[{"_1":2,"_608":-5,"_609":-5},"loaderData",{"_3":4,"_95":96,"_447":448,"_607":-5},"root",{"_5":6,"_34":35,"_62":63,"_93":94},"ctx",{"_7":8,"_19":20,"_21":-5,"_22":23,"_24":25,"_26":27,"_28":27,"_29":27,"_30":31,"_32":33},"auth",{"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5,"_18":-5},"sessionClaims","sessionId","sessionStatus","userId","actor","orgId","orgRole","orgSlug","orgPermissions","factorVerificationAge","environmentVariableKeys",[],"user","preferences_keybindings","default","preferences_theme","light","preferences_fixers",false,"preferences_copilot","preferences_selection_ai","preferences_model","claude-3-5-haiku-20241022","notification_count",0,"ENV",{"_36":37,"_38":39,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59,"_60":61},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","VAL_RUNTIME_NAME","prod","RENDER_GIT_COMMIT","5b25ff509fb278d43796a55a7b776310489290ca","RENDER_GIT_BRANCH","main","CLERK_PUBLISHABLE_KEY","pk_live_Y2xlcmsudmFsLnRvd24k","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","API_URL","https://api.val.town","WORKER_FINGERPRINT","fd7e47ba602df66e0f857b45e802b861","clerkState",{"_64":65},"__internal_clerk_state",{"_66":67,"_68":45,"_69":70,"_71":70,"_72":27,"_73":70,"_74":70,"_75":70,"_76":70,"_77":70,"_78":70,"_79":70,"_80":70,"_81":82,"_91":27,"_92":27},"__clerk_ssr_state",{"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5,"_18":-5},"__publishableKey","__proxyUrl","","__domain","__isSatellite","__signInUrl","__signUpUrl","__afterSignInUrl","__afterSignUpUrl","__signInForceRedirectUrl","__signUpForceRedirectUrl","__signInFallbackRedirectUrl","__signUpFallbackRedirectUrl","__clerk_debug",{"_83":27,"_84":70,"_85":86,"_87":70,"_88":45,"_89":27,"_90":70},"isSignedIn","proxyUrl","reason","session-token-and-uat-missing","message","publishableKey","isSatellite","domain","__telemetryDisabled","__telemetryDebug","remixVersion",2,"routes/_app.x.$handle.$name",{"_97":98,"_99":100,"_101":43,"_102":70,"_103":-7,"_104":105,"_143":144,"_413":414,"_415":416,"_421":422,"_383":-5,"_444":-5,"_445":-5,"_446":27},"handle","maxm","name","monke","branchName","path","version","project",{"_106":107,"_99":100,"_108":109,"_110":-5,"_111":27,"_112":113,"_114":-5,"_115":-5,"_12":116,"_117":-5,"_118":119,"_120":-5,"_121":33,"_122":123,"_124":123,"_125":126,"_127":126,"_128":126,"_129":123,"_130":27,"_21":131,"_134":135},"id","57fa9f5c-0a6a-11f0-b501-569c3dd06744","createdAt",["D",1743011230732],"deletedAt","legacyEnvVars","privacy","public","description","imageUrl","418c1e39-eb48-4f2f-8c97-122ece94631c","pinnedFileId","pinPreview",true,"screenshotVersion","likeCount","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","isOwned",{"_97":98,"_132":133},"profileImageUrl","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvdXBsb2FkZWQvaW1nXzJnTEtkSHlOMWxWZm1GRHdKYUVTNklYM3FpdiJ9","branch",{"_103":136,"_99":43,"_106":137,"_138":107,"_139":-5,"_140":-5,"_141":-5,"_142":-5},30,"57faf236-0a6a-11f0-b501-569c3dd06744","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectUserId","tree",[145,174,181,377,387,396,405],{"_106":146,"_99":147,"_148":149,"_150":-5,"_151":137,"_152":153,"_154":119,"_108":155,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":162,"_102":173},"c17c3d46-0a6a-11f0-9622-569c3dd06744","deps","fileId","c17c3dfa-0a6a-11f0-9622-569c3dd06744","parentId","branchId","branchVersion",5,"latest",["D",1743011407738],"fileType","directory","content","deleted","subdomain","children",[163],{"_106":164,"_99":165,"_148":166,"_150":149,"_151":137,"_152":167,"_154":119,"_108":168,"_103":33,"_156":169,"_158":170,"_159":27,"_160":-5,"_161":171,"_102":172},"dfa9a7ea-0a6a-11f0-bf1a-569c3dd06744","color.ts","dfa9a89e-0a6a-11f0-bf1a-569c3dd06744",17,["D",1743011458365],"script","export { Color, meanDistance } from \"https://deno.land/x/colors@v1.0.0/mod.ts\";\n",[],[147,165],[147],{"_106":175,"_99":100,"_148":176,"_150":-5,"_151":137,"_152":177,"_154":119,"_108":178,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":179,"_102":180},"d5d9b11a-0a6a-11f0-8e07-569c3dd06744","d5d9b1ec-0a6a-11f0-8e07-569c3dd06744",8,["D",1743011441905],[],[100],{"_106":182,"_99":183,"_148":184,"_150":-5,"_151":137,"_152":185,"_154":119,"_108":186,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":187,"_102":376},"ba764ca8-0a6a-11f0-ba55-569c3dd06744","src","ba764d34-0a6a-11f0-ba55-569c3dd06744",1,["D",1743011395955],[188,214,257,299,325,351,359,367],{"_106":189,"_99":190,"_148":191,"_150":184,"_151":137,"_152":192,"_154":119,"_108":193,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":194,"_102":213},"d074ff72-0a6a-11f0-9d7c-569c3dd06744","blur","d0750008-0a6a-11f0-9d7c-569c3dd06744",7,["D",1743011432857],[195,204],{"_106":196,"_99":197,"_148":198,"_150":191,"_151":137,"_152":199,"_154":119,"_108":200,"_103":33,"_156":169,"_158":201,"_159":27,"_160":-5,"_161":202,"_102":203},"e03c2e3a-0a6a-11f0-9052-569c3dd06744","box.ts","e03c2f02-0a6a-11f0-9052-569c3dd06744",28,["D",1743011459321],"import { Color } from \"../../deps/color.ts\";\n\n/** Box blur with r = 3 */\nexport function boxBlur(image: { pixels: Color[]; width: number }) {\n let i = 0;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i < (image.pixels.length)) {\n const sumNeighbours = [0, 0, 0];\n let numAdded = 0;\n\n if (i > image.width + 1) {\n // 1\n sumNeighbours[0] += image.pixels[i - image.width - 1].r;\n sumNeighbours[1] += image.pixels[i - image.width - 1].g;\n