{"group":{"group":{"id":62,"name":"Image Functions","lockable":false,"created_at":"2018-08-28T17:49:22.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Can you picture the solution to these problems?","is_default":false,"created_by":26769,"badge_id":62,"featured":false,"trending":false,"solution_count_in_trending_period":9,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":624,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCan you picture the solution to these problems?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}","description_html":"\u003cdiv style = \"text-align: start; line-height: normal; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; perspective-origin: 289.5px 10.5px; transform-origin: 289.5px 10.5px; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; perspective-origin: 266.5px 10.5px; transform-origin: 266.5px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCan you picture the solution to these problems?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","published_at":"2020-03-23T19:14:26.000Z"},"current_player":null},"problems":[{"id":540,"title":"Convert hex color specification to MATLAB RGB","description":"Here's something that comes up all the time if you deal with web pages.\r\n\r\nGiven a \u003chttp://www.w3schools.com/html/html_colors.asp hexadecimal color specification\u003e of the form #FFFFFF, convert it to a 3 element MATLAB color specification. Remember that MATLAB RGB color values are between 0 and 1.\r\n\r\nThe input is a string starting with '#'. The output should be a 1-by-3 vector.\r\n\r\nExamples:\r\n\r\n Input  '#0000FF'\r\n Output [0 0 1]\r\n \r\n Input  '#33FF00'\r\n Output [0.2 1 0]","description_html":"\u003cp\u003eHere's something that comes up all the time if you deal with web pages.\u003c/p\u003e\u003cp\u003eGiven a \u003ca href=\"http://www.w3schools.com/html/html_colors.asp\"\u003ehexadecimal color specification\u003c/a\u003e of the form #FFFFFF, convert it to a 3 element MATLAB color specification. Remember that MATLAB RGB color values are between 0 and 1.\u003c/p\u003e\u003cp\u003eThe input is a string starting with '#'. The output should be a 1-by-3 vector.\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre\u003e Input  '#0000FF'\r\n Output [0 0 1]\u003c/pre\u003e\u003cpre\u003e Input  '#33FF00'\r\n Output [0.2 1 0]\u003c/pre\u003e","function_template":"function rgb = color_hex2rgb(hex)\r\n  rgb = [0 0 0];\r\nend","test_suite":"%%\r\nhex =  '#0000FF';\r\nrgb = [0 0 1];\r\nassert(isequal(color_hex2rgb(hex),rgb))\r\n\r\n%%\r\nhex =  '#33FF00';\r\nrgb = [0.2 1 0];\r\nassert(isequal(color_hex2rgb(hex),rgb))\r\n\r\n%%\r\nhex =  '#FFCCFF';\r\nrgb = [1 0.8 1];\r\nassert(isequal(color_hex2rgb(hex),rgb))\r\n\r\n%%\r\nhex =  '#999999';\r\nrgb = [.6 .6 .6];\r\nassert(isequal(color_hex2rgb(hex),rgb))","published":true,"deleted":false,"likes_count":7,"comments_count":0,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":256,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-30T18:18:52.000Z","updated_at":"2026-03-09T20:38:06.000Z","published_at":"2012-03-30T18:26:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere's something that comes up all the time if you deal with web pages.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.w3schools.com/html/html_colors.asp\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehexadecimal color specification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of the form #FFFFFF, convert it to a 3 element MATLAB color specification. Remember that MATLAB RGB color values are between 0 and 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is a string starting with '#'. The output should be a 1-by-3 vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input  '#0000FF'\\n Output [0 0 1]\\n\\n Input  '#33FF00'\\n Output [0.2 1 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1948,"title":"Convert this color - RGB Vector to Hex String","description":"Given a 3 element RGB vector corresponding to a color (given by the MATLAB color spec \u003chttp://www.mathworks.com/help/matlab/ref/colorspec.html\u003e), return the corresponding hex value string associated with it. This hex value string is often used when coding in HTML to specify a color. \r\n\r\nFor example, given the vector [0, 1, 1], you would return the string 00FFFF and given the vector [.5 .5 .2], you would return the string 7F7F33. We will assume that in this case once we multiply the vector by 255, we will round down to the nearest integer. If anyone thinks that we should not assume rounding down, feel free to comment :) \r\n\r\n","description_html":"\u003cp\u003eGiven a 3 element RGB vector corresponding to a color (given by the MATLAB color spec \u003ca href = \"http://www.mathworks.com/help/matlab/ref/colorspec.html\"\u003ehttp://www.mathworks.com/help/matlab/ref/colorspec.html\u003c/a\u003e), return the corresponding hex value string associated with it. This hex value string is often used when coding in HTML to specify a color.\u003c/p\u003e\u003cp\u003eFor example, given the vector [0, 1, 1], you would return the string 00FFFF and given the vector [.5 .5 .2], you would return the string 7F7F33. We will assume that in this case once we multiply the vector by 255, we will round down to the nearest integer. If anyone thinks that we should not assume rounding down, feel free to comment :)\u003c/p\u003e","function_template":"function hexString = rgb2hex(rgbColor)\r\n  hexString = rgbColor;\r\nend","test_suite":"%%\r\nx = [0,0,0.0001];\r\ny_correct = '000000';\r\nassert(isequal(rgb2hex(x),y_correct))\r\n\r\n%%\r\nx = [1,1,1];\r\ny_correct = 'FFFFFF';\r\nassert(isequal(rgb2hex(x),y_correct))\r\n\r\n%%\r\nx = [.5,.5,.2];\r\ny_correct = '7F7F33';\r\nassert(isequal(rgb2hex(x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":3743,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":139,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-10-20T03:34:55.000Z","updated_at":"2026-02-04T11:54:49.000Z","published_at":"2013-10-20T03:34:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a 3 element RGB vector corresponding to a color (given by the MATLAB color spec\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/help/matlab/ref/colorspec.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.mathworks.com/help/matlab/ref/colorspec.html\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e), return the corresponding hex value string associated with it. This hex value string is often used when coding in HTML to specify a color.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, given the vector [0, 1, 1], you would return the string 00FFFF and given the vector [.5 .5 .2], you would return the string 7F7F33. We will assume that in this case once we multiply the vector by 255, we will round down to the nearest integer. If anyone thinks that we should not assume rounding down, feel free to comment :)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42612,"title":"Convert ColorSpec string to RGB triplet","description":"Given a ColorSpec string, either in short or long form, return the corresponding RGB triplet. If the input is not a valid color, return an empty vector.