Introduction to scikit-learn & scikit-image¶
%matplotlib inline
scikit-learn - Machine Learning in Python¶
scikit-learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. The following examples show some of scikit-learn’s power. For a complete list, go to the official homepage under examples or tutorials.
Blind source separation using FastICA¶
This example of estimating sources from noisy data is adapted from plot_ica_blind_source_separation.
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from sklearn.decomposition import FastICA, PCA
# Generate sample data
n_samples = 2000
time = np.linspace(0, 8, n_samples)
s1 = np.sin(2 * time) # Signal 1: sinusoidal signal
s2 = np.sign(np.sin(3 * time)) # Signal 2: square signal
s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal
S = np.c_[s1, s2, s3]
S += 0.2 * np.random.normal(size=S.shape) # Add noise
S /= S.std(axis=0) # Standardize data
# Mix data
A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix
X = np.dot(S, A.T) # Generate observations
# Compute ICA
ica = FastICA(n_components=3)
S_ = ica.fit_transform(X) # Reconstruct signals
A_ = ica.mixing_ # Get estimated mixing matrix
# For comparison, compute PCA
pca = PCA(n_components=3)
H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components
# Plot results
plt.figure(figsize=(12, 4))
models = [X, S, S_, H]
names = ['Observations (mixed signal)', 'True Sources',
'ICA recovered signals', 'PCA recovered signals']
colors = ['red', 'steelblue', 'orange']
for ii, (model, name) in enumerate(zip(models, names), 1):
plt.subplot(2, 2, ii)
plt.title(name)
for sig, color in zip(model.T, colors):
plt.plot(sig, color=color)
plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.46)
plt.show()
Anomaly detection with Local Outlier Factor (LOF)¶
This example presents the Local Outlier Factor (LOF) estimator. The LOF algorithm is an unsupervised outlier detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outlier samples that have a substantially lower density than their neighbors. This example is adapted from plot_lof.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import LocalOutlierFactor
# Generate train data
X = 0.3 * np.random.randn(100, 2)
# Generate some abnormal novel observations
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
X = np.r_[X + 2, X - 2, X_outliers]
# fit the model
clf = LocalOutlierFactor(n_neighbors=20)
y_pred = clf.fit_predict(X)
y_pred_outliers = y_pred[200:]
/opt/miniconda-latest/envs/neuro/lib/python3.6/site-packages/sklearn/neighbors/lof.py:236: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to "auto". This will change the predict method behavior.
FutureWarning)
# Plot the level sets of the decision function
xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
Z = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.title("Local Outlier Factor (LOF)")
plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
a = plt.scatter(X[:200, 0], X[:200, 1], c='white', edgecolor='k', s=20)
b = plt.scatter(X[200:, 0], X[200:, 1], c='red', edgecolor='k', s=20)
plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([a, b], ["normal observations", "abnormal observations"], loc="upper left")
plt.show()
SVM: Maximum margin separating hyperplane¶
Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with a linear kernel. This example is adapted from plot_separating_hyperplane.
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_blobs
# we create 40 separable points
X, y = make_blobs(n_samples=40, centers=2, random_state=6)
# fit the model, don't regularize for illustration purposes
clf = svm.SVC(kernel='linear', C=1000)
clf.fit(X, y)
SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
kernel='linear', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False)
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
# plot the decision function
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
# create grid to evaluate model
xx = np.linspace(xlim[0], xlim[1], 30)
yy = np.linspace(ylim[0], ylim[1], 30)
YY, XX = np.meshgrid(yy, xx)
xy = np.vstack([XX.ravel(), YY.ravel()]).T
Z = clf.decision_function(xy).reshape(XX.shape)
# plot decision boundary and margins
ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
linestyles=['--', '-', '--'])
# plot support vectors
ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,
linewidth=1, facecolors='none')
plt.show()
Scikit-Image - Image processing in python¶
scikit-image is a collection of algorithms for image processing and is based on scikit-learn. The following examples show some of scikit-image’s power. For a complete list, go to the official homepage under examples.
Sliding window histogram¶
Histogram matching can be used for object detection in images. This example extracts a single coin from the skimage.data.coins image and uses histogram matching to attempt to locate it within the original image. This example is adapted from plot_windowed_histogram.
from __future__ import division
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from skimage import data, transform
from skimage.util import img_as_ubyte
from skimage.morphology import disk
from skimage.filters import rank
def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
# Compute normalized windowed histogram feature vector for each pixel
px_histograms = rank.windowed_histogram(image, selem, n_bins=n_bins)
# Reshape coin histogram to (1,1,N) for broadcast when we want to use it in
# arithmetic operations with the windowed histograms from the image
reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape)
# Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y));
# a measure of distance between histograms
X = px_histograms
Y = reference_hist
num = (X - Y) ** 2
denom = X + Y
denom[denom == 0] = np.infty
frac = num / denom
chi_sqr = 0.5 * np.sum(frac, axis=2)
