chore: fix the invalid svg images.
This commit is contained in:
parent
11e9972df6
commit
232d9eb19b
GPG Key ID:
9D18A22A7DCD5A9B
@ -178,9 +178,12 @@ const options: z.input<typeof Options> = {
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},
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},
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thumbnail: ({ src, width, height }) => {
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if (src.endsWith('.svg')) {
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return src;
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}
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if (isProd()) {
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// Add upyun thumbnail support.
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return `${src}!upyun520/both/${width}x${height}/quality/100/unsharp/true/progressive/true`;
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return `${src}!upyun520/both/${width}x${height}/format/webp/quality/100/unsharp/true/progressive/true`;
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}
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// See https://docs.astro.build/en/reference/image-service-reference/#local-services
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// Remember to add the localhost to you image service settings.
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59
package-lock.json
generated
59
package-lock.json
generated
@ -42,8 +42,8 @@
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"resize-sensor": "^0.0.6",
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"rimraf": "^5.0.7",
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"sharp": "^0.33.4",
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"typescript": "^5.4.5",
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"unist-util-visit": "^5.0.0"
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"typescript": "^5.5.2",
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"unist-util-select": "^5.1.0"
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}
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},
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"node_modules/@ampproject/remapping": {
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@ -3290,6 +3290,13 @@
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"url": "https://github.com/sponsors/sindresorhus"
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}
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},
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"node_modules/boolbase": {
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"version": "1.0.0",
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"resolved": "https://registry.npmjs.org/boolbase/-/boolbase-1.0.0.tgz",
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"integrity": "sha512-JZOSA7Mo9sNGB8+UjSgzdLtokWAky1zbztM3WRLCbZ70/3cTANmQmOdR7y2g+J0e2WXywy1yS468tY+IruqEww==",
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"dev": true,
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"license": "ISC"
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},
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"node_modules/bootstrap": {
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"version": "5.3.3",
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"resolved": "https://registry.npmjs.org/bootstrap/-/bootstrap-5.3.3.tgz",
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@ -3833,6 +3840,23 @@
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"node": ">= 8"
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}
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},
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"node_modules/css-selector-parser": {
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"version": "3.0.5",
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"resolved": "https://registry.npmjs.org/css-selector-parser/-/css-selector-parser-3.0.5.tgz",
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"integrity": "sha512-3itoDFbKUNx1eKmVpYMFyqKX04Ww9osZ+dLgrk6GEv6KMVeXUhUnp4I5X+evw+u3ZxVU6RFXSSRxlTeMh8bA+g==",
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"dev": true,
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"funding": [
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{
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"type": "github",
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"url": "https://github.com/sponsors/mdevils"
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},
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{
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"type": "patreon",
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"url": "https://patreon.com/mdevils"
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}
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],
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"license": "MIT"
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},
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"node_modules/cssesc": {
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"version": "3.0.0",
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"resolved": "https://registry.npmjs.org/cssesc/-/cssesc-3.0.0.tgz",
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@ -6701,6 +6725,19 @@
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"url": "https://github.com/sponsors/sindresorhus"
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}
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},
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"node_modules/nth-check": {
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"version": "2.1.1",
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"resolved": "https://registry.npmjs.org/nth-check/-/nth-check-2.1.1.tgz",
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"integrity": "sha512-lqjrjmaOoAnWfMmBPL+XNnynZh2+swxiX3WUE0s4yEHI6m+AwrK2UZOimIRl3X/4QctVqS8AiZjFqyOGrMXb/w==",
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"dev": true,
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"license": "BSD-2-Clause",
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"dependencies": {
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"boolbase": "^1.0.0"
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},
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"funding": {
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"url": "https://github.com/fb55/nth-check?sponsor=1"
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}
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},
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"node_modules/obuf": {
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"version": "1.1.2",
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"resolved": "https://registry.npmjs.org/obuf/-/obuf-1.1.2.tgz",
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@ -8654,6 +8691,24 @@
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"url": "https://opencollective.com/unified"
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}
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},
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"node_modules/unist-util-select": {
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"version": "5.1.0",
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"resolved": "https://registry.npmjs.org/unist-util-select/-/unist-util-select-5.1.0.tgz",
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"integrity": "sha512-4A5mfokSHG/rNQ4g7gSbdEs+H586xyd24sdJqF1IWamqrLHvYb+DH48fzxowyOhOfK7YSqX+XlCojAyuuyyT2A==",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"@types/unist": "^3.0.0",
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"css-selector-parser": "^3.0.0",
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"devlop": "^1.1.0",
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"nth-check": "^2.0.0",
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"zwitch": "^2.0.0"
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},
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"funding": {
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"type": "opencollective",
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"url": "https://opencollective.com/unified"
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}
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},
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"node_modules/unist-util-stringify-position": {
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"version": "4.0.0",
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"resolved": "https://registry.npmjs.org/unist-util-stringify-position/-/unist-util-stringify-position-4.0.0.tgz",
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@ -74,7 +74,7 @@
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"resize-sensor": "^0.0.6",
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"rimraf": "^5.0.7",
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"sharp": "^0.33.4",
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"typescript": "^5.4.5",
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"unist-util-visit": "^5.0.0"
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"typescript": "^5.5.2",
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"unist-util-select": "^5.1.0"
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}
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}
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@ -1,8 +1,10 @@
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import type { Literal, Node, Parent } from 'unist';
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import { visit } from 'unist-util-visit';
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import { selectAll } from 'unist-util-select';
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import options from '../options';
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import { imageMetadata } from '../src/helpers/images';
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import { urlJoin } from '../src/helpers/tools';
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export type ImageNode = Parent & {
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type ImageNode = Parent & {
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url: string;
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alt: string;
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name: string;
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@ -11,43 +13,72 @@ export type ImageNode = Parent & {
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attributes: (Literal & { name: string })[];
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};
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type MdxJsxAttribute = {
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type: 'mdxJsxAttribute';
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name: string;
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value: string;
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};
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type MdxJsxFlowElement = Parent & {
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name: string;
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attributes: MdxJsxAttribute[];
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};
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export const astroImage = () => {
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return async (tree: Node) => {
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const images: ImageNode[] = [];
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// Find all the local image node.
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visit(tree, 'image', (node: Node, _, __: Parent) => {
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const imageNode = node as ImageNode;
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// Skip remote images.
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if (imageNode.url.startsWith('http')) {
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return;
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// Find all the img node.
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const imgs = selectAll('mdxJsxFlowElement', tree)
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.map((node) => node as MdxJsxFlowElement)
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.filter((node) => node.name === 'img');
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for (const img of imgs) {
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const srcAttribute = img.attributes.find((attribute) => attribute.name === 'src');
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if (srcAttribute) {
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const src = srcAttribute.value;
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if (src.startsWith('/')) {
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srcAttribute.value = urlJoin(options.assetsPrefix(), src);
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}
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}
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}
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images.push(imageNode);
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});
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// Find all the image node.
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const imageNodes = selectAll('image', tree)
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.map((node) => node as ImageNode)
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.filter((imageNode) => !imageNode.url.startsWith('http'))
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.map(transformAstroImage);
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// Process images.
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await Promise.all(images.map(transformAstroImage));
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// Process image with blur metadata.
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await Promise.all(imageNodes);
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return tree;
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};
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};
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const transformAstroImage = async (imageNode: ImageNode) => {
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const metadata = await imageMetadata(imageNode.url);
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if (metadata == null) {
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throw new Error(`Failed to get image metadata: ${imageNode.url}`);
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}
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// Convert original node to next/image
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imageNode.type = 'mdxJsxFlowElement';
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imageNode.name = 'Image';
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imageNode.attributes = [
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{ type: 'mdxJsxAttribute', name: 'alt', value: imageNode.alt },
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{ type: 'mdxJsxAttribute', name: 'src', value: metadata.src },
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{ type: 'mdxJsxAttribute', name: 'width', value: imageNode.width ?? metadata.width },
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{ type: 'mdxJsxAttribute', name: 'height', value: imageNode.height ?? metadata.height },
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{ type: 'mdxJsxAttribute', name: 'blurDataURL', value: metadata.blurDataURL },
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{ type: 'mdxJsxAttribute', name: 'blurWidth', value: metadata.blurWidth },
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{ type: 'mdxJsxAttribute', name: 'blurHeight', value: metadata.blurHeight },
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];
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try {
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const metadata = await imageMetadata(imageNode.url);
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if (metadata == null) {
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throw new Error(`Failed to get image metadata: ${imageNode.url}`);
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}
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imageNode.attributes = [
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{ type: 'mdxJsxAttribute', name: 'alt', value: imageNode.alt },
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{ type: 'mdxJsxAttribute', name: 'src', value: metadata.src },
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{ type: 'mdxJsxAttribute', name: 'width', value: imageNode.width ?? metadata.width },
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{ type: 'mdxJsxAttribute', name: 'height', value: imageNode.height ?? metadata.height },
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{ type: 'mdxJsxAttribute', name: 'blurDataURL', value: metadata.blurDataURL },
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{ type: 'mdxJsxAttribute', name: 'blurWidth', value: metadata.blurWidth },
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{ type: 'mdxJsxAttribute', name: 'blurHeight', value: metadata.blurHeight },
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];
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} catch (error) {
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imageNode.attributes = [
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{ type: 'mdxJsxAttribute', name: 'alt', value: imageNode.alt },
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{
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type: 'mdxJsxAttribute',
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name: 'src',
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value: imageNode.url.startsWith('/') ? urlJoin(options.assetsPrefix(), imageNode.url) : imageNode.url,
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},
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];
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}
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};
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BIN
public/images/2024/06/2024062101010412.jpg
Normal file
BIN
public/images/2024/06/2024062101010412.jpg
Normal file
Binary file not shown.
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After Width: | Height: | Size: 170 KiB |
@ -6,7 +6,7 @@ import options from '@/options';
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const pinnedSlug = options.settings.post.category ?? [];
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const pinnedCategories = pinnedSlug
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.map((slug) => getCategory(undefined, slug))
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.flatMap((category) => (category !== undefined ? [category] : []));
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.filter((category) => category !== undefined);
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---
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<div class="row g-2 g-md-4 list-grouped mt-3 mt-md-4">
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@ -46,28 +46,28 @@ cover: /images/2024/04/2024041405050511.png
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这也使得这道题在弄懂原理后相当无趣，但考虑我这朋友确实经验尚浅，所以我还是给他继续讲下去，顺带着给了代码实现。这篇博客便是当时内容的摘录整理。
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<img src={'/images/recaps/algo-minimal-costs/step1.svg'} width={400} height={400} alt={'Step 1'} />
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<img src='/images/recaps/algo-minimal-costs/step1.svg' width='400px' height='400px' alt='Step 1' />
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<center>
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做任何算法题，第一步是理解题意，第二步是设想最简单的情况，再慢慢推导到复杂情况。首先，我们先不考虑存在阻塞的情况。最简单场景里，顾客和维修点在一个
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1 x 1 的格子的一条边上，这个时候他们间的最短距离为 1。
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</center>
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<img src={'/images/recaps/algo-minimal-costs/step2.svg'} width={400} height={400} alt={'Step 2'} />
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<img src='/images/recaps/algo-minimal-costs/step2.svg' width='400px' height='400px' alt='Step 2' />
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<center>然后我们更进一步，如果他们在一个格子的对角线上呢？他们间的最短路径有两条，为 2。</center>
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<img src={'/images/recaps/algo-minimal-costs/step3.svg'} width={400} height={400} alt={'Step 3'} />
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<img src='/images/recaps/algo-minimal-costs/step3.svg' width='400px' height='400px' alt='Step 3' />
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<center>
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结合初中的几何学知识，我们首先知道一个基本知识，两点之间，直线最短。所以，维修点和顾客在同一条直线上时，他们之间的距离就是直线距离。
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</center>
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<img src={'/images/recaps/algo-minimal-costs/step4.svg'} width={800} height={400} alt={'Step 4'} />
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<img src='/images/recaps/algo-minimal-costs/step4.svg' width='800px' height='400px' alt='Step 4' />
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<center>然后我们再稍微复杂一点，此时顾客和维修点之间是田字格，最短路径就有三条，距离为 3。</center>
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<img src={'/images/recaps/algo-minimal-costs/step5.svg'} width={800} height={400} alt={'Step 5'} />
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<img src='/images/recaps/algo-minimal-costs/step5.svg' width='800px' height='400px' alt='Step 5' />
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<center>
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等到田字格的时候，相信聪明的你已经发现了规律。那就是顾客到维修点的最短距离，等于他们所形成的矩形的横纵两条边边长的总和。按照上面右侧图片所示的箭头所行走的距离都等于这个最短路径。
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@ -75,7 +75,7 @@ cover: /images/2024/04/2024041405050511.png
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一般情况下，面试场景的编码题已经可以开始写了。对应的编程思路就是，从维修点出发，在与顾客构成的矩形边界里面，不断逼近，只要能走通那么我们之间就有了最短距离。再把不同维修点到顾客的最短距离排序，选出最小的距离来进行计算费用。
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<img src={'/images/recaps/algo-minimal-costs/step6.svg'} width={800} height={400} alt={'Step 6'} />
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<img src='/images/recaps/algo-minimal-costs/step6.svg' width='800px' height='400px' alt='Step 6' />
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倘若以上面的推论作为最终编码的方式，虽然不能说完全错误，但是在当下这个面试很卷的时代，还是有可能被 PASS，为什么呢？因为我们还没有引入阻塞的概念。我们随便画两种阻塞的情况，并且假定这里都属于在当时条件下的最短路径，那么阁下又该如何应对？😆
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@ -88,11 +88,11 @@ cover: /images/2024/04/2024041405050511.png
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以此为基础，我们就可以稍微来复习一下大学的算法知识了，贪心算法（贪婪算法）。贪心算法的定义网上随随便便都能找到，这里就不再复述，我们更多地是需要去思考在这个场景的贪心算法如何使用。
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<img src={'/images/recaps/algo-minimal-costs/step7.svg'} width={800} height={400} alt={'Step 7'} />
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<img src='/images/recaps/algo-minimal-costs/step7.svg' width='800px' height='400px' alt='Step 7' />
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贪心算法的第一步，就是找寻从顾客开始，所有可能能行走方向距离为 1 的点有哪些（图中蓝色的点）。接着，我们可以以这些距离为 1 的点为基础，去找寻所有距离为 2 的点（图中绿色的点）。以此类推，直到所有的点都没有下一个可以行走的点了。而每计算一次距离为 N 的点的时候，都可以尝试看看里面是否有对应的维修点，如果有，那么终止检索，这个 N 便是最短距离。
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<img src={'/images/recaps/algo-minimal-costs/step8.svg'} width={800} height={400} alt={'Step 8'} />
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<img src='/images/recaps/algo-minimal-costs/step8.svg' width='800px' height='400px' alt='Step 8' />
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如上图所示，在我们查找距离为 4 的点的时候，我们就能找到目标维修店，那么我们可以认定，起最短距离就是 4。
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@ -166,3 +166,5 @@ private boolean availablePoint(int[][] routines, int x, int y) {
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return x >= 0 && x < routines.length && y >= 0 && y <= routines[0].length && (routines[x][y] == 0 || routines[x][y] == -1);
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}
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```
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@ -13,7 +13,7 @@ const title = `${query} 查询结果`;
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const searchResults = searchPosts(query)
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.map((slug) => posts.find((post) => post.slug === slug))
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.flatMap((post) => (post == null ? [] : [post]))
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.filter((post) => post !== undefined)
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.slice(0, options.settings.pagination.search);
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---
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