sumNeighbours[2] += image.pixels[i - image.width - 1].b;\n numAdded += 1;\n }\n if (i > image.width) {\n // 2\n sumNeighbours[0] += image.pixels[i - image.width].r;\n sumNeighbours[1] += image.pixels[i - image.width].g;\n sumNeighbours[2] += image.pixels[i - image.width].b;\n\n // 3\n sumNeighbours[0] += image.pixels[i - image.width + 1].r;\n sumNeighbours[1] += image.pixels[i - image.width + 1].g;\n sumNeighbours[2] += image.pixels[i - image.width + 1].b;\n numAdded += 2;\n }\n\n if (i % image.width > 0) {\n // 4\n sumNeighbours[0] += image.pixels[i - 1].r;\n sumNeighbours[1] += image.pixels[i - 1].g;\n sumNeighbours[2] += image.pixels[i - 1].b;\n numAdded += 1;\n }\n // 5\n sumNeighbours[0] += image.pixels[i].r;\n sumNeighbours[1] += image.pixels[i].g;\n sumNeighbours[2] += image.pixels[i].b;\n numAdded += 1;\n\n if ((i % image.width) - image.width < -1) {\n // 6\n sumNeighbours[0] += image.pixels[i + 1].r;\n sumNeighbours[1] += image.pixels[i + 1].g;\n sumNeighbours[2] += image.pixels[i + 1].b;\n numAdded += 1;\n }\n\n if (image.pixels.length - i > image.width) {\n // 7\n sumNeighbours[0] += image.pixels[i + image.width].r;\n sumNeighbours[1] += image.pixels[i + image.width].g;\n sumNeighbours[2] += image.pixels[i + image.width].b;\n\n // 8\n sumNeighbours[0] += image.pixels[i + image.width - 1].r;\n sumNeighbours[1] += image.pixels[i + image.width - 1].g;\n sumNeighbours[2] += image.pixels[i + image.width - 1].b;\n numAdded += 2;\n\n if (image.pixels.length - i > image.width + 1) {\n // 9\n sumNeighbours[0] += image.pixels[i + image.width + 1].r;\n sumNeighbours[1] += image.pixels[i + image.width + 1].g;\n sumNeighbours[2] += image.pixels[i + image.width + 1].b;\n numAdded += 1;\n }\n }\n\n image.pixels[i].r = Math.trunc(sumNeighbours[0] / numAdded);\n image.pixels[i].g = Math.trunc(sumNeighbours[1] / numAdded);\n image.pixels[i].b = Math.trunc(sumNeighbours[2] / numAdded);\n i += 1;\n }\n}\n",[],[183,190,197],{"_106":205,"_99":206,"_148":207,"_150":191,"_151":137,"_152":208,"_154":119,"_108":209,"_103":33,"_156":169,"_158":210,"_159":27,"_160":-5,"_161":211,"_102":212},"e0270a46-0a6a-11f0-8ea4-569c3dd06744","mod.ts","e0270b04-0a6a-11f0-8ea4-569c3dd06744",27,["D",1743011459190],"export { boxBlur } from \"./box.ts\";\n",[],[183,190,206],[183,190],{"_106":215,"_99":216,"_148":217,"_150":184,"_151":137,"_152":218,"_154":119,"_108":219,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":220,"_102":256},"c25b8e24-0a6a-11f0-b0d8-569c3dd06744","dither","c25b8ed8-0a6a-11f0-b0d8-569c3dd06744",6,["D",1743011409201],[221,230,238,247],{"_106":222,"_99":223,"_148":224,"_150":217,"_151":137,"_152":225,"_154":119,"_108":226,"_103":33,"_156":169,"_158":227,"_159":27,"_160":-5,"_161":228,"_102":229},"e0141238-0a6a-11f0-a4d2-569c3dd06744","fs.ts","e01412ec-0a6a-11f0-a4d2-569c3dd06744",25,["D",1743011459065],"import { Color } from \"../../deps/color.ts\";\nimport { findClosestColor } from \"../util/closest.ts\";\n\n/**\n * Dither the image into a smaller palette\n * Very fast dithering\n * Use twoRowSierra for more accuracy\n * Use monochromeFs for monochrome dithering\n */\nexport function floydSteinberg(\n pixels: Color[],\n width: number,\n palette: Color[],\n): Color[] {\n let i = 0;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i < (pixels.length)) {\n const newC = findClosestColor(pixels[i], palette);\n const err = {\n r: Math.floor((pixels[i].r - newC.r) / 16),\n g: Math.floor((pixels[i].r - newC.g) / 16),\n b: Math.floor((pixels[i].r - newC.b) / 16),\n };\n\n // Match our quantized palette\n pixels[i] = newC;\n\n // Spread error to neighbouring pixels\n if (i < pixels.length - 1) {\n // Spread error to next pixel\n pixels[i + 1].r += err.r * 7;\n pixels[i + 1].g += err.g * 7;\n pixels[i + 1].b += err.b * 7;\n // Spread error to lower pixels\n if (i < pixels.length - width - 1) {\n pixels[i + width - 1].r += err.r * 3;\n pixels[i + width].r += err.r * 5;\n pixels[i + width + 1].r += err.r * 1;\n pixels[i + width - 1].g += err.g * 3;\n pixels[i + width].g += err.g * 5;\n pixels[i + width + 1].g += err.g * 1;\n pixels[i + width - 1].b += err.b * 3;\n pixels[i + width].b += err.b * 5;\n pixels[i + width + 1].b += err.b * 1;\n }\n }\n i += 1;\n }\n return pixels;\n}\n\n/**\n * Dither the image into monochrome\n * Uses Floyd-Steinberg matrix\n */\nexport function monochromeFs(pixels: Color[], width: number): Color[] {\n let i = 0;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i < (pixels.length)) {\n // We shall use \"black\" and \"white\" as our quantized palette\n const newR = pixels[i].r < 129 ? 0 : 255;\n const errR = Math.floor((pixels[i].r - newR) / 16);\n\n // Match our quantized palette\n pixels[i].r = newR;\n\n if (i < pixels.length - 1) {\n // Spread error to next pixel\n pixels[i + 1].r += errR * 7;\n // Spread error to lower pixels\n if (i < pixels.length - width - 1) {\n pixels[i + width - 1].r += errR * 3;\n pixels[i + width].r += errR * 5;\n pixels[i + width + 1].r += errR * 1;\n }\n }\n\n // Convert our color to black or white completely\n pixels[i].b = pixels[i].g = pixels[i].r;\n i += 1;\n }\n return pixels;\n}\n",[],[183,216,223],{"_106":231,"_99":206,"_148":232,"_150":217,"_151":137,"_152":233,"_154":119,"_108":234,"_103":33,"_156":169,"_158":235,"_159":27,"_160":-5,"_161":236,"_102":237},"dfbdf68c-0a6a-11f0-9622-569c3dd06744","dfbdf718-0a6a-11f0-9622-569c3dd06744",19,["D",1743011458502],"export * from \"./fs.ts\";\nexport * from \"./quick.ts\";\nexport * from \"./sierra.ts\";\n",[],[183,216,206],{"_106":239,"_99":240,"_148":241,"_150":217,"_151":137,"_152":242,"_154":119,"_108":243,"_103":33,"_156":169,"_158":244,"_159":27,"_160":-5,"_161":245,"_102":246},"dfb42b8e-0a6a-11f0-bf1a-569c3dd06744","quick.ts","dfb42c6a-0a6a-11f0-bf1a-569c3dd06744",18,["D",1743011458433],"import { Color } from \"../../deps/color.ts\";\nimport { findClosestColor } from \"../util/closest.ts\";\n\n/**\n * Dither the image into a smaller palette\n * Uses a quick, two-row dither.