feat: add some ai stuff
This commit is contained in:
parent
a7851e5058
commit
578ee0d3fa
@ -15,5 +15,5 @@ steps:
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from_secret: FTP_PASSWORD
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from_secret: FTP_PASSWORD
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commands:
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commands:
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- pwd
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- pwd
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- cd /drone/src/view/public
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- cd view/public
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- lftp -e "set sftp:auto-confirm true; set ftp:ssl-force true; set xfer:timeout 10000; debug 3; open -u $FTP_USERNAME,$FTP_PASSWORD sftp://ssh.jim-fx.com:2221; mkdir -p share/karl; cd share/karl; mirror -p --scan-all-first --overwrite --verbose -R --skip-noaccess; quit;"
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- lftp -e "set sftp:auto-confirm true; set ftp:ssl-force true; set xfer:timeout 10000; debug 3; open -u $FTP_USERNAME,$FTP_PASSWORD sftp://ssh.jim-fx.com:2221; mkdir -p share/karl; cd share/karl; mirror -p --scan-all-first --overwrite --verbose -R --skip-noaccess; quit;"
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3
.vscode/settings.json
vendored
Normal file
3
.vscode/settings.json
vendored
Normal file
@ -0,0 +1,3 @@
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{
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"python.pythonPath": "/home/jim/.pyenv/versions/3.8.6/bin/python"
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}
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1
server/.python-version
Normal file
1
server/.python-version
Normal file
@ -0,0 +1 @@
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3.8.6
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3
server/requirements.txt
Normal file
3
server/requirements.txt
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@ -0,0 +1,3 @@
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keras_applications>=1.0.7,<=1.0.8
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image-classifiers==1.0.0
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efficientnet==1.0.0
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3
server/test.py
Normal file
3
server/test.py
Normal file
@ -0,0 +1,3 @@
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from segmentation_models import Unet
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model = Unet('resnet34', encoder_weights='imagenet')
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182
view/package-lock.json
generated
182
view/package-lock.json
generated
@ -8,6 +8,9 @@
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"name": "svelte-app",
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"name": "svelte-app",
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"version": "1.0.0",
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"version": "1.0.0",
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"dependencies": {
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"dependencies": {
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"@tensorflow-models/deeplab": "^0.2.1",
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"@tensorflow/tfjs-backend-webgl": "^3.3.0",
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"@tensorflow/tfjs-converter": "^3.3.0",
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"ogl": "^0.0.65",
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"ogl": "^0.0.65",
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"sirv-cli": "^1.0.0",
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"sirv-cli": "^1.0.0",
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"svelte-file-dropzone": "^0.0.15"
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"svelte-file-dropzone": "^0.0.15"
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@ -173,6 +176,73 @@
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"integrity": "sha512-1fMXF3YP4pZZVozF8j/ZLfvnR8NSIljt56UhbZ5PeeDmmGHpgpdwQt7ITlGvYaQukCvuBRMLEiKiYC+oeIg4cg==",
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"integrity": "sha512-1fMXF3YP4pZZVozF8j/ZLfvnR8NSIljt56UhbZ5PeeDmmGHpgpdwQt7ITlGvYaQukCvuBRMLEiKiYC+oeIg4cg==",
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"dev": true
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"dev": true
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},
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},
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"node_modules/@tensorflow-models/deeplab": {
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"version": "0.2.1",
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"resolved": "https://registry.npmjs.org/@tensorflow-models/deeplab/-/deeplab-0.2.1.tgz",
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"integrity": "sha512-P30Q0M7DoxU/npMlAbyFG8fVE/eGp7UordxxBHkBixa4JCOFvuug5Q7vNT28b4UIM/i85BuJOImRIETab+HYFg==",
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"peerDependencies": {
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"@tensorflow/tfjs-converter": "^3.0.0",
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"@tensorflow/tfjs-core": "^3.0.0"
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}
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},
