import numpy as np
from sklearn_theano.feature_extraction import GoogLeNetClassifier
from sklearn_theano.datasets import load_sample_image
X = load_sample_image("sloth_closeup.jpg")
top_n_classes = 5
goog_clf = GoogLeNetClassifier(top_n=top_n_classes)
goog_preds = goog_clf.predict(X)[0]
goog_probs = goog_clf.predict_proba(X)[0]
# Want the sorted from greatest probability to least
sort_indices = np.argsort(goog_probs)[::-1]
for n, (pred, prob) in enumerate(
zip(goog_preds[sort_indices], goog_probs[sort_indices])):
print("Class prediction (probability): %s (%.4f)" % (pred, prob))
probability outputs are plotted as a bar graph. The red box represents the
cropping window on the input - because input is forced to be 231x231, a center
box of size 231x231 is taken from the larger image to perform classification.
In general, the top N probability outputs represent the most likely potential
classes for the OverfeatClassifier.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from sklearn_theano.datasets import load_sample_image
from sklearn_theano.feature_extraction import OverfeatClassifier
X = load_sample_image("sloth_closeup.jpg")
top_n_classes = 5
clf = OverfeatClassifier(top_n=top_n_classes)
prediction = clf.predict(X)
# Shortened the labels for plotting
prediction = [p.split(",")[0] for p in prediction.ravel()]
prediction_probs = clf.predict_proba(X)
crop_width = clf.crop_bounds_[1] - clf.crop_bounds_[0]
crop_height = clf.crop_bounds_[3] - clf.crop_bounds_[2]
crop_box = Rectangle((clf.crop_bounds_[0], clf.crop_bounds_[2]), crop_width, crop_height,
fc='darkred', ec='darkred', alpha=.35)
f, axarr = plt.subplots(2, 1)
plt.suptitle("Top %i classification and cropping box" % top_n_classes)
axarr[0].imshow(X)
axarr[0].add_patch(crop_box)
axarr[0].autoscale(enable=False)
from sklearn_theano.datasets import load_sample_image
from sklearn_theano.feature_extraction import OverfeatLocalizer
from sklearn.mixture import GMM
def convert_gmm_to_box(gmm, color, alpha):
midpoint = gmm.means_
std = 3 * np.sqrt(clf.covars_)
width = std[:, 0]
height = std[:, 1]
upper_left_point = (midpoint[:, 0] - width // 2,
midpoint[:, 1] - height // 2)
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("cat_and_dog.jpg")
dog_label = 'dog.n.01'
cat_label = 'cat.n.01'
clf = OverfeatLocalizer(top_n=1,
match_strings=[dog_label, cat_label])
points = clf.predict(X)
dog_points = points[0]
cat_points = points[1]
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
clf = GMM()
clf.fit(dog_points)
dog_box = convert_gmm_to_box(clf, "darkred", .6)
clf.fit(cat_points)
axarr[1, 1].scatter(sloth_points[:, 0], sloth_points[:, 1], color='orange',
s=50)
print("Localization complete!")
def convert_gmm_to_box(gmm, color, alpha):
midpoint = gmm.means_
std = 3 * np.sqrt(clf.covars_)
width = std[:, 0]
height = std[:, 1]
upper_left_point = (midpoint[:, 0] - width // 2,
midpoint[:, 1] - height // 2)
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("cat_and_dog.jpg")
dog_label = 'dog.n.01'
cat_label = 'cat.n.01'
print("Starting localization")
clf = OverfeatLocalizer(top_n=1,
match_strings=[dog_label, cat_label])
points = clf.predict(X)
print("Localization complete!")
dog_points = points[0]
cat_points = points[1]
plt.figure()
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
clf = GMM()
from matplotlib.patches import Rectangle
from sklearn_theano.datasets import load_sample_image
from sklearn_theano.feature_extraction import OverfeatLocalizer
from sklearn_theano.feature_extraction import OverfeatTransformer
def convert_points_to_box(points, color, alpha):
upper_left_point = (points[:, 0].min(), points[:, 1].min())
width = points[:, 0].max() - points[:, 0].min()
height = points[:, 1].max() - points[:, 1].min()
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
# Show the original image
f, axarr = plt.subplots(2, 3)
X = load_sample_image("sloth.jpg")
axarr[0, 0].imshow(X)
axarr[0, 0].axis('off')
# Show a single box
axarr[0, 1].imshow(X)
axarr[0, 1].axis('off')
r = Rectangle((0, 0), 231, 231, fc='yellow', ec='black', alpha=.8)
axarr[0, 1].add_patch(r)
# Show all the boxes being processed
axarr[0, 2].imshow(X)
axarr[0, 2].axis('off')
clf = OverfeatTransformer(force_reshape=False)
X_tf = clf.transform(X)
x_points = np.linspace(0, X.shape[1] - 231, X_tf[0].shape[3])
def convert_points_to_box(points, color, alpha):
upper_left_point = (points[:, 0].min(), points[:, 1].min())
width = points[:, 0].max() - points[:, 0].min()
height = points[:, 1].max() - points[:, 1].min()
return Rectangle(upper_left_point,
width,
height,
ec=color,
fc=color,
alpha=alpha)
# Show the original image
f, axarr = plt.subplots(2, 3)
X = load_sample_image("sloth.jpg")
axarr[0, 0].imshow(X)
axarr[0, 0].axis('off')
# Show a single box
axarr[0, 1].imshow(X)
axarr[0, 1].axis('off')
r = Rectangle((0, 0), 231, 231, fc='yellow', ec='black', alpha=.8)
axarr[0, 1].add_patch(r)
# Show all the boxes being processed
axarr[0, 2].imshow(X)
axarr[0, 2].axis('off')
clf = OverfeatTransformer(force_reshape=False)
X_tf = clf.transform(X)
x_points = np.linspace(0, X.shape[1] - 231, X_tf[0].shape[3])
