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Parameter | Description | Possible Values / Example |
---|---|---|
total_images | Total number of synthetic images to generate. | Integer (e.g., |
cuts_n_image | Number of plant cutouts to include in each image. | min: Minimum cutouts (e.g., max: Maximum cutouts (e.g., |
reuse_cutouts | Whether the same cutouts can be reused in multiple images. |
|
morphological.area | Range of the cutout area in pixels. | min: |
morphological.blur_effect | Threshold range for cutout blur effect. | min: optional, max: optional |
morphological.eccentricity | Shape eccentricity range. | min: optional, max: optional |
morphological.extends_border | Whether to include only cutouts that extend the image border. |
|
morphological.num_components | Range for the number of components in a cutout. | min: optional, max: optional |
morphological.solidity | Threshold for solidity, indicating shape compactness. | min: |
morphological.green_sum | Range for the sum of green pixel values in the cutout. | min: optional, max: optional |
morphological.is_primary | Whether to include only primary cutouts. |
|
morphological.perimeter | Range for cutout perimeter length. | min: optional, max: optional |
category.family | Exact family name to filter by. | String (e.g., |
category.genus | Exact genus name to filter by. | String (e.g., |
category.group | Plant group to filter by. | String (e.g., |
category.duration | Plant lifecycle duration to include. | List (e.g., |
category.growth_habit | Filter based on growth habit of plants. | List (e.g., |
category.species | Exact species name to filter by. | String (e.g., |
category.subclass | Exact subclass to filter by. | String (e.g., |
category.common_name | List of plant common names to include. | Example: |
common_name_weights | Assign weights to plant common names for frequency control. | Example: |
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The SemiF-SyntheticPipeline offers participants flexibility to adjust configurations through YAML files or directly from the command line, making it easy to experiment with different setups during the hackathon. This modular design ensures you can focus on specific tasks like generating recipes, moving cutouts, or synthesizing images, while optimizing performance using parallel processing and seamless AWS and MongoDB integrations.
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