Day #215 - Deep Dive OpenCV

	#cv2.getStructuringElement
	#===============================
	#Generate Different Kernel Combinations
	kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
	kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPLSE,(5,5))
	kernel3 = cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
	#cv2.filter2D
	#==============
	#OpenCV provides a function cv2.filter2D() to convolve a kernel with an image
	#Example1
	import cv2
	import numpy as np
	original_image = cv2.imread(r'E:\Opencv_Examples\goalkeeper.jpg')
	kernel1 = cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
	kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
	kernel3 = cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
	conv1 = cv2.filter2D(original_image ,-1,kernel1)
	conv2 = cv2.filter2D(original_image ,-1,kernel2)
	conv3 = cv2.filter2D(original_image ,-1,kernel3)
	cv2.imshow('conv1',conv1)
	cv2.imshow('conv2',conv2)
	cv2.imshow('conv3',conv3)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	#cv2.calcHist
	#=============
	#cv2.calcHist() function to find the histogram
	#cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])
	#channels - grayscale image [0]. [0], [1] or [2] to calculate histogram of blue, green or red channel
	#mask : mask image. To find histogram of full image, it is given as "None"
	#histSize : this represents our BIN count. Need to be given in square brackets. For full scale, we pass [256].
	#ranges : this is our RANGE. Normally, it is [0,256].
	##Example2
	import cv2
	original_image = cv2.imread(r'E:\Opencv_Examples\goalkeeper.jpg',cv2.IMREAD_GRAYSCALE)
	hist = cv2.calcHist(original_image,[0],None,[256],[0,256])
	original_image = cv2.imread(r'E:\Opencv_Examples\goalkeeper.jpg',cv2.IMREAD_COLOR)
	hist_r = cv2.calcHist(original_image,[0],None,[256],[0,256])
	hist_g = cv2.calcHist(original_image,[1],None,[256],[0,256])
	hist_b = cv2.calcHist(original_image,[2],None,[256],[0,256])
	from matplotlib import pyplot as plt
	plt.hist(hist_b, bins=10)
	plt.ylabel('Values')
	plt.show()
	#cv2.threshold
	#===============
	#If pixel value is greater than a threshold value, it is assigned one value (may be white)
	#assigned another value (may be black)
	#Minot changes from https://docs.opencv.org/3.4/d7/d4d/tutorial_py_thresholding.html
	import cv2
	import numpy as np
	from matplotlib import pyplot as plt
	img = cv2.imread(r'E:\Opencv_Examples\goalkeeper.jpg',cv2.IMREAD_GRAYSCALE)
	ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
	ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
	ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
	ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
	ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)
	titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
	images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
	for i in range(6):
	plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
	plt.title(titles[i])
	plt.xticks([]),plt.yticks([])
	plt.show()
	#cv2.calcBackProject
	#===================
	#cv2.calcBackProject(). Its parameters are almost same as the cv2.calcHist()
	#object histogram should be normalized before passing on to the backproject function
	#One of input is histogram of object we want to find it
	#cv2.merge
	#=========
	import cv2
	img = cv2.imread(r'E:\Opencv_Examples\goalkeeper.jpg',cv2.IMREAD_COLOR)
	b,g,r = cv2.split(img)
	cv2.imshow('b',b)
	cv2.imshow('g',g)
	cv2.imshow('r',r)
	img2 = cv2.merge((b,g,g))
	cv2.imshow('bgg',img2)
	img3 = cv2.merge((b,g,r))
	cv2.imshow('bgr',img3)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	#cv2.bitwise_and
	#===============
	#This includes bitwise AND, OR, NOT and XOR operations.
	#They will be highly useful while extracting any part of the image
	#cv2.bitwise_and
	#cv2.bitwise_not
	#https://docs.opencv.org/3.2.0/d0/d86/tutorial_py_image_arithmetics.html

view raw OpencvExamples.py hosted with ❤ by GitHub

Happy Mastering DL!!!