\r\n\r\n*Examples*\r\n\r\n  str2rgb('red') returns [1 0 0]\r\n  str2rgb('k') returns [0 0 0]\r\n  str2rgb('pizza') returns []\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eGiven a ColorSpec string, either in short or long form, return the corresponding RGB triplet. If the input is not a valid color, return an empty vector.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003estr2rgb('red') returns [1 0 0]\r\nstr2rgb('k') returns [0 0 0]\r\nstr2rgb('pizza') returns []\r\n\u003c/pre\u003e","function_template":"function rgb = str2rgb(str)\r\n  rgb = [];\r\nend","test_suite":"%%\r\nassert(isequal(str2rgb('y'),[1 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('m'),[1 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('c'),[0 1 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('r'),[1 0 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('g'),[0 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('b'),[0 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('w'),[1 1 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('k'),[0 0 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('yellow'),[1 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('magenta'),[1 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('cyan'),[0 1 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('red'),[1 0 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('green'),[0 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('blue'),[0 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('white'),[1 1 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('black'),[0 0 0]))\r\n%%\r\nassert(isequal(str2rgb('y'),[1 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('m'),[1 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('c'),[0 1 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('r'),[1 0 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('g'),[0 1 0]))\r\n\r\n%%\r\nassert(isequal(str2rgb('b'),[0 0 1]))\r\n\r\n%%\r\nassert(isequal(str2rgb('w'),[1 1 1]))\r\n\r\n%%\r\nstr = 'adefhijlnopqstuvxz';\r\nfor ii=1:length(str)\r\n  assert(isequal(str2rgb(str(ii)),[]))\r\nend\r\n\r\n%%\r\nassert(isequal(str2rgb('cyanide'),[]))\r\n\r\n%%\r\nassert(isequal(str2rgb('yellowfin tuna'),[]))\r\n\r\n%%\r\nassert(isequal(str2rgb('blue jays'),[]))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":"2015-09-12T18:48:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-09-12T18:42:23.000Z","updated_at":"2026-02-04T15:39:05.000Z","published_at":"2015-09-12T18:48:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a ColorSpec string, either in short or long form, return the corresponding RGB triplet. If the input is not a valid color, return an empty vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[str2rgb('red') returns [1 0 0]\\nstr2rgb('k') returns [0 0 0]\\nstr2rgb('pizza') returns []]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42613,"title":"Convert RGB triplet to ColorSpec string","description":"This is the inverse to \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42612-convert-colorspec-string-to-rgb-triplet this problem\u003e. Given an RGB triplet, return the corresponding ColorSpec string. If the input does not have a known string name, return an empty string. A second optional input indicates whether to return the short (default) or long form.\r\n\r\n*Examples*\r\n\r\n  rgb2str([1 0 0]) returns 'r'\r\n  rgb2str([0 1 0],'short') returns 'g'\r\n  rgb2str([0 0 1],'long') returns 'blue'\r\n  rgb2str([0.9 0.7 0.1]) returns ''","description_html":"\u003cp\u003eThis is the inverse to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42612-convert-colorspec-string-to-rgb-triplet\"\u003ethis problem\u003c/a\u003e. Given an RGB triplet, return the corresponding ColorSpec string. If the input does not have a known string name, return an empty string. A second optional input indicates whether to return the short (default) or long form.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ergb2str([1 0 0]) returns 'r'\r\nrgb2str([0 1 0],'short') returns 'g'\r\nrgb2str([0 0 1],'long') returns 'blue'\r\nrgb2str([0.9 0.7 0.1]) returns ''\r\n\u003c/pre\u003e","function_template":"function str = rgb2str(rgb,option)\r\n  str = '';\r\nend","test_suite":"%%\r\nrgb = [1 1 0;\r\n       1 0 1;\r\n       0 1 1;\r\n       1 0 0;\r\n       0 1 0;\r\n       0 0 1;\r\n       1 1 1;\r\n       0 0 0];\r\nstr = 'ymcrgbwk';\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:)),str(ii)))\r\nend\r\n\r\n%%\r\nrgb = [1 1 0;\r\n       1 0 1;\r\n       0 1 1;\r\n       1 0 0;\r\n       0 1 0;\r\n       0 0 1;\r\n       1 1 1;\r\n       0 0 0];\r\nstr = 'ymcrgbwk';\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:),'short'),str(ii)))\r\nend\r\n\r\n%%\r\nrgb = [1 1 0;\r\n       1 0 1;\r\n       0 1 1;\r\n       1 0 0;\r\n       0 1 0;\r\n       0 0 1;\r\n       1 1 1;\r\n       0 0 0];\r\nstr = {'yellow','magenta','cyan','red','green','blue','white','black'};\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:),'long'),str{ii}))\r\nend\r\n\r\n%%\r\nrng('default');\r\nrgb = rand(8,3);\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:)),''))\r\nend\r\n\r\n%%\r\nrng(673);\r\nrgb = round(rand(8,3)*10)/10;\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:),'short'),''))\r\nend\r\n\r\n%%\r\nrgb = round(rand(8,3)*10)/10;\r\nfor ii=1:size(rgb,1)\r\n  assert(isequal(rgb2str(rgb(ii,:),'long'),''))\r\nend\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":39,"test_suite_updated_at":"2015-09-12T19:11:51.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-09-12T18:56:56.000Z","updated_at":"2026-02-04T15:43:05.000Z","published_at":"2015-09-12T19:11:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is the inverse to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/42612-convert-colorspec-string-to-rgb-triplet\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethis problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Given an RGB triplet, return the corresponding ColorSpec string. If the input does not have a known string name, return an empty string. A second optional input indicates whether to return the short (default) or long form.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[rgb2str([1 0 0]) returns 'r'\\nrgb2str([0 1 0],'short') returns 'g'\\nrgb2str([0 0 1],'long') returns 'blue'\\nrgb2str([0.9 0.7 0.1]) returns '']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42849,"title":"RGB to CMYK","description":"Convert an RGB code to the corresponding CMYK code for printing.\r\n\r\n\r\nThe RGB input is a [1×3] double array between 0 and 1.\r\n\r\nCMYK must be a [1×4] double array between 0 and 1.","description_html":"\u003cp\u003eConvert an RGB code to the corresponding CMYK code for printing.\u003c/p\u003e\u003cp\u003eThe RGB input is a [1×3] double array between 0 and 1.\u003c/p\u003e\u003cp\u003eCMYK must be a [1×4] double array between 0 and 1.