# Image Batch Processor - Resize, Convert & Compress (`ntriqpro/image-batch-optimizer`) Actor Process hundreds of images at once. Resize, convert formats, compress for web, and optimize automatically. - **URL**: https://apify.com/ntriqpro/image-batch-optimizer.md - **Developed by:** [daehwan kim](https://apify.com/ntriqpro) (community) - **Categories:** AI, Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $10.00 / 1,000 image optimizeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Image Batch Optimizer Optimize up to 50 images per run: convert formats (WebP/JPEG/PNG), resize, and compress. ### Features - **Batch Processing**: Handle up to 50 images per run - **Format Conversion**: Convert to WebP (recommended), JPEG, PNG, or keep original format - **Resizing**: Resize images with customizable fit modes (inside, cover, contain, fill, outside) - **Compression**: Adjust quality (1-100) for optimal file size vs visual quality tradeoff - **Detailed Output**: Get original/optimized sizes, compression ratio, and final dimensions - **Reliable**: Skip failed downloads and continue processing remaining images - **Pay Per Use**: Only pay $0.01 per image processed ### Input | Property | Type | Description | Example | |----------|------|-------------|---------| | `imageUrls` | Array[String] | List of image URLs to optimize (max 50) | `["https://example.com/photo.png"]` | | `outputFormat` | String | Target format: `webp`, `jpeg`, `png`, `original` | `"webp"` | | `quality` | Integer | Compression quality (1-100) | `80` | | `maxWidth` | Integer | Maximum width in pixels (0 = no resize) | `800` | | `maxHeight` | Integer | Maximum height in pixels (0 = no resize) | `600` | | `fit` | String | Resize fit mode: `inside`, `cover`, `contain`, `fill`, `outside` | `"inside"` | ### Output Dataset contains one record per processed image: ```json { "originalUrl": "https://example.com/photo.png", "outputUrl": "https://api.apify.com/v2/key-value-stores/.../photo.webp", "originalSize": 245600, "outputSize": 102340, "compressionRatio": "58.3%", "width": 800, "height": 600, "outputFormat": "webp", "disclaimer": "This tool optimizes images..." } ```` ### Pricing - **$0.01 per image optimized** (Pay-Per-Event model) - Failed images (download errors) are not charged ### Example Input: ```json { "imageUrls": ["https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png"], "outputFormat": "webp", "quality": 80, "maxWidth": 600, "maxHeight": 600, "fit": "inside" } ``` Result: PNG (280px, ~10KB) → WebP (280×280px, ~3KB), 70% compression, $0.01 charge. ### Disclaimer This tool optimizes images using the open source Sharp library (Apache 2.0 license). Output quality depends on input image format and content. Original files are not modified. Processed images are stored temporarily in Apify Key-Value Store and may expire. Always verify output before use in production. ### Data Source - **Image Processing**: Sharp (Apache 2.0) — https://sharp.pixelplumbing.com/ *** ### 🔗 Related Actors by ntriqpro Extend this actor with the ntriqpro intelligence network: - [**video-intelligence-analyzer**](https://apify.com/ntriqpro/video-intelligence-analyzer) — Video/image AI analyzer (transcripts, scenes, objects) - [**vehicle-damage-assessment**](https://apify.com/ntriqpro/vehicle-damage-assessment) — Vehicle damage AI assessment - [**table-chart-extractor**](https://apify.com/ntriqpro/table-chart-extractor) — Table and chart data extraction ### ⭐ Love it? Leave a Review Your rating helps professionals discover this actor. [Rate it here](https://apify.com/ntriqpro/image-batch-optimizer/reviews). # Actor input Schema ## `imageUrls` (type: `array`): List of image URLs to optimize (max 50) ## `outputFormat` (type: `string`): Target image format ## `quality` (type: `integer`): Compression quality. Higher = better quality, larger file. ## `maxWidth` (type: `integer`): Maximum output width in pixels. 0 keeps original. ## `maxHeight` (type: `integer`): Maximum output height in pixels. 0 keeps original. ## `fit` (type: `string`): How to fit the image within max dimensions ## Actor input object example ```json { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png" ], "outputFormat": "webp", "quality": 80, "fit": "inside" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png" ], "outputFormat": "webp", "quality": 80, "maxWidth": 0, "maxHeight": 0, "fit": "inside" }; // Run the Actor and wait for it to finish const run = await client.actor("ntriqpro/image-batch-optimizer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "imageUrls": ["https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png"], "outputFormat": "webp", "quality": 80, "maxWidth": 0, "maxHeight": 0, "fit": "inside", } # Run the Actor and wait for it to finish run = client.actor("ntriqpro/image-batch-optimizer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "imageUrls": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png" ], "outputFormat": "webp", "quality": 80, "maxWidth": 0, "maxHeight": 0, "fit": "inside" }' | apify call ntriqpro/image-batch-optimizer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ntriqpro/image-batch-optimizer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Image Batch Processor - Resize, Convert & Compress", "description": "Process hundreds of images at once. Resize, convert formats, compress for web, and optimize automatically.", "version": "1.0", "x-build-id": "F6a0ukqONDKwfkdS1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ntriqpro~image-batch-optimizer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ntriqpro-image-batch-optimizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ntriqpro~image-batch-optimizer/runs": { "post": { "operationId": "runs-sync-ntriqpro-image-batch-optimizer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ntriqpro~image-batch-optimizer/run-sync": { "post": { "operationId": "run-sync-ntriqpro-image-batch-optimizer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "imageUrls" ], "properties": { "imageUrls": { "title": "Image URLs", "type": "array", "description": "List of image URLs to optimize (max 50)", "items": { "type": "string" } }, "outputFormat": { "title": "Output Format", "enum": [ "webp", "jpeg", "png", "original" ], "type": "string", "description": "Target image format" }, "quality": { "title": "Quality (1-100)", "type": "integer", "description": "Compression quality. Higher = better quality, larger file." }, "maxWidth": { "title": "Max Width (px, 0 = no change)", "type": "integer", "description": "Maximum output width in pixels. 0 keeps original." }, "maxHeight": { "title": "Max Height (px, 0 = no change)", "type": "integer", "description": "Maximum output height in pixels. 0 keeps original." }, "fit": { "title": "Resize Fit Mode", "enum": [ "inside", "cover", "contain", "fill", "outside" ], "type": "string", "description": "How to fit the image within max dimensions" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```