mmagic.evaluation.metrics.connectivity_error

Evaluation metrics used in Image Matting.

Module Contents

Classes

ConnectivityError

Connectivity error for evaluating alpha matte prediction.

class mmagic.evaluation.metrics.connectivity_error.ConnectivityError(step=0.1, norm_constant=1000, **kwargs)

Bases: mmagic.evaluation.metrics.base_sample_wise_metric.BaseSampleWiseMetric

Connectivity error for evaluating alpha matte prediction.

Note

Current implementation assume image / alpha / trimap array in numpy format and with pixel value ranging from 0 to 255.

Note

pred_alpha should be masked by trimap before passing into this metric

Parameters

• step (float) – Step of threshold when computing intersection between alpha and pred_alpha. Default to 0.1 .

• norm_const (int) – Divide the result to reduce its magnitude. Default to 1000.

Default prefix: ‘’

Metrics:

• ConnectivityError (float): Connectivity Error

metric = ConnectivityError

prepare(module: torch.nn.Module, dataloader: torch.utils.data.dataloader.DataLoader)

process(data_batch: Sequence[dict], data_samples: Sequence[dict]) → None

Process one batch of data samples and predictions. The processed results should be stored in self.results, which will be used to compute the metrics when all batches have been processed.

Parameters

• data_batch (Sequence[dict]) – A batch of data from the dataloader.

• predictions (Sequence[dict]) – A batch of outputs from the model.

compute_metrics(results: List)

Compute the metrics from processed results.

Parameters

results (dict) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of the metrics, and the values are corresponding results.

Return type

Dict