can you convert this code from python to matlab please

"""

MoinMoin - Daltonize ImageCorrection - Effect

Please note: Daltonize ImageCorrection needs * Python Image Library (PIL) from http://www.pythonware.com/products/pil/ * NumPy from http://numpy.scipy.org/ You can call Daltonize from the command-line with "daltonize.py C:\image.png" Explanations: * Normally this module is called from Moin.AttachFile.get_file * @param filename, fpath is the filename/fullpath to an image in the attachment dir of a page. * @param color_deficit can either be - 'd' for Deuteranope image correction - 'p' for Protanope image correction - 't' for Tritanope image correct Idea: * Since daltonizing an image takes quite some time and we don't want visually impaired users to wait so long until the page is loaded, this module has a command-line option built-in which could be called as a separate process after a file upload of a non visually impaired user in "AttachFile", e.g "spawnlp(os.NO_WAIT...)" * "AttachFile": If an image attachment is deleted or overwritten by a new version please make sure to delete the daltonized images and redaltonize them. * But all in all: Concrete implementation of ImageCorrection needs further thinking and discussion. This is only a first prototype as proof of concept. @copyright: 2007 by Oliver Siemoneit @license: GNU GPL, see COPYING for details. """ import os.path def execute(filename,fpath, color_deficit='d'): modified_filename = "%s-%s-%s" % ('daltonize', color_deficit, filename) head, tail = os.path.split(fpath) # Save transformed image to the cache dir # head = head.replace('attachments', 'cache') modified_fpath = os.path.join(head, modified_filename) # Look if requested image is already available if os.path.isfile(modified_fpath): return (modified_filename, modified_fpath) helpers_available = True try: import numpy from PIL import Image except: helpers_available = False if not helpers_available: return (filename, fpath) # Get image data im = Image.open(fpath) if im.mode in ['1', 'L']: # Don't process black/white or grayscale images return (filename, fpath) im = im.copy() im = im.convert('RGB') RGB = numpy.asarray(im, dtype=float) # Transformation matrix for Deuteranope (a form of red/green color deficit) lms2lmsd = numpy.array([[1, 0, 0], [0.494207, 0, 1.24827], [0, 0, 1]]) # Transformation matrix for Protanope (another form of red/green color deficit) lms2lmsp = numpy.array([[0, 2.02344, -2.52581], [0, 1, 0], [0, 0, 1]]) # Transformation matrix for Tritanope (a blue/yellow deficit - very rare) lms2lmst = numpy.array([[1, 0, 0], [0, 1, 0], [-0.395913, 0.801109, 0]]) # Colorspace transformation matrices rgb2lms = numpy.array([[17.8824, 43.5161, 4.11935], [3.45565, 27.1554, 3.86714], [0.0299566, 0.184309, 1.46709]]) lms2rgb = numpy.linalg.inv(rgb2lms) # Daltonize image correction matrix err2mod = numpy.array([[0, 0, 0], [0.7, 1, 0], [0.7, 0, 1]]) # Get the requested image correction if color_deficit == 'd': lms2lms_deficit = lms2lmsd elif color_deficit == 'p': lms2lms_deficit = lms2lmsp elif color_deficit == 't': lms2lms_deficit = lms2lmst else: return (filename, fpath) # Transform to LMS space LMS = numpy.zeros_like(RGB) for i in range(RGB.shape[0]): for j in range(RGB.shape[1]): rgb = RGB[i, j, :3] LMS[i, j, :3] = numpy.dot(rgb2lms, rgb) # Calculate image as seen by the color blind _LMS = numpy.zeros_like(RGB) for i in range(RGB.shape[0]): for j in range(RGB.shape[1]): lms = LMS[i, j, :3] _LMS[i, j, :3] = numpy.dot(lms2lms_deficit, lms) _RGB = numpy.zeros_like(RGB) for i in range(RGB.shape[0]): for j in range(RGB.shape[1]): _lms = _LMS[i, j, :3] _RGB[i, j, :3] = numpy.dot(lms2rgb, _lms) ## # Save simulation how image is perceived by a color blind ## for i in range(RGB.shape[0]): ## for j in range(RGB.shape[1]): ## _RGB[i,j,0] = max(0, _RGB[i,j,0]) ## _RGB[i,j,0] = min(255, _RGB[i,j,0]) ## _RGB[i,j,1] = max(0, _RGB[i,j,1]) ## _RGB[i,j,1] = min(255, _RGB[i,j,1]) ## _RGB[i,j,2] = max(0, _RGB[i,j,2]) ## _RGB[i,j,2] = min(255, _RGB[i,j,2]) ## simulation = _RGB.astype('uint8') ## im_simulation = Image.fromarray(simulation, mode='RGB') ## simulation_filename = "%s-%s-%s" % ('daltonize-simulation', color_deficit, filename) ## simulation_fpath = os.path.join(head, simulation_filename) ## im_simulation.save(simulation_fpath) # Calculate error between images error = (RGB - _RGB) # Daltonize ERR = numpy.zeros_like(RGB) for i in range(RGB.shape[0]): for j in range(RGB.shape[1]): err = error[i, j, :3] ERR[i, j, :3] = numpy.dot(err2mod, err) dtpn = ERR + RGB for i in range(RGB.shape[0]): for j in range(RGB.shape[1]): dtpn[i, j, 0] = max(0, dtpn[i, j, 0]) dtpn[i, j, 0] = min(255, dtpn[i, j, 0]) dtpn[i, j, 1] = max(0, dtpn[i, j, 1]) dtpn[i, j, 1] = min(255, dtpn[i, j, 1]) dtpn[i, j, 2] = max(0, dtpn[i, j, 2]) dtpn[i, j, 2] = min(255, dtpn[i, j, 2]) result = dtpn.astype('uint8') # Save daltonized image im_converted = Image.fromarray(result, mode='RGB') im_converted.save(modified_fpath) return (modified_filename, modified_fpath) if __name__ == '__main__': import sys print "Daltonize image correction for color blind users" if len(sys.argv) != 2: print "Calling syntax: daltonize.py [fullpath to image file]" print "Example: daltonize.py C:/wikiinstance/data/pages/PageName/attachments/pic.png" sys.exit(1) if not (os.path.isfile(sys.argv[1])): print "Given file does not exist" sys.exit(1) extpos = sys.argv[1].rfind(".") if not (extpos > 0 and sys.argv[1][extpos:].lower() in ['.gif', '.jpg', '.jpeg', '.png', '.bmp', '.ico', ]): print "Given file is not an image" sys.exit(1) path, fname = os.path.split(sys.argv[1]) print "Please wait. Daltonizing in progress..." colorblindness = {'d': 'Deuteranope', 'p': 'Protanope', 't': 'Tritanope', } for col_deficit in ['d', 'p', 't']: print "Creating %s corrected version" % colorblindness[col_deficit] modified_filename, modified_fpath = execute(fname, sys.argv[1], col_deficit) if modified_fpath == sys.argv[1]: print "Error while processing image: PIL/NumPy missing and/or source file is a grayscale image." else: print "Image successfully daltonized" execute("image.jpg","C:\\Users\\HP\\Desktop",'d')