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"node_modules/@tensorflow/tfjs-backend-cpu": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.3.0.tgz",
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"integrity": "sha512-DLctv+PUZni26kQW1hq8jwQQ8u+GGc/p764WQIC4/IDagGtfGAUW1mHzWcTxtni2l4re1VrwE41ogWLhv4sGHg==",
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"dependencies": {
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"@types/seedrandom": "2.4.27",
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"seedrandom": "2.4.3"
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},
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"engines": {
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"yarn": ">= 1.3.2"
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},
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"peerDependencies": {
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"@tensorflow/tfjs-core": "3.3.0"
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}
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},
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"node_modules/@tensorflow/tfjs-backend-webgl": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.3.0.tgz",
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"integrity": "sha512-GWCtXbrjPTyye3ooId9GlcNDwnIMskZarUpNIQ5g/zeISLfwEQoutA/UqJF+HzuEHgGMsWFkmaO3xKVT7UMpdg==",
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"dependencies": {
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"@tensorflow/tfjs-backend-cpu": "3.3.0",
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"@types/offscreencanvas": "~2019.3.0",
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"@types/seedrandom": "2.4.27",
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"@types/webgl-ext": "0.0.30",
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"@types/webgl2": "0.0.5",
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"seedrandom": "2.4.3"
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},
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"engines": {
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"yarn": ">= 1.3.2"
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},
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"peerDependencies": {
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"@tensorflow/tfjs-core": "3.3.0"
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}
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},
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"node_modules/@tensorflow/tfjs-converter": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-3.3.0.tgz",
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"integrity": "sha512-k57wN4yelePhmO9orcT/wzGMIuyedrMpVtg0FhxpV6BQu0+TZ/ti3W4Kb97GWJsoHKXMoing9SnioKfVnBW6hw==",
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"peerDependencies": {
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"@tensorflow/tfjs-core": "3.3.0"
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}
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},
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"node_modules/@tensorflow/tfjs-core": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-3.3.0.tgz",
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"integrity": "sha512-6G+LcCiQBl4Kza5mDbWbf8QSWBTW3l7SDjGhQzMO1ITtQatHzxkuHGHcJ4CTUJvNA0JmKf4QJWOvlFqEmxwyLQ==",
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"peer": true,
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"dependencies": {
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"@types/offscreencanvas": "~2019.3.0",
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"@types/seedrandom": "2.4.27",
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"@types/webgl-ext": "0.0.30",
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"node-fetch": "~2.6.1",
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"seedrandom": "2.4.3"
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},
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"engines": {
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"yarn": ">= 1.3.2"
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}
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},
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"node_modules/@tsconfig/svelte": {
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"node_modules/@tsconfig/svelte": {
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"version": "1.0.10",
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"version": "1.0.10",
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"resolved": "https://registry.npmjs.org/@tsconfig/svelte/-/svelte-1.0.10.tgz",
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"resolved": "https://registry.npmjs.org/@tsconfig/svelte/-/svelte-1.0.10.tgz",
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@ -191,6 +261,11 @@
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"integrity": "sha512-/Ctrftx/zp4m8JOujM5ZhwzlWLx22nbQJiVqz8/zE15gOeEW+uly3FSX4fGFpcfEvFzXcMCJwq9lGVWgyARXhg==",
|
"integrity": "sha512-/Ctrftx/zp4m8JOujM5ZhwzlWLx22nbQJiVqz8/zE15gOeEW+uly3FSX4fGFpcfEvFzXcMCJwq9lGVWgyARXhg==",
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"dev": true
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"dev": true
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},
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},
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"node_modules/@types/offscreencanvas": {
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"version": "2019.3.0",
