peeush-the-developer

# gray is the numpy array of grayscale of binary image.

# Perform erosion operations on the binary image
for i in range(1, 6):
  # Parameter as None means that we apply default kernel
  # (3,3) 8-neighborhood rectangle kernel.
  eroded = cv2.erode(gray.copy(), None, iterations=i)
  cv2.imshow(f'Eroded: {i} times', eroded)

# Wait for the key press to move further.

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

# Perform Dilation operation
for i in range(1, 6):
  # Parameter as None means that we apply default kernel
  # (3,3) 8-neighborhood rectangle kernel.
  dilated = cv2.dilate(gray.copy(), None, iterations=i)
  cv2.imshow(f"Dilated: {i} times", dilated)

cv2.waitKey(0)

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

# Perform Opening: Erosion followed by Dilation.
kernel_sizes = [(3, 3), (5, 5), (7, 7)]
for kernel_size in kernel_sizes:
  # Get 8-neighborhood kernel based on the kernel size
  kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
  # Apply kernel to the grayscale binary image
  opened = cv2.morphologyEx(gray.copy(), cv2.MORPH_OPEN, kernel)
  # Display "Opened" image

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

kernel_sizes = [(3, 3), (5, 5), (7, 7)]

# Perform Closing: Dilation followed by Erosion.
for kernel_size in kernel_sizes:
  # Get 8-neighborhood kernel based on the kernel size
  kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
  # Apply kernel to the grayscale binary image
  closed = cv2.morphologyEx(gray.copy(), cv2.MORPH_CLOSE, kernel)

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

kernel_sizes = [(3, 3), (5, 5), (7, 7)]

# Perform Morpholigical_gradient: Difference between Dilation and Erosion
for kernel_size in kernel_sizes:
  # Get 8-neighborhood kernel based on the kernel size
  kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernel_size)
  # Apply kernel to the grayscale binary image
  grad = cv2.morphologyEx(gray.copy(), cv2.MORPH_GRADIENT, kernel)

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

# kernel defined. The size of kernel is based on the problem statement.
# Following kernel is for revealing License plate region in the image.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 5))

# Perform TopHat: Difference between grayscale image and opening. This will give bright foreground over dark background
top_hat = cv2.morphologyEx(gray.copy(), cv2.MORPH_TOPHAT, kernel)
# Display output after TopHat operation
cv2.imshow("TopHat", top_hat)

peeush-the-developer

# gray is the numpy array of grayscale of binary image.

# kernel defined. The size of kernel is based on the problem statement.
# Following kernel is for revealing License plate region in the image.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 5))

# Perform BlackHat: Difference between grayscale image and closing. This will give dark foreground over light background
black_hat = cv2.morphologyEx(gray.copy(), cv2.MORPH_BLACKHAT, kernel)
# Display output after BlackHat operation
cv2.imshow("BlackHat", black_hat)

peeush-the-developer

python3 -m venv {env_name}
source {env_name}/bin/activate

python3 -m pip install requests bs4 pandas

peeush-the-developer

import datetime
import requests
from bs4 import BeautifulSoup
import pandas as pd

peeush-the-developer

response = requests.get('https://www.nytimes.com/interactive/2017/06/23/opinion/trumps-lies.html')