python
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
def text_mining(text):
text = text.lower()
tokens = word_tokenize(text)
stop_words = set(stopwords.words('english'))
filtered_tokens = [word for word in tokens if word not in stop_words]
word_freq = nltk.FreqDist(filtered_tokens)
return word_freq
text = "This is an example text for text mining. We will analyze the frequency of words in this text."
word_freq = text_mining(text)
for word, freq in word_freq.items():
print(word, freq)
python
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
image = cv2.imread('image.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 3)
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
