OpenCV Notes

Color channels

• Hue: Measures the color of the pixel.
• Saturation: Measures the intensity of color of the pixel.
• Value: Measures the brighness of the pixel.

Hue range

• Red (0-60)
• Yellow (60-120)
• Green (120-180)
• Cyan (180-240)
• Blue (240-300)
• Magenta (300-360)

RGB Channels

• (0,0,0) is a black color.
• (255,0,0) is a pure red color.
• (0,255,0) is a pure green color.

	#import the libraries
	import cv2 as cv
	import numpy as np
	#read the image
	img = cv.imread(r"E:\5.jpg")
	#convert the BGR image to HSV colour space
	hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
	cv.imshow("hsv", hsv)
	cv.waitKey(0)
	hsv[:,:,2] = 200 # Changes the V value
	cv.imshow("hsv", hsv)
	cv.waitKey(0)
	rgbimg = cv.cvtColor(hsv, cv.COLOR_HSV2RGB)
	cv.imshow("rgbimg", rgbimg)
	cv.waitKey(0)

view raw hsvexample1.py hosted with ❤ by GitHub

 

Smoothing Functions

• Image Smoothing - convolving the image with a low-pass filter kernel. It is useful for removing noise
• Bilateral filter - replaces the intensity of each pixel with a weighted average of intensity values. cv.bilateralFilter() is highly effective in noise removal while keeping edges sharp

	#https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html
	import cv2 as cv
	import numpy as np
	from matplotlib import pyplot as plt
	img = cv.imread('opencv-logo-white.png')
	blur = cv.blur(img,(5,5))
	import cv2 as cv
	import numpy as np
	from matplotlib import pyplot as plt
	img = cv.imread('opencv-logo-white.png')
	blur = cv.bilateralFilter(img,9,75,75)
	#Adding (blending) two images using OpenCV
	#https://docs.opencv.org/3.4/d5/dc4/tutorial_adding_images.html
	src1 = cv.imread(cv.samples.findFile('LinuxLogo.jpg'))
	src2 = cv.imread(cv.samples.findFile('WindowsLogo.jpg'))
	beta = (1.0 - alpha)
	dst = cv.addWeighted(src1, alpha, src2, beta, 0.0)

view raw examplebookmarks.py hosted with ❤ by GitHub

Color Codes

Mask for custom color range

	#https://stackoverflow.com/questions/60152862/highlight-areas-with-different-colour-image-with-the-area-that-surround-them
	import cv2
	import numpy as np
	# Load image
	image = cv2.imread(r"E:\2.jpg")
	# Color threshold
	#(hMin = 0 , sMin = 0, vMin = 105), (hMax = 179 , sMax = 255, vMax = 255)
	original = image.copy()
	hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
	lower = np.array([0, 0, 105])
	upper = np.array([179, 255, 255])
	mask = cv2.inRange(hsv, lower, upper)
	result = cv2.bitwise_and(original,original,mask=mask)
	cv2.imshow('mask', mask)
	cv2.imshow('result', result)
	cv2.imshow('original', original)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
	def nothing(x):
	pass
	def findranges():
	# Create a window
	cv2.namedWindow('image')
	# Create trackbars for color change
	# Hue is from 0-179 for Opencv
	cv2.createTrackbar('HMin', 'image', 0, 179, nothing)
	cv2.createTrackbar('SMin', 'image', 0, 255, nothing)
	cv2.createTrackbar('VMin', 'image', 0, 255, nothing)
	cv2.createTrackbar('HMax', 'image', 0, 179, nothing)
	cv2.createTrackbar('SMax', 'image', 0, 255, nothing)
	cv2.createTrackbar('VMax', 'image', 0, 255, nothing)
	# Set default value for Max HSV trackbars
	cv2.setTrackbarPos('HMax', 'image', 179)
	cv2.setTrackbarPos('SMax', 'image', 255)
	cv2.setTrackbarPos('VMax', 'image', 255)
	# Initialize HSV min/max values
	hMin = sMin = vMin = hMax = sMax = vMax = 0
	phMin = psMin = pvMin = phMax = psMax = pvMax = 0
	while(1):
	# Get current positions of all trackbars
	hMin = cv2.getTrackbarPos('HMin', 'image')
	sMin = cv2.getTrackbarPos('SMin', 'image')
	vMin = cv2.getTrackbarPos('VMin', 'image')
	hMax = cv2.getTrackbarPos('HMax', 'image')
	sMax = cv2.getTrackbarPos('SMax', 'image')
	vMax = cv2.getTrackbarPos('VMax', 'image')
	# Set minimum and maximum HSV values to display
	lower = np.array([hMin, sMin, vMin])
	upper = np.array([hMax, sMax, vMax])
	# Convert to HSV format and color threshold
	hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
	mask = cv2.inRange(hsv, lower, upper)
	result = cv2.bitwise_and(image, image, mask=mask)
	# Print if there is a change in HSV value
	if((phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
	print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
	phMin = hMin
	psMin = sMin
	pvMin = vMin
	phMax = hMax
	psMax = sMax
	pvMax = vMax
	# Display result image
	cv2.imshow('image', result)
	if cv2.waitKey(10) & 0xFF == ord('q'):
	break
	cv2.destroyAllWindows()
	findranges()

view raw hsvrangemaskexample.py hosted with ❤ by GitHub

	#https://gist.github.com/BIGBALLON/cb6ab73f6aaaa068ab6756611bb324b2
	from PIL import Image, ImageOps
	import os
	from collections import defaultdict
	import cv2
	class padimages:
	def padding(self,img, expected_size):
	desired_size = expected_size
	delta_width = desired_size - img.size[0]
	delta_height = desired_size - img.size[1]
	pad_width = delta_width // 2
	pad_height = delta_height // 2
	padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
	return ImageOps.expand(img, padding)
	def resize_with_padding(self,img, expected_size):
	delta_width = expected_size[0] - img.size[0]
	delta_height = expected_size[1] - img.size[1]
	pad_width = int(delta_width // 2)
	pad_height = int(delta_height // 2)
	padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
	return ImageOps.expand(img, padding)
	imgresizer = padimages()
	images_path = r'E:\Dataset_July_22\Input'
	results_path = r'E:\Dataset_July_22\Results'
	file_names=[]
	for (dirpath, dirnames, filenames) in os.walk(images_path):
	file_names.append(filenames)
	print(file_names)
	for file_nms in file_names:
	for file_name in file_nms:
	input_path = images_path + '/' + '/' + file_name
	result_path = results_path + '/' + '/Res_' + file_name
	print(input_path)
	img = Image.open(input_path)
	width, height = img.size
	maxval = width
	if height > width:
	maxval = height
	img = imgresizer.resize_with_padding(img, (maxval, maxval))
	img.save(result_path)

view raw imagepadding.py hosted with ❤ by GitHub

Keep Thinking!!!!