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abundance_model.R

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    opencv 2.59 KiB
    
    point2f(y,x) # inverted!
    BGR 	#not rgb ...
    Scalar = 4 element vector
    CV_RGB(r, g, b) #from bgs
    
    Types:
    	#depth
    	CV_8U 	unsigned char
    	CV_8S 	char
    	CV_16U 	unsigned short
    	CV_16S 	short
    	CV_32S 	int
    	CV_32F 	float
    	CV_64F 	double
    	# + channels C1..4 	eg RGBA:
    	# eg Mat M(2,2, CV_8UC3, Scalar(0,0,255));
    	0 to 255 for CV_8U images
    	0 to 65535 for CV_16U images
    	0 to 1 for CV_32F images
    
    
    
    Camera Calibration:
    	distortion_coefficients - 5 values
    	camera_matrix - 3x3 matrix - eg focal length etc.
    
    	different methods:
    		https://docs.opencv.org/4.x/d7/d21/tutorial_interactive_calibration.html
    		https://docs.opencv.org/4.x/d6/d55/tutorial_table_of_content_calib3d.html
    
    		# using chess board, to get values
    		https://github.com/abidrahmank/OpenCV2-Python-Tutorials/blob/master/source/py_tutorials/py_calib3d/py_calibration/py_calibration.rs
    
    
    roi - region of interest
    	x, y, w, h
    
    	// roi as subset of full data image
    	Mat D (A, Rect(10, 10, 100, 100) ); // using a rectangle
    	Mat E = A(Range::all(), Range(1,3)); // using row and column boundaries
    
    Matrix
    	Fill matrix with random values
    		Mat R = Mat(3, 2, CV_8UC3);
    		randu(R, Scalar::all(0), Scalar::all(255));
    
    	Formatted output
    		cout << "R (csv)     = " << endl << format(R, Formatter::FMT_CSV   ) << endl << endl;
    		#sa FMT_C/PYTHON/NUMPY/CSV
    
    MISC
    	glob( dir, file )
    	CommandLineParser
    	randu() # https://docs.opencv.org/4.x/d2/de8/group__core__array.html#ga1ba1026dca0807b27057ba6a49d258c0
    	cvtColor(img, img, COLOR_BGR2Luv); #convert color
    
    
    BS:
    https://docs.opencv.org/4.x/da/d5c/tutorial_canny_detector.html
    https://docs.opencv.org/4.x/d1/dc5/tutorial_background_subtraction.html
    https://web.archive.org/web/20140418093037/http://bmc.univ-bpclermont.fr/
    https://web.archive.org/web/20140221195750/http://docs.opencv.org/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html
    https://www.pyimagesearch.com/2020/07/27/opencv-grabcut-foreground-segmentation-and-extraction/
    https://learnopencv.com/applications-of-foreground-background-separation-with-semantic-segmentation/
    https://docs.opencv.org/4.x/d2/d55/group__bgsegm.html
    https://docs.opencv.org/4.x/d5/de8/samples_2cpp_2segment_objects_8cpp-example.html#_a15
    
    tuts:))
    https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html
    https://datahacker.rs/opencv-thresholding/
    
    # median frame as bg + frame difference
    https://learnopencv.com/simple-background-estimation-in-videos-using-opencv-c-python/
    
    https://learnopencv.com/contour-detection-using-opencv-python-c/
    https://learnopencv.com/deep-learning-with-opencvs-dnn-module-a-definitive-guide/
    
    moments
    https://docs.opencv.org/4.x/d0/d49/tutorial_moments.html