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GeoCap

geometry caption & geography fossil caption

Install

Requirements are provided in deploy/requirements.txt. It's recommended to use python 3.10.

Running a module

A run.py is provided for running a module. This is an elegant workaround for importing errors from different packages. Examples:

python run.py --module data.rule.generate --num_basic_geo_samples 10  # default entry is main()
python run.py --module data.format --action to_llava  # you can also specify the entry function (--action)

Generating rules for geometric shapes / synthetic fossil samples

Running the following command can generate rules for geometric shapes in dataset/rules.json:

python run.py --module data.rule.generate --stage 1 --num_basic_geo_samples 10

or generate rules for synthetic fossil samples:

python run.py --module data.rule.generate --stage 2 --num_fossil_samples 10

Each data sample contains two parts:

  • shapes: parameters and special information of each geometric shape.
  • relations: relationship between two shapes in form of [head_shape_idx, tail_shape_idx, relation_type]

Example data sample

{
  "shapes": [
    {
      "type": "line"
      //...
    },
    {
      "type": "ellipse"
      //...
    }
  ],
  "relations": [[0, 1, "tangent line"]]
}

You can control the generation process with the following arguments:

  • max_num_shapes: the maximum number of shapes in each sample. Default is 10 and there are arguments for controling the proportion of different shapes and relations, for example:
  • polygon_shape_level: the proportion of polygon in all shapes
  • line_shape_level: the proportion of line in all shapes
  • ...
  • polygon_tangent_line_level: the proportion of generating a tangent line in all polygon relations
  • polygon_shared_edge_level: the proportion of generating a new polygon that have a shared edge with a given polygon
  • ellipse_concentric_level: the proportion of generating a set of ellipses that is concentric with a given ellipse
  • ...

Each 'level' argument is an integer (with a default value) representing the relative level within its shape/relation block. For more details, please refer to RuleArgs in common/args.py. All 'level' arguments will be transformed into probabilities using L1 normalization (sum normalization).

If more ellipse is expected, you can set a higher level for ellipse_shape_level:

python run.py --module data.rule.generate --polygon_shape_level 1 --line_shape_level 1 --ellipse_shape_level 3 --spiral_shape_level 1

Running Module 'draw'

Two python files, pil_backend.py and plt_backend.py is provided, in which the former one is written in pillow, providing continuous change of shape, and a relatively less noisy image; the latter, in comparison, provides hand-drawing line style and more natural noise. plt_backend.py is recommended to use and draw.py will automatically use this version. You can change the preferred version by setting argument backend to plt or pil.

To use plt_backend.py, the following arguments are expected:

  • rules: "list[dict[str, Any]]". Mandatory. The rules you would like to draw.
  • random_seed: int|None. The default value is None. Control the random seed.
  • randomize: bool. The default value is True. Enable the noise-applying procedure.
  • size: "tuple[float, float]". The deault value is (6.4, 6.4).
  • dpi: int. The default value is 100. dpi * size = resolution.
  • line_weight: int. The default value is 4. Control the line weight. If randomize is enabled, the line weight will be randomly chosen in a certain range near the value.
  • xkcd: bool. The default value is False. Enable the hand-drawing line style.
  • color:None|tuple[int,int,int]. The default value is None. If a color in RGB form is provided, that rule will be drawn in the given color. The the value is None, that rule will be drawn in random colors.
  • n_white_line:None|int. The default value is None. If an integer is given, the white lines will be drawn in that certain amount. Otherwise, the value is randomly chosen.
  • Gaussian_mean: float. The default value is 0. Control the mean value of the Gaussian noise. The higher the value is, the grayer the image will be.
  • Gaussian_var: float. The default value is 10. Control the variance of the Gaussian Noise. The higher the value is, the stronger the Gaussian Noise will be.
  • Perlin_lattice: int. The default value is 20. Control the number of lattices while generating Perlin noise. The value is not recommended to change and may cause the crash the the module.
  • Perlin_power: float. The default value is 16. Control the power of the Perlin noise, will affect the contrast ratio of the noise and the image.
  • Perlin_bias: float. The default value is -16. Control the bias of the Perlin noise. The lower it is, the brighter the image will be.
  • stylish: bool. The default value is False. Setting to true will sharpen the image.

To simply generate a picture with default settings, use the following command:

python run.py --module data.draw.draw --backend plt

Running caption

python run.py --module data.caption.caption [ --caption_batchsize ${BatchSize} ] [ --caption_llm ${LLM ID} ] [ --numeric_ratio ${ratio} ]

Only part of the shapes will add numeric values, controlled by ${ratio}.

Generating VQA questions

python run.py --module data.vqa.question --numeric_ratio 1

The questions will be generated (by default) in data/vqa.

Evaluating VQA questions

python run.py --module eval.evaluate --eval_model {model_name}_{model_size} --eval_batchsize {batchsize}

The evaluation results will be saved in eval/results/{model_name}_{model_size}.

Implementation detail

Rule

generate random rules with conditions.

write how you generate rules here...

Draw

Draw shapes according to the rule.

In the default backend (which means plt_backend.py), the python script will receive the rules.json in path dataset/rules.json and handle each rule by turn. After handling all the rules, a basic image will be generated. Then, the script will add noise to the image and generate a final image. The noise here contains Gaussian noise and Perlin noise.

To run the script, use the following command:

python run.py --module data.draw.draw --backend plt --stage 1

More arguments are provided in DrawArgs in common/args.py. And you may also read readme file in the root directory for more details.

Currently, the Stable Diffusion part is not merged into the project. The script diffusion_backend_new.py is not available.

Warning: With noise enabled, the script will encounter performance issues if there are too many ellipses standing by for drawing. Please consider reducing the number of ellipses in the rule / wait patiently :).

Caption

generate image captions according to the rule.

write how you convert rules to intermediate format and generate captions...

Contributing

Fork and open a pull request. Follow the instructions below or your PR will fail.

  1. Use Pylance (basic level) to lint your code while doing your work. Refer to https://docs.pydantic.dev/latest/integrations/visual_studio_code/#configure-vs-code to configure your VSCode. NOTE: Be cautious of using # type: ignore to suppress type errors, as you may be ignoring valuable traces of bugs; usually typing.cast() is more preferred.

  2. Use black to format your code before opening a PR:

    pip install black
black . --line-length 120 --extend-exclude llava

Note: If you want to add external modules which will not pass the linter, you can add them to pyrightconfig.json and .github/workflows/lint_format.yaml.