\n * Creates a column pattern. Use Floyd-Steinberg \n * or Sierra to avoid patterns.\n */\nexport function quick2(\n pixels: Color[],\n width: number,\n palette: Color[],\n): Color[] {\n let i = pixels.length - 1;\n const twoW = width << 2;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i >= 0) {\n const newC = findClosestColor(pixels[i], palette);\n const err = {\n r: Math.floor((pixels[i].r - newC.r) / 16),\n g: Math.floor((pixels[i].r - newC.g) / 16),\n b: Math.floor((pixels[i].r - newC.b) / 16),\n };\n\n // Match our quantized palette\n pixels[i] = newC;\n\n // Spread error to neighbouring pixels\n if (i >= 1) {\n // Spread error to next pixel\n pixels[i - 1].r += err.r * 4;\n pixels[i - 1].g += err.g * 4;\n pixels[i - 1].b += err.b * 4;\n if (i >= 2) {\n // Spread error to next pixel\n pixels[i - 2].r += err.r * 2;\n pixels[i - 2].g += err.g * 2;\n pixels[i - 2].b += err.b * 2;\n // Spread error to lower pixels\n const k = i - width;\n if (k >= 2) {\n const k = i - width;\n pixels[k - 2].r += err.r;\n pixels[k - 1].r += err.r * 2;\n pixels[k].r += err.r * 2;\n pixels[k + 1].r += err.r * 2;\n pixels[k + 2].r += err.r;\n\n pixels[k - 2].g += err.g;\n pixels[k - 1].g += err.g * 2;\n pixels[k].g += err.g * 2;\n pixels[k + 1].g += err.g * 2;\n pixels[k + 2].g += err.g;\n\n pixels[k - 2].b += err.b;\n pixels[k - 1].b += err.b * 2;\n pixels[k].b += err.b * 2;\n pixels[k + 1].b += err.b * 2;\n pixels[k + 2].b += err.b;\n\n if (i >= twoW + 1) {\n pixels[i - twoW + 1].r += err.r * 2;\n pixels[i - twoW + 1].g += err.g * 2;\n pixels[i - twoW + 1].b += err.b * 2;\n }\n }\n }\n }\n i -= 1;\n }\n return pixels;\n}\n\n/**\n * Dither the image into a smaller palette\n * Starts from the mid point of the image\n * I have no idea why I did this\n */\nexport function bidirectional(\n pixels: Color[],\n width: number,\n palette: Color[],\n): Color[] {\n const mid = Math.trunc(pixels.length / 2);\n let i = mid;\n let j = mid + 1;\n while (j < (pixels.length)) {\n const newC = findClosestColor(pixels[j], palette);\n const err = {\n r: Math.floor((pixels[j].r - newC.r) / 16),\n g: Math.floor((pixels[j].r - newC.g) / 16),\n b: Math.floor((pixels[j].r - newC.b) / 16),\n };\n\n // Match our quantized palette\n pixels[j] = newC;\n\n // Spread error to neighbouring pixels\n if (j < pixels.length - 1) {\n // Spread error to next pixel\n pixels[j + 1].r += err.r * 4;\n pixels[j + 1].g += err.g * 4;\n pixels[j + 1].b += err.b * 4;\n if (j < pixels.length - 2) {\n // Spread error to next pixel\n pixels[j + 2].r += err.r * 2;\n pixels[j + 2].g += err.g * 2;\n pixels[j + 2].b += err.b * 2;\n // Spread error to lower pixels\n if (j < pixels.length - width - 2) {\n pixels[j + width - 2].r += err.r;\n pixels[j + width - 1].r += err.r * 2;\n pixels[j + width].r += err.r * 4;\n pixels[j + width + 1].r += err.r * 2;\n pixels[j + width + 2].r += err.r;\n\n pixels[j + width - 2].g += err.g;\n pixels[j + width - 1].g += err.g * 2;\n pixels[j + width].g += err.g * 4;\n pixels[j + width + 1].g += err.g * 2;\n pixels[j + width + 2].g += err.g;\n pixels[j + width - 2].b += err.b;\n pixels[j + width - 1].b += err.b * 2;\n pixels[j + width].b += err.b * 4;\n pixels[j + width + 1].b += err.b * 2;\n pixels[j + width + 2].b += err.b;\n }\n }\n }\n j += 1;\n }\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i > 0) {\n const newC = findClosestColor(pixels[i], palette);\n const err = {\n r: Math.floor((pixels[i].r - newC.r) / 16),\n g: Math.floor((pixels[i].r - newC.g) / 16),\n b: Math.floor((pixels[i].r - newC.b) / 16),\n };\n\n // Match our quantized palette\n pixels[i] = newC;\n\n // Spread error to neighbouring pixels\n if (i >= 1) {\n // Spread error to next pixel\n pixels[i - 1].r += err.r * 4;\n pixels[i - 1].g += err.g * 4;\n pixels[i - 1].b += err.b * 4;\n if (i >= 2) {\n // Spread error to next pixel\n pixels[i - 2].r += err.r * 3;\n pixels[i - 2].g += err.g * 3;\n pixels[i - 2].b += err.b * 3;\n // Spread error to lower pixels\n if (i >= width + 2) {\n pixels[i - width - 2].r += err.r;\n pixels[i - width - 1].r += err.r * 2;\n pixels[i - width].r += err.r * 3;\n pixels[i - width + 1].r += err.r * 2;\n pixels[i - width + 2].r += err.r;\n\n pixels[i - width - 2].g += err.g;\n pixels[i - width - 1].g += err.g * 2;\n pixels[i - width].g += err.g * 3;\n pixels[i - width + 1].g += err.g * 2;\n pixels[i - width + 2].g += err.g;\n\n pixels[i - width - 2].b += err.b;\n pixels[i - width - 1].b += err.b * 2;\n pixels[i - width].b += err.b * 3;\n pixels[i - width + 1].b += err.b * 2;\n pixels[i - width + 2].b += err.b;\n }\n }\n }\n i -= 1;\n }\n return pixels;\n}\n\n/**\n * Dither the image into monochrome\n * Uses the same matrix as above\n */\nexport function monochromeQ(pixels: Color[], width: number): Color[] {\n let i = pixels.length - 1;\n const twoW = width << 2;\n\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i >= 0) {\n // We shall use \"black\" and \"white\" as our quantized palette\n const newR = pixels[i].r < 129 ? 