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"resolved": "https://registry.npmjs.org/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz",
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|
"integrity": "sha512-esIJx9bQg+QYF0ra8GnvfianIY8qWB0GBx54PK5Eps6m+xTj86KLavHv6qDhzKcu5UUOgNfJ2pWaIIV7TRUd9Q=="
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},
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"node_modules/@types/pug": {
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"node_modules/@types/pug": {
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"version": "2.0.4",
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"version": "2.0.4",
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"resolved": "https://registry.npmjs.org/@types/pug/-/pug-2.0.4.tgz",
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"resolved": "https://registry.npmjs.org/@types/pug/-/pug-2.0.4.tgz",
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@ -215,6 +290,21 @@
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"@types/node": "*"
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"@types/node": "*"
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}
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}
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},
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},
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"node_modules/@types/seedrandom": {
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"version": "2.4.27",
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"resolved": "https://registry.npmjs.org/@types/seedrandom/-/seedrandom-2.4.27.tgz",
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"integrity": "sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE="
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},
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"node_modules/@types/webgl-ext": {
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"version": "0.0.30",
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"resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
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"integrity": "sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg=="
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},
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"node_modules/@types/webgl2": {
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"version": "0.0.5",
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"resolved": "https://registry.npmjs.org/@types/webgl2/-/webgl2-0.0.5.tgz",
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"integrity": "sha512-oGaKsBbxQOY5+aJFV3KECDhGaXt+yZJt2y/OZsnQGLRkH6Fvr7rv4pCt3SRH1somIHfej/c4u7NSpCyd9x+1Ow=="
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},
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"node_modules/acorn": {
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"node_modules/acorn": {
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"version": "7.4.1",
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"version": "7.4.1",
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"resolved": "https://registry.npmjs.org/acorn/-/acorn-7.4.1.tgz",
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"resolved": "https://registry.npmjs.org/acorn/-/acorn-7.4.1.tgz",
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@ -1202,6 +1292,15 @@
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"integrity": "sha1-sGJ44h/Gw3+lMTcysEEry2rhX1E=",
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"integrity": "sha1-sGJ44h/Gw3+lMTcysEEry2rhX1E=",
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"dev": true
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"dev": true
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},
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},
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"node_modules/node-fetch": {
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"version": "2.6.1",
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"resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.6.1.tgz",
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|
"integrity": "sha512-V4aYg89jEoVRxRb2fJdAg8FHvI7cEyYdVAh94HH0UIK8oJxUfkjlDQN9RbMx+bEjP7+ggMiFRprSti032Oipxw==",
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"peer": true,
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"engines": {
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"node": "4.x || >=6.0.0"
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}
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},
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"node_modules/normalize-path": {
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"node_modules/normalize-path": {
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"version": "3.0.0",
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"version": "3.0.0",
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"resolved": "https://registry.npmjs.org/normalize-path/-/normalize-path-3.0.0.tgz",
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"resolved": "https://registry.npmjs.org/normalize-path/-/normalize-path-3.0.0.tgz",
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@ -1539,6 +1638,11 @@
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}
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}
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]
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]
