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utils.py
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import numpy as np
import matplotlib.pyplot as plt
plt.ion()
def plot_margin(X, Y, model, title=''):
"""
Represents the performance of a svm model over data.
Parameters
----------
X: ndarray
data points. (shape:(n_samples, dim))
Y: ndarray
groundtruth labels. (shape:(n_samples,))
model: SVC
A sklearn.SVC fit model.
title: str
an optional title for the plot.
"""
fig, ax = plt.subplots(1, 2)
ax[0].scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap='jet')
if False and model.kernel == 'linear':
# get the separating hyperplane
w = model.coef_[0]
a = -w[0] / w[1]
xx = np.linspace(np.min(X), np.max(X))
yy = a * xx - (model.intercept_[0]) / w[1]
# plot the parallels to the separating hyperplane that pass through the
# support vectors
margin = 1 / np.sqrt(np.sum(model.coef_ ** 2))
yy_down = yy + a * margin
yy_up = yy - a * margin
# plot the line, the points, and the nearest vectors to the plane
ax[1].plot(xx, yy, 'k-')
ax[1].plot(xx, yy_down, 'k--')
ax[1].plot(xx, yy_up, 'k--')
# ax[1].scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1], s=80,
# facecolors='none', zorder=10)
# ax[1].scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap='jet')
plt.axis('tight')
x_min = np.min(X[:, 0])
x_max = np.max(X[:, 0])
y_min = np.min(X[:, 1])
y_max = np.max(X[:, 1])
XX, YY = np.mgrid[x_min:x_max:500j, y_min:y_max:500j]
Z = model.predict(np.concatenate((np.c_[XX.ravel(), YY.ravel()], np.ones((250000, 1))), axis=-1))
# Put the result into a color plot
Z = Z.reshape(XX.shape)
# plt.figure(1, figsize=(4, 3))
ax[1].pcolormesh(XX, YY, Z, cmap=plt.cm.Paired)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.title(title)
plt.waitforbuttonpress()
def visualize_predictions(X_img, gt, pred):
"""
Visualizes predictions of people vs non people classification.
In an endless loop.
Parameters
----------
X_img: ndarray
Images of people or non people. (shape:(n_images, h, w))
gt: ndarray
Groundtruth labels. (shape:(n_images,))
pred: ndarray
Predicted labels. (shape:(n_images,))
Returns
-------
None
"""
labels = ['non_people', 'people']
while True:
# sample
idx = np.random.choice(np.arange(0, X_img.shape[0]))
img = X_img[idx]
plt.imshow(X_img[idx], cmap='gray')
title = 'GT: {}, Pred: {}'.format(labels[int(gt[idx])], labels[int(pred[idx])])
plt.title(title)
plt.waitforbuttonpress()
def people_visualization(X,y):
plt.subplot(121)
plt.title('Class 0. Non people')
X_0 = X[y == 0.0]
random_idx_1 = np.random.choice(np.arange(0, X_0.shape[0]))
plt.imshow(X_0[random_idx_1], cmap='gray')
plt.grid(b=False)
plt.subplot(122)
plt.title('Class 1. People')
X_1 = X[y == 1.0]
random_idx_2 = np.random.choice(np.arange(0, X_1.shape[0]))
plt.imshow(X_1[random_idx_2], cmap='gray')
plt.grid(b=False)
plt.show()
plt.waitforbuttonpress()
def people_visualize_prediction(X,y,y_pred):
labels = ['Non people', 'People']
num_row, num_col = 2, 6
f,subplots = plt.subplots(num_row, num_col, sharex='col', sharey='row')
for i in range(num_row):
for j in range(num_col):
idx = np.random.choice(np.arange(0, X.shape[0]))
subplots[i,j].imshow(X[idx], cmap='gray', interpolation='nearest', aspect='auto')
title = 'GT: {} \n Pred: {}'.format(labels[int(y[idx])], labels[int(y_pred[idx])])
color_title = 'green' if int(y[idx]) == int(y_pred[idx]) else 'red'
subplots[i,j].set_title(title, color=color_title, fontweight="bold")
subplots[i,j].grid(b=False)
f.set_size_inches(13.5, 7.5)
plt.waitforbuttonpress()
def plot_pegasos_margin(X, Y, model, title=''):
"""
Represents the performance of a svm model over data.
Parameters
----------
X: ndarray
data points. (shape:(n_samples, dim))
Y: ndarray
groundtruth labels. (shape:(n_samples,))
model: SVC
A sklearn.SVC fit model.
title: str
an optional title for the plot.
"""
fig, ax = plt.subplots(1, 2)
ax[0].scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap='jet')
plt.axis('tight')
x_min = np.min(X[:, 0])
x_max = np.max(X[:, 0])
y_min = np.min(X[:, 1])
y_max = np.max(X[:, 1])
XX, YY = np.mgrid[x_min:x_max:500j, y_min:y_max:500j]
Z = model.predict(np.c_[XX.ravel(), YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
# plt.figure(1, figsize=(4, 3))
ax[1].pcolormesh(XX, YY, Z, cmap=plt.cm.Paired)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.title(title)
plt.waitforbuttonpress()