\u003c/p\u003e","function_template":"function CMYK = rgb2cmyk(RGB)\r\n  CMYK = RGB;\r\nend","test_suite":"%%\r\nRGB   = [0,0,0];\r\nCMYK  = [0,0,0,1];\r\nassert(isequal(rgb2cmyk(RGB),CMYK))\r\n%%\r\nRGB   = [1,1,1];\r\nCMYK  = [0,0,0,0];\r\nassert(isequal(rgb2cmyk(RGB),CMYK))\r\n%%\r\nRGB   = [1,0,0];\r\nCMYK  = [0,1,1,0];\r\nassert(isequal(rgb2cmyk(RGB),CMYK))\r\n%%\r\nRGB   = [1,1,0];\r\nCMYK  = [0,0,1,0];\r\nassert(isequal(rgb2cmyk(RGB),CMYK))\r\n%%\r\nRGB   = [150,25,0]/255;\r\nCMYK  = [0,5/6,1,7/17];\r\nerror = abs(rgb2cmyk(RGB)-CMYK);\r\nassert(all(error\u003c1e-4))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":12767,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":133,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-05-05T15:25:57.000Z","updated_at":"2026-02-03T09:25:44.000Z","published_at":"2016-05-05T15:26:16.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConvert an RGB code to the corresponding CMYK code for printing.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe RGB input is a [1×3] double array between 0 and 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCMYK must be a [1×4] double array between 0 and 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":626,"title":"Make blocks of color","description":"Given a (Nx3) sequence of RGB colors, I want to create a (numRowBlocks x numColBlocks x 3) image comprising (blockSize x blockSize) blocks of those colors in a (numRowBlocks x numColBlocks) pattern.\r\n\r\ni.e. for this signature:\r\n  makeColorBlocks(blockSize, numColBlocks, numRowBlocks, RGBvec)\r\n\r\n(Using a weird RGB to make the point)\r\n\r\n    \u003e\u003e makeColorBlocks(2, 3, 2, [1 11 10;2 22 20; 3 33 30; 4 44 40; 5 55 50; 6 66 60])\r\n  \r\n  ans(:,:,1) =\r\n  \r\n      1    1    2    2    3    3\r\n      1    1    2    2    3    3\r\n      4    4    5    5    6    6\r\n      4    4    5    5    6    6\r\n  \r\n  \r\n  ans(:,:,2) =\r\n  \r\n     11   11   22   22   33   33\r\n     11   11   22   22   33   33\r\n     44   44   55   55   66   66\r\n     44   44   55   55   66   66\r\n  \r\n  \r\n  ans(:,:,3) =\r\n  \r\n     10   10   20   20   30   30\r\n     10   10   20   20   30   30\r\n     40   40   50   50   60   60\r\n     40   40   50   50   60   60\r\n\r\nThough it can not be mechanically graded as such, I am looking for easy to read an understand code.  Something easy to understand is preferred.  Thanks to \u003chttp://blogs.mathworks.com/pick/ Brett\u003e for the question.","description_html":"\u003cp\u003eGiven a (Nx3) sequence of RGB colors, I want to create a (numRowBlocks x numColBlocks x 3) image comprising (blockSize x blockSize) blocks of those colors in a (numRowBlocks x numColBlocks) pattern.\u003c/p\u003e\u003cp\u003ei.e. for this signature:\r\n  makeColorBlocks(blockSize, numColBlocks, numRowBlocks, RGBvec)\u003c/p\u003e\u003cp\u003e(Using a weird RGB to make the point)\u003c/p\u003e\u003cpre\u003e    \u003e\u003e makeColorBlocks(2, 3, 2, [1 11 10;2 22 20; 3 33 30; 4 44 40; 5 55 50; 6 66 60])\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans(:,:,1) =\r\n\u003c/pre\u003e\u003cpre\u003e      1    1    2    2    3    3\r\n      1    1    2    2    3    3\r\n      4    4    5    5    6    6\r\n      4    4    5    5    6    6\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans(:,:,2) =\r\n\u003c/pre\u003e\u003cpre\u003e     11   11   22   22   33   33\r\n     11   11   22   22   33   33\r\n     44   44   55   55   66   66\r\n     44   44   55   55   66   66\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans(:,:,3) =\r\n\u003c/pre\u003e\u003cpre\u003e     10   10   20   20   30   30\r\n     10   10   20   20   30   30\r\n     40   40   50   50   60   60\r\n     40   40   50   50   60   60\u003c/pre\u003e\u003cp\u003eThough it can not be mechanically graded as such, I am looking for easy to read an understand code.  Something easy to understand is preferred.  Thanks to \u003ca href=\"http://blogs.mathworks.com/pick/\"\u003eBrett\u003c/a\u003e for the question.\u003c/p\u003e","function_template":"function img = makeColorBlocks(blockSize, numColBlocks, numRowBlocks, RGBvec)\r\n  img = 1;\r\nend","test_suite":"%%\r\nblockSize    = 2; \r\nnumColBlocks = 6; \r\nnumRowBlocks = 4;\r\nRGBvec = spring(numColBlocks*numRowBlocks)*255;\r\n\r\ny_correct(:,:,1) =[\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255\r\n  255  255  255  255  255  255  255  255  255  255  255  255];\r\n\r\ny_correct(:,:,2) = [\r\n    0    0   11   11   22   22   33   33   44   44   55   55\r\n    0    0   11   11   22   22   33   33   44   44   55   55\r\n   67   67   78   78   89   89  100  100  111  111  122  122\r\n   67   67   78   78   89   89  100  100  111  111  122  122\r\n  133  133  144  144  155  155  166  166  177  177  188  188\r\n  133  133  144  144  155  155  166  166  177  177  188  188\r\n  200  200  211  211  222  222  233  233  244  244  255  255\r\n  200  200  211  211  222  222  233  233  244  244  255  255];\r\n\r\ny_correct(:,:,3) = [\r\n  255  255  244  244  233  233  222  222  211  211  200  200\r\n  255  255  244  244  233  233  222  222  211  211  200  200\r\n  188  188  177  177  166  166  155  155  144  144  133  133\r\n  188  188  177  177  166  166  155  155  144  144  133  133\r\n  122  122  111  111  100  100   89   89   78   78   67   67\r\n  122  122  111  111  100  100   89   89   78   78   67   67\r\n   55   55   44   44   33   33   22   22   11   11    0    0\r\n   55   55   44   44   33   33   22   22   11   11    0    0];\r\n\r\n\r\nassert(isequal(makeColorBlocks(blockSize, numColBlocks, numRowBlocks, RGBvec),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":240,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-04-26T14:21:49.000Z","updated_at":"2026-04-02T22:17:59.000Z","published_at":"2012-04-26T14:21:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a (Nx3) sequence of RGB colors, I want to create a (numRowBlocks x numColBlocks x 3) image comprising (blockSize x blockSize) blocks of those colors in a (numRowBlocks x numColBlocks) pattern.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ei.e. for this signature: makeColorBlocks(blockSize, numColBlocks, numRowBlocks, RGBvec)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(Using a weird RGB to make the point)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    \u003e\u003e makeColorBlocks(2, 3, 2, [1 11 10;2 22 20; 3 33 30; 4 44 40; 5 55 50; 6 66 60])\\n\\nans(:,:,1) =\\n\\n      1    1    2    2    3    3\\n      1    1    2    2    3    3\\n      4    4    5    5    6    6\\n      4    4    5    5    6    6\\n\\nans(:,:,2) =\\n\\n     11   11   22   22   33   33\\n     11   11   22   22   33   33\\n     44   44   55   55   66   66\\n     44   44   55   55   66   66\\n\\nans(:,:,3) =\\n\\n     10   10   20   20   30   30\\n     10   10   20   20   30   30\\n     40   40   50   50   60   60\\n     40   40   50   50   60   60]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThough it can not be mechanically graded as such, I am looking for easy to read an understand code. Something easy to understand is preferred. Thanks to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://blogs.mathworks.com/pick/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBrett\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for the question.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":818,"title":"Change a specific color in an image","description":"The ability to change colors can be a useful tool in image processing. Given an m x n x 3 array (much like CData in images), find all instances of a specific color (c1) and change those pixels to another color (c2). Both colors are inputs to the function. The output is the modified 3D array. If there are no instances of the color to be changed, the output will be the same as the input 3D array.