Source code for mmagic.evaluation.metrics.connectivity_error

# Copyright (c) OpenMMLab. All rights reserved.
"""Evaluation metrics used in Image Matting."""

from typing import List, Sequence

import cv2
import numpy as np
import torch.nn as nn
from mmengine.model import is_model_wrapper
from import DataLoader

from mmagic.registry import METRICS
from .base_sample_wise_metric import BaseSampleWiseMetric
from .metrics_utils import _fetch_data_and_check, average

[docs]class ConnectivityError(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 Args: 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 """
[docs] metric = 'ConnectivityError'
def __init__( self, step=0.1, norm_constant=1000, **kwargs, ) -> None: self.step = step self.norm_constant = norm_constant super().__init__(**kwargs)
[docs] def prepare(self, module: nn.Module, dataloader: DataLoader): self.size = len(dataloader.dataset) if is_model_wrapper(module): module = module.module self.data_preprocessor = module.data_preprocessor
[docs] def process(self, 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. Args: data_batch (Sequence[dict]): A batch of data from the dataloader. predictions (Sequence[dict]): A batch of outputs from the model. """ for data_sample in data_samples: pred_alpha, gt_alpha, trimap = _fetch_data_and_check(data_sample) thresh_steps = np.arange(0, 1 + self.step, self.step) round_down_map = -np.ones_like(gt_alpha) for i in range(1, len(thresh_steps)): gt_alpha_thresh = gt_alpha >= thresh_steps[i] pred_alpha_thresh = pred_alpha >= thresh_steps[i] intersection = gt_alpha_thresh & pred_alpha_thresh intersection = intersection.astype(np.uint8) # connected components _, output, stats, _ = cv2.connectedComponentsWithStats( intersection, connectivity=4) # start from 1 in dim 0 to exclude background size = stats[1:, -1] # largest connected component of the intersection omega = np.zeros_like(gt_alpha) if len(size) != 0: max_id = np.argmax(size) # plus one to include background omega[output == max_id + 1] = 1 mask = (round_down_map == -1) & (omega == 0) round_down_map[mask] = thresh_steps[i - 1] round_down_map[round_down_map == -1] = 1 gt_alpha_diff = gt_alpha - round_down_map pred_alpha_diff = pred_alpha - round_down_map # only calculate difference larger than or equal to 0.15 gt_alpha_phi = 1 - gt_alpha_diff * (gt_alpha_diff >= 0.15) pred_alpha_phi = 1 - pred_alpha_diff * (pred_alpha_diff >= 0.15) connectivity_error = np.sum( np.abs(gt_alpha_phi - pred_alpha_phi) * (trimap == 128)) # divide by 1000 to reduce the magnitude of the result connectivity_error /= self.norm_constant self.results.append({'conn_err': connectivity_error})
[docs] def compute_metrics(self, results: List): """Compute the metrics from processed results. Args: results (dict): The processed results of each batch. Returns: Dict: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. """ conn_err = average(results, 'conn_err') return {'ConnectivityError': conn_err}
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