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},
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},
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"node_modules/seedrandom": {
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"version": "2.4.3",
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"resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
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"integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw="
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},
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"node_modules/semiver": {
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"node_modules/semiver": {
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"version": "1.1.0",
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"version": "1.1.0",
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"resolved": "https://registry.npmjs.org/semiver/-/semiver-1.1.0.tgz",
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"resolved": "https://registry.npmjs.org/semiver/-/semiver-1.1.0.tgz",
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@ -2126,6 +2230,53 @@
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}
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}
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}
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}
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},
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},
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"@tensorflow-models/deeplab": {
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"version": "0.2.1",
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"resolved": "https://registry.npmjs.org/@tensorflow-models/deeplab/-/deeplab-0.2.1.tgz",
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|
"integrity": "sha512-P30Q0M7DoxU/npMlAbyFG8fVE/eGp7UordxxBHkBixa4JCOFvuug5Q7vNT28b4UIM/i85BuJOImRIETab+HYFg==",
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"requires": {}
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},
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"@tensorflow/tfjs-backend-cpu": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.3.0.tgz",
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|
"integrity": "sha512-DLctv+PUZni26kQW1hq8jwQQ8u+GGc/p764WQIC4/IDagGtfGAUW1mHzWcTxtni2l4re1VrwE41ogWLhv4sGHg==",
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"requires": {
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"@types/seedrandom": "2.4.27",
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"seedrandom": "2.4.3"
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}
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},
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"@tensorflow/tfjs-backend-webgl": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.3.0.tgz",
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|
"integrity": "sha512-GWCtXbrjPTyye3ooId9GlcNDwnIMskZarUpNIQ5g/zeISLfwEQoutA/UqJF+HzuEHgGMsWFkmaO3xKVT7UMpdg==",
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"requires": {
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"@tensorflow/tfjs-backend-cpu": "3.3.0",
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"@types/offscreencanvas": "~2019.3.0",
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"@types/seedrandom": "2.4.27",
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"@types/webgl-ext": "0.0.30",
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"@types/webgl2": "0.0.5",
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"seedrandom": "2.4.3"
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}
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},
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"@tensorflow/tfjs-converter": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-converter/-/tfjs-converter-3.3.0.tgz",
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"integrity": "sha512-k57wN4yelePhmO9orcT/wzGMIuyedrMpVtg0FhxpV6BQu0+TZ/ti3W4Kb97GWJsoHKXMoing9SnioKfVnBW6hw==",
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"requires": {}
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},
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"@tensorflow/tfjs-core": {
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"version": "3.3.0",
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-3.3.0.tgz",
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|
"integrity": "sha512-6G+LcCiQBl4Kza5mDbWbf8QSWBTW3l7SDjGhQzMO1ITtQatHzxkuHGHcJ4CTUJvNA0JmKf4QJWOvlFqEmxwyLQ==",
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"peer": true,
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"requires": {
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"@types/offscreencanvas": "~2019.3.0",
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"@types/seedrandom": "2.4.27",
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"@types/webgl-ext": "0.0.30",
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"node-fetch": "~2.6.1",
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"seedrandom": "2.4.3"
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}
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},
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"@tsconfig/svelte": {
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"@tsconfig/svelte": {
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"version": "1.0.10",
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"version": "1.0.10",
