NikaTsanka

import numpy as np
import cv2
import glob
import yaml
#import pathlib

# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((7*7,3), np.float32)

NikaTsanka

import cv2
import matplotlib.pyplot as plt

# Open the image
img = cv2.imread('dancing-spider.jpg')

# Apply Canny
edges = cv2.Canny(img, 100, 200, 3, L2gradient=True)

plt.figure()

NikaTsanka

import cv2
import matplotlib.pyplot as plt

# Open the image
img = cv2.imread('shapes.jpg')

# Apply gray scale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply gaussian blur

NikaTsanka

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

# Open the image
img = np.array(Image.open('dancing-spider.jpg')).astype(np.uint8)

# Apply gray scale
gray_img = np.round(0.299 * img[:, :, 0] +
                    0.587 * img[:, :, 1] +

NikaTsanka

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

# Open the image
img = np.array(Image.open('dancing-spider.jpg')).astype(np.uint8)

# Sobel Operator
h, w, d = img.shape

NikaTsanka

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

# Open the image
img = np.array(Image.open('dancing-spider.jpg')).astype(np.uint8)

# Apply gray scale
gray_img = np.round(0.299 * img[:, :, 0] +
                    0.587 * img[:, :, 1] +

NikaTsanka

import math
from matplotlib import pyplot as plt

# Let's initialize two lists with the real
# and imaginary numbers.
real_list = [36, 22, 45, 15]
imag_list = [0, 0, 0, 0]

results_f = []  # Forward
results_i = []  # Inverse