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utils.py
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executable file
·212 lines (156 loc) · 5.03 KB
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import math
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as PLT
def to_cpu(tensor):
return tensor.detach().cpu()
def _sigmoid(x):
return torch.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4)
def mean_precision(eval_segm, gt_segm):
check_size(eval_segm, gt_segm)
cl, n_cl = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
mAP = [0] * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
n_ij = np.sum(curr_eval_mask)
val = n_ii / float(n_ij)
if math.isnan(val):
mAP[i] = 0.
else:
mAP[i] = val
# print(mAP)
return mAP
def both_mean_precision(eval_segm, gt_segm):
check_size(eval_segm, gt_segm)
cl, n_cl = both_extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
mAP = [0] * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
n_ij = np.sum(curr_eval_mask)
val = n_ii / float(n_ij)
if math.isnan(val):
mAP[i] = 0.
else:
mAP[i] = val
# print(mAP)
return mAP
def mean_IU(eval_segm, gt_segm):
'''
(1/n_cl) * sum_i(n_ii / (t_i + sum_j(n_ji) - n_ii))
'''
check_size(eval_segm, gt_segm)
cl, n_cl = union_classes(eval_segm, gt_segm)
_, n_cl_gt = extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
IU = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
# PLT.imshow(curr_eval_mask, cmap=PLT.cm.jet)
# PLT.show()
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
n_ij = np.sum(curr_eval_mask)
# print(n_ii, t_i, n_ij)
# print('n_ii', n_ii)
# print('t_i', t_i)
# print('n_ij', n_ij)
# print(n_ii, t_i, n_ij)
IU[i] = n_ii / (t_i + n_ij - n_ii)
# print('IU', IU)
return IU
def both_mean_IU(eval_segm, gt_segm):
'''
(1/n_cl) * sum_i(n_ii / (t_i + sum_j(n_ji) - n_ii))
'''
check_size(eval_segm, gt_segm)
cl, n_cl = both_union_classes(eval_segm, gt_segm)
_, n_cl_gt = both_extract_classes(gt_segm)
eval_mask, gt_mask = extract_both_masks(eval_segm, gt_segm, cl, n_cl)
IU = list([0]) * n_cl
for i, c in enumerate(cl):
curr_eval_mask = eval_mask[i, :, :]
curr_gt_mask = gt_mask[i, :, :]
if (np.sum(curr_eval_mask) == 0) or (np.sum(curr_gt_mask) == 0):
continue
# PLT.imshow(curr_eval_mask, cmap=PLT.cm.jet)
# PLT.show()
n_ii = np.sum(np.logical_and(curr_eval_mask, curr_gt_mask))
t_i = np.sum(curr_gt_mask)
n_ij = np.sum(curr_eval_mask)
# print(n_ii, t_i, n_ij)
# print('n_ii', n_ii)
# print('t_i', t_i)
# print('n_ij', n_ij)
# print(n_ii, t_i, n_ij)
IU[i] = n_ii / (t_i + n_ij - n_ii)
# print('IU', IU)
return IU
'''
Auxiliary functions used during evaluation.
'''
def get_pixel_area(segm):
return segm.shape[0] * segm.shape[1]
def extract_both_masks(eval_segm, gt_segm, cl, n_cl):
eval_mask = extract_masks(eval_segm, cl, n_cl)
gt_mask = extract_masks(gt_segm, cl, n_cl)
return eval_mask, gt_mask
def extract_classes(segm):
cl = np.unique(segm)
n_cl = len(cl)
return cl, n_cl
def both_extract_classes(segm):
cl = np.array([0,1,2])
n_cl = 3
return cl, n_cl
def union_classes(eval_segm, gt_segm):
eval_cl, _ = extract_classes(eval_segm)
gt_cl, _ = extract_classes(gt_segm)
cl = np.union1d(eval_cl, gt_cl)
n_cl = len(cl)
# n_cl = 9
return cl, n_cl
def both_union_classes(eval_segm, gt_segm):
eval_cl, _ = both_extract_classes(eval_segm)
gt_cl, _ = both_extract_classes(gt_segm)
cl = np.union1d(eval_cl, gt_cl)
n_cl = len(cl)
# n_cl = 9
return cl, n_cl
def extract_masks(segm, cl, n_cl):
h, w = segm_size(segm)
masks = np.zeros((n_cl, h, w))
for i, c in enumerate(cl):
masks[i, :, :] = segm == c
return masks
def segm_size(segm):
try:
height = segm.shape[0]
width = segm.shape[1]
except IndexError:
raise
return height, width
def check_size(eval_segm, gt_segm):
h_e, w_e = segm_size(eval_segm)
h_g, w_g = segm_size(gt_segm)
if (h_e != h_g) or (w_e != w_g):
raise EvalSegErr("DiffDim: Different dimensions of matrices!")
'''
Exceptions
'''
class EvalSegErr(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.value)