Category: tutorials

import matplotlib
matplotlib.use('GTKAgg')

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
y = iris.target
h = .02  # step size in the mesh

# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title('Data')
plt.show()

import matplotlib
matplotlib.use('GTKAgg')

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target

h = .02  # step size in the mesh

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0  # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)

# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))

# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)',
'SVC with RBF kernel',
'SVC with polynomial (degree 3) kernel']

for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
plt.subplot(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])

plt.show()

import cv2
import numpy as np

# read image into matrix.
m =  cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# print image properties.
print "width: " + str(w)
print "height: " + str(h)
print "bpp: " + str(bpp)

import cv2
import numpy as np

# read image into matrix.
m =  cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# print pixel value
y = 1
x = 1
print m[y][x]

import cv2
import numpy as np

# read image into matrix.
m =  cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# iterate over the entire image.
for py in range(0,h):
for px in range(0,w):
print m[py][px]

import cv2
import numpy as np

# read image into matrix.
m =  cv2.imread("python.png")

# get image properties.
h,w,bpp = np.shape(m)

# iterate over the entire image.
for py in range(0,h):
for px in range(0,w):
m[py][px][0] = 0

# display image
cv2.imshow('matrix', m)
cv2.waitKey(0)

cv2.imwrite('filename.png',m)

#! /usr/bin/python

import cv2

vc = cv2.VideoCapture('video.mp4')
c=1
fps = 24

if vc.isOpened():
    rval , frame = vc.read()
else:
    rval = False

while rval:
    rval, frame = vc.read()
    cv2.imshow("Result",frame)
    cv2.waitKey(1000 / fps);
    vc.release()

#! /usr/bin/python

import cv2

face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface.xml')
vc = cv2.VideoCapture('video.mp4')

if vc.isOpened():
    rval , frame = vc.read()
else:
    rval = False

roi = [0,0,0,0]

while rval:
    rval, frame = vc.read()

    # resize frame for speed.
    frame = cv2.resize(frame, (300,200))
    
    # face detection.
    faces = face_cascade.detectMultiScale(frame, 1.8, 2)
    nfaces = 0
    for (x,y,w,h) in faces:
    cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
    nfaces = nfaces + 1
    roi = [x,y,w,h]

    # undetected face, show old on position.
    if nfaces == 0:
        cv2.rectangle(frame,(roi[0],roi[1]),(roi[0]+roi[2],roi[1]+roi[3]),(0,0,255),2)

    # show result
    cv2.imshow("Result",frame)
    cv2.waitKey(1);
    vc.release()

#!/usr/bin/env python
import gmaillib
from collections import Counter


def getMails(cnt,account, start,amount):
    emails = account.inbox(start, amount)

    for email in emails:
        cnt[email.sender_addr] += 1

amountOfMails = 100
cnt = Counter()
username = raw_input("Gmail account: ")
password = raw_input("Password: ")

account = gmaillib.account(username, password)

getMails(cnt,account,0,amountOfMails)
print(cnt)

#!/usr/bin/env python
import gmaillib
from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt

amount = 100
cnt = Counter()
username = raw_input("Gmail account: ")
password = raw_input("Password: ")

account = gmaillib.account(username, password)

emails = account.inbox(0, amount)

data = ""
for email in emails:
    data = data + str(email.body)

wordcloud = WordCloud().generate(data)
plt.imshow(wordcloud)
plt.axis("off")
plt.show()