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Copy pathutils.py
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86 lines (74 loc) · 2.45 KB
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#Import packages
import numpy as np
import torch
import cv2
import matplotlib.pyplot as plt
from matplotlib import cm as CM
from scipy.ndimage.filters import gaussian_filter
#Useful functions
def save_checkpoint(state, filename='../checkpoint.pth.tar'):
torch.save(state, filename)
def zeropad(img, h, w, target=False):
if not target:
color = [0, 0, 0]
padded = cv2.copyMakeBorder(img, 0, h, 0, w, cv2.BORDER_CONSTANT, value=color)
else:
padded = cv2.copyMakeBorder(img, 0, h, 0, w, cv2.BORDER_CONSTANT, value=0)
return padded
def vis_input(im, target=None, pred0=None, pred1=None, pred2=None):
if im.size(0) == 4:
depth = im[3, :, :].cpu().numpy()
im = im[:3, :, :]
plt.subplot(1,1,1).imshow(depth, cmap=CM.jet)
plt.title('depth')
plt.show()
elif (pred1 is None) or (pred2 is None):
i = 2
elif pred2 is not None:
i = 3
else:
i = 1
if im.size(0) == 3:
im = im.permute(1, 2, 0).cpu()
im = cv2.normalize(np.float32(im), None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
im = im.astype(np.uint8)
else:
if im.size(0) == 1:
im = im.squeeze(0)
im = im.cpu().numpy()
plt.subplot(2,i,1).imshow(im)
target = target.cpu().numpy()
target = target.squeeze(0) if target.shape[0]==1 else target
plt.subplot(2,i,2).imshow(target, cmap=CM.jet)
count = target.sum()
plt.title('GT: ' + str(count.item()))
if pred0 is not None:
pred0 = pred0.cpu().numpy()
plt.subplot(2,i,4).imshow(pred0, cmap=CM.jet)
count = pred0.sum()
plt.title('Pred: ' + str(count.item()))
if pred1 is not None:
pred1 = pred1.cpu().numpy()
plt.subplot(2,i,5).imshow(pred1, cmap=CM.jet)
count = pred1.sum()
plt.title('Pred: ' + str(count.item()))
if pred2 is not None:
pred2 = pred2.cpu().numpy()
plt.subplot(2,i,6).imshow(pred2, cmap=CM.jet)
count = pred2.sum()
plt.title('Pred: ' + str(count.item()))
plt.show()
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count