31:T545,process_image = /* prettier-ignore */ { const image = await FileAttachment("1024px-Garo_people_celebrate_New_year_2021.jpg").image(); const inputCanvas = DOM.canvas(image.width, image.height); const threshCanvas = Object.assign( DOM.canvas(image.width, image.height) ); const laplCanvas = Object.assign( DOM.canvas(image.width, image.height) ); let srcImage = cv.imread(image); cv.imshow(inputCanvas, srcImage); let thresh = cv.Mat.zeros( image.width, image.height, cv.CV_8UC3); let lapl = cv.Mat.zeros(image.width, image.height, cv.CV_8UC3); cv.threshold(srcImage, thresh, ex1_threshold, 255, threshTypeA[tTypeA]); cv.imshow(threshCanvas, thresh); cv.cvtColor(thresh, lapl, cv.COLOR_RGB2GRAY, 0); cv.adaptiveThreshold( lapl, lapl, maxValue, aMethod[+adaptiveMethod], +inv, blockSize, 2 ); cv.imshow(laplCanvas, lapl); srcImage.delete(); thresh.delete(); lapl.delete(); const css = "max-width:100%"; inputCanvas.style.cssText += css; threshCanvas.style.cssText += css; laplCanvas.style.cssText += css; return html`

${inputCanvas}

${threshCanvas}

${laplCanvas}

`; }32:[{"id":"10ee56138062ca76375367a6d9b014c30e4127a2cd701f8ce2e00c8736d444007d5ec2eb4482ec93397b6922f86342dc111a8a527534961bb6d981ae80ef2d79","href":"https://static.observableusercontent.com/files/10ee56138062ca76375367a6d9b014c30e4127a2cd701f8ce2e00c8736d444007d5ec2eb4482ec93397b6922f86342dc111a8a527534961bb6d981ae80ef2d79","name":"1024px-Garo_people_celebrate_New_year_2021.jpg","type":"image/jpeg","lastModified":1641043228387.274,"size":226968}] 2f:{"title":"Dithering 02: genuary 2022 challenge","cells":[{"id":0,"value":"# Dithering 02: genuary 2022 challenge \n\n\n[Garo people celebrate New year 2021](https://commons.wikimedia.org/wiki/File:Garo_people_celebrate_New_year_2021.jpg)","pinned":false,"hidden":false,"mode":"md"},{"id":211,"value":"primage = /* prettier-ignore */ {\n const image = await FileAttachment(\n \"1024px-Garo_people_celebrate_New_year_2021.jpg\"\n ).image();\n const laplCanvas = Object.assign(DOM.canvas(image.width, image.height));\n\n let srcImage = cv.imread(image);\n let lapl = cv.Mat.zeros(image.width, image.height, cv.CV_8UC3);\n\n let frame;\n\n while(true){\n let t = tre?0:2\n cv.threshold(srcImage, lapl, 133, 255, t); // cv.THRESH_BINARY\n cv.cvtColor(lapl, lapl, cv.COLOR_RGB2GRAY, 0);\n cv.adaptiveThreshold( lapl, lapl, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, 0, BS[0], 2 );\n cv.imshow(laplCanvas, lapl);\n\n BS[0] += 2\n if (BS[0] > 255) {BS[0] = 3;}\n yield html`

${laplCanvas}

`; \n }\n srcImage.delete();\n lapl.delete();\n\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"viewof tre = Inputs.toggle({\n label: \"Change Thresh hold type between BINARY and TRUNC\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":184,"value":"BS = [3]","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"---\n\n# test area","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"viewof ex1_threshold = Inputs.range([0, 254], {\n value: 176,\n step: 1,\n label: \"Threshold\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":65,"value":"viewof tTypeA = Inputs.select(\n [\"THRESH_BINARY\", \"THRESH_TRUNC\", \"THRESH_TOZERO\"],\n {\n label: \"thresholding type to apply\",\n value: \"THRESH_BINARY\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"threshTypeA = ({\n THRESH_BINARY: cv.THRESH_BINARY,\n THRESH_TRUNC: cv.THRESH_TRUNC,\n THRESH_TOZERO: cv.THRESH_TOZERO\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"viewof maxValue = Inputs.range([0, 255], {\n value: 176,\n step: 1,\n label: \"MaxValue\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":114,"value":"viewof inv = Inputs.toggle({ label: \"Invert Colours\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"viewof adaptiveMethod = Inputs.toggle({\n label: \"adaptiveMethod MEAN or GAUSSIAN\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":149,"value":"viewof blockSize = Inputs.range([3, 300], {\n value: 3,\n step: 2,\n label: \"Block Size\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"aMethod = [cv.ADAPTIVE_THRESH_MEAN_C, cv.ADAPTIVE_THRESH_GAUSSIAN_C]","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":8,"value":"import { cv, handleError, registerFile } from \"@mootari/opencv-js\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}