# Google Gemini Watermark Remover (`tempting_finch/google-gemini-watermark-remover`) Actor Detect and remove the Gemini watermark from AI-generated images using template matching and inpainting. - **URL**: https://apify.com/tempting\_finch/google-gemini-watermark-remover.md - **Developed by:** [Karl Goyer](https://apify.com/tempting_finch) (community) - **Categories:** AI, Developer tools, Social media - **Stats:** 2 total users, 1 monthly users, 75.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Google Gemini Watermark Remover An [Apify actor](https://apify.com) that detects and removes the Gemini watermark (4-pointed star ✦) from AI-generated images. ### How it works 1. **Detection** — Multi-scale template matching (`TM_CCOEFF_NORMED` with alpha mask) locates the watermark in the bottom-right region of the image. 2. **Inpainting** — The watermark area is reconstructed using either **LaMa** (deep learning, high quality) or **Telea** (OpenCV, fast fallback). ### Input | Parameter | Type | Default | Description | |---|---|---|---| | `imageUrls` | `string[]` | *required* | URLs of images to process | | `detectionThreshold` | `number` | `0.50` | Minimum match confidence (0–1) | | `outputFormat` | `string` | `PNG` | Output format: `PNG`, `JPEG`, or `WEBP` | | `jpegQuality` | `number` | `95` | Quality for JPEG/WEBP (1–100) | | `inpaintMethod` | `string` | `auto` | `auto`, `telea`, or `lama` | ### Output - **Key-Value Store** — Each cleaned image saved as `{name}_cleaned.{format}` - **Dataset** — JSON records with `originalUrl`, `outputKey`, `watermarkDetected`, `confidence`, `bbox` ### Run locally ```bash pip install -r requirements.txt python -c " import asyncio from src.main import main asyncio.run(main()) " ```` With Apify CLI: ```bash apify run ``` ### Deploy to Apify ```bash apify login apify push ``` ### Docker build ```bash docker build -f .actor/Dockerfile -t gemini-watermark-remover . ``` ### License MIT # Actor input Schema ## `imageUrls` (type: `array`): List of image URLs to process. Each image will be downloaded, checked for the Gemini watermark, and cleaned. ## `detectionThreshold` (type: `number`): Minimum confidence score (0-1) for template matching. Lower = more sensitive, higher = fewer false positives. ## `outputFormat` (type: `string`): Image format for the cleaned output files. ## `jpegQuality` (type: `integer`): Quality for JPEG/WEBP output (1-100). Only used when outputFormat is JPEG or WEBP. ## Actor input object example ```json { "imageUrls": [ "https://i.ibb.co/xtdBMMxY/Gemini-Generated-Image-lmxxdwlmxxdwlmxx.png" ], "detectionThreshold": 0.4, "outputFormat": "PNG", "jpegQuality": 95 } ``` # 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://i.ibb.co/xtdBMMxY/Gemini-Generated-Image-lmxxdwlmxxdwlmxx.png" ] }; // Run the Actor and wait for it to finish const run = await client.actor("tempting_finch/google-gemini-watermark-remover").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://i.ibb.co/xtdBMMxY/Gemini-Generated-Image-lmxxdwlmxxdwlmxx.png"] } # Run the Actor and wait for it to finish run = client.actor("tempting_finch/google-gemini-watermark-remover").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://i.ibb.co/xtdBMMxY/Gemini-Generated-Image-lmxxdwlmxxdwlmxx.png" ] }' | apify call tempting_finch/google-gemini-watermark-remover --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=tempting_finch/google-gemini-watermark-remover", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Gemini Watermark Remover", "description": "Detect and remove the Gemini watermark from AI-generated images using template matching and inpainting.", "version": "0.1", "x-build-id": "cow3pD0nBALmzr8hk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/tempting_finch~google-gemini-watermark-remover/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-tempting_finch-google-gemini-watermark-remover", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/tempting_finch~google-gemini-watermark-remover/runs": { "post": { "operationId": "runs-sync-tempting_finch-google-gemini-watermark-remover", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/tempting_finch~google-gemini-watermark-remover/run-sync": { "post": { "operationId": "run-sync-tempting_finch-google-gemini-watermark-remover", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 process. Each image will be downloaded, checked for the Gemini watermark, and cleaned.", "items": { "type": "string" } }, "detectionThreshold": { "title": "Detection Threshold", "minimum": 0.1, "maximum": 1, "type": "number", "description": "Minimum confidence score (0-1) for template matching. Lower = more sensitive, higher = fewer false positives.", "default": 0.4 }, "outputFormat": { "title": "Output Format", "enum": [ "PNG", "JPEG", "WEBP" ], "type": "string", "description": "Image format for the cleaned output files.", "default": "PNG" }, "jpegQuality": { "title": "JPEG Quality", "minimum": 1, "maximum": 100, "type": "integer", "description": "Quality for JPEG/WEBP output (1-100). Only used when outputFormat is JPEG or WEBP.", "default": 95 } } }, "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 } } } } } } } } } } ```