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utils.py
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utils.py
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import torch
import cv2
import sys
import time
import pdb
import numpy as np
import os
from skimage import color, segmentation, color
from skimage.segmentation import slic
from skimage.future import graph
from skimage.measure import regionprops
from skimage.filters import gaussian
# Paths Init
IMG_ROOT = './image'
MASK_ROOT = './mask'
# Parameters Init
_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)
TOTAL_BAR_LENGTH = 65.
last_time = time.time()
begin_time = last_time
def get_loss(pred, target):
if type(label) == type(tuple):
shape_loss = F.cross_entropy(pred[0], target)
color1_loss = F.cross_entropy(pred[1], target)
color2_loss = F.cross_entropy(pred[2], target)
return shape_loss, color1_loss, color2_loss
else:
return F.cross_entropy(pred, target)
def pill_mask(img_root, mask_root):
''' Mask Pill Mask
-Reference Paper-
http://www.scitepress.org/Papers/2017/61358/61358.pdf
'''
img_list = os.listdir(img_root)
img_list = sorted(img_list, key=lambda x: int(os.path.splitext(x)[0]))
for i, img_name in enumerate(img_list):
print(i)
img = cv2.imread(os.path.join(img_root, img_name))
shear = int(img.shape[0] * 0.12)
pad_img = np.zeros((shear, img.shape[1]))
img = img[:(img.shape[0]-shear), :, :]
# Gaussian Smoothing Filter
img = (gaussian(img, sigma=2, multichannel=True)*255).astype(np.uint8)
# SLIC (Simple Linear Iterative Clustering)
labels = slic(img, n_segments=150, compactness=12, max_iter=10)
labels = labels + 1
regions = regionprops(labels)
# Create RAG(Region Adjacency Graph)
rag = graph.rag_mean_color(img, labels)
for region in regions:
rag.node[region['label']]['centroid'] = region['centroid']
# Post-processing
labels = graph.cut_threshold(labels, rag, 29)
background = np.argmax(np.bincount(labels.flatten()))
labels[labels!=background] = 255
labels[labels==background] = 0
labels = np.concatenate((labels, pad_img),axis=0)
cv2.imwrite(os.path.join(mask_root, img_name), labels)
def progress_bar(current, total, msg=None):
''' Source Code from 'kuangliu/pytorch-cifar'
(https://github.com/kuangliu/pytorch-cifar/blob/master/utils.py)
'''
global last_time, begin_time
if current == 0:
begin_time = time.time() # Reset for new bar.
cur_len = int(TOTAL_BAR_LENGTH*current/total)
rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
sys.stdout.write(' [')
for i in range(cur_len):
sys.stdout.write('=')
sys.stdout.write('>')
for i in range(rest_len):
sys.stdout.write('.')
sys.stdout.write(']')
cur_time = time.time()
step_time = cur_time - last_time
last_time = cur_time
tot_time = cur_time - begin_time
L = []
L.append(' Step: %s' % format_time(step_time))
L.append(' | Tot: %s' % format_time(tot_time))
if msg:
L.append(' | ' + msg)
msg = ''.join(L)
sys.stdout.write(msg)
for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
sys.stdout.write(' ')
# Go back to the center of the bar.
for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
sys.stdout.write('\b')
sys.stdout.write(' %d/%d ' % (current+1, total))
if current < total-1:
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
sys.stdout.flush()
def format_time(seconds):
''' Source Code from 'kuangliu/pytorch-cifar'
(https://github.com/kuangliu/pytorch-cifar/blob/master/utils.py)
'''
days = int(seconds / 3600/24)
seconds = seconds - days*3600*24
hours = int(seconds / 3600)
seconds = seconds - hours*3600
minutes = int(seconds / 60)
seconds = seconds - minutes*60
secondsf = int(seconds)
seconds = seconds - secondsf
millis = int(seconds*1000)
f = ''
i = 1
if days > 0:
f += str(days) + 'D'
i += 1
if hours > 0 and i <= 2:
f += str(hours) + 'h'
i += 1
if minutes > 0 and i <= 2:
f += str(minutes) + 'm'
i += 1
if secondsf > 0 and i <= 2:
f += str(secondsf) + 's'
i += 1
if millis > 0 and i <= 2:
f += str(millis) + 'ms'
i += 1
if f == '':
f = '0ms'
return f
class Checkpoint:
def __init__(self, model, optimizer=None, epoch=0, best_loss=9999):
self.model = model
self.optimizer = optimizer
self.epoch = epoch
self.best_loss = best_loss
def load(self, path):
checkpoint = torch.load(path)
self.model.load_state_dict(checkpoint["model_state"])
self.epoch = checkpoint["epoch"]
self.best_loss = checkpoint["best_loss"]
if self.optimizer:
self.optimizer.load_state_dict(checkpoint["optimizer_state"])
def save(self, path):
state_dict = self.model.module.state_dict()
torch.save({"model_state": state_dict,
"optimizer_state": self.optimizer.state_dict(),
"epoch": self.epoch,
"best_loss": self.best_loss}, path)