# Image Comparator (`noisy_alchemy/image-comparator`) Actor Compare a source image against multiple targets using deep-learning model to determine visual similarity. Ideal for e-commerce matching, copyright detection, and image deduplication. Accepts both URLs and Base64 encoded images to provide highly accurate similarity scoring. - **URL**: https://apify.com/noisy\_alchemy/image-comparator.md - **Developed by:** [Aljeandro](https://apify.com/noisy_alchemy) (community) - **Categories:** AI, Other, Agents - **Stats:** 2 total users, 1 monthly users, 91.2% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $2.50 / 1,000 image processeds 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 ## πŸ” Image Comparator β€” AI-Powered Visual Similarity **Instantly compare images and get a similarity score + match verdict.** Works with public URLs **and** base64-encoded images β€” no preprocessing needed. --- ### What it does This actor uses a state-of-the-art **deep learning vision model** to measure how visually similar two images are. It converts each image into a high-dimensional embedding vector, then computes the **cosine similarity** between them. You provide: | Input | Description | |---|---| | **1 source image** | The reference image you want to compare against | | **1 or more target images** | The images you want to check for similarity | You get back: | Output | Description | |---|---| | **similarityScore** | A float between 0.0 and 1.0 (higher = more similar) | | **isSimilar** | `true` / `false` β€” whether the score meets your threshold | --- ### Use cases - **Duplicate detection** β€” Find near-duplicate product images across catalogs. - **Brand monitoring** β€” Check if scraped images match your reference assets. - **Content moderation** β€” Flag re-uploaded or slightly altered images. - **Visual QA** β€” Verify screenshots or rendered pages against golden references. - **E-commerce matching** β€” Match supplier images to your existing product photos. --- ### πŸ’° Pricing This actor uses **pay-per-event** pricing based on the total number of images processed, plus a minimal base charge for the actor start. | Event | Metric | Rate | |---|---|---| | **Actor Start** | `apify-actor-start` | **$0.00005 per run** (covers the first 5s of compute) | | **Image Processed** | `image-processed` | **$2.50 per 1,000 images** ($0.0025 per image) | **Examples:** | Scenario | Total images | Image Cost | Actor Start Cost | Total Cost | |---|---|---|---|---| | 1 source vs 1 target | 2 | $0.005 | $0.00005 | $0.00505 | | 1 source vs 10 targets | 11 | $0.0275 | $0.00005 | $0.02755 | | 1 source vs 100 targets | 101 | $0.2525 | $0.00005 | $0.25255 | | 1 source vs 1,000 targets | 1,001 | $2.5025 | $0.00005 | $2.50255 | --- ### How similarity scoring works | Score range | Interpretation | |---|---| | **0.95 – 1.00** | Near-identical or the same image (possibly resized/compressed). | | **0.85 – 0.95** | Very similar β€” same subject, minor differences (angle, lighting). | | **0.70 – 0.85** | Related β€” same category or concept, but visually distinct. | | **Below 0.70** | Different images. | --- ### Performance | Metric | Value | |---|---| | **Model load** | Instant β€” weights are pre-baked into the Docker image | | **Per-image embedding** | ~20–50 ms (CPU) / ~5–10 ms (GPU) | | **Concurrency** | Up to 10 images downloaded in parallel | | **Batch processing** | Images are embedded in batches of 16 for throughput | #### Expected Execution Times (1-to-1 comparison) On standard Apify CPU instances, you can expect the following benchmarks for a single source vs. target comparison: - **~0.3 to 0.5 seconds ("Hot" Start)** β€” If the exact same URLs/Base64 strings have been run before, the actor instantly grabs the embeddings from the local cache. - **~1 to 2 seconds ("Cold" Start)** β€” The lightweight ONNX model loads instantly from disk (< 50MB), downloads the images, and computes embeddings via vectorized numpy arrays. No more 15+ second HuggingFace model download delays! > **πŸ’‘ Note on Batches:** If you run large batches (e.g., 1 source vs 100 targets), the sub-second model loading time is a flat fee, and the remaining 100 images will process rapidly in memory. The actor is designed for **speed**: 1. **Pure ONNX Runtime** β€” The massive PyTorch dependency has been stripped out. The AI model runs on a highly optimized ONNX graph, cutting RAM usage to < 500MB and starting instantly. 2. **Async image fetching** β€” All images are downloaded concurrently using `httpx` with a configurable semaphore and standard browser User-Agents to bypass strict CDNs. 3. **Vectorized Math** β€” Cosine similarity is computed simultaneously across all images using a single, blazing-fast `numpy.dot()` matrix instruction. 4. **Resilience** β€” Malformed images, 404s, or 100MB+ files are automatically skipped and logged as errors without crashing your batch run. 5. **Smart Embedding Cache** β€” Automatically saves computed embeddings in an Apify Key-Value Store (`embedding-cache`). If you run the actor again with the same URLs or base64 images, it instantly reuses the cached dataβ€”skipping the download and inference steps entirely to save massive amounts of processing time. --- ### Limitations - **Image format** β€” Supports JPEG, PNG, WebP, BMP, TIFF. Animated GIFs use the first frame only. - **Image size** β€” Very large images (>20 MP) may slow down processing. The model internally resizes to 224Γ—224 px. - **Pixel-level diff** β€” This actor measures **semantic** similarity (does it look like the same thing?), not pixel-exact differences. Two photos of the same cat from different angles will score high; a red square and a blue square will score low, even if they're the same shape. - **Timeout** β€” Image downloads time out after 30 seconds per image. --- ### Integrations & API usage Call this actor from any language via the [Apify API](https://docs.apify.com/api/v2): ```bash curl -X POST "https://api.apify.com/v2/acts//runs?token=" \ -H "Content-Type: application/json" \ -d '{ "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": ["https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg"] }' ```` #### Python ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("").call(run_input={ "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg", ], }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): for result in item["results"]: print(f"Target #{result['targetIndex']}: " f"score={result['similarityScore']:.4f} " f"match={result['isSimilar']}") ``` #### JavaScript / Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: '' }); const run = await client.actor('').call({ sourceImage: 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg', targetImages: [ 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg', ], }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items[0].results.forEach((r) => { console.log(`Target #${r.targetIndex}: score=${r.similarityScore} match=${r.isSimilar}`); }); ``` *** ### Support & Feedback Found a bug or have a feature request? Open an issue on the actor's GitHub repository or reach out via the Apify community. *** *** ## πŸ“‹ Input Description Detailed reference for every input field accepted by this actor. *** #### πŸ–ΌοΈ Source Image `sourceImage` **Optional** The reference image to compare all target images against. This is the "baseline" β€” every target will be scored based on how similar it is to this image. Accepts two formats: - **Public URL** β€” Any directly accessible image URL (e.g., `https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg`) - **Base64-encoded string** β€” With or without the `data:image/...;base64,` prefix > **πŸ’‘ Pro tip:** For fastest results, use URLs pointing to images hosted on a CDN or fast server. Base64 input avoids the network download but increases the payload size. > **⚠️ Note:** If left empty, a sample image will be used for demonstration purposes. This is useful for testing the actor before integrating it into your pipeline. **Type:** `string` **Default:** ``` https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg ``` **Example (URL):** ``` https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg/1200px-Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg ``` **Example (base64 with data-URI):** ``` data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD... ``` **Example (raw base64):** ``` /9j/4AAQSkZJRgABAQAAAQABAAD... ``` *** #### 🎯 Target Images `targetImages` **Optional** A list of one or more images to compare against the source image. Each item in the array is scored independently β€” you get one result object per target. Each array element accepts the same two formats as `sourceImage`: - **Public URL** - **Base64-encoded string** (with or without data-URI prefix) You can freely mix URLs and base64 strings in the same array. > **πŸ’‘ Pro tip:** For **1-to-1 comparison**, pass a single-element array. For **1-to-N batch comparison**, pass as many targets as you need β€” they are all processed in a single run with batched inference for maximum speed. > **⚠️ Pricing note:** You are charged based on the **total number of images** processed (1 source + N targets). See the Pricing section above. > **⚠️ Blank entries:** Any blank or empty strings in the array are automatically filtered out. If all entries are blank, the default sample images will be used. **Type:** `array` of `string` **Default:** ```json [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg" ] ``` **Example (URLs only):** ```json [ "https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Kittyply_edit1.jpg/1200px-Kittyply_edit1.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/GoldenGateBridge-001.jpg/1200px-GoldenGateBridge-001.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Camponotus_flavomarginatus_ant.jpg/1200px-Camponotus_flavomarginatus_ant.jpg" ] ``` **Example (mixed URLs + base64):** ```json [ "https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Kittyply_edit1.jpg/1200px-Kittyply_edit1.jpg", "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg...", "/9j/4AAQSkZJRgABAQAAAQABAAD..." ] ``` *** #### 🎚️ Threshold `threshold` **Optional** The similarity score cutoff (between `-1.0` and `1.0`) that determines whether `isSimilar` is `true` or `false`. Any target image that scores equal to or higher than this value will be marked as a match. > **πŸ’‘ Pro tip:** A threshold of `0.85` works great for detecting near-duplicates or identical subjects from different angles. Increase it to `0.95` for strict exact-duplicate detection. **Type:** `number` **Default:** `0.85` *** *** ## πŸ“₯ Input Example Full JSON input with all fields: ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg", "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD..." ] } ``` #### Minimal input (uses all defaults) ```json {} ``` #### 1-to-1 comparison ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg/1200px-Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Kittyply_edit1.jpg/1200px-Kittyply_edit1.jpg" ] } ``` #### 1-to-N comparison with custom threshold ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Kittyply_edit1.jpg/1200px-Kittyply_edit1.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/GoldenGateBridge-001.jpg/1200px-GoldenGateBridge-001.