EssentialCV 0.26
A small module to simplify essential OpenCV functions.
License: MIT
Keywords: OpenCV,Simple,Essentials,haar

EssentialCV
------------

[![PyPI](https://img.shields.io/pypi/v/EssentialCV.svg)](https://pypi.python.org/pypi/EssentialCV)

* This is a simplified module for the great OpenCV Library.
* Most of the essential functions in the library are condensed to a minimal and easy to understand code.
	
* This project is usefull for simple and small works only. 
* Some Functions use values that cannot be passed while calling. These values are from OpenCV library
	that do not have a huge impact on simple usage.
		example: `cv.threshold(frame, th1, th2, cv.THRESH_BINARY)`
		Here everything except cv.THRESH_BINARY can be passed while calling the function.

* That said, this project aims to simplify OpenCV functions. By using this module, you are expected to have some knowledge with OpenCV.

Installation:  
--------------

* `pip install EssentialCV`

In this module:
---------------

* Image will be sometimes reffered as Frame or img.  
* Threshold will be called thresh    
* Blank is a blank image used for masking.  
* My personal best methods for different functions will be mentioned.  
	
Requirements:  
--------------

* OpenCV  
* Matplotlib  
* Numpy (included with OpenCV)  

Documentation:
--------------

* You can ignore passing variables which already contain a default value. As this project is aimed to simplify things like that.  
* Default values are used if you don't pass the required values.  

* class Edge:  
	
	1. `Edge.CannyEdge(frame, th1, th2)`  
	
		* Canny Edge Detection.
		* This takes 3 values. The Image, and 2 Thresholds.
		* Returns image.

	2. `Edge.DilateEdge(frame, th1, th2, iterations,  strength = (7,7))`  
 
		* Dilate Edges.
		* This takes 5 values. The Image, and 2 Thresholds, iterations needed and strength with default (7,7). 
		* Change the default while calling according to your need. 
		* Returns image.

	3. `Edge.LapEdge(frame)`  

		* Laplacian Edge Detection.
		* This takes 1 value. The Image.
		* Returns image.

	4. `Edge.SobelEdge(frame)`  

		* Sobel Edge Detection. Best.
		* This takes 1 value. The Image.
		* Returns image.

* class Threshold:  
	
	1. `Threshold.simpleThresh(frame, th1, th2)`  

		* Simple Threshold.
		* This takes 3 values. The Image, and 2 Thresholds.
		* Returns ret -> th1 and thresholded image as thresh.

	2. `Threshold.simpleThresh_inv(frame, th1, th2):`  

		* Inverse Simple Threshold.
		* This takes 3 values. The Image, and 2 Thresholds.
		* Returns ret -> th1 and thresholded image as thresh.

	3. `Threshold.adaptiveThresh(frame, th1, kernel = 11, mean_tune = 3):`  

		* Adaptive Threshold. Best.
		* This takes 4 values. The Image, and max Threshold, kernel size default 11 and Mean_tune default 3.
		* Returns thresholded image.

	4. `Threshold.adaptiveThresh_inv(frame, th1, kernel = 11, mean_tune = 3)`  

		* Inverse Adaptive Threshold.
		* This takes 4 values. The Image, and max Threshold, kernel size default 11 and Mean_tune default 3.
		* Returns inverse thresholded image.

	5. `Threshold.adaptiveThresh_gauss(frame, th1, kernel = 11, mean_tune = 3)`  

		* Adaptive Threshold Gaussian.
		* This takes 4 values. The Image, and max Threshold, kernel size default 11 and Mean_tune default 3.
		* Returns thresholded image.

* class Blur:  
	
	1. `Blur.avgBlur(img, strength = (3,3))`  

		* Average Blur.
		* This takes 2 values. The Image, and strength default (3,3).
		* Returns blurred image.

	2. `Blur.GaussBlur(img, strength = (3,3))`  

		* Gaussian Blur.
		* This takes 2 values. The Image, and strength default (3,3).
		* Returns blurred image.

	3. `Blur.medBlur(img, strength = 3)`  

		* Median Blur.
		* This takes 2 values. The Image, and strength default 3.
		* Returns blurred image.