\r\n\r\nAssume the class of all colors is double. So, for example, black is [0 0 0] and white is [1 1 1].","description_html":"\u003cp\u003eThe ability to change colors can be a useful tool in image processing. Given an m x n x 3 array (much like CData in images), find all instances of a specific color (c1) and change those pixels to another color (c2). Both colors are inputs to the function. The output is the modified 3D array. If there are no instances of the color to be changed, the output will be the same as the input 3D array.\u003c/p\u003e\u003cp\u003eAssume the class of all colors is double. So, for example, black is [0 0 0] and white is [1 1 1].\u003c/p\u003e","function_template":"function cdata_new = changeColor(cdata,c1,c2)\r\n  cdata_new = [];\r\nend","test_suite":"%%\r\nr = ones(100);\r\ng = zeros(100);\r\nb = zeros(100);\r\ncdata = cat(3,r,g,b);\r\nc1 = [1 0 0];\r\nc2 = [0 0 1];\r\ncdata_new = cat(3,b,g,r);\r\nassert(isequal(changeColor(cdata,c1,c2),cdata_new))\r\n\r\n%%\r\ncdata = rand([400,600,3])*0.9;\r\nc1 = [0 0.5 1];\r\nc2 = [1 1 1];\r\nassert(isequal(changeColor(cdata,c1,c2),cdata))\r\n\r\n%%\r\nind = randi(100,[50 1]);\r\nc1 = rand([1 3]); c1(3) = 0.95;\r\nc2 = [1 1 1];\r\ncdata = rand([100,1,3])*0.8;\r\ncdata_new = cdata;\r\nfor i=1:50\r\n    for j=1:3\r\n        cdata(ind(i),1,j) = c1(j);\r\n        cdata_new(ind(i),1,j) = c2(j);\r\n    end\r\nend\r\nassert(isequal(changeColor(cdata,c1,c2),cdata_new))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":75,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-07-09T04:56:55.000Z","updated_at":"2026-03-31T14:50:08.000Z","published_at":"2012-07-09T05:00:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ability to change colors can be a useful tool in image processing. Given an m x n x 3 array (much like CData in images), find all instances of a specific color (c1) and change those pixels to another color (c2). Both colors are inputs to the function. The output is the modified 3D array. If there are no instances of the color to be changed, the output will be the same as the input 3D array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume the class of all colors is double. So, for example, black is [0 0 0] and white is [1 1 1].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1683,"title":"Create different color vectors.","description":"When producing figures with multiple lines on, you often want the lines to all be visible and different colors. Given the need for n colors, produce a cell of n different RGB color vectors, preferably as different as possible!\r\n\r\n*Examples*\r\n\r\n1) Input: x = 3; Output: y = {[1 0 0], [0 1 0], [0 0 1]}\r\n\r\n2) Input: x = 5; Output: y = {[0 0 0], [0.5 0.5 0.5], [1 0 0], [0 1 0], [0 0 1]}\r\n\r\nNote: to avoid colors being too close to white, it must NOT be that sum(y{i}) \u003e 2.5","description_html":"\u003cp\u003eWhen producing figures with multiple lines on, you often want the lines to all be visible and different colors. Given the need for n colors, produce a cell of n different RGB color vectors, preferably as different as possible!\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples\u003c/b\u003e\u003c/p\u003e\u003cp\u003e1) Input: x = 3; Output: y = {[1 0 0], [0 1 0], [0 0 1]}\u003c/p\u003e\u003cp\u003e2) Input: x = 5; Output: y = {[0 0 0], [0.5 0.5 0.5], [1 0 0], [0 1 0], [0 0 1]}\u003c/p\u003e\u003cp\u003eNote: to avoid colors being too close to white, it must NOT be that sum(y{i}) \u003e 2.5\u003c/p\u003e","function_template":"function y = get_colorVs(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = 3;\r\ny = get_colorVs(x);\r\nc0 = 0;\r\nc1 = 0;\r\ncw = 0;\r\ncs = 0;\r\ncd = 0;\r\nfor i = 1:3\r\n cs = cs + (length(y{i})~=3);\r\n c0 = c0 + sum(y{i}\u003c0);\r\n c1 = c1 + sum(y{i}\u003e1);\r\n cw = cw + (sum(y{i}) \u003e 2.5);\r\nend\r\nfor i = 1:x-1\r\n for j = i+1:x\r\n  cd = cd + (sum(y{i}==y{j}) == 3);\r\n end\r\nend\r\nassert(isequal(cs+c0+c1+cw+cd,0))\r\n\r\n%%\r\nx = 12;\r\ny = get_colorVs(x);\r\nc0 = 0;\r\nc1 = 0;\r\ncw = 0;\r\ncs = 0;\r\ncd = 0;\r\nfor i = 1:3\r\n cs = cs + (length(y{i})~=3);\r\n c0 = c0 + sum(y{i}\u003c0);\r\n c1 = c1 + sum(y{i}\u003e1);\r\n cw = cw + (sum(y{i}) \u003e 2.5);\r\nend\r\nfor i = 1:x-1\r\n for j = i+1:x\r\n  cd = cd + (sum(y{i}==y{j}) == 3);\r\n end\r\nend\r\nassert(isequal(cs+c0+c1+cw+cd,0))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":15191,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":36,"test_suite_updated_at":"2013-06-27T17:35:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-06-27T17:21:04.000Z","updated_at":"2026-03-31T14:54:39.000Z","published_at":"2013-06-27T17:33:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen producing figures with multiple lines on, you often want the lines to all be visible and different colors. Given the need for n colors, produce a cell of n different RGB color vectors, preferably as different as possible!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1) Input: x = 3; Output: y = {[1 0 0], [0 1 0], [0 0 1]}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2) Input: x = 5; Output: y = {[0 0 0], [0.5 0.5 0.5], [1 0 0], [0 1 0], [0 0 1]}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote: to avoid colors being too close to white, it must NOT be that sum(y{i}) \u0026gt; 2.5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1368,"title":"Create an 8-color version of an image","description":"This problem was inspired by a tweet I saw from @MATLAB regarding \u003chttp://www.mathworks.com/matlabcentral/fileexchange/37816-the-warholer?s_eid=PSM_3808 The Warholer\u003e.\r\n\r\nGiven an MxNx3 array (A) representing an image and a threshold for each channel (r/g/b), generate a new array (B) whose elements are 255 if greater than the threshold for that channel or 0 otherwise. This means the new \"image\" will only have 8 colors.\r\n\r\nYou may assume that the inputs are on the interval [0,255].\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eThis problem was inspired by a tweet I saw from @MATLAB regarding \u003ca href = \"http://www.mathworks.com/matlabcentral/fileexchange/37816-the-warholer?s_eid=PSM_3808\"\u003eThe Warholer\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven an MxNx3 array (A) representing an image and a threshold for each channel (r/g/b), generate a new array (B) whose elements are 255 if greater than the threshold for that channel or 0 otherwise. This means the new \"image\" will only have 8 colors.\u003c/p\u003e\u003cp\u003eYou may assume that the inputs are on the interval [0,255].