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