#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
try:
import cStringIO as StringIO
except:
from io import StringIO
import hashlib
import json
import math
import os
import re
import shlex
import subprocess
import sys
import tarfile
import tempfile
import zipfile
import six
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import chainer
from chainer import cuda
from chainer.training import extensions
# -----------------------------------------------------------------------------
# CV Util
# -----------------------------------------------------------------------------
def resize_img_with_max_size(img, max_size=500*500):
"""Resize image with max size (height x width)"""
from skimage.transform import rescale
height, width = img.shape[:2]
scale = max_size / (height * width)
resizing_scale = 1
if scale < 1:
resizing_scale = np.sqrt(scale)
img = rescale(img, resizing_scale, preserve_range=True)
img = img.astype(np.uint8)
return img, resizing_scale
# -----------------------------------------------------------------------------
# Chainer Util
# -----------------------------------------------------------------------------
def copy_chainermodel(src, dst):
from chainer import link
assert isinstance(src, link.Chain)
assert isinstance(dst, link.Chain)
print('Copying layers %s -> %s:' %
(src.__class__.__name__, dst.__class__.__name__))
for child in src.children():
if child.name not in dst.__dict__:
continue
dst_child = dst[child.name]
if type(child) != type(dst_child):
continue
if isinstance(child, link.Chain):
copy_chainermodel(child, dst_child)
if isinstance(child, link.Link):
match = True
for a, b in zip(child.namedparams(), dst_child.namedparams()):
if a[0] != b[0]:
match = False
break
if a[1].data.shape != b[1].data.shape:
match = False
break
if not match:
print('Ignore %s because of parameter mismatch.' % child.name)
continue
for a, b in zip(child.namedparams(), dst_child.namedparams()):
b[1].data = a[1].data
print(' layer: %s -> %s' % (child.name, dst_child.name))
# -----------------------------------------------------------------------------
# Data Util
# -----------------------------------------------------------------------------
def download(url, path, quiet=False):
def is_google_drive_url(url):
m = re.match('^https?://drive.google.com/uc\?id=.*$', url)
return m is not None
if is_google_drive_url(url):
client = 'gdown'
else:
client = 'wget'
cmd = '{client} {url} -O {path}'.format(client=client, url=url, path=path)
if quiet:
cmd += ' --quiet'
subprocess.call(shlex.split(cmd))
return path
def cached_download(url, path, md5=None, quiet=False):
def check_md5(path, md5, quiet=False):
if not quiet:
print('Checking md5 of file: {}'.format(path))
is_same = hashlib.md5(open(path, 'rb').read()).hexdigest() == md5
return is_same
if os.path.exists(path) and not md5:
return path
elif os.path.exists(path) and md5 and check_md5(path, md5):
return path
else:
return download(url, path, quiet=quiet)
def extract_file(path, to_directory='.'):
if path.endswith('.zip'):
opener, mode = zipfile.ZipFile, 'r'
elif path.endswith('.tar'):
opener, mode = tarfile.open, 'r'
elif path.endswith('.tar.gz') or path.endswith('.tgz'):
opener, mode = tarfile.open, 'r:gz'
elif path.endswith('.tar.bz2') or path.endswith('.tbz'):
opener, mode = tarfile.open, 'r:bz2'
else:
raise ValueError("Could not extract '%s' as no appropriate "
"extractor is found" % path)
cwd = os.getcwd()
os.chdir(to_directory)
try:
file = opener(path, mode)
try:
file.extractall()
finally:
file.close()
finally:
os.chdir(cwd)
# -----------------------------------------------------------------------------
# Color Util
# -----------------------------------------------------------------------------
def bitget(byteval, idx):
return ((byteval & (1 << idx)) != 0)
def labelcolormap(N=256):
cmap = np.zeros((N, 3))
for i in range(0, N):
id = i
r, g, b = 0, 0, 0
for j in range(0, 8):
r = np.bitwise_or(r, (bitget(id, 0) << 7-j))
g = np.bitwise_or(g, (bitget(id, 1) << 7-j))
b = np.bitwise_or(b, (bitget(id, 2) << 7-j))
id = (id >> 3)
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
cmap = cmap.astype(np.float32) / 255
return cmap
# -----------------------------------------------------------------------------
# Evaluation
# -----------------------------------------------------------------------------
def _fast_hist(label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
hist = np.bincount(
n_class * label_true[mask].astype(int) +
label_pred[mask], minlength=n_class**2).reshape(n_class, n_class)
return hist
def label_accuracy_score(label_true, label_pred, n_class):
"""Returns accuracy score evaluation result.