# Generate a similarity measure. It needs to be low when distance is high
# and high when distance is low; taking the reciprocal will do this.
# Chi squared will always be >= 0, add small value to prevent divide by 0.
similarity = 1 / (chi_sqr + 1.0e-4)
return similarity
# Load the `skimage.data.coins` image
img = img_as_ubyte(data.coins())
# Quantize to 16 levels of greyscale; this way the output image will have a
# 16-dimensional feature vector per pixel
quantized_img = img // 16
# Select the coin from the 4th column, second row.
# Co-ordinate ordering: [x1,y1,x2,y2]
coin_coords = [184, 100, 228, 148] # 44 x 44 region
coin = quantized_img[coin_coords[1]:coin_coords[3],
coin_coords[0]:coin_coords[2]]
# Compute coin histogram and normalize
coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0, 16))
coin_hist = coin_hist.astype(float) / np.sum(coin_hist)
# Compute a disk shaped mask that will define the shape of our sliding window
# Example coin is ~44px across, so make a disk 61px wide (2 * rad + 1) to be
# big enough for other coins too.
selem = disk(30)
# Compute the similarity across the complete image
similarity = windowed_histogram_similarity(quantized_img, selem, coin_hist,
coin_hist.shape[0])
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))
axes[0].imshow(quantized_img, cmap='gray')
axes[0].set_title('Quantized image')
axes[0].axis('off')
axes[1].imshow(coin, cmap='gray')
axes[1].set_title('Coin from 2nd row, 4th column')
axes[1].axis('off')
axes[2].imshow(img, cmap='gray')
axes[2].imshow(similarity, cmap='hot', alpha=0.5)
axes[2].set_title('Original image with overlaid similarity')
axes[2].axis('off')
plt.tight_layout()
plt.show()
Local Thresholding¶
If the image background is relatively uniform, then you can use a global threshold value as presented above. However, if there is large variation in the background intensity, adaptive thresholding (a.k.a. local or dynamic thresholding) may produce better results. This example is adapted from plot_thresholding.
from skimage.filters import threshold_otsu, threshold_local
image = data.page()
global_thresh = threshold_otsu(image)
binary_global = image > global_thresh
block_size = 35
adaptive_thresh = threshold_local(image, block_size, offset=10)
binary_adaptive = image > adaptive_thresh
fig, axes = plt.subplots(ncols=3, figsize=(16, 6))
ax = axes.ravel()
plt.gray()
ax[0].imshow(image)
ax[0].set_title('Original')
ax[1].imshow(binary_global)
ax[1].set_title('Global thresholding')
ax[2].imshow(binary_adaptive)
ax[2].set_title('Adaptive thresholding')
for a in ax:
a.axis('off')
plt.show()
Finding local maxima¶
The peak_local_max function returns the coordinates of local peaks (maxima) in an image. A maximum filter is used for finding local maxima. This operation dilates the original image and merges neighboring local maxima closer than the size of the dilation. Locations, where the original image is equal to the dilated image, are returned as local maxima. This example is adapted from plot_peak_local_max.
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.feature import peak_local_max
from skimage import data, img_as_float
im = img_as_float(data.coins())
# image_max is the dilation of im with a 20*20 structuring element
# It is used within peak_local_max function
image_max = ndi.maximum_filter(im, size=20, mode='constant')
# Comparison between image_max and im to find the coordinates of local maxima
coordinates = peak_local_max(im, min_distance=20)
# display results
fig, axes = plt.subplots(1, 3, figsize=(12, 5), sharex=True, sharey=True,
subplot_kw={'adjustable': 'box'})
ax = axes.ravel()
ax[0].imshow(im, cmap=plt.cm.gray)
ax[0].axis('off')
ax[0].set_title('Original')
ax[1].imshow(image_max, cmap=plt.cm.gray)
ax[1].axis('off')
ax[1].set_title('Maximum filter')
ax[2].imshow(im, cmap=plt.cm.gray)
ax[2].autoscale(False)
ax[2].plot(coordinates[:, 1], coordinates[:, 0], 'r.')
ax[2].axis('off')
ax[2].set_title('Peak local max')
fig.tight_layout()
plt.show()
Label image region¶
This example shows how to segment an image with image labeling. The following steps are applied:
Thresholding with automatic Otsu method
Close small holes with binary closing
Remove artifacts touching image border
Measure image regions to filter small objects
This example is adapted from plot_label.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from skimage import data
from skimage.filters import threshold_otsu
from skimage.segmentation import clear_border
from skimage.measure import label, regionprops
from skimage.morphology import closing, square
from skimage.color import label2rgb
image = data.coins()[50:-50, 50:-50]
# apply threshold
thresh = threshold_otsu(image)
bw = closing(image > thresh, square(3))
# remove artifacts connected to image border
cleared = clear_border(bw)
# label image regions
label_image = label(cleared)
image_label_overlay = label2rgb(label_image, image=image)
fig, ax = plt.subplots(figsize=(10, 6))
ax.imshow(image_label_overlay)
for region in regionprops(label_image):
# take regions with large enough areas
if region.area >= 100:
# draw rectangle around segmented coins
minr, minc, maxr, maxc = region.bbox
rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, edgecolor='red', linewidth=2)
ax.add_patch(rect)
ax.set_axis_off()
plt.tight_layout()
plt.show()