\u003c/p\u003e","function_template":"function B = warholer(A,t)\r\n  B = A;\r\nend","test_suite":"%%\r\nA = randi([51 255],[600 800 3]);\r\nt = [0 25 50];\r\nB = warholer(A,t);\r\nassert(all(B(:)==255))\r\n\r\n%%\r\nA = randi(100,[800 600 3]);\r\nt = [100 255 100];\r\nB = warholer(A,t);\r\nassert(all(B(:)==0))\r\n\r\n%%\r\nA = zeros(200,200,3);\r\nt = randi(255,1,3);\r\nB = warholer(A,t);\r\nassert(all(B(:)==0))\r\n\r\n%%\r\nA = 255*ones(100,100,3);\r\nt = randi(254,1,3);\r\nB = warholer(A,t);\r\nassert(all(B(:)==255))\r\n\r\n%%\r\nA(:,:,1) = randi([0 120],[480 320]);\r\nA(:,:,2) = randi([80 200],[480 320]);\r\nA(:,:,3) = randi([160 240],[480 320]);\r\nt = [150 50 150];\r\nB = warholer(A,t);\r\nassert(all(all(B(:,:,1)==0)))\r\nassert(all(all(B(:,:,2)==255)))\r\nassert(all(all(B(:,:,3)==255)))\r\n\r\n%%\r\nA(:,:,1) = randi([0 100],[480 320]);\r\nA(:,:,2) = randi([80 180],[480 320]);\r\nA(:,:,3) = randi([100 240],[480 320]);\r\nt = [180 60 250];\r\nB = warholer(A,t);\r\nassert(all(all(B(:,:,1)==0)))\r\nassert(all(all(B(:,:,2)==255)))\r\nassert(all(all(B(:,:,3)==0)))\r\n\r\n%%\r\nA(:,:,1) = magic(5)*10;\r\nA(:,:,2) = spiral(5)*10;\r\nA(:,:,3) = toeplitz(1:5)*50;\r\nt = [159 99 149];\r\nB_correct(:,:,1) = [255 255 0   0   0;\r\n                    255 0   0   0   255;\r\n                    0   0   0   255 255;\r\n                    0   0   255 255 0;\r\n                    0   255 255 0   0];\r\nB_correct(:,:,2) = [255 255 255 255 255;\r\n                    255 0   0   0   255;\r\n                    255 0   0   0   255;\r\n                    255 0   0   0   255;\r\n                    255 255 255 255 255];\r\nB_correct(:,:,3) = [0   0   255 255 255;\r\n                    0   0   0   255 255;\r\n                    255 0   0   0   255;\r\n                    255 255 0   0   0;\r\n                    255 255 255 0   0];\r\nassert(isequal(warholer(A,t),B_correct))\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":62,"test_suite_updated_at":"2013-08-05T02:37:21.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-03-20T23:26:39.000Z","updated_at":"2026-03-31T14:55:47.000Z","published_at":"2013-08-05T02:37:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem was inspired by a tweet I saw from @MATLAB regarding\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/fileexchange/37816-the-warholer?s_eid=PSM_3808\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eThe Warholer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an MxNx3 array (A) representing an image and a threshold for each channel (r/g/b), generate a new array (B) whose elements are 255 if greater than the threshold for that channel or 0 otherwise. This means the new \\\"image\\\" will only have 8 colors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou may assume that the inputs are on the interval [0,255].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2195,"title":"Sum of the pixel values for the blue color","description":"Calculate the sum of the pixel values for the blue color, with a picture as an input.\r\nNOTE: The picture will be provided as a matrix of size Height*Width*Colors\r\nTo test your algorithm, you can import the picture to MATLAB with the command 'inputImage=imread('http://static0.therichestimages.com/wp-content/uploads/2014/02/san-sebastian.jpg')'; and introduce 'inputImage' in your algorithm. The output should be '54784197'\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440001px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 461px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 332px 230.5px; transform-origin: 332px 230.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 309px 10.5px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCalculate the sum of the pixel values for the blue color, with a picture as an input.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 309px 10.5px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNOTE:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e The picture will be provided as a matrix of size Height*Width*Colors\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 309px 31.5px; text-align: left; transform-origin: 309px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo test your algorithm, you can import the picture to MATLAB with the command\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003e'inputImage=imread('http://static0.therichestimages.com/wp-content/uploads/2014/02/san-sebastian.jpg')';\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and introduce 'inputImage' in your algorithm. The output should be '54784197'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 329px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 309px 164.5px; text-align: center; transform-origin: 309px 164.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = sum_blue_pixels(x)\r\n  y = x;\r\nend","test_suite":"% Changed on 11-Jun-2024 to only test a built-in image...\r\n%%\r\npath_to_builtin_image = which(\"peppers.png\");\r\nimg = imread(path_to_builtin_image);\r\nassert(isequal(sum_blue_pixels(img), 11331211));\r\n% %\r\n% x=imread('http://static0.therichestimages.com/wp-content/uploads/2014/02/san-sebastian.jpg');\r\n% assert(isequal(sum_blue_pixels(x),54784197));\r\n% %\r\n% x=imread('http://grxworkshop.com/wp-content/uploads/2013/11/223-126-guggenheim-large.jpg');\r\n% assert(isequal(sum_blue_pixels(x),120866847));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":9,"created_by":22877,"edited_by":26769,"edited_at":"2024-06-11T13:53:10.000Z","deleted_by":null,"deleted_at":null,"solvers_count":69,"test_suite_updated_at":"2024-06-11T13:53:10.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-02-18T13:35:31.000Z","updated_at":"2026-04-02T22:22:01.000Z","published_at":"2014-02-18T13:36:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCalculate the sum of the pixel values for the blue color, with a picture as an input.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The picture will be provided as a matrix of size Height*Width*Colors\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo test your algorithm, you can import the picture to MATLAB with the command\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'inputImage=imread('http://static0.therichestimages.com/wp-content/uploads/2014/02/san-sebastian.jpg')';\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and introduce 'inputImage' in your algorithm. The output should be '54784197'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"center\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"469\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"898\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.JPEG\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.JPEG\",\"contentType\":\"image/JPEG\",\"content\":\"data:image/JPEG;base64,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- Mean Filter","description":"Assume you are given an \"image\" matrix of size NxM. Reduce the image noise by implementing a mean filter window of size 9 (a 3x3 averaging window). The image matrix will have integer elements from 0 to 255 (to reflect 8 bits of resolution).\r\n\r\nThe averaging window is a square matrix of odd size where the center element (pixel) is the one that is being operated on. Replace the center pixel with the average of the pixels the matrix window encompasses. This is accomplished for every pixel. The output should be the filtered image matrix. Each average should be rounded to the nearest integer \r\n\r\nNote: If the window filter overlaps the edge of an image use zero (0) for missing values \r\n\r\nExample: \r\n\r\nOperating on the first element of matrix\r\n\r\n [1  2  3  4  5; 6  7  8  9  10; 11 12 13 14 15]\r\n\r\nwould have the window:\r\n\r\n [0 0 0; 0 1 2; 0 6 7]\r\n\r\nthis will result in:\r\n\r\n B(1,1) = 2","description_html":"\u003cp\u003eAssume you are given an \"image\" matrix of size NxM. Reduce the image noise by implementing a mean filter window of size 9 (a 3x3 averaging window). The image matrix will have integer elements from 0 to 255 (to reflect 8 bits of resolution).