ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/input.jpg')
cv2.imshow('Original', image)

# Create our shapening kernel, we don't normalize since the 
# the values in the matrix sum to 1
kernel_sharpening = np.array([[-1,-1,-1], 
                              [-1,9,-1], 

ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/elephant.jpg')
cv2.imshow('Original Image', image)
cv2.waitKey(0)

# Creating our 3 x 3 kernel
kernel_3x3 = np.ones((3, 3), np.float32) / 9

ChongyeWang

import cv2
import numpy as np

# If you're wondering why only two dimensions, well this is a grayscale image, 
# if we doing a colored image, we'd use 
# rectangle = np.zeros((300, 300, 3),np.uint8)

# Making a sqare
square = np.zeros((300, 300), np.uint8)
cv2.rectangle(square, (50, 50), (250, 250), 255, -2)

ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/input.jpg')

# Create a matrix of ones, then multiply it by a scaler of 100 
# This gives a matrix with same dimesions of our image with all values being 100
M = np.ones(image.shape, dtype = "uint8") * 175 

# We use this to add this matrix M, to our image

ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/input.jpg')
height, width = image.shape[:2]

# Let's get the starting pixel coordiantes (top  left of cropping rectangle)
start_row, start_col = int(height * .25), int(width * .25)

# Let's get the ending pixel coordinates (bottom right)

ChongyeWang

import cv2

image = cv2.imread('images/input.jpg')

smaller = cv2.pyrDown(image)
larger = cv2.pyrUp(smaller)

cv2.imshow('Original', image )

cv2.imshow('Smaller ', smaller )

ChongyeWang

import cv2
import numpy as np

# load our input image
image = cv2.imread('images/input.jpg')

# Let's make our image 3/4 of it's original size
image_scaled = cv2.resize(image, None, fx=0.75, fy=0.75)
cv2.imshow('Scaling - Linear Interpolation', image_scaled) 
cv2.waitKey()

ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/input.jpg')
height, width = image.shape[:2]

# Divide by two to rototate the image around its centre
rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), 90, .5)

rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))

ChongyeWang

import cv2
import numpy as np

image = cv2.imread('images/input.jpg')

# Store height and width of the image
height, width = image.shape[:2]

quarter_height, quarter_width = height/4, width/4

ChongyeWang

class Solution(object):
    def permuteUnique(self, nums):
        """
        :type nums: List[int]
        :rtype: List[List[int]]
        """
        nums = sorted(nums)
        temp = []
        result = []
        used = [0 for _ in range(len(nums))]