	4. `Blur.biBlur(img, diameter = 10, color = 35, space = 25)`  

		* Bilateral Blur. Best.
		* This takes 4 values. The Image, and diameter, sigmaColor, sigmaSpace.
		* The diameter, sigmaColor, sigmaSpace can be ignored if you dont need complications.
		* Returns blurred image.

* class Transform:
	
	1. `Transform.translate(img, x, y)`   

		* Translate (move) image to (x,y).
		* This takes 3 values. The Image, and x and y coordinates.
		* Returns image.

	2. `Transform.rotate(img, angle, rotPoint = None)`  

		* Rotate image to with angle along (x,y).
		* This takes 3 values. The Image, angle and rotPoint => (x,y) coordinates. rotPoint Default => None (0,0).
		* Returns image. 

	3. `Transform.flip(img, axis)`  

		* Flip along x or y or both axis.
		* This takes 2 values. The Image, axis.
		* axis : 
			 0  => x
			 1  => y
			 -1 => xy
		* Returns image.

* class Rescale:  
	
	1. `Rescale.rescaleframe(frame, scale)`  

		* Rescale image.
		* This takes 2 values. The Image, and scale factor.
		* Returns image.

	2. `Rescale.changeRes(frame, width, height)`  

		* Change resoltuion.
		* This takes 3 values. The Image, width and height.
		* Returns image.

* class Colorspace:  
	
	1. BGR to other color formats.  
		
		* This takes 1 values. The Image.
		* Returns image.

	* To Gray:  
		`Colorspace.toGray(frame)`
	
	* To HSV:  
		`Colorspace.toHSV(frame)`
	
	* To LAB:  
		`Colorspace.toLAB(frame)`
	
	* To RGB:  
		`Colorspace.toRGB(frame)`

	2. From other color formats BGR.  
		
		* This takes 1 values. The Image.
		* Returns image.

	* from Gray:  
		`Colorspace.fromGray(frame)`
	
	* from HSV:  
		`Colorspace.fromHSV(frame)`
	
	* from LAB:  
		`Colorspace.fromLAB(frame)`
	
	* from RGB:  
		`Colorspace.fromRGB(frame)`

* class Colors:  
	
	1. `Colors.colorsplit(img, depth)`  

		* Split colors of an image.
		* This takes 2 values. The Image and Depth.
		* Depth:
			1 --> GRAY
			2 --> BGR
		* Returns 3 image of blue, green and red of the specified depth.

	2. `Colors.colormerge(b,g,r)`  

		* Merge 3 color splitted image to get the final image.

* class Bitwise:  

	1. Bitwise Funtions.  
	
		* Overview of each values. Don't mind this if using the Mask class. It will do this automatically for you:
			* dst => output array. Specify a Blank image for color image input. Leave None for Gray.
			* mask => input array. Specify a Mask image for color image input. Leave None for Gray. 
			* one => Image to which mask is to be applied.
			* two=> Same as "one" in case of color image. If "one" is gray, use mask image.

		* For operations without mask image, Only need to pass images "one" and "two".

	* Bitwise AND:  
		`Bitwise.bitAnd(one, two, dst = None, mask = None)`

	* Bitwise OR:  
		`Bitwise.bitOr(one, two, dst = None, mask = None)`

	* Bitwise XOR:  
		`Bitwise.bitXor(one, two, dst = None, mask = None)`

	* Bitwise NOT:  
		`Bitwise.bitNot(one, dst = None, mask = None)`

* class Mask:  

	1. `Mask.grayMasking(img, mask)`  

		* Gray Masking.
		* Use only to mask gray image.
		* Takes 2 values,Gray Image and Mask image.
		* Returns masked image.

	2. `Mask.colorMasking(img, mask)`  

		* Color Masking.
		* Use only to mask Color image.
		* Takes 2 values,Color Image and Mask image.
		* Returns masked image.

* class MaskMaker:  

	1. `MaskMaker.create(img, material)`  

		* Creates basic rectanglur and circular mask in the centre if the image with half the size.
		* Takes image to use as masking reference and material (shape).
		* materials:
			rectangle
			circle
		* KeyError is raised if the input material is not valid.
		* Returns mask image.

LICENSE
-------

MIT License

Copyright (c) 2021 rednek46

Permission is hereby granted, free of charge, to any person obtaining a copy
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The above copyright notice and this permission notice shall be included in all
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