0 : 255;\n const err = { r: Math.floor((pixels[i].r - newR) / 16) };\n\n // Match our quantized palette\n pixels[i].r = newR;\n\n // Spread error to neighbouring pixels\n if (i >= 1) {\n // Spread error to next pixel\n pixels[i - 1].r += err.r * 4;\n if (i >= 2) {\n // Spread error to next pixel\n pixels[i - 2].r += err.r * 2;\n // Spread error to lower pixels\n const k = i - width;\n if (k >= 2) {\n const k = i - width;\n pixels[k - 2].r += err.r;\n pixels[k - 1].r += err.r * 2;\n pixels[k].r += err.r * 2;\n pixels[k + 1].r += err.r * 2;\n pixels[k + 2].r += err.r;\n\n if (i >= twoW + 1) {\n pixels[i - twoW + 1].r += err.r * 2;\n }\n }\n }\n }\n\n // Convert our color to black or white completely\n pixels[i].b = pixels[i].g = pixels[i].r;\n i -= 1;\n }\n return pixels;\n}\n",[],[183,216,240],{"_106":248,"_99":249,"_148":250,"_150":217,"_151":137,"_152":251,"_154":119,"_108":252,"_103":33,"_156":169,"_158":253,"_159":27,"_160":-5,"_161":254,"_102":255},"e01d41be-0a6a-11f0-8ea4-569c3dd06744","sierra.ts","e01d4272-0a6a-11f0-8ea4-569c3dd06744",26,["D",1743011459125],"import { Color } from \"../../deps/color.ts\";\nimport { findClosestColor } from \"../util/closest.ts\";\n\n/**\n * Dither the image into a smaller palette\n * Uses two-row Sierra matrix\n */\nexport function sierra2(\n pixels: Color[],\n width: number,\n palette: Color[],\n): Color[] {\n let i = 0;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i < (pixels.length)) {\n const newC = findClosestColor(pixels[i], palette);\n const err = {\n r: Math.floor((pixels[i].r - newC.r) / 16),\n g: Math.floor((pixels[i].r - newC.g) / 16),\n b: Math.floor((pixels[i].r - newC.b) / 16),\n };\n\n // Match our quantized palette\n pixels[i] = newC;\n\n // Spread error to neighbouring pixels\n if (i < pixels.length - 1) {\n // Spread error to next pixel\n pixels[i + 1].r += err.r * 4;\n pixels[i + 1].g += err.g * 4;\n pixels[i + 1].b += err.b * 4;\n if (i < pixels.length - 2) {\n // Spread error to next pixel\n pixels[i + 2].r += err.r * 3;\n pixels[i + 2].g += err.g * 3;\n pixels[i + 2].b += err.b * 3;\n // Spread error to lower pixels\n if (i < pixels.length - width - 2) {\n pixels[i + width - 2].r += err.r;\n pixels[i + width - 1].r += err.r * 2;\n pixels[i + width].r += err.r * 3;\n pixels[i + width + 1].r += err.r * 2;\n pixels[i + width + 2].r += err.r;\n\n pixels[i + width - 2].g += err.g;\n pixels[i + width - 1].g += err.g * 2;\n pixels[i + width].g += err.g * 3;\n pixels[i + width + 1].g += err.g * 2;\n pixels[i + width + 2].g += err.g;\n\n pixels[i + width - 2].b += err.b;\n pixels[i + width - 1].b += err.b * 2;\n pixels[i + width].b += err.b * 3;\n pixels[i + width + 1].b += err.b * 2;\n pixels[i + width + 2].b += err.b;\n }\n }\n }\n i += 1;\n }\n return pixels;\n}\n\n/**\n * Dither the image into a smaller palette\n * Uses Sierra lite matrix\n * Use twoRowSierraDither for more accuracy\n */\nexport function sierraLite(\n pixels: Color[],\n width: number,\n palette: Color[],\n): Color[] {\n let i = pixels.length - 1;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i >= 0) {\n const newC = findClosestColor(pixels[i], palette);\n const err = {\n r: Math.floor((pixels[i].r - newC.r) >> 2),\n g: Math.floor((pixels[i].r - newC.g) >> 2),\n b: Math.floor((pixels[i].r - newC.b) >> 2),\n };\n\n // Match our quantized palette\n pixels[i] = newC;\n\n // Spread error to neighbouring pixels\n if (i > 0) {\n // Spread error to next pixel\n if (i % width !== 0) {\n pixels[i - 1].r += err.r << 1;\n pixels[i - 1].g += err.g << 1;\n pixels[i - 1].b += err.b << 1;\n }\n // Spread error to lower pixels\n if (i > width + 1) {\n pixels[i - width + 1].r += err.r;\n pixels[i - width].r += err.r;\n pixels[i - width + 1].g += err.g;\n pixels[i - width].g += err.g;\n pixels[i - width + 1].b += err.b;\n pixels[i - width].b += err.b;\n }\n }\n i -= 1;\n }\n return pixels;\n}\n",[],[183,216,249],[183,216],{"_106":258,"_99":259,"_148":260,"_150":184,"_151":137,"_152":94,"_154":119,"_108":261,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":262,"_102":298},"bfac896c-0a6a-11f0-9052-569c3dd06744","quantize","bfac89f8-0a6a-11f0-9052-569c3dd06744",["D",1743011404699],[263,272,281,289],{"_106":264,"_99":265,"_148":266,"_150":260,"_151":137,"_152":267,"_154":119,"_108":268,"_103":33,"_156":169,"_158":269,"_159":27,"_160":-5,"_161":270,"_102":271},"dfd1aac4-0a6a-11f0-a0f5-569c3dd06744","common.ts","dfd1ab96-0a6a-11f0-a0f5-569c3dd06744",20,["D",1743011458629],"import { ColorHistogram } from \"../mod.ts\";\nimport { Color } from \"../../deps/color.ts\";\n\nexport function getAverageColor(\n vbox: ColorRange,\n histo: ColorHistogram,\n): Color {\n let total = 0;\n let totalR = 0, totalG = 0, totalB = 0;\n let ri = vbox.r.min;\n while (ri <= vbox.r.max) {\n let gi = vbox.g.min;\n while (gi <= vbox.g.max) {\n let bi = vbox.b.min;\n while (bi <= vbox.b.max) {\n const count = histo.getQuantized([ri, gi, bi]) || 0;\n total += count;\n totalR += count * (ri + 0.5) * 8;\n totalG += count * (gi + 0.5) * 8;\n totalB += count * (bi + 0.5) * 8;\n bi += 1;\n }\n gi += 1;\n }\n ri += 1;\n }\n if (total) {\n return new Color(\n ~~(totalR / total),\n ~~(totalG / total),\n ~~(totalB / total),\n 255,\n );\n }\n // In case box is empty\n return new Color(\n Math.trunc(8 * (vbox.r.min + vbox.r.max + 1) / 2),\n Math.trunc(8 * (vbox.g.min + vbox.g.max + 1) / 2),\n Math.trunc(8 * (vbox.b.min + vbox.b.max + 1) / 2),\n 255,\n );\n}\n\n/** The vbox */\nexport interface ColorRange {\n r: { min: number; max: number };\n g: { min: number; max: number };\n b: { min: number; max: number };\n}\n\n/** Get the minimum and maximum RGB values. */\nexport function getColorRange(\n colors: Color[],\n): ColorRange {\n const range = {\n r: { min: 1000, max: 0 },\n g: { min: 1000, max: 0 },\n b: { min: 1000, max: 0 },\n };\n let i = 0;\n while (i < colors.length) {\n if ((colors[i].r >> 3) < range.r.min) range.r.min = colors[i].r >> 3;\n if ((colors[i].r >> 3) > range.r.max) range.r.max = colors[i].r >> 3;\n\n if ((colors[i].g >> 3) < range.g.min) range.g.min = colors[i].g >> 3;\n if ((colors[i].g >> 3) > range.g.max) range.g.max = colors[i].g >> 3;\n\n if ((colors[i].b >> 3) < range.b.min) range.b.min = colors[i].b >> 3;\n if ((colors[i].b >> 3) > range.b.max) range.b.max = colors[i].b >> 3;\n\n i += 1;\n }\n return range;\n}\n",[],[183,259,265],{"_106":273,"_99":274,"_148":275,"_150":260,"_151":137,"_152":276,"_154":119,"_108":277,"_103":33,"_156":169,"_158":278,"_159":27,"_160":-5,"_161":279,"_102":280},"e046e64a-0a6a-11f0-bc70-569c3dd06744","medianCut.ts","e046e730-0a6a-11f0-bc70-569c3dd06744",29,["D",1743011459398],"import { ColorHistogram, getHistogram } from \"../mod.ts\";\nimport { Color } from \"../../deps/color.ts\";\nimport { getAverageColor, getColorRange } from \"./common.ts\";\nimport type { ColorRange } from \"./common.ts\";\n\nexport function quantizeByMedianCut(\n pixels: Color[],\n extractCount: number,\n): Color[] {\n const vbox = getColorRange(pixels);\n const histo = getHistogram(pixels);\n return quantize(vbox, histo, extractCount);\n}\n\nfunction quantize(\n vbox: ColorRange,\n histo: ColorHistogram,\n extractCount: number,\n): Color[] {\n const vboxes: ColorRange[] = [vbox];\n\n // Avoid an infinite loop\n const maxIter = 1000;\n let i = 0;\n\n const firstExtractCount = ~~(extractCount >> 1);\n let generated = 1;\n\n while (i < maxIter) {\n const lastBox = vboxes.shift();\n if (!lastBox) break; // This shouldn't happen\n if (!vboxSize(lastBox, histo)) {\n vboxes.push(lastBox);\n i += 1;\n continue;\n }\n const cut = medianCutApply(lastBox, histo);\n\n if (cut) {\n vboxes.push(cut[0], cut[1]);\n generated += 1;\n } else vboxes.push(lastBox);\n if (generated >= firstExtractCount) break;\n }\n\n vboxes.sort((a, b) =>\n (vboxSize(b, histo) * vboxVolume(b)) - (vboxSize(a, histo) * vboxVolume(a))\n );\n\n const secondExtractCount = extractCount - vboxes.length;\n i = 0;\n generated = 0;\n\n while (i < maxIter) {\n const lastBox = vboxes.shift();\n if (!lastBox) break; // This shouldn't happen\n if (!vboxSize(lastBox, histo)) {\n vboxes.push(lastBox);\n i += 1;\n continue;\n }\n const cut = medianCutApply(lastBox, histo);\n\n if (cut) {\n vboxes.push(cut[0], cut[1]);\n generated += 1;\n } else vboxes.push(lastBox);\n if (generated >= secondExtractCount) break;\n }\n vboxes.sort((a, b) => vboxSize(b, histo) - vboxSize(a, histo));\n return vboxes.map((x) => getAverageColor(x, histo)).slice(0, extractCount);\n}\n\n/** Get number of colors in vbox */\nfunction vboxSize(vbox: ColorRange, histo: ColorHistogram): number {\n let count = 0;\n let ri = vbox.r.min;\n while (ri <= vbox.r.max) {\n let gi = vbox.g.min;\n while (gi <= vbox.g.max) {\n let bi = vbox.b.min;\n while (bi <= vbox.b.max) {\n count += histo.get(new Color(ri, gi, bi, 255)) || 0;\n bi += 1;\n }\n gi += 1;\n }\n ri += 1;\n }\n return count;\n}\n\n/** Get volume by dimensions of vbox */\nfunction vboxVolume(vbox: ColorRange): number {\n return ~~(vbox.r.max - vbox.r.min) * ~~(vbox.g.max - vbox.g.min) *\n ~~(vbox.b.max - vbox.b.min);\n}\n\n/** Cut vbox into two */\nfunction medianCutApply(\n vbox: ColorRange,\n histo: ColorHistogram,\n): [ColorRange, ColorRange] | false {\n const count = vboxSize(vbox, histo);\n\n if (!count || count === 1) return false;\n const rw = vbox.r.max - vbox.r.min + 1;\n const gw = vbox.g.max - vbox.g.min + 1;\n const bw = vbox.b.max - vbox.b.min + 1;\n\n const axis = Math.max(rw, gw, bw);\n\n // Find partial sums along each axis\n const sumAlongAxis = [];\n // avoid running another loop to compute sum\n let totalSum = 0;\n switch (axis) {\n case rw: {\n let i = vbox.r.min;\n while (i <= vbox.r.max) {\n let tempSum = 0;\n let j = vbox.g.min;\n while (j < vbox.g.max) {\n let k = vbox.b.min;\n while (k < vbox.b.max) {\n tempSum += histo.getQuantized([i, j, k]) || 0;\n k += 1;\n }\n j += 1;\n }\n totalSum += tempSum;\n sumAlongAxis[i] = totalSum;\n i += 1;\n }\n break;\n }\n case gw: {\n let i = vbox.g.min;\n while (i <= vbox.g.max) {\n let tempSum = 0;\n let j = vbox.r.min;\n while (j < vbox.r.max) {\n let k = vbox.b.min;\n while (k < vbox.b.max) {\n tempSum += histo.getQuantized([j, i, k]) || 0;\n k += 1;\n }\n j += 1;\n }\n totalSum += tempSum;\n sumAlongAxis[i] = totalSum;\n i += 1;\n }\n break;\n }\n default: {\n let i = vbox.b.min;\n while (i <= vbox.b.max) {\n let tempSum = 0;\n let j = vbox.r.min;\n while (j < vbox.r.max) {\n let k = vbox.g.min;\n while (k < vbox.g.max) {\n tempSum += histo.getQuantized([j, k, i]) || 0;\n k += 1;\n }\n j += 1;\n }\n totalSum += tempSum;\n sumAlongAxis[i] = totalSum;\n i += 1;\n }\n break;\n }\n }\n\n // Apply median cut\n switch (axis) {\n case rw: {\n let i = vbox.r.min;\n while (i <= vbox.r.max) {\n // Find the mid point through linear search\n if (sumAlongAxis[i] < totalSum / 2) {\n let cutAt = 0;\n const vbox1 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const