# Pinterest Lens & Reverse Image Search API (`dev00/pinterest-lens-apify`) Actor Reverse image search on Pinterest. Input an image URL or base64 stream to extract matching pins, associated links, and related visual elements. - **URL**: https://apify.com/dev00/pinterest-lens-apify.md - **Developed by:** [dev00](https://apify.com/dev00) (community) - **Categories:** Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 visual search by images 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 ## 📷 Pinterest Lens & Reverse Image Search API Actor The **Pinterest Lens & Reverse Image Search Actor** is an advanced web scraper and visual matching engine on the Apify platform. It enables seamless integration with Pinterest's visual search index, allowing you to search billions of images, discover visual duplicates, find similar products, and crawl rich pin metadata. By passing either a public image URL or a base64 encoded image string, this Actor queries the target visual index and returns structured lists of matching Pins, shop links, pinner profiles, and website domains. --- ### 🌟 Key Features * **Instant Visual Search**: Provide any image to immediately discover visually similar Pins and matching web assets across the web. * **Automatic Focal Coordinate Calculation**: The Actor automatically analyzes the input image and calculates the optimal bounding box coordinates (internally mapping the search to normalized coordinates `0, 0, 1, 1` to target the entire image) so you don't need to specify manual cropping offsets. * **Base64 Payload Support**: Directly submit base64-encoded strings (with or without MIME data headers), perfect for local application integrations where web-accessible URLs are not available. * **Destination Link Crawler**: Automatically extracts the outbound URL/redirect link of each matched Pin, making it easy to identify product merchant sites. * **Structured Data Export**: Download results in JSON, CSV, Excel, XML, or HTML formats via the Apify dataset store. --- ### 🛠️ Input Parameters The Actor accepts the following input settings in JSON format: | Field | Type | Description | | :--- | :--- | :--- | | **image_url** | `string` | Public HTTP/HTTPS URL of the image to search. | | **image_base64** | `string` | Base64 encoded string of the image (supporting prefix header format). | *Note: One of `image_url` or `image_base64` must be provided.* #### Input JSON Example ```json { "image_url": "https://images.unsplash.com/photo-1579546929518-9e396f3cc809?w=300" } ```` *** ### 📤 Output Structure When the Actor executes, it stores the results directly in the default Dataset. Additionally, the raw JSON payload is saved under the `OUTPUT` key of the Key-Value Store. #### Output Dataset Row Example ```json { "domain": "creativemarket.com", "is_repin": false, "created_at": "Fri, 06 Dec 2024 20:36:02 +0000", "type": "pin", "id": "112730796954872157", "image_medium_url": "https://i.pinimg.com/400x/a4/39/71/a43971425c73c0f9ce138c7c74419af4.jpg", "link": "https://creativemarket.com/product/91166409-Abstract-moving-neon-orange-light-green-yellow-blue-bright-pink-purple-gradient-background", "description": "Abstract moving neon orange light green yellow blue bright pink purple gradient background...", "title": "Abstract Neon Fluid Animation" } ``` *** ### 🚀 Use Cases - **E-Commerce Visual Shopping**: Seamlessly recommend matching inventory by letting users search using photos. - **Intellectual Property Protection**: Scan Pinterest to verify if your designs or images are being used without authorization. - **Visual Social Listening**: Track trends and viral designs across Pinterest boards. # Actor input Schema ## `image_url` (type: `string`): The public HTTP/HTTPS URL of the image to search. ## `image_base64` (type: `string`): Raw image data encoded as base64 (e.g., data:image/jpeg;base64,...). ## Actor input object example ```json { "image_url": "https://images.unsplash.com/photo-1579546929518-9e396f3cc809?w=300" } ``` # 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 = { "image_url": "https://images.unsplash.com/photo-1579546929518-9e396f3cc809?w=300" }; // Run the Actor and wait for it to finish const run = await client.actor("dev00/pinterest-lens-apify").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 = { "image_url": "https://images.unsplash.com/photo-1579546929518-9e396f3cc809?w=300" } # Run the Actor and wait for it to finish run = client.actor("dev00/pinterest-lens-apify").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 '{ "image_url": "https://images.unsplash.com/photo-1579546929518-9e396f3cc809?w=300" }' | apify call dev00/pinterest-lens-apify --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dev00/pinterest-lens-apify", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pinterest Lens & Reverse Image Search API", "description": "Reverse image search on Pinterest. Input an image URL or base64 stream to extract matching pins, associated links, and related visual elements.", "version": "1.0", "x-build-id": "XytZ5nJpHJBWcPO4I" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dev00~pinterest-lens-apify/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dev00-pinterest-lens-apify", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dev00~pinterest-lens-apify/runs": { "post": { "operationId": "runs-sync-dev00-pinterest-lens-apify", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dev00~pinterest-lens-apify/run-sync": { "post": { "operationId": "run-sync-dev00-pinterest-lens-apify", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "image_url": { "title": "Image URL", "type": "string", "description": "The public HTTP/HTTPS URL of the image to search." }, "image_base64": { "title": "Image Base64 Data", "type": "string", "description": "Raw image data encoded as base64 (e.g., data:image/jpeg;base64,...)." } } }, "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 } } } } } } } } } } ```