values(c)
plt.xlabel('Predicted Emotion')
plt.ylabel('Real Emotion')
plt.show()