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"resolved": "https://registry.npmjs.org/@tsconfig/svelte/-/svelte-1.0.10.tgz",
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"resolved": "https://registry.npmjs.org/@tsconfig/svelte/-/svelte-1.0.10.tgz",
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@ -2144,6 +2295,11 @@
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"integrity": "sha512-/Ctrftx/zp4m8JOujM5ZhwzlWLx22nbQJiVqz8/zE15gOeEW+uly3FSX4fGFpcfEvFzXcMCJwq9lGVWgyARXhg==",
|
"integrity": "sha512-/Ctrftx/zp4m8JOujM5ZhwzlWLx22nbQJiVqz8/zE15gOeEW+uly3FSX4fGFpcfEvFzXcMCJwq9lGVWgyARXhg==",
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"dev": true
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"dev": true
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},
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},
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"@types/offscreencanvas": {
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"version": "2019.3.0",
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"resolved": "https://registry.npmjs.org/@types/offscreencanvas/-/offscreencanvas-2019.3.0.tgz",
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"integrity": "sha512-esIJx9bQg+QYF0ra8GnvfianIY8qWB0GBx54PK5Eps6m+xTj86KLavHv6qDhzKcu5UUOgNfJ2pWaIIV7TRUd9Q=="
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},
|
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"@types/pug": {
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"@types/pug": {
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"version": "2.0.4",
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"version": "2.0.4",
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"resolved": "https://registry.npmjs.org/@types/pug/-/pug-2.0.4.tgz",
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"resolved": "https://registry.npmjs.org/@types/pug/-/pug-2.0.4.tgz",
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"@types/node": "*"
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"@types/node": "*"
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}
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}
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},
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},
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"@types/seedrandom": {
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"version": "2.4.27",
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"resolved": "https://registry.npmjs.org/@types/seedrandom/-/seedrandom-2.4.27.tgz",
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"integrity": "sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE="
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},
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"@types/webgl-ext": {
|
||||||
|
"version": "0.0.30",
|
||||||
|
"resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
|
||||||
|
"integrity": "sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg=="
|
||||||
|
},
|
||||||
|
"@types/webgl2": {
|
||||||
|
"version": "0.0.5",
|
||||||
|
"resolved": "https://registry.npmjs.org/@types/webgl2/-/webgl2-0.0.5.tgz",
|
||||||
|
"integrity": "sha512-oGaKsBbxQOY5+aJFV3KECDhGaXt+yZJt2y/OZsnQGLRkH6Fvr7rv4pCt3SRH1somIHfej/c4u7NSpCyd9x+1Ow=="
|
||||||
|
},
|
||||||
"acorn": {
|
"acorn": {
|
||||||
"version": "7.4.1",
|
"version": "7.4.1",
|
||||||
"resolved": "https://registry.npmjs.org/acorn/-/acorn-7.4.1.tgz",
|
"resolved": "https://registry.npmjs.org/acorn/-/acorn-7.4.1.tgz",
|
||||||
@ -2987,6 +3158,12 @@
|
|||||||
"integrity": "sha1-sGJ44h/Gw3+lMTcysEEry2rhX1E=",
|
"integrity": "sha1-sGJ44h/Gw3+lMTcysEEry2rhX1E=",
|
||||||
"dev": true
|
"dev": true
|
||||||
},
|
},
|
||||||
|
"node-fetch": {
|
||||||
|
"version": "2.6.1",
|
||||||
|
"resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.6.1.tgz",
|
||||||
|
"integrity": "sha512-V4aYg89jEoVRxRb2fJdAg8FHvI7cEyYdVAh94HH0UIK8oJxUfkjlDQN9RbMx+bEjP7+ggMiFRprSti032Oipxw==",
|
||||||
|
"peer": true
|
||||||
|
},
|
||||||
"normalize-path": {
|
"normalize-path": {
|
||||||
"version": "3.0.0",
|
"version": "3.0.0",
|
||||||
"resolved": "https://registry.npmjs.org/normalize-path/-/normalize-path-3.0.0.tgz",
|
"resolved": "https://registry.npmjs.org/normalize-path/-/normalize-path-3.0.0.tgz",
|
||||||
@ -3249,6 +3426,11 @@
|
|||||||
"integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==",
|
"integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==",
|
||||||
"dev": true
|
"dev": true
|
||||||
},
|
},
|
||||||
|
"seedrandom": {
|
||||||
|
"version": "2.4.3",
|
||||||
|
"resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
|
||||||
|
"integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw="
|
||||||
|
},
|
||||||
"semiver": {
|
"semiver": {
|
||||||
"version": "1.1.0",
|
"version": "1.1.0",
|
||||||
"resolved": "https://registry.npmjs.org/semiver/-/semiver-1.1.0.tgz",
|
"resolved": "https://registry.npmjs.org/semiver/-/semiver-1.1.0.tgz",
|
||||||
|
|||||||
@ -29,6 +29,9 @@
|
|||||||
"typescript": "^4.0.0"
|
"typescript": "^4.0.0"
|
||||||
},
|
},
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
|
"@tensorflow-models/deeplab": "^0.2.1",
|
||||||
|
"@tensorflow/tfjs-backend-webgl": "^3.3.0",
|
||||||
|
"@tensorflow/tfjs-converter": "^3.3.0",
|
||||||
"ogl": "^0.0.65",
|
"ogl": "^0.0.65",
|
||||||
"sirv-cli": "^1.0.0",
|
"sirv-cli": "^1.0.0",
|
||||||
"svelte-file-dropzone": "^0.0.15"
|
"svelte-file-dropzone": "^0.0.15"
|
||||||
|
|||||||
@ -3,27 +3,69 @@