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Camponotus_flavomarginatus_ant.jpg/1200px-Camponotus_flavomarginatus_ant.jpg" ] } ``` #### Pure base64 input (no URLs) ```json { "sourceImage": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg...", "targetImages": [ "/9j/4AAQSkZJRgABAQAAAQABAAD..." ] } ``` *** *** ## πŸ“€ Output Description The actor pushes a single JSON object to the default dataset. Below is the detailed schema of every output field. *** #### sourceImage **Required** Truncated echo of your source image input (max 120 characters). Useful for identifying which source was used when reviewing results. **Type:** `string` **Example:** ``` https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg ``` *** #### totalTargets **Required** The number of target images that were compared against the source. **Type:** `integer` **Example:** ``` 3 ``` *** #### totalImagesProcessed **Required** The total number of images processed in this run (1 source + N targets). This is the number used for billing. **Type:** `integer` **Example:** ``` 4 ``` *** #### results **Required** Array of comparison results β€” one entry per target image. **Type:** `array` of `object` Each object in the array contains: ##### results\[].targetIndex **Required** Zero-based index matching the position in your `targetImages` input array. Use this to map results back to your original input list. **Type:** `integer` **Example:** `0` *** ##### results\[].targetImage **Required** Truncated echo of the target image input (max 120 characters). For base64 inputs, this will be the first 120 characters of the encoded string. **Type:** `string` **Example:** ``` https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg ``` *** ##### results\[].similarityScore **Required** Cosine similarity between the source and this target image. Ranges from `0.0` (completely different) to `1.0` (identical). This is the raw AI confidence score. **Type:** `number` **Minimum:** `0.0` **Maximum:** `1.0` **Example:** `0.943218` *** ##### results\[].isSimilar **Required** Boolean verdict: `true` if `similarityScore >= 0.85` (our internal similarity threshold), otherwise `false`. This is the quick yes/no answer β€” use `similarityScore` for more nuanced decisions. **Type:** `boolean` **Example:** `true` *** # # #### error **Optional** *(only present when the run fails)* Human-readable error message describing what went wrong. Present instead of the normal output fields when the actor encounters an unrecoverable error. **Type:** `string` **Example:** ``` HTTP error fetching image: 404 – https://upload.wikimedia.org/wikipedia/commons/nonexistent-image.jpg ``` *** *** ## πŸ“€ Output Example #### Successful run β€” 1 source vs 2 targets ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "totalTargets": 2, "totalImagesProcessed": 3, "results": [ { "targetIndex": 0, "targetImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1…", "similarityScore": 0.943218, "isSimilar": true }, { "targetIndex": 1, "targetImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorL…", "similarityScore": 0.612745, "isSimilar": false } ] } ``` #### Successful run β€” 1 source vs 1 target (exact duplicate) ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01…", "totalTargets": 1, "totalImagesProcessed": 2, "results": [ { "targetIndex": 0, "targetImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/b/bb/Kittyply_edit1.jpg/1200px-Kittyply_edit1.jpg", "similarityScore": 0.998714, "isSimilar": true } ] } ``` #### Failed run β€” bad URL ```json { "error": "HTTP error fetching image: 404 – https://upload.wikimedia.org/wikipedia/commons/nonexistent-image.jpg" } ``` # Actor input Schema ## `sourceImage` (type: `string`): The reference image to compare against. Accepts a public URL or a base64-encoded image string (with or without data-URI prefix). If left empty, a sample image will be used. ## `targetImages` (type: `array`): A list of images to compare with the source. Each item can be a public URL or a base64-encoded image string. If left empty, sample images will be used. ## Actor input object example ```json { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg" ] } ``` # Actor output Schema ## `resultsDataset` (type: `string`): The default dataset containing the comparison scores. # 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 = { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg" ] }; // Run the Actor and wait for it to finish const run = await client.actor("noisy_alchemy/image-comparator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg", ], } # Run the Actor and wait for it to finish run = client.actor("noisy_alchemy/image-comparator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg", "targetImages": [ "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg", "https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/YellowLabradorLooking_new.jpg/1200px-YellowLabradorLooking_new.jpg" ] }' | apify call noisy_alchemy/image-comparator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=noisy_alchemy/image-comparator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Image Comparator", "description": "Compare a source image against multiple targets using deep-learning model to determine visual similarity. Ideal for e-commerce matching, copyright detection, and image deduplication. Accepts both URLs and Base64 encoded images to provide highly accurate similarity scoring.", "version": "0.0", "x-build-id": "P1UxoD52HfaAHWphk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/noisy_alchemy~image-comparator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-noisy_alchemy-image-comparator", "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/noisy_alchemy~image-comparator/runs": { "post": { "operationId": "runs-sync-noisy_alchemy-image-comparator", "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/noisy_alchemy~image-comparator/run-sync": { "post": { "operationId": "run-sync-noisy_alchemy-image-comparator", "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": { "sourceImage": { "title": "Source Image", "type": "string", "description": "The reference image to compare against. Accepts a public URL or a base64-encoded image string (with or without data-URI prefix). If left empty, a sample image will be used.", "default": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg" }, "targetImages": { "title": "Target Images", "type": "array", "description": "A list of images to compare with the source. Each item can be a public URL or a base64-encoded image string. 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