vbox2 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const left = i - vbox.r.min;\n const right = vbox.r.max - i;\n if (left <= right) {\n cutAt = Math.min(vbox.r.max - 1, Math.trunc(i + right / 2));\n } else cutAt = Math.max(vbox.r.min, Math.trunc(i - 1 - left / 2));\n\n while (!sumAlongAxis[cutAt]) cutAt += 1;\n\n vbox1.r.max = cutAt;\n vbox2.r.min = cutAt + 1;\n return [vbox1, vbox2];\n }\n i += 1;\n }\n break;\n }\n case gw: {\n let i = vbox.g.min;\n while (i <= vbox.g.max) {\n // Find the mid point through linear search\n if (sumAlongAxis[i] < totalSum / 2) {\n let cutAt = 0;\n const vbox1 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const vbox2 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const left = i - vbox.g.min;\n const right = vbox.g.max - i;\n if (left <= right) {\n cutAt = Math.min(vbox.g.max - 1, Math.trunc(i + right / 2));\n } else cutAt = Math.max(vbox.g.min, Math.trunc(i - 1 - left / 2));\n while (!sumAlongAxis[cutAt]) cutAt += 1;\n\n vbox1.g.max = cutAt;\n vbox2.g.min = cutAt + 1;\n return [vbox1, vbox2];\n }\n i += 1;\n }\n break;\n }\n default: {\n let i = vbox.b.min;\n while (i <= vbox.b.max) {\n // Find the mid point through linear search\n if (sumAlongAxis[i] < totalSum / 2) {\n let cutAt = 0;\n const vbox1 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const vbox2 = {\n r: { min: vbox.r.min, max: vbox.r.max },\n g: { min: vbox.g.min, max: vbox.g.max },\n b: { min: vbox.b.min, max: vbox.b.max },\n };\n const left = i - vbox.b.min;\n const right = vbox.b.max - i;\n if (left <= right) {\n cutAt = Math.min(vbox.b.max - 1, Math.trunc(i + right / 2));\n } else cutAt = Math.max(vbox.b.min, Math.trunc(i - 1 - left / 2));\n while (!sumAlongAxis[cutAt]) cutAt += 1;\n\n vbox1.b.max = cutAt;\n vbox2.b.min = cutAt + 1;\n return [vbox1, vbox2];\n }\n i += 1;\n }\n break;\n }\n }\n\n return false;\n}\n",[],[183,259,274],{"_106":282,"_99":206,"_148":283,"_150":260,"_151":137,"_152":284,"_154":119,"_108":285,"_103":33,"_156":169,"_158":286,"_159":27,"_160":-5,"_161":287,"_102":288},"df70fa08-0a6a-11f0-8a13-569c3dd06744","df70fada-0a6a-11f0-8a13-569c3dd06744",13,["D",1743011457996],"export { quantizeByMedianCut } from \"./medianCut.ts\";\n\nexport { quantizeByPopularity } from \"./popularity.ts\";\n",[],[183,259,206],{"_106":290,"_99":291,"_148":292,"_150":260,"_151":137,"_152":293,"_154":119,"_108":294,"_103":33,"_156":169,"_158":295,"_159":27,"_160":-5,"_161":296,"_102":297},"dfdfc05a-0a6a-11f0-a99a-569c3dd06744","popularity.ts","dfdfc118-0a6a-11f0-a99a-569c3dd06744",21,["D",1743011458721],"/// Uses Modified Median Cut Algorithm\n/// TypeScript port of Leptonica\n/// http://www.leptonica.org/\n\nimport { ColorHistogram, getHistogram } from \"../mod.ts\";\nimport { Color } from \"../../deps/color.ts\";\n\nexport function quantizeByPopularity(\n pixels: Color[],\n extractCount: number,\n): Color[] {\n const histo = getHistogram(pixels);\n const result: [number, number][] = [];\n\n histo.raw.forEach((v, i) => {\n if (v) result.push([i, v]);\n });\n\n result.sort((a, b) => b[1] - a[1]);\n const res = [];\n for (const i of result.slice(0, extractCount)) {\n /*\n const colors = pixels.filter((color) =>\n ((color.r >> 3) << (10)) + ((color.g >> 3) << 5) +\n (color.b >> 3) === i[0]\n );\n console.log(colors)\n */\n // Temporarily use the quantized colors\n res.push(ColorHistogram.getColor(i[0]));\n }\n return res;\n}\n",[],[183,259,291],[183,259],{"_106":300,"_99":301,"_148":302,"_150":184,"_151":137,"_152":303,"_154":119,"_108":304,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":305,"_102":324},"c172d1b6-0a6a-11f0-bf1a-569c3dd06744","structures","c172d26a-0a6a-11f0-bf1a-569c3dd06744",4,["D",1743011407676],[306,315],{"_106":307,"_99":308,"_148":309,"_150":302,"_151":137,"_152":310,"_154":119,"_108":311,"_103":33,"_156":169,"_158":312,"_159":27,"_160":-5,"_161":313,"_102":314},"df8b322e-0a6a-11f0-9622-569c3dd06744","image.ts","df8b32d8-0a6a-11f0-9622-569c3dd06744",15,["D",1743011458169],"import { Color } from \"../../deps/color.ts\";\nimport { boxBlur } from \"../blur/mod.ts\";\nimport { recolor } from \"../util/recolor.ts\";\n\nimport {\n bidirectional,\n floydSteinberg,\n monochromeFs,\n monochromeQ,\n quick2,\n sierra2,\n sierraLite,\n} from \"../dither/mod.ts\";\n\nexport interface DitherOptions {\n method:\n | \"floyd_steinberg\"\n | \"sierra_2\"\n | \"sierra_lite\"\n | \"quick\"\n | \"bidirectional\";\n}\n\nexport type BlurType = \"box\";\n\nexport interface ImageData {\n data: Uint8ClampedArray;\n width: number;\n height: number;\n channels: number; // always 4, for compat with retraigo/vectorizer\n colorSpace: \"srgb\" | \"display-p3\";\n}\n\n/**\n * Image with width, height, and pixel data\n * All methods mutate the image itself\n */\nexport class Image implements ImageData {\n pixels: Color[];\n width: number;\n height: number;\n /** Canvas ImageData always returns RGBA values */\n channels = 4;\n /** We are only gonna work with sRGB images */\n colorSpace: \"srgb\" = \"srgb\";\n constructor(pixels: Uint8ClampedArray, width: number, height?: number) {\n if (!height) height = pixels.length / width;\n if (width !== Math.trunc(width) || width <= 0) {\n throw new TypeError(\"Width must be a natural number.\");\n }\n if (height !== Math.trunc(height) || height <= 0) {\n throw new TypeError(\"Height must be a natural number.