# Importing Brush Traces You may import PNG masks as pre-labels for the [Brush labeling tool](/labeling/labeling-tools/tools/brush-bucket.md) in Ango Hub. ## How to Import Masks as Brush Annotations #### 1. Verify Brush Tool Classes Navigate to **Settings > Category Schema** in your project to ensure that the necessary Brush tool classes are available in the project. #### 2. Assign Colors * For **instance segmentation** tasks, randomly assign a unique RGB color value to each instance in your mask. * For **semantic segmentation** tasks, randomly assign a unique RGB color value to each class in your mask. #### 3. Prepare Masks Prepare the masks you intend to import. Ensure the masks meet the following criteria: * The imported mask must be a 4-channel RGBA image * The width and height of the mask image must match the image asset's dimensions * The **background** value of the mask must be solid white and transparent * **Example:** `(RGBA: 255, 255, 255, 0)` * Use randomly assigned unique colors in the previous step, with fully opaque (255) alpha channel. Make sure that the mask’s **foreground** brush content must match the exact color values of the "brush" field in the JSON file. * **Example:** * Mask Image: `(RGBA: 244, 67, 54, 255)`, * JSON File: `"brush": [244, 67, 54]` The following would be a valid PNG mask: {% file src="/files/w4ezv6dr5oBWDYz27rz9" %} You can use the following Python script to convert an RGB mask with a solid black background into the required RGBA format: ```python import numpy as np from skimage import io def rgb_mask_to_rgba_mask(input_mask_path, output_mask_path): input_mask = io.imread(input_mask_path) output_mask = np.zeros((input_mask.shape[0], input_mask.shape[1], 4), dtype=np.uint8) mask_flat = input_mask.reshape(-1, 3) unique_colors = np.unique(mask_flat, axis=0) for color in unique_colors: if np.array_equal(color, [0, 0, 0]): indices = np.where(np.all(input_mask == color, axis=-1)) output_mask[indices] = np.array([255, 255, 255, 0], dtype=np.uint8) else: indices = np.where(np.all(input_mask == color, axis=-1)) output_mask[indices] = np.array(list(color) + [255], dtype=np.uint8) io.imsave(output_mask_path, output_mask) ``` #### 4. Prepare JSON File Prepare the JSON to import to Hub, in the following format: Where `externalId` is the external ID of the asset to pre-annotate, `brushDataUrl` is a link to your publicly accessible PNG mask, and `tools` is the list of instances available in the mask with randomly assigned unique RGB color information (`"brush"`) and the `"schemaId"` of each class. #### For Brush Traces with Publicly Accessible URLs ```json [ { "externalId": "0a6e52c9-1eda-426f-8bcb-e6b1cd45668c.png", "brushDataUrl": "https://domain.s3.eu-central-1.amazonaws.com/brush-import-samples/0a6e52c9-1eda-426f-8bcb-e6b1cd45668c.png", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] }, { "externalId": "0e9ee946-ebaa-4847-99ee-ce7386795333.png", "brushDataUrl": "https://domain.s3.eu-central-1.amazonaws.com/brush-import-samples/0e9ee946-ebaa-4847-99ee-ce7386795333.png", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] }, { "externalId": "0eaf2333-5533-4353-ac3d-55774b6adb47.png", "brushDataUrl": "https://domain.s3.eu-central-1.amazonaws.com/brush-import-samples/0eaf2333-5533-4353-ac3d-55774b6adb47.png", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] }, { "externalId": "0ebf0551-6c2d-4b6e-9a46-d5e1a5f22e72.png", "brushDataUrl": "https://domain.s3.eu-central-1.amazonaws.com/brush-import-samples/0ebf0551-6c2d-4b6e-9a46-d5e1a5f22e72.png", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] } ] ``` {% hint style="warning" %} If your masks are stored in a cloud storage service, ensure the bucket's CORS settings are set properly, or your masks will not be visible on Hub. [Here is a guide](/data/storages/set-up-cors.md) on how to set up CORS in such a way that your masks will be visible. {% endhint %} #### For Brush Traces in Private Buckets * Integrate your private bucket with Ango Hub by following the instructions on the [Storages](/data/storages.md) docs page. * Navigate to your organization's *Organization* page, then navigate to the *Storages* tab. * Copy the ID of the storage where your brush traces are stored. * Prepare a JSON with the following format: ```json [ { "externalId": "0a6e52c9-1eda-426f-8bcb-e6b1cd45668c.png", "brushDataUrl": "https://url.com/e6b1cd45668c.png?storageId=", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] }, { "externalId": "0e9ee946-ebaa-4847-99ee-ce7386795333.png", "brushDataUrl": "https://url.com/ce7386795333.png?storageId=", "tools": [{"brush": [244, 67, 54], "schemaId": "29fde0dbe3eca1115f18368"}] } ] ``` #### 5. Import Annotations In your project, navigate to the *Assets* tab and ensure that the assets you wish to prelabel are in the *Start* stage, then click on *Import*. Drag and drop the JSON file you have just prepared. Your assets will be pre-labeled. To programmatically import brush traces, you can use the [import\_labels](/sdk/sdk-documentation/project-level-sdk-functions/import_labels.md) function available in our SDK. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.imerit.net/data/importing-and-exporting-annotations/importing-annotations/importing-brush-traces.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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