|
|||||||
self.addEventListener('message', function (e) {
|
self.addEventListener('message', function (e) {
|
||||||
|
|
||||||
|
|
||||||
const { data: { i, arr: pixels } } = e;
|
const { data: { i, pixels, correctDistortion = true, width, height } } = e;
|
||||||
|
|
||||||
let store = {};
|
// This is the old method just by counting pixels
|
||||||
|
const oldStore = {};
|
||||||
|
|
||||||
|
//This method applies some weight to the pixels
|
||||||
|
const store = {};
|
||||||
|
let totalPixelValue = 0;
|
||||||
|
|
||||||
let total = pixels.length / 4;
|
let total = pixels.length / 4;
|
||||||
|
|
||||||
const threshold = 200;
|
const threshold = 200;
|
||||||
|
|
||||||
for (let i = 0; i < total; i++) {
|
for (let i = 0; i < total; i++) {
|
||||||
|
|
||||||
|
|
||||||
const r = pixels[i * 4 + 0] > threshold ? 1 : 0;
|
const r = pixels[i * 4 + 0] > threshold ? 1 : 0;
|
||||||
const g = pixels[i * 4 + 1] > threshold ? 1 : 0;
|
const g = pixels[i * 4 + 1] > threshold ? 1 : 0;
|
||||||
const b = pixels[i * 4 + 2] > threshold ? 1 : 0;
|
const b = pixels[i * 4 + 2] > threshold ? 1 : 0;
|
||||||
const id = r + "-" + g + "-" + b;
|
const id = r + "-" + g + "-" + b;
|
||||||
store[id] = store[id] + 1 || 1;
|
|
||||||
|
//Caclulate y position of pixel
|
||||||
|
const y = Math.floor(i / width);
|
||||||
|
|
||||||
|
// KarlKilian Formel
|
||||||
|
const pixelValue = Math.cos(360 / (Math.pow(height, 2)) * Math.pow(y, 2) + (-360 / height) * y + 90);
|
||||||
|
|
||||||
|
oldStore[id] = oldStore[id] + 1 || 1;
|
||||||
|
|
||||||
|
if (id in store) {
|
||||||
|
store[id] += pixelValue;
|
||||||
|
} else {
|
||||||
|
store[id] = pixelValue;
|
||||||
|
}
|
||||||
|
|
||||||
|
totalPixelValue += pixelValue;
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
|
const finalResult = {};
|
||||||
|
|
||||||
Object.keys(store).forEach(k => {
|
|
||||||
store[k] = store[k] / total;
|
// Normalize stores
|
||||||
|
Object.keys(oldStore).forEach(k => {
|
||||||
|
finalResult[k] = {
|
||||||
|
distortedValue: oldStore[k] / total
|
||||||
|
}
|
||||||
});
|
});
|
||||||
|
|
||||||
|
Object.keys(store).forEach(k => {
|
||||||
|
finalResult[k].value = store[k] / totalPixelValue;
|
||||||
|
});
|
||||||
|
|
||||||
self.postMessage({ res: store, i });
|
// Conver to array for easier
|
||||||
|
const result = Object.keys(finalResult).map(key => {
|
||||||
|
const [r, g, b] = key.split("-").map((n) => parseInt(n) * 255);
|
||||||
|
return {
|
||||||
|
id: key,
|
||||||
|
color: `rgb(${r},${g},${b})`,
|
||||||
|
value: finalResult[key].value,
|
||||||
|
distortedValue: finalResult[key].distortedValue
|
||||||
|
}
|
||||||
|
}).sort((a, b) => a.value > b.value ? -1 : 1)
|
||||||
|
|
||||||
|
self.postMessage({ result, i });
|
||||||
}, false);
|
}, false);
|
||||||
@ -9,57 +9,82 @@ import includePaths from 'rollup-plugin-includepaths';
|
|||||||
import glslify from 'rollup-plugin-glslify';
|
import glslify from 'rollup-plugin-glslify';
|
||||||
const production = !process.env.ROLLUP_WATCH;
|
const production = !process.env.ROLLUP_WATCH;
|
||||||
|
|
||||||
export default {
|
export default [
|
||||||
input: 'src/main.ts',
|
{
|
||||||
output: {
|
input: 'src/ai.ts',
|
||||||
sourcemap: true,
|
output: {
|
||||||
format: 'iife',
|
sourcemap: true,
|
||||||
name: 'app',
|
format: 'iife',
|
||||||
file: 'public/build/bundle.js'
|
name: 'app',
|
||||||
|
file: 'public/build/ai-worker.js'
|
||||||
|
},
|
||||||
|
plugins: [
|
||||||
|
resolve({
|
||||||
|
browser: true,
|
||||||
|
dedupe: ['svelte']
|
||||||
|
}),
|
||||||
|
commonjs(),
|
||||||
|
typescript({
|
||||||
|
sourceMap: !production,
|
||||||
|
inlineSources: !production
|
||||||
|
}),
|
||||||
|
|
||||||
|
// If we're building for production (npm run build
|
||||||
|
// instead of npm run dev), minify
|
||||||
|
production && terser()
|
||||||
|
]
|
||||||
},
|
},
|
||||||
plugins: [
|
{
|
||||||
|
input: 'src/main.ts',
|
||||||
|
output: {
|
||||||
|
sourcemap: true,
|
||||||
|
format: 'iife',
|
||||||
|
name: 'app',
|
||||||
|
file: 'public/build/bundle.js'
|
||||||
|
},
|
||||||
|
plugins: [
|
||||||
|
|
||||||
includePaths({
|
includePaths({
|
||||||
paths: ["src"],
|
paths: ["src"],
|
||||||
extensions: [".ts", ".svelte"]
|
extensions: [".ts", ".svelte"]
|
||||||
}),
|
}),
|
||||||
|
|
||||||
svelte({
|
svelte({
|
||||||
preprocess: sveltePreprocess({ sourceMap: !production }),
|
preprocess: sveltePreprocess({ sourceMap: !production }),
|
||||||
compilerOptions: {
|
compilerOptions: {
|
||||||
// enable run-time checks when not in production
|
// enable run-time checks when not in production
|
||||||
dev: !production
|
dev: !production
|
||||||
|
}
|
||||||
|
}),
|
||||||
|
// we'll extract any component CSS out into
|
||||||
|
// a separate file - better for performance
|
||||||
|
css({ output: 'bundle.css' }),
|
||||||
|
|
||||||
|
glslify(),
|
||||||
|
|
||||||
|
// If you have external dependencies installed from
|
||||||
|
// npm, you'll most likely need these plugins. In
|
||||||
|
// some cases you'll need additional configuration -
|
||||||
|
// consult the documentation for details:
|
||||||
|
// https://github.com/rollup/plugins/tree/master/packages/commonjs
|
||||||
|
resolve({
|
||||||
|
browser: true,
|
||||||
|
dedupe: ['svelte']
|
||||||
|
}),
|
||||||
|
commonjs(),
|
||||||
|
typescript({
|
||||||
|
sourceMap: !production,
|
||||||
|
inlineSources: !production
|
||||||
|
}),
|
||||||
|
|
||||||
|
// If we're building for production (npm run build
|
||||||
|
// instead of npm run dev), minify