- overall accuracy
- mean accuracy
- mean IU
- fwavacc
"""
hist = _fast_hist(label_true.flatten(), label_pred.flatten(), n_class)
acc = np.diag(hist).sum() / hist.sum()
acc_cls = np.diag(hist) / hist.sum(axis=1)
acc_cls = np.nanmean(acc_cls)
iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
mean_iu = np.nanmean(iu)
freq = hist.sum(axis=1) / hist.sum()
fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
return acc, acc_cls, mean_iu, fwavacc
# -----------------------------------------------------------------------------
# Visualization
# -----------------------------------------------------------------------------
def draw_label(label, img, n_class, label_titles, bg_label=0):
"""Convert label to rgb with label titles.
@param label_title: label title for each labels.
@type label_title: dict
"""
from PIL import Image
from scipy.misc import fromimage
from skimage.color import label2rgb
from skimage.transform import resize
colors = labelcolormap(n_class)
label_viz = label2rgb(label, img, colors=colors[1:], bg_label=bg_label)
# label 0 color: (0, 0, 0, 0) -> (0, 0, 0, 255)
label_viz[label == 0] = 0
# plot label titles on image using matplotlib
plt.subplots_adjust(left=0, right=1, top=1, bottom=0,
wspace=0, hspace=0)
plt.margins(0, 0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.axis('off')
# plot image
plt.imshow(label_viz)
# plot legend
plt_handlers = []
plt_titles = []
for label_value in np.unique(label):
if label_value not in label_titles:
continue
fc = colors[label_value]
p = plt.Rectangle((0, 0), 1, 1, fc=fc)
plt_handlers.append(p)
plt_titles.append(label_titles[label_value])
plt.legend(plt_handlers, plt_titles, loc='lower right', framealpha=0.5)
# convert plotted figure to np.ndarray
f = StringIO.StringIO()
plt.savefig(f, bbox_inches='tight', pad_inches=0)
result_img_pil = Image.open(f)
result_img = fromimage(result_img_pil, mode='RGB')
result_img = resize(result_img, img.shape, preserve_range=True)
result_img = result_img.astype(img.dtype)
return result_img
def centerize(src, dst_shape, margin_color=None):
"""Centerize image for specified image size
@param src: image to centerize
@param dst_shape: image shape (height, width) or (height, width, channel)
"""
if src.shape[:2] == dst_shape[:2]:
return src
centerized = np.zeros(dst_shape, dtype=src.dtype)
if margin_color:
centerized[:, :] = margin_color
pad_vertical, pad_horizontal = 0, 0
h, w = src.shape[:2]
dst_h, dst_w = dst_shape[:2]
if h < dst_h:
pad_vertical = (dst_h - h) // 2
if w < dst_w:
pad_horizontal = (dst_w - w) // 2
centerized[pad_vertical:pad_vertical+h,
pad_horizontal:pad_horizontal+w] = src
return centerized
def _tile_images(imgs, tile_shape, concatenated_image):
"""Concatenate images whose sizes are same.
@param imgs: image list which should be concatenated
@param tile_shape: shape for which images should be concatenated
@param concatenated_image: returned image.
if it is None, new image will be created.
"""
y_num, x_num = tile_shape
one_width = imgs[0].shape[1]
one_height = imgs[0].shape[0]
if concatenated_image is None:
if len(imgs[0].shape) == 3:
concatenated_image = np.zeros(
(one_height * y_num, one_width * x_num, 3), dtype=np.uint8)
else:
concatenated_image = np.zeros(
(one_height * y_num, one_width * x_num), dtype=np.uint8)
for y in range(y_num):
for x in range(x_num):
i = x + y * x_num
if i >= len(imgs):
pass
else:
concatenated_image[y*one_height:(y+1)*one_height,
x*one_width:(x+1)*one_width, ] = imgs[i]
return concatenated_image
def get_tile_image(imgs, tile_shape=None, result_img=None, margin_color=None):
"""Concatenate images whose sizes are different.
@param imgs: image list which should be concatenated
@param tile_shape: shape for which images should be concatenated
@param result_img: numpy array to put result image
"""
from skimage.transform import resize
def get_tile_shape(img_num):
x_num = 0
y_num = int(math.sqrt(img_num))
while x_num * y_num < img_num:
x_num += 1
return x_num, y_num
if tile_shape is None:
tile_shape = get_tile_shape(len(imgs))
# get max tile size to which each image should be resized
max_height, max_width = np.inf, np.inf
for img in imgs:
max_height = min([max_height, img.shape[0]])
max_width = min([max_width, img.shape[1]])
# resize and concatenate images
for i, img in enumerate(imgs):
h, w = img.shape[:2]
dtype = img.dtype
h_scale, w_scale = max_height / h, max_width / w
scale = min([h_scale, w_scale])
h, w = int(scale * h), int(scale * w)
img = resize(img, (h, w), preserve_range=True).astype(dtype)
if len(img.shape) == 3:
img = centerize(img, (max_height, max_width, 3), margin_color)
else:
img = centerize(img, (max_height, max_width), margin_color)
imgs[i] = img
return _tile_images(imgs, tile_shape, result_img)
