def main():
import argparse
parser = argparse.ArgumentParser(
description="""Denoise measured field strength (H) curves.""",
formatter_class=argparse.RawDescriptionHelpFormatter)
# -------------------------------------------------
# ungrouped meta options
parser.add_argument('-v',
'--version',
action='version',
version=('%(prog)s ' + __version__))
parser.add_argument(
'-l',
'--list',
dest='listfiles',
action='store_true',
help='List available stress levels (measurement data files) and exit.')
parser.set_defaults(listfiles=False)
# -------------------------------------------------
# data options
group_data = parser.add_argument_group('data', 'Data file options.')
group_data.add_argument(
'-s',
'--sigma',
dest='sigma',
default=0,
type=int,
metavar='x',
help=
'Stress level, selects the corresponding data file. (This expects only the number in MPa, leaving out the unit.) (default: %(default)s).'
)
group_data.add_argument(
'-p',
'--path',
dest='path',
default=".",
type=str,
metavar='my/directory/path',
help=
'Path where to look for measurement data files (default: current working directory).'
)
# -------------------------------------------------
# http://parezcoydigo.wordpress.com/2012/08/04/from-argparse-to-dictionary-in-python-2-7/
kwargs = vars(parser.parse_args())
if kwargs["listfiles"]:
util.listfiles(kwargs["path"])
sys.exit(0)
else:
lam = scrub(kwargs["sigma"], kwargs["path"], show=True, verbose=True)
def main(path):
# get data files in specified directory
#
data_items = util.listfiles(path, verbose=False)
for sigma, input_filename in data_items:
output_filename = re.sub(r'\.mat$', r'_denoised.mat', input_filename)
print("Scrubbing '%s' --> '%s'..." % (input_filename, output_filename))
H = filter_H.scrub(sigma, path)
B = filter_B.scrub(sigma, path)
pol = filter_pol.scrub(sigma, path)
lam = filter_magnetostriction.scrub(sigma, path)
assert H.shape == B.shape == pol.shape == lam.shape
A = np.empty((H.shape[0], 4), dtype=np.float64)
A[:, 0] = H[:]
A[:, 1] = B[:]
A[:, 2] = pol[:]
A[:, 3] = lam[:]
scipy.io.savemat(output_filename, mdict={'A': A})
print("All done.")
def runthreading():
pool = ThreadPoolExecutor(1)
jpgname = listfiles(path, "jpg")
for item in jpgname:
# 识别过的就不再识别了
if len(item) > 30:
pool.submit(main, item)
def filltree(self,path):
self.currentfiles.clear()
lst = os.listdir(self.dir)
files = util.listfiles(path)
dirs = util.listdirs(path)
counter = 0
for p in dirs:
self.currentfiles.append([p + "/"])
counter += 1
for p in files:
self.currentfiles.append([p])
counter += 1
self.len = len(lst)
def main(path):
# get list of raw data files in specified directory
#
data_items = util.listfiles(path, verbose=False)
for sigma,input_filename in data_items:
# for dummy,input_filename in data_items:
if sigma != 0: # XXX DEBUG
continue
input_filename = re.sub( r'\.mat$', r'_denoised.mat', input_filename ) # denoised data files
output_filename = re.sub( r'\.mat$', r'_singlevalued.mat', input_filename )
print( "Single-valuizing '%s' --> '%s'..." % (input_filename, output_filename) )
try:
data = scipy.io.loadmat(input_filename)
except FileNotFoundError:
import sys
print( "Data file named '%s' not found, exiting (use --list to see available data files)" % (input_filename), file=sys.stderr )
sys.exit(1)
A = data["A"]
H = A[:,0] # Field strength H (A/m)
B = A[:,1] # Flux density B (T)
pol = A[:,2] # magnetic polarization J = B - mu0*H
lam = A[:,3] # Magnetostriction lambda (ppm)
assert H.shape == B.shape == pol.shape == lam.shape
# # de-hysterize
# xx,yy = fit_1d_weighted_average_localr(B,H)
#
# # symmetrize w.r.t. B = 0
# #
# # we average the positive and negative parts.
# #
# imid = yy.shape[0]//2
# ymid = yy[imid]
## tmp = 0.5 * ((ymid - yy[imid-1::-1]) + (yy[imid+1:] - ymid))
# tmp = 0.5 * (yy[imid+1:] - yy[imid-1::-1]) # equivalent
# yy2 = np.empty_like(yy)
# yy2[imid-1::-1] = ymid - tmp
# yy2[imid+1:] = ymid + tmp
# yy2[imid] = ymid
#
# # DEBUG TEST - swap pos/neg parts
# tmp_p = yy[imid+1:] - ymid
# tmp_n = ymid - yy[imid-1::-1]
# yy3 = np.empty_like(yy)
# yy3[imid-1::-1] = ymid - tmp_p
# yy3[imid+1:] = ymid + tmp_n
# yy3[imid] = ymid
# xx,yy = fit_1d_doeverything(B,H)
# xx,yy = take_positive_half(xx, yy)
xout = []
yout = []
xdata = [B, pol, H]
ydata = [H, H, lam]
xlabels = [r"$B$", r"$pol$", r"$H$"]
ylabels = [r"$H$", r"$H$", r"$\lambda