|
||||||
|
production && terser()
|
||||||
|
],
|
||||||
|
watch: {
|
||||||
|
clearScreen: false,
|
||||||
|
chokidar: {
|
||||||
|
followSymlinks: true
|
||||||
}
|
}
|
||||||
}),
|
|
||||||
// we'll extract any component CSS out into
|
|
||||||
// a separate file - better for performance
|
|
||||||
css({ output: 'bundle.css' }),
|
|
||||||
|
|
||||||
glslify(),
|
|
||||||
|
|
||||||
// If you have external dependencies installed from
|
|
||||||
// npm, you'll most likely need these plugins. In
|
|
||||||
// some cases you'll need additional configuration -
|
|
||||||
// consult the documentation for details:
|
|
||||||
// https://github.com/rollup/plugins/tree/master/packages/commonjs
|
|
||||||
resolve({
|
|
||||||
browser: true,
|
|
||||||
dedupe: ['svelte']
|
|
||||||
}),
|
|
||||||
commonjs(),
|
|
||||||
typescript({
|
|
||||||
sourceMap: !production,
|
|
||||||
inlineSources: !production
|
|
||||||
}),
|
|
||||||
|
|
||||||
// If we're building for production (npm run build
|
|
||||||
// instead of npm run dev), minify
|
|
||||||
production && terser()
|
|
||||||
],
|
|
||||||
watch: {
|
|
||||||
clearScreen: false,
|
|
||||||
chokidar: {
|
|
||||||
followSymlinks: true
|
|
||||||
}
|
}
|
||||||
}
|
}];
|
||||||
};
|
|
||||||
|
|||||||
36
view/src/ai.ts
Normal file
36
view/src/ai.ts
Normal file
@ -0,0 +1,36 @@
|
|||||||
|
import '@tensorflow/tfjs-backend-webgl';
|
||||||
|
import * as tfconv from '@tensorflow/tfjs-converter';
|
||||||
|
import * as deeplab from '@tensorflow-models/deeplab';
|
||||||
|
|
||||||
|
const createModel = async () => {
|
||||||
|
|
||||||
|
console.log("Ezzzz")
|
||||||
|
const base = 'pascal'; // set to your preferred model, out of `pascal`,
|
||||||
|
// `cityscapes` and `ade20k`
|
||||||
|
const quantizationBytes = 2; // either 1, 2 or 4
|
||||||
|
// use the getURL utility function to get the URL to the pre-trained weights
|
||||||
|
const modelUrl = deeplab.getURL(base, quantizationBytes);
|
||||||
|
const rawModel = await tfconv.loadGraphModel(modelUrl);
|
||||||
|
const modelName = 'pascal'; // set to your preferred model, out of `pascal`,
|
||||||
|
// `cityscapes` and `ade20k`
|
||||||
|
return new deeplab.SemanticSegmentation(rawModel);
|
||||||
|
};
|
||||||
|
|
||||||
|
const model = createModel();
|
||||||
|
model.then(() => console.log(`Loaded the model successfully!`));
|
||||||
|
|
||||||
|
self.addEventListener('message', async (e) => {
|
||||||
|
|
||||||
|
const { pixels, width, height } = e.data;
|
||||||
|
|
||||||
|
console.log(pixels, width, height)
|
||||||
|
|
||||||
|
var array = new Uint8ClampedArray(pixels);
|
||||||
|
|
||||||
|
var image = new ImageData(array, width, height);
|
||||||
|
|
||||||
|
console.log("model", await (await model).segment(image))
|
||||||
|
|
||||||
|
console.log(e);
|
||||||
|
|
||||||
|
});
|
||||||
@ -5,7 +5,7 @@
|
|||||||
|
|
||||||
import { quartInOut } from "svelte/easing";
|
import { quartInOut } from "svelte/easing";
|
||||||
|
|
||||||
let visible = true;
|
let correctDistortion = true;
|
||||||
|
|
||||||
function scaleX(node, { duration, delay }) {
|
function scaleX(node, { duration, delay }) {
|
||||||
return {
|
return {
|
||||||
@ -20,23 +20,18 @@
|
|||||||
};
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
const prom = countPixels(img.overlayData)
|
const prom = countPixels(img, correctDistortion);
|
||||||
.then((res) => {
|
|
||||||
return Object.keys(res).map((c) => {
|
|
||||||
const [r, g, b] = c.split("-").map((n) => parseInt(n) * 255);
|
|
||||||
|
|
||||||
return {
|
|
||||||
r,
|
|
||||||
g,
|
|
||||||
b,
|
|
||||||
amount: res[c],
|
|
||||||
};
|
|
||||||
});
|
|
||||||
})
|
|
||||||
.then((colors) => colors.sort((a, b) => (a.amount > b.amount ? -1 : 1)));
|
|
||||||
</script>
|
</script>
|
||||||
|
|
||||||
<h3>ANALYZER</h3>
|
<div id="header">
|
||||||
|
<h3>ANALYZER</h3>
|
||||||
|
<label for="correct-distort">Correct Distortion</label>
|
||||||
|
<input
|
||||||
|
id="correct-distort"
|
||||||
|
type="checkbox"
|
||||||
|
bind:checked={correctDistortion}
|
||||||
|
/>
|
||||||
|
</div>
|
||||||
|
|
||||||
{#await prom}
|
{#await prom}
|
||||||
<p>Loading...</p>
|
<p>Loading...</p>
|
||||||
@ -44,20 +39,36 @@
|
|||||||
<div class="list">
|
<div class="list">
|
||||||
{#each result as color, i}
|
{#each result as color, i}
|
||||||
<p>
|
<p>
|
||||||
{Math.floor(color.amount * 1000) / 10}%
|
{Math.floor(
|
||||||
|
(correctDistortion ? color.value : color.distortedValue) * 1000
|
||||||
|
) / 10}%
|
||||||
</p>
|
</p>
|
||||||
<div
|
<div
|
||||||
transition:scaleX={{ duration: 500, delay: i * 200 }}
|
transition:scaleX={{ duration: 500, delay: i * 200 }}
|
||||||
class="bar"
|
class="bar"
|
||||||
style={`width: ${color.amount * 100}%; background-color: rgb(${
|
style={`width: ${
|
||||||
color.r
|
(correctDistortion ? color.value : color.distortedValue) * 100
|
||||||
}, ${color.g}, ${color.b})`}
|
}%; background-color:${color.color}`}
|
||||||
/>
|
/>
|
||||||
{/each}
|
{/each}
|
||||||
</div>
|
</div>
|
||||||
{/await}
|
{/await}
|
||||||
|
|
||||||
<style>
|
<style>
|
||||||
|
#header {
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
}
|
||||||
|
|
||||||
|
#header > label {
|
||||||
|
margin-left: 10px;
|
||||||
|
}
|
||||||
|
|
||||||
|
#header > input {
|
||||||
|
margin: 0px;
|
||||||
|
margin-left: 5px;
|
||||||
|
}
|
||||||
|
|
||||||
.list {
|
.list {
|
||||||
display: grid;
|
display: grid;
|
||||||
grid-template-rows: auto;
|
grid-template-rows: auto;
|
||||||
@ -74,5 +85,6 @@
|
|||||||
height: 20px;
|
height: 20px;
|
||||||
margin-bottom: 20px;
|
margin-bottom: 20px;
|
||||||
transform-origin: 0px 0px;
|
transform-origin: 0px 0px;
|
||||||
|
transition: width 0.3s ease;
|
||||||
}
|
}
|
||||||
</style>
|
</style>
|
||||||
|
|||||||
@ -45,6 +45,8 @@
|
|||||||
let my = 0;
|
let my = 0;
|
||||||
let downX, downOffset;
|
let downX, downOffset;
|
||||||
|
|
||||||
|
let debugValue = 0;
|
||||||
|
|
||||||
let isStrPressed = false;
|
let isStrPressed = false;
|
||||||
let isSpacePressed = false;
|
let isSpacePressed = false;
|
||||||
let lastActiveTool;
|
let lastActiveTool;
|
||||||
@ -152,6 +154,16 @@
|
|||||||
|
|
||||||
isOriginal = e.target.id === "cx1";
|
isOriginal = e.target.id === "cx1";
|
||||||
|
|
||||||
|
//Caclulate y position of pixel
|
||||||
|
const y = my / wrapperHeightRatio;
|
||||||
|
|
||||||
|
// KarlKilian Formel
|
||||||
|
debugValue = Math.cos(
|
||||||
|
(360 / Math.pow(image.height, 2)) * Math.pow(y, 2) +
|
||||||
|
(-360 / image.height) * y +
|
||||||
|
90
|
||||||
|
);
|
||||||
|
|
||||||
if (isDown) {
|
if (isDown) {
|
||||||
if (activeTool === "pan") {
|
if (activeTool === "pan") {
|
||||||
// TODO fix overflowiung
|
// TODO fix overflowiung
|
||||||
@ -291,7 +303,7 @@
|
|||||||
}%)); opacity: ${(layerOpacity / 100) * 0.5};`}
|
}%)); opacity: ${(layerOpacity / 100) * 0.5};`}
|
||||||
/>
|
/>
|
||||||
|
|
||||||
<!-- <p>{topLeftY}|{my}</p> -->
|
<p>{debugValue}|{my}</p>
|
||||||
|
|
||||||
<canvas class:visible={mode === "3d"} bind:this={canvas3D} />
|
<canvas class:visible={mode === "3d"} bind:this={canvas3D} />
|
||||||
</div>
|
</div>
|
||||||
|
|||||||
20
view/src/helpers/AI.ts
Normal file
20
view/src/helpers/AI.ts
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
const worker = new Worker("build/ai-worker.js");
|
||||||
|
|
||||||
|
let i = 0;
|
||||||
|
|
||||||
|
let cb = {};
|
||||||
|
|
||||||
|
worker.addEventListener("message", ev => {
|
||||||
|
if (ev.data.i in cb) {
|
||||||
|
cb[ev.data.i](ev.data.res);
|
||||||
|
}
|
||||||
|
})
|
||||||
|
|
||||||
|
const analyze = (img: Image) => new Promise((res, rej) => {
|
||||||
|
i++;
|
||||||
|
const _i = i;
|
||||||
|
worker.postMessage({ i: _i, pixels: img.data, width: img.width, height: img.height });
|
||||||
|
cb[_i] = res;
|
||||||
|
});
|
||||||
|
|
||||||
|
export default { analyze }
|
||||||
@ -1,3 +1,5 @@
|
|||||||
|
import { images } from "../stores";
|
||||||
|
|
||||||
const worker = new Worker("worker.js");
|
const worker = new Worker("worker.js");
|
||||||
|
|
||||||
let i = 0;
|
let i = 0;
|
||||||
@ -6,13 +8,20 @@ let cb = {};
|
|||||||
|
|
||||||
worker.addEventListener("message", ev => {
|
worker.addEventListener("message", ev => {
|
||||||
if (ev.data.i in cb) {
|
if (ev.data.i in cb) {
|
||||||
cb[ev.data.i](ev.data.res);
|
cb[ev.data.i](ev.data.result);
|
||||||
}
|
}
|
||||||
})
|
})
|
||||||
|
|
||||||
export default (arr: ArrayBuffer) => new Promise((res, rej) => {
|
interface res {
|
||||||
|
color: string
|
||||||
|
distortedValue: number
|
||||||
|
id: string
|
||||||
|
value: number
|
||||||
|
}
|
||||||
|
|
||||||
|
export default (img: Image, correctDistortion: boolean): Promise<res[]> => new Promise((res, rej) => {
|
||||||
i++;
|
i++;
|
||||||
const _i = i;
|
const _i = i;
|
||||||
worker.postMessage({ i: _i, arr });
|
worker.postMessage({ i: _i, pixels: img.overlayData, width: img.width, height: img.height, correctDistortion });
|
||||||
cb[_i] = res;
|
cb[_i] = res;
|
||||||
});
|
});
|
||||||
@ -1,3 +1,4 @@
|
|||||||
export { default as bufToImageUrl } from "./BuffToImg";
|
export { default as bufToImageUrl } from "./BuffToImg";
|
||||||
export { default as fileToImage } from "./FileToImage";
|
export { default as fileToImage } from "./FileToImage";
|
||||||
export { default as countPixels } from "./CountPixels";
|
export { default as countPixels } from "./CountPixels";
|
||||||
|
export { default as AI } from "./AI";
|
||||||
@ -2,7 +2,7 @@
|
|||||||
import { Cross } from "../icons";
|
import { Cross } from "../icons";
|
||||||
import { fly, fade } from "svelte/transition";
|
import { fly, fade } from "svelte/transition";
|
||||||
import { images as imageData, route } from "stores";
|
import { images as imageData, route } from "stores";
|
||||||
import { bufToImageUrl } from "helpers";
|
import { bufToImageUrl, AI } from "../helpers";
|
||||||
import type { Writable } from "svelte/store";
|
import type { Writable } from "svelte/store";
|
||||||
|
|
||||||
import Toast from "../components/Toast";
|
import Toast from "../components/Toast";
|
||||||
@ -59,6 +59,7 @@
|
|||||||
showAnalyzerIndex = undefined;
|
showAnalyzerIndex = undefined;
|
||||||
} else {
|
} else {
|
||||||
showAnalyzerIndex = i;
|
showAnalyzerIndex = i;
|
||||||
|
//AI.analyze(img);
|
||||||
}
|
}
|
||||||
} else {
|
} else {
|
||||||
showAnalyzerIndex = undefined;
|
showAnalyzerIndex = undefined;
|
||||||
|
|||||||
7
view/tsconfig.json
Executable file → Normal file
7
view/tsconfig.json
Executable file → Normal file
@ -1,9 +1,8 @@
|
|||||||
{
|
{
|
||||||
"extends": "@tsconfig/svelte/tsconfig.json",
|
"extends": "@tsconfig/svelte/tsconfig.json",
|
||||||
|
"compilerOptions": {
|
||||||
"compileOptions":{
|
"baseUrl": "src"
|
||||||
"baseUrl": "src",
|
|
||||||
},
|
},
|
||||||
"include": ["src/**/*"],
|
"include": ["./src/**/*"],
|
||||||
"exclude": ["node_modules/*", "__sapper__/*", "public/*"]
|
"exclude": ["node_modules/*", "__sapper__/*", "public/*"]
|
||||||
}
|
}
|
||||||
Loading…
x
Reference in New Issue
Block a user