data_juicer.ops.filter.image_watermark_filter module

class data_juicer.ops.filter.image_watermark_filter.ImageWatermarkFilter(hf_watermark_model: str = 'amrul-hzz/watermark_detector', trust_remote_code: bool = False, prob_threshold: float = 0.8, any_or_all: str = 'any', *args, **kwargs)

Bases: Filter

Filter to keep samples whose images have no watermark with high probability.

__init__(hf_watermark_model: str = 'amrul-hzz/watermark_detector', trust_remote_code: bool = False, prob_threshold: float = 0.8, any_or_all: str = 'any', *args, **kwargs)

Initialization method.

Parameters:

• hf_watermark_model – watermark detection model name on huggingface.

• prob_threshold – the predicted watermark probability threshold for samples. range from 0 to 1. Samples with watermark probability less than this threshold will be kept.

• any_or_all – keep this sample with ‘any’ or ‘all’ strategy of all images. ‘any’: keep this sample if any images meet the condition. ‘all’: keep this sample only if all images meet the condition.

• args – extra args

• kwargs – extra args

compute_stats_single(sample, rank=None, context=False)

Compute stats for the sample which is used as a metric to decide whether to filter this sample.

Parameters:

• sample – input sample.

• context – whether to store context information of intermediate vars in the sample temporarily.

Returns:

sample with computed stats

process_single(sample, rank=None)

For sample level, sample –> Boolean.

Parameters:

sample – sample to decide whether to filter

Returns:

true for keeping and false for filtering