\");\n }\n this.width = width;\n this.height = height;\n this.pixels = new Array(pixels.length / 4);\n let i = 0;\n\n while (i < pixels.length) {\n this.pixels[i / 4] = new Color(\n pixels[i],\n pixels[i + 1],\n pixels[i + 2],\n pixels[i + 3],\n );\n i += 4;\n }\n }\n /** Blur the image. Currently only supports box blur. */\n blur(method: BlurType) {\n switch (method) {\n case \"box\": {\n boxBlur(this);\n break;\n }\n default: {\n throw new TypeError(`Unknown blur method ${method}`);\n }\n }\n }\n /** Recolor the image with dithering */\n dither(\n palette: Color[],\n options: DitherOptions = { method: \"quick\" },\n ) {\n switch (options.method) {\n case \"floyd_steinberg\": {\n floydSteinberg(this.pixels, this.width, palette);\n break;\n }\n case \"quick\": {\n quick2(this.pixels, this.width, palette);\n break;\n }\n case \"sierra_2\": {\n sierra2(this.pixels, this.width, palette);\n break;\n }\n case \"sierra_lite\": {\n sierraLite(this.pixels, this.width, palette);\n break;\n }\n case \"bidirectional\": {\n bidirectional(this.pixels, this.width, palette);\n break;\n }\n default: {\n throw new TypeError(`Unknown dithering method: ${options.method}`);\n }\n }\n }\n /** Make the image grayscale */\n grayscale(): void {\n let i = 0;\n while (i < this.pixels.length) {\n this.pixels[i] = this.pixels[i].grayscale;\n i += 1;\n }\n }\n /** Invert colors in the image */\n invert(): void {\n let i = 0;\n while (i < this.pixels.length) {\n this.pixels[i] = this.pixels[i].invert;\n i += 1;\n }\n }\n /** Apply a function on every pixel in the image */\n map(fn: (c: Color) => Color): void {\n let i = 0;\n while (i < this.pixels.length) {\n this.pixels[i] = fn(this.pixels[i]);\n i += 1;\n }\n }\n /** Recolor the image using just black and white */\n monochrome(\n dither = false,\n ditherMode: \"floyd_steinberg\" | \"quick\" = \"floyd_steinberg\",\n ): void {\n if (dither) {\n ditherMode === \"floyd_steinberg\"\n ? monochromeFs(this.pixels, this.width)\n : monochromeQ(this.pixels, this.width);\n } else this.recolor([new Color(\"#000000\"), new Color(\"#ffffff\")]);\n }\n /** Recolor the image without dithering */\n recolor(palette: Color[]) {\n recolor(this.pixels, palette);\n }\n /** Convert to an ImageData object */\n toImageData(): ImageData {\n return {\n data: this.data,\n width: this.width,\n height: this.height,\n channels: this.channels,\n colorSpace: this.colorSpace,\n };\n }\n /** Get a Uint8ClampedArray with RGBA values */\n get data(): Uint8ClampedArray {\n const data = new Uint8ClampedArray(this.pixels.length * 4);\n let i = 0;\n\n while (i < data.length) {\n const c = this.pixels[i / 4];\n data[i] = c.r;\n data[i + 1] = c.g;\n data[i + 2] = c.b;\n data[i + 3] = c.a;\n i += 4;\n }\n return data;\n }\n /** Get a Uint8ClampedArray of the grayscale image with RGBA values */\n get grayscaleData(): Uint8ClampedArray {\n const data = new Uint8ClampedArray(this.pixels.length);\n let i = 0;\n while (i < data.length) {\n const c = this.pixels[i];\n data[i] = c.grayscale.r;\n i += 1;\n }\n return data;\n }\n}\n",[],[183,301,308],{"_106":316,"_99":317,"_148":318,"_150":302,"_151":137,"_152":319,"_154":119,"_108":320,"_103":33,"_156":169,"_158":321,"_159":27,"_160":-5,"_161":322,"_102":323},"dfe9fcfa-0a6a-11f0-a4d2-569c3dd06744","kernel.ts","dfe9fdb8-0a6a-11f0-a4d2-569c3dd06744",22,["D",1743011458789],"/** A simple square matrix */\n\nexport class Kernel {\n size: number;\n data: number[][];\n constructor(size: number, ...values: number[]) {\n if(values.length / size !== size) throw new Error(\"Kernel must be square.\")\n this.size = size;\n this.data = [];\n let i = 0;\n while (i < size) {\n const row: number[] = [];\n let j = 0;\n while (j < size) {\n row[j] = values[i + j];\n j += 1;\n }\n this.data[i] = row;\n i += 1;\n }\n }\n}\n",[],[183,301,317],[183,301],{"_106":326,"_99":327,"_148":328,"_150":184,"_151":137,"_152":329,"_154":119,"_108":330,"_103":33,"_156":157,"_158":-5,"_159":27,"_160":-5,"_161":331,"_102":350},"c09cda34-0a6a-11f0-a097-569c3dd06744","util","c09cdbc4-0a6a-11f0-a097-569c3dd06744",3,["D",1743011406274],[332,341],{"_106":333,"_99":334,"_148":335,"_150":328,"_151":137,"_152":336,"_154":119,"_108":337,"_103":33,"_156":169,"_158":338,"_159":27,"_160":-5,"_161":339,"_102":340},"df821428-0a6a-11f0-9622-569c3dd06744","closest.ts","df8214fa-0a6a-11f0-9622-569c3dd06744",14,["D",1743011458109],"import { Color, meanDistance } from \"../../deps/color.ts\";\n\nexport function findClosestColor(color: Color, palette: Color[]): Color {\n const closest = {\n dist: Infinity,\n i: 0,\n };\n let i = 0;\n while (i < palette.length) {\n const m = meanDistance(color, palette[i]);\n if (m < closest.dist) {\n closest.dist = m;\n closest.i = i;\n }\n i += 1;\n }\n return palette[closest.i];\n}\n",[],[183,327,334],{"_106":342,"_99":343,"_148":344,"_150":328,"_151":137,"_152":345,"_154":119,"_108":346,"_103":33,"_156":169,"_158":347,"_159":27,"_160":-5,"_161":348,"_102":349},"dff40722-0a6a-11f0-9052-569c3dd06744","recolor.ts","dff407e0-0a6a-11f0-9052-569c3dd06744",23,["D",1743011458855],"import { Color } from \"../../deps/color.ts\";\nimport { findClosestColor } from \"./closest.ts\";\n\n/** Recolor the image without dithering */\nexport function recolor(\n pixels: Color[],\n palette: Color[],\n): Color[] {\n let i = 0;\n // pixels is an array of pixels with r, g, b values\n // width is the width of the image in pixels\n while (i < (pixels.length)) {\n const newC = findClosestColor(pixels[i], palette);\n\n // Match our quantized palette\n pixels[i] = newC;\n i += 1;\n }\n return pixels;\n}\n",[],[183,327,343],[183,327],{"_106":352,"_99":353,"_148":354,"_150":184,"_151":137,"_152":136,"_154":119,"_108":355,"_103":33,"_156":169,"_158":356,"_159":27,"_160":-5,"_161":357,"_102":358},"e05d506a-0a6a-11f0-a99a-569c3dd06744","histogram.ts","e05d50f6-0a6a-11f0-a99a-569c3dd06744",["D",1743011459543],"import { Color } from \"../deps/color.ts\";\n\n/**\n * Histogram of colors with reduced space\n * Effectively quantizes the image into 32768 colors\n */\nexport class ColorHistogram {\n #data: Uint32Array;\n constructor() {\n this.