| import cv2
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| import os
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| import numpy as np
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|
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| from collections import OrderedDict
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| from PIL import Image as PILImage
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| from utils.transforms import transform_parsing
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|
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| LABELS = ['Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'Upper-clothes', 'Dress', 'Coat', \
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| 'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm', 'Left-leg',
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| 'Right-leg', 'Left-shoe', 'Right-shoe']
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|
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|
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| def get_palette(num_cls):
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| """ Returns the color map for visualizing the segmentation mask.
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| Args:
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| num_cls: Number of classes
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| Returns:
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| The color map
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| """
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|
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| n = num_cls
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| palette = [0] * (n * 3)
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| for j in range(0, n):
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| lab = j
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| palette[j * 3 + 0] = 0
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| palette[j * 3 + 1] = 0
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| palette[j * 3 + 2] = 0
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| i = 0
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| while lab:
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| palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
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| palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
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| palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
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| i += 1
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| lab >>= 3
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| return palette
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|
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|
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| def get_confusion_matrix(gt_label, pred_label, num_classes):
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| """
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| Calcute the confusion matrix by given label and pred
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| :param gt_label: the ground truth label
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| :param pred_label: the pred label
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| :param num_classes: the nunber of class
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| :return: the confusion matrix
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| """
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| index = (gt_label * num_classes + pred_label).astype('int32')
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| label_count = np.bincount(index)
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| confusion_matrix = np.zeros((num_classes, num_classes))
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|
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| for i_label in range(num_classes):
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| for i_pred_label in range(num_classes):
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| cur_index = i_label * num_classes + i_pred_label
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| if cur_index < len(label_count):
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| confusion_matrix[i_label, i_pred_label] = label_count[cur_index]
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|
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| return confusion_matrix
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|
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|
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| def compute_mean_ioU(preds, scales, centers, num_classes, datadir, input_size=[473, 473], dataset='val'):
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| val_file = os.path.join(datadir, dataset + '_id.txt')
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| val_id = [i_id.strip() for i_id in open(val_file)]
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|
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| confusion_matrix = np.zeros((num_classes, num_classes))
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|
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| for i, pred_out in enumerate(preds):
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| im_name = val_id[i]
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| gt_path = os.path.join(datadir, dataset + '_segmentations', im_name + '.png')
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| gt = np.array(PILImage.open(gt_path))
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| h, w = gt.shape
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| s = scales[i]
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| c = centers[i]
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| pred = transform_parsing(pred_out, c, s, w, h, input_size)
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|
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| gt = np.asarray(gt, dtype=np.int32)
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| pred = np.asarray(pred, dtype=np.int32)
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|
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| ignore_index = gt != 255
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|
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| gt = gt[ignore_index]
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| pred = pred[ignore_index]
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|
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| confusion_matrix += get_confusion_matrix(gt, pred, num_classes)
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|
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| pos = confusion_matrix.sum(1)
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| res = confusion_matrix.sum(0)
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| tp = np.diag(confusion_matrix)
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|
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| pixel_accuracy = (tp.sum() / pos.sum()) * 100
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| mean_accuracy = ((tp / np.maximum(1.0, pos)).mean()) * 100
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| IoU_array = (tp / np.maximum(1.0, pos + res - tp))
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| IoU_array = IoU_array * 100
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| mean_IoU = IoU_array.mean()
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| print('Pixel accuracy: %f \n' % pixel_accuracy)
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| print('Mean accuracy: %f \n' % mean_accuracy)
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| print('Mean IU: %f \n' % mean_IoU)
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| name_value = []
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|
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| for i, (label, iou) in enumerate(zip(LABELS, IoU_array)):
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| name_value.append((label, iou))
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|
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| name_value.append(('Pixel accuracy', pixel_accuracy))
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| name_value.append(('Mean accuracy', mean_accuracy))
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| name_value.append(('Mean IU', mean_IoU))
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| name_value = OrderedDict(name_value)
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| return name_value
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|
|
|
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| def compute_mean_ioU_file(preds_dir, num_classes, datadir, dataset='val'):
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| list_path = os.path.join(datadir, dataset + '_id.txt')
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| val_id = [i_id.strip() for i_id in open(list_path)]
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|
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| confusion_matrix = np.zeros((num_classes, num_classes))
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|
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| for i, im_name in enumerate(val_id):
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| gt_path = os.path.join(datadir, 'segmentations', im_name + '.png')
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| gt = cv2.imread(gt_path, cv2.IMREAD_GRAYSCALE)
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|
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| pred_path = os.path.join(preds_dir, im_name + '.png')
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| pred = np.asarray(PILImage.open(pred_path))
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|
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| gt = np.asarray(gt, dtype=np.int32)
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| pred = np.asarray(pred, dtype=np.int32)
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|
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| ignore_index = gt != 255
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|
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| gt = gt[ignore_index]
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| pred = pred[ignore_index]
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|
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| confusion_matrix += get_confusion_matrix(gt, pred, num_classes)
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|
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| pos = confusion_matrix.sum(1)
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| res = confusion_matrix.sum(0)
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| tp = np.diag(confusion_matrix)
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|
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| pixel_accuracy = (tp.sum() / pos.sum()) * 100
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| mean_accuracy = ((tp / np.maximum(1.0, pos)).mean()) * 100
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| IoU_array = (tp / np.maximum(1.0, pos + res - tp))
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| IoU_array = IoU_array * 100
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| mean_IoU = IoU_array.mean()
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| print('Pixel accuracy: %f \n' % pixel_accuracy)
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| print('Mean accuracy: %f \n' % mean_accuracy)
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| print('Mean IU: %f \n' % mean_IoU)
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| name_value = []
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|
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| for i, (label, iou) in enumerate(zip(LABELS, IoU_array)):
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| name_value.append((label, iou))
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|
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| name_value.append(('Pixel accuracy', pixel_accuracy))
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| name_value.append(('Mean accuracy', mean_accuracy))
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| name_value.append(('Mean IU', mean_IoU))
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| name_value = OrderedDict(name_value)
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| return name_value
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|
|