\u003c/p\u003e\u003cp\u003eThe averaging window is a square matrix of odd size where the center element (pixel) is the one that is being operated on. Replace the center pixel with the average of the pixels the matrix window encompasses. This is accomplished for every pixel. The output should be the filtered image matrix. Each average should be rounded to the nearest integer\u003c/p\u003e\u003cp\u003eNote: If the window filter overlaps the edge of an image use zero (0) for missing values\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cp\u003eOperating on the first element of matrix\u003c/p\u003e\u003cpre\u003e [1  2  3  4  5; 6  7  8  9  10; 11 12 13 14 15]\u003c/pre\u003e\u003cp\u003ewould have the window:\u003c/p\u003e\u003cpre\u003e [0 0 0; 0 1 2; 0 6 7]\u003c/pre\u003e\u003cp\u003ethis will result in:\u003c/p\u003e\u003cpre\u003e B(1,1) = 2\u003c/pre\u003e","function_template":"function B = med_filt(A)\r\n  B = A;\r\nend","test_suite":"%%\r\nA = 127*ones(5,4)\r\nB_correct = [56 85 85 56; 85 127 127 85; 85 127 127 85; 85 127 127 85; 56 85 85 56]\r\nassert(isequal(med_filt(A),B_correct))\r\nclear all\r\n%%\r\nA(1,:) = [0:7:63];\r\nA(2,:) = [63:7:127];\r\nA(3,:) = [128:7:191];\r\nA(4,:) = [192:7:256];\r\nA\r\nB_correct = [16 26 30 35 40 44 49 54 58 40; 45 71 78 85 92 99 106 113 120 82; 87 135 142 149 156 163 170 177 184 125; 73 111 116 121 125 130 135 139 144 98]\r\nassert(isequal(med_filt(A),B_correct))\r\n%%\r\nclear all\r\nA = [71 128 192 246 215; 174 246 65 140 65; 168 87 130 35 208; 42 150 179 38 62; 30 57 228 66 238]\r\nB_correct = [69 97 113 103 74; 97 140 141 144 101; 96 138 119 102 61; 59 119 108 132 72; 31 76 80 90 45]\r\nassert(isequal(med_filt(A),B_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":9441,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":64,"test_suite_updated_at":"2013-01-07T17:07:06.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-07T16:33:13.000Z","updated_at":"2026-03-31T14:57:35.000Z","published_at":"2013-01-07T16:35:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssume you are given an \\\"image\\\" matrix of size NxM. Reduce the image noise by implementing a mean filter window of size 9 (a 3x3 averaging window). The image matrix will have integer elements from 0 to 255 (to reflect 8 bits of resolution).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe averaging window is a square matrix of odd size where the center element (pixel) is the one that is being operated on. Replace the center pixel with the average of the pixels the matrix window encompasses. This is accomplished for every pixel. The output should be the filtered image matrix. Each average should be rounded to the nearest integer\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote: If the window filter overlaps the edge of an image use zero (0) for missing values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOperating on the first element of matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ [1  2  3  4  5; 6  7  8  9  10; 11 12 13 14 15]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewould have the window:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ [0 0 0; 0 1 2; 0 6 7]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethis will result in:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ B(1,1) = 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":839,"title":"Compute the dilation of a binary image","description":"A basic operation in image analysis is the dilation. Given an image where each pixel is either on or off (black/white, true/false), and a neighbourhood, the result of the operation at one pixel is 'on' (white/true) if any of the pixels covered by the neighbourhood, when centred on that pixel, is 'on'.\r\n\r\nTake for example a 3x3 neighbourhood. When centring it on any one pixel, it covers that pixel plus its 8 direct neighbours. If any of those 9 pixels is 'on' in the input, that pixel will be 'on' in the output. Looking at it in another way, any 'on' pixel in the input will cause all its 8 direct neighbours to be 'on' in the output.\r\n\r\nYour task is to write an algorithm that takes a matrix with 1s and 0s, and computes the dilation. Do not use any toolbox functions, the test suite checks against them (don't use variable names containing things like 'dilate' or 'conv' or 'filt'!).","description_html":"\u003cp\u003eA basic operation in image analysis is the dilation. Given an image where each pixel is either on or off (black/white, true/false), and a neighbourhood, the result of the operation at one pixel is 'on' (white/true) if any of the pixels covered by the neighbourhood, when centred on that pixel, is 'on'.\u003c/p\u003e\u003cp\u003eTake for example a 3x3 neighbourhood. When centring it on any one pixel, it covers that pixel plus its 8 direct neighbours. If any of those 9 pixels is 'on' in the input, that pixel will be 'on' in the output. Looking at it in another way, any 'on' pixel in the input will cause all its 8 direct neighbours to be 'on' in the output.\u003c/p\u003e\u003cp\u003eYour task is to write an algorithm that takes a matrix with 1s and 0s, and computes the dilation. Do not use any toolbox functions, the test suite checks against them (don't use variable names containing things like 'dilate' or 'conv' or 'filt'!).\u003c/p\u003e","function_template":"function out = mymorphop(in)\r\nout = in;\r\nend","test_suite":"%%\r\nf = fopen('mymorphop.m','rt');\r\ncode = lower(fread(f,Inf,'*char'))';\r\nfclose(f);\r\nassert(isempty(strfind(code,'dilat')))\r\nassert(isempty(strfind(code,'erode')))\r\nassert(isempty(strfind(code,'bwmorph')))\r\nassert(isempty(strfind(code,'filt')))\r\nassert(isempty(strfind(code,'conv')))\r\n\r\n%%\r\nin = zeros(3,3);\r\nout = in;\r\nassert(isequal(mymorphop(in),out));\r\n\r\n%%\r\nin = zeros(10,5);\r\nin(4,3) = 1;\r\nout = in;\r\nout(3:5,2:4) = 1;\r\nassert(isequal(mymorphop(in),out));\r\n\r\n%%\r\nin =  [0,0,1,0,0,1,0\r\n       0,1,0,0,0,1,1\r\n       1,1,0,0,0,0,0\r\n       1,0,0,0,0,0,1\r\n       1,0,0,0,0,1,1];\r\nout = [1,1,1,1,1,1,1\r\n       1,1,1,1,1,1,1\r\n       1,1,1,0,1,1,1\r\n       1,1,1,0,1,1,1\r\n       1,1,0,0,1,1,1];\r\nassert(isequal(mymorphop(in),out));\r\n\r\n%%\r\nin =  [0,0,1,0,0,0,0,0,1\r\n       0,1,0,0,0,0,0,0,1\r\n       1,1,0,0,0,0,0,0,1\r\n       1,0,0,0,0,0,0,0,1\r\n       1,0,0,0,0,0,0,0,1];\r\nout = [1,1,1,1,0,0,0,1,1\r\n       1,1,1,1,0,0,0,1,1\r\n       1,1,1,0,0,0,0,1,1\r\n       1,1,1,0,0,0,0,1,1\r\n       1,1,0,0,0,0,0,1,1];\r\nassert(isequal(mymorphop(in),out));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":1411,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-07-17T20:49:47.000Z","updated_at":"2026-03-31T15:00:48.000Z","published_at":"2012-07-17T21:03:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA basic operation in image analysis is the dilation. Given an image where each pixel is either on or off (black/white, true/false), and a neighbourhood, the result of the operation at one pixel is 'on' (white/true) if any of the pixels covered by the neighbourhood, when centred on that pixel, is 'on'.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTake for example a 3x3 neighbourhood. When centring it on any one pixel, it covers that pixel plus its 8 direct neighbours. If any of those 9 pixels is 'on' in the input, that pixel will be 'on' in the output. Looking at it in another way, any 'on' pixel in the input will cause all its 8 direct neighbours to be 'on' in the output.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to write an algorithm that takes a matrix with 1s and 0s, and computes the dilation. Do not use any toolbox functions, the test suite checks against them (don't use variable names containing things like 'dilate' or 'conv' or 'filt'!).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":852,"title":"Index of neighbor pixel with steepest gradient","description":"Unlike in various applications, where the gradient of a two dimensional matrix is calculated in x and y direction, the gradient of a digital elevation model (DEM) is usually returned as the steepest gradient. The steepest gradient is the largest downward slope of a pixel to one of its eight neighbors.\r\nIn this problem, your task will be to return the linear index of the steepest neighbor for each pixel in a gridded DEM. Pixels that don't have downward neighbors should receive the index value zero.\r\nAn example should help. The DEM is\r\ndem = [1 5 9; ...\r\n       4 5 6; ...\r\n       8 7 3];\r\nThe result should be\r\nIX  = [0 1 4; ...\r\n       1 1 9; ...\r\n       2 9 0];\r\nThe results may not be unique, but the test cases have been built so that this is not a problem. The spatial resolution of the dem is dx=1 and dy=1. Note that the diagonal distance is hypot(dx,dy).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 369.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 184.8px; transform-origin: 407px 184.8px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 358.5px 8px; transform-origin: 358.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eUnlike in various applications, where the gradient of a two dimensional matrix is calculated in x and y direction, the gradient of a digital elevation model (DEM) is usually returned as the steepest gradient. The steepest gradient is the largest downward slope of a pixel to one of its eight neighbors.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.5px 8px; transform-origin: 382.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn this problem, your task will be to return the linear index of the steepest neighbor for each pixel in a gridded DEM. Pixels that don't have downward neighbors should receive the index value zero.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 115.5px 8px; transform-origin: 115.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAn example should help. The DEM is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30.65px; transform-origin: 404px 30.65px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 68px 8.5px; tab-size: 4; transform-origin: 68px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003edem = [1 5 9; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 12px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 12px 8.5px; \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 68px 8.5px; tab-size: 4; transform-origin: 68px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003e       4 5 6; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 12px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 12px 8.5px; \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e       8 7 3];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 65px 8px; transform-origin: 65px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe result should be\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30.65px; transform-origin: 404px 30.65px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 68px 8.5px; tab-size: 4; transform-origin: 68px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003eIX  = [0 1 4; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 12px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 12px 8.5px; \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 68px 8.5px; tab-size: 4; transform-origin: 68px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 56px 8.5px; transform-origin: 56px 8.5px; \"\u003e       1 1 9; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 12px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 12px 8.5px; \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e       2 9 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe results may not be unique, but the test cases have been built so that this is not a problem. The spatial resolution of the dem is dx=1 and dy=1. Note that the diagonal distance is hypot(dx,dy).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function IX = sgix(dem)\r\n  IX = [];\r\nend","test_suite":"%%\r\ndem = [1 5 9; ...\r\n       4 5 6; ...\r\n       8 7 3];\r\nIX  = [0 1 4; ...\r\n       1 1 9; ...\r\n       2 9 0];\r\nassert(isequal(sgix(dem),IX))\r\n\r\n%%\r\ndem = [1 4 7; ...\r\n       2 5 8; ...\r\n       3 6 9];\r\nIX  = [0 1 4; ...\r\n       1 2 5; ...\r\n       2 3 6];\r\nassert(isequal(sgix(dem),IX))\r\n\r\n%%\r\ndem = [1 2 ; ...\r\n       3 4];\r\ndem = dem + randi(1e3);\r\nIX  = [0 1; ...\r\n       1 1];\r\nassert(isequal(sgix(dem),IX))\r\n\r\n\r\n%%\r\ndem = [1 5 4 9; ...\r\n       4 5 7 7; ...\r\n       6 6 6 8];\r\nIX  = [0 1 0 7; ...\r\n       1 1 7 7; ...\r\n       2 2 5 9];\r\nassert(isequal(sgix(dem),IX))","published":true,"deleted":false,"likes_count":2,"comments_count":7,"created_by":569,"edited_by":223089,"edited_at":"2022-12-22T13:29:53.000Z","deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2022-12-22T13:29:53.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-07-20T08:58:26.000Z","updated_at":"2026-04-02T22:28:48.000Z","published_at":"2012-07-20T08:58:26.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUnlike in various applications, where the gradient of a two dimensional matrix is calculated in x and y direction, the gradient of a digital elevation model (DEM) is usually returned as the steepest gradient. The steepest gradient is the largest downward slope of a pixel to one of its eight neighbors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, your task will be to return the linear index of the steepest neighbor for each pixel in a gridded DEM. Pixels that don't have downward neighbors should receive the index value zero.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn example should help. The DEM is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[dem = [1 5 9; ...\\n       4 5 6; ...\\n       8 7 3];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe result should be\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[IX  = [0 1 4; ...\\n       1 1 9; ...\\n       2 9 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe results may not be unique, but the test cases have been built so that this is not a problem. The spatial resolution of the dem is dx=1 and dy=1. Note that the diagonal distance is hypot(dx,dy).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42616,"title":"Detect circles in images","description":"Given an image and a target radius range, specified as [rmin rmax], find circles in the image. Your function should output an m-by-2 array of circle centers (x,y positions in the image) and an m-by-1 vector of radii corresponding to m circles.\r\n\r\nYour detector will be judged on its \u003chttps://en.wikipedia.org/wiki/Precision_and_recall precision and recall\u003e. The recall must be 0.75 or higher and the precision must be 0.5 or higher to pass. \r\n\r\nFor example, if an image has 4 true circles, you can miss at most 1 of the circles and have at most 4 false detections.\r\n\r\n*Additional notes:*\r\n\r\n* Circles can be brighter or darker than the background.\r\n* A detection is considered a match if its position and radius is within 5 pixels of a true circle.\r\n* To make things easier, the target number of circles (N) is provided as an input. Pat yourself on the back if you do not need it.","description_html":"\u003cp\u003eGiven an image and a target radius range, specified as [rmin rmax], find circles in the image. Your function should output an m-by-2 array of circle centers (x,y positions in the image) and an m-by-1 vector of radii corresponding to m circles.\u003c/p\u003e\u003cp\u003eYour detector will be judged on its \u003ca href = \"https://en.wikipedia.org/wiki/Precision_and_recall\"\u003eprecision and recall\u003c/a\u003e. The recall must be 0.75 or higher and the precision must be 0.5 or higher to pass.\u003c/p\u003e\u003cp\u003eFor example, if an image has 4 true circles, you can miss at most 1 of the circles and have at most 4 false detections.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdditional notes:\u003c/b\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003eCircles can be brighter or darker than the background.\u003c/li\u003e\u003cli\u003eA detection is considered a match if its position and radius is within 5 pixels of a true circle.\u003c/li\u003e\u003cli\u003eTo make things easier, the target number of circles (N) is provided as an input. Pat yourself on the back if you do not need it.\u003c/li\u003e\u003c/ul\u003e","function_template":"function [centers,radii] = detectcircles(I,R,N)\r\n  centers = [];\r\n  radii = [];\r\nend","test_suite":"%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','circles.png'));\r\n[centers,radii] = detectcircles(I,[18 20],13);\r\nc = [119 222; 185 218; 124 116; 37 37; 178 184; 93 167; 37 72; 71 38; 93 132; 122 186; 97 96; 71 74; 151 204];\r\nr = 19*ones(13,1);\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','circlesBrightDark.png'));\r\n[centers,radii] = detectcircles(I,[32 64],6);\r\nc = [75 250; 100 100; 250 400; 300 120; 450 240; 330 370];\r\nr = [35; 50; 60; 40; 50; 55];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','coins.png'));\r\n[centers,radii] = detectcircles(I,[24 30],10);\r\nc = [236 174; 149 35; 56 50; 266 103; 217 71; 120 209; 110 85; 175 120; 96 146; 37 107];\r\nr = [25; 29; 25; 24; 29; 29; 24; 29; 29; 29];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','coloredChips.png'));\r\n[centers,radii] = detectcircles(I,[20 28],26);\r\nc = [83 177; 304 336; 420 88; 434 165; 244 166; 327 297; 273 53; 130 44; 271 281; 408 265; 312 192; 420 346; 146 199; 228 232; 329 135; 175 297; 366 224; 150 258; 217 107; 345 119; 445 68; 372 293; 150 342; 251 8; 259 217; 198 107];\r\nr = [23; 24; 23; 23; 23; 23; 23; 23; 23; 23; 23; 24; 23; 23; 23; 24; 23; 24; 23; 23; 23; 24; 25; 23; 23; 25];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','eight.tif'));\r\n[centers,radii] = detectcircles(I,[35 40],4);\r\nc = [198 189; 247 72; 62 141; 124 58];\r\nr = [37; 37; 38; 37];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','moon.tif'));\r\n[centers,radii] = detectcircles(I,[200 210],1);\r\nc = [253 287];\r\nr = [205];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','pillsetc.png'));\r\n[centers,radii] = detectcircles(I,[15 55],4);\r\nc = [103 240; 252 326; 119 130; 319 84];\r\nr = [17; 17; 50; 37];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','tape.png'));\r\n[centers,radii] = detectcircles(I,[75 85],1);\r\nc = [236 172];\r\nr = [80];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','testpat1.png'));\r\n[centers,radii] = detectcircles(I,[110 120],1);\r\nc = [128 128];\r\nr = [116];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)\r\n\r\n%%\r\nI = imread(fullfile(matlabroot,'toolbox','images','imdata','toysnoflash.png'));\r\n[centers,radii] = detectcircles(I,[90 100],1);\r\nc = [267 506];\r\nr = [94];\r\nd1 = squeeze(sqrt(sum(bsxfun(@minus,centers,permute(c,str2num('3 2 1'))).^2,2)));\r\nd2 = squeeze(sqrt(sum(bsxfun(@minus,radii,permute(r,str2num('3 2 1'))).^2,2)));\r\nmask = d1\u003c5 \u0026 d2\u003c5;\r\nRe = mean(any(mask));\r\nPr = sum(any(mask))/size(mask,1);\r\nassert(Pr\u003e=0.5)\r\nassert(Re\u003e=0.75)","published":true,"deleted":false,"likes_count":4,"comments_count":4,"created_by":4793,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2015-09-18T20:16:44.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-09-17T22:38:48.000Z","updated_at":"2026-04-02T22:38:30.000Z","published_at":"2015-09-17T23:22:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an image and a target radius range, specified as [rmin rmax], find circles in the image. Your function should output an m-by-2 array of circle centers (x,y positions in the image) and an m-by-1 vector of radii corresponding to m circles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour detector will be judged on its\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Precision_and_recall\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eprecision and recall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The recall must be 0.75 or higher and the precision must be 0.5 or higher to pass.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if an image has 4 true circles, you can miss at most 1 of the circles and have at most 4 false detections.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAdditional notes:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCircles can be brighter or darker than the background.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA detection is considered a match if its position and radius is within 5 pixels of a true circle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo make things easier, the target number of circles (N) is provided as an input. Pat yourself on the back if you do not need it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"no_progress_badge":{"id":53,"name":"Unknown","symbol":"unknown","description":"Partially completed groups","description_html":null,"image_location":"/images/responsive/supporting/matlabcentral/cody/badges/problem_groups_unknown_2.png","bonus":null,"players_count":0,"active":false,"created_by":null,"updated_by":null,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"created_at":"2018-01-10T23:20:29.000Z","updated_at":"2018-01-10T23:20:29.000Z","community_badge_id":null,"award_multiples":false}}