# Image Processor — Batch Resize, Convert, Compress & Watermark (`accurate_pouch/image-processor`) Actor Batch image processing: resize, convert to WebP/AVIF, compress, watermark, thumbnail, crop, rotate, strip EXIF. Powered by sharp. 5 images free per run. - **URL**: https://apify.com/accurate\_pouch/image-processor.md - **Developed by:** [Manchitt Sanan](https://apify.com/accurate_pouch) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Processor — Batch Resize, Convert, Compress & Watermark Batch image processing in the cloud. Resize, convert to WebP/AVIF, compress, add watermarks, generate thumbnails, crop, rotate, and strip EXIF metadata. Powered by sharp. No ImageMagick installation required.. --- ### Why this exists sharp is the most popular image processing library in Node.js (8M+ weekly downloads), but no general-purpose batch image processor exists on Apify. E-commerce sites need bulk WebP conversion for SEO (faster pages = better rankings). Agencies need batch watermarking and thumbnail generation. --- ### Quick start Convert an image to WebP: ```json { "images": [ { "url": "https://via.placeholder.com/800x600.png", "operation": "convert", "format": "webp", "quality": 85 } ] } ```` *** ### Supported operations | Operation | Description | Required fields | |-----------|-------------|----------------| | `resize` | Resize to dimensions | `width` and/or `height`, optional `fit` | | `crop` | Extract region | `x`, `y`, `width`, `height` | | `rotate` | Rotate by degrees | `degrees` | | `flip` | Mirror image | `axis` (horizontal/vertical) | | `convert` | Change format | `format` (webp/avif/png/jpeg/gif/tiff) | | `compress` | Reduce file size | `quality` (1-100), optional `format` | | `watermark` | Add text or image overlay | `watermarkText` or `watermarkUrl`, optional `watermarkPosition`, `watermarkOpacity`, `watermarkScale` | | `thumbnail` | Generate small preview | `width`, `height` (default 150x150) | | `strip-exif` | Remove metadata | *(no extra fields)* | | `get-exif` | Read metadata | *(no extra fields, returns JSON)* | *** ### Input Each item in the `images` array: | Field | Type | Required | Description | |-------|------|----------|-------------| | `url` | string | Yes | Source image URL | | `operation` | enum | Yes | One of the operations above | | `format` | string | No | Target format: webp, avif, png, jpeg, gif, tiff | | `quality` | number | No | 1-100. Default: 80 (60 for AVIF) | | `width` | number | No | For resize/crop/thumbnail | | `height` | number | No | For resize/crop/thumbnail | | `fit` | enum | No | cover, contain, fill, inside, outside | | `x`, `y` | number | No | For crop (top-left corner) | | `degrees` | number | No | For rotate | | `axis` | enum | No | horizontal or vertical (for flip) | | `watermarkText` | string | No | Text to overlay (for watermark) | | `watermarkUrl` | string | No | URL to an image to overlay as watermark | | `watermarkPosition` | enum | No | top-left, top-right, bottom-left, bottom-right, center | | `watermarkOpacity` | number | No | 0-1. Default: 0.5 | | `watermarkScale` | number | No | Watermark size as fraction of base image width. Default: 0.2 | *** ### Output Processed images are stored in the Apify **Key-Value Store**. Each operation also produces a dataset entry: ```json { "sourceUrl": "https://via.placeholder.com/800x600.png", "operation": "convert", "outputKey": "800x600-convert.webp", "outputFormat": "webp", "originalSize": 12845, "outputSize": 4230, "compressionRatio": 67.1, "width": 800, "height": 600, "status": "success", "error": null } ``` *** ### Pricing **$0.003 per image processed** (pay-per-event pricing). - One operation = one charge (resize + convert = 2 operations = 2 images). - Errors and dry runs are never charged. - 100 images = $0.30 *** ### Performance - Powered by sharp (libvips) — processes images in milliseconds - WebP conversion typically saves 40-70% file size vs JPEG - AVIF saves even more but encodes slower — good for archival, not real-time - Memory: ~256MB sufficient for images up to 10,000x10,000 pixels *** ### Limitations - **No AI-based background removal.** Watermarks support both text and image overlays with configurable position, opacity, and scale. - **Max image size:** ~100MB input. Very large images may exceed memory on the default 256MB plan. - **SVG input:** Rasterized on read. Output is always raster (PNG, JPEG, WebP, etc). - **Animated GIF:** Only the first frame is processed. *** ### Related Tools by manchittlab - **[Broken Link Checker](https://apify.com/accurate_pouch/broken-link-checker)** — Recursively crawl your website and find every broken link, 404, redirect, and timeout. - **[Email Validator Pro](https://apify.com/accurate_pouch/email-validator)** — Bulk email validation with SMTP check, 5,300+ disposable domains, deliverability scoring. - **[Lighthouse Auditor](https://apify.com/accurate_pouch/lighthouse-auditor)** — Batch Lighthouse audits for performance, SEO, accessibility, and Core Web Vitals. - **[Domain Age Checker](https://apify.com/accurate_pouch/domain-age-checker)** — Bulk RDAP domain age, registration, and expiration lookup. - **[Tech Stack Detector](https://apify.com/accurate_pouch/tech-stack-detector)** — Detect frameworks, CMS, analytics, CDN, and 100+ technologies for any URL. - **[Google Sheets Reader & Writer](https://apify.com/accurate_pouch/google-sheets-rw)** — Read any Google Sheet to JSON or append rows. Service Account auth. *** ### Run on Apify [![Run on Apify](https://apify.com/static/run-on-apify.svg)](https://apify.com/accurate_pouch/image-processor) No setup needed. Click above to run in the cloud. $0.003 per operation. # Actor input Schema ## `images` (type: `array`): Array of image operation objects. Each specifies a source URL and the operation to perform. ## `outputPrefix` (type: `string`): Prefix added to all output filenames in the key-value store. ## `dryRun` (type: `boolean`): Process images but don't charge. Results and outputs are still stored. ## Actor input object example ```json { "images": [ { "url": "https://picsum.photos/800/600", "operation": "convert", "format": "webp", "quality": 85 } ], "outputPrefix": "", "dryRun": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "images": [ { "url": "https://picsum.photos/800/600", "operation": "convert", "format": "webp", "quality": 85 } ] }; // Run the Actor and wait for it to finish const run = await client.actor("accurate_pouch/image-processor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "images": [{ "url": "https://picsum.photos/800/600", "operation": "convert", "format": "webp", "quality": 85, }] } # Run the Actor and wait for it to finish run = client.actor("accurate_pouch/image-processor").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 '{ "images": [ { "url": "https://picsum.photos/800/600", "operation": "convert", "format": "webp", "quality": 85 } ] }' | apify call accurate_pouch/image-processor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=accurate_pouch/image-processor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Image Processor — Batch Resize, Convert, Compress & Watermark", "description": "Batch image processing: resize, convert to WebP/AVIF, compress, watermark, thumbnail, crop, rotate, strip EXIF. Powered by sharp. 5 images free per run.", "version": "0.1", "x-build-id": "4b1eySwQclsMTarJb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/accurate_pouch~image-processor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-accurate_pouch-image-processor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/accurate_pouch~image-processor/runs": { "post": { "operationId": "runs-sync-accurate_pouch-image-processor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/accurate_pouch~image-processor/run-sync": { "post": { "operationId": "run-sync-accurate_pouch-image-processor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "images" ], "properties": { "images": { "title": "Image Operations", "type": "array", "description": "Array of image operation objects. Each specifies a source URL and the operation to perform." }, "outputPrefix": { "title": "Output Filename Prefix", "type": "string", "description": "Prefix added to all output filenames in the key-value store.", "default": "" }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Process images but don't charge. Results and outputs are still stored.", "default": false } } }, "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 } } } } } } } } } } ```