#data = new Uint32Array(32768);\n }\n #getIndex(color: Color) {\n // ignore alpha\n const index = ((color.r >> 3) << (10)) + ((color.g >> 3) << 5) +\n (color.b >> 3);\n return index;\n }\n get(color: Color): number {\n const index = this.#getIndex(color);\n return this.#data[index];\n }\n getQuantized(color: [number, number, number]): number {\n const index = (color[0] << 10) + (color[1] << 5) + color[2];\n return this.#data[index];\n }\n add(color: Color, amount: number): number {\n const index = this.#getIndex(color);\n return Atomics.add(this.#data, index, amount);\n }\n get raw(): Uint32Array {\n return this.#data;\n }\n get length(): number {\n return this.#data.filter((x) => x).length;\n }\n static getColor(index: number): Color {\n const ri = index >> 10;\n const gi = (index - (ri << 10)) >> 5;\n const bi = (index - (ri << 10) - (gi << 5));\n return new Color(ri << 3, gi << 3, bi << 3, 255);\n }\n}\n\n/** Get a histogram of frequency of colors. */\nexport function getHistogram(colors: Color[]): ColorHistogram {\n const histo = new ColorHistogram();\n let i = 0;\n while (i < colors.length) {\n const hIndex = colors[i];\n histo.add(hIndex, 1);\n i += 1;\n }\n return histo;\n}\n",[],[183,353],{"_106":360,"_99":206,"_148":361,"_150":184,"_151":137,"_152":362,"_154":119,"_108":363,"_103":33,"_156":169,"_158":364,"_159":27,"_160":-5,"_161":365,"_102":366},"df4d612e-0a6a-11f0-bc70-569c3dd06744","df4d61e2-0a6a-11f0-bc70-569c3dd06744",10,["D",1743011457757],"export * from \"./quantize/mod.ts\";\n\nexport * from \"./histogram.ts\";\n\nexport * from \"./pixels.ts\";\n\nexport * from \"./dither/mod.ts\";\n\nexport * from \"./structures/image.ts\";\n\n// For backwards compat. TODO: Remove soon\nexport { Color } from \"../deps/color.ts\";\n",[],[183,206],{"_106":368,"_99":369,"_148":370,"_150":184,"_151":137,"_152":371,"_154":119,"_108":372,"_103":33,"_156":169,"_158":373,"_159":27,"_160":-5,"_161":374,"_102":375},"e00a0572-0a6a-11f0-bc70-569c3dd06744","pixels.ts","e00a0658-0a6a-11f0-bc70-569c3dd06744",24,["D",1743011458999],"import {\n contentType,\n parseMediaType,\n} from \"https://deno.land/std@0.181.0/media_types/mod.ts\";\n\nimport {\n createCanvas,\n loadImage,\n} from \"https://deno.land/x/canvas@v1.4.2/mod.ts\";\nimport { Image } from \"./structures/image.ts\";\n\nexport async function getPixels(path: string) {\n const data = /https?:\\/\\/.+/.test(path)\n ? await getImageFromWeb(path)\n : await getImageFromLocal(path);\n const image = await loadImage(data.data);\n\n const canvas = createCanvas(image.width(), image.height());\n\n const ctx = canvas.getContext(\"2d\");\n\n ctx.drawImage(image, 0, 0, canvas.width, canvas.height);\n const d = ctx.getImageData(0, 0, canvas.width, canvas.height).data;\n return new Image(d, canvas.width, canvas.height)\n}\n\nasync function getImageFromWeb(path: string) {\n const res = await fetch(path);\n if (res.ok) {\n const data = new Uint8Array(await res.arrayBuffer());\n const mediaType = parseMediaType(res.headers.get(\"content-type\") ?? \"\")[0];\n return {\n data,\n mediaType,\n };\n } else throw new Error(\"Unable to load image.\");\n}\n\nasync function getImageFromLocal(path: string) {\n try {\n const data = await Deno.readFile(path);\n const mediaType = contentType(\n path.split(\".\").reverse()[0],\n );\n return {\n data,\n mediaType,\n };\n } catch (_e) {\n throw new Error(\"Unable to load image.\");\n }\n}\n",[],[183,369],[183],{"_106":378,"_99":379,"_148":380,"_150":-5,"_151":137,"_152":381,"_154":119,"_108":382,"_103":33,"_156":383,"_158":384,"_159":27,"_160":-5,"_161":385,"_102":386},"df6535f6-0a6a-11f0-bc70-569c3dd06744",".gitignore","df65368c-0a6a-11f0-bc70-569c3dd06744",12,["D",1743011457919],"file",".vscode\ntest\n",[],[379],{"_106":388,"_99":389,"_148":390,"_150":-5,"_151":137,"_152":391,"_154":119,"_108":392,"_103":33,"_156":383,"_158":393,"_159":27,"_160":-5,"_161":394,"_102":395},"df9e90a8-0a6a-11f0-bc70-569c3dd06744","LICENSE","df9e91ca-0a6a-11f0-bc70-569c3dd06744",16,["D",1743011458295],"MIT License\n\nCopyright (c) 2022 La Lala Land\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n",[],[389],{"_106":397,"_99":398,"_148":399,"_150":-5,"_151":137,"_152":400,"_154":119,"_108":401,"_103":33,"_156":383,"_158":402,"_159":27,"_160":-5,"_161":403,"_102":404},"df5ad0d4-0a6a-11f0-bf1a-569c3dd06744","README.md","df5ad19c-0a6a-11f0-bf1a-569c3dd06744",11,["D",1743011457850],"# monke\n\nAn image-related module in TypeScript.\n\nCheck https://deno.land/x/monke/mod.ts for documentation.\n\n\nSupports:\n- Recoloring an image with a different palette\n- Recoloring an image with dithering\n- Quantizing a palette\n- Image filters (blur, grayscale, invert, etc)\n\n~~Provides a class `Color` for general color-related stuff.~~\n`Color` has been moved to [retraigo/colors](https://github.com/retraigo/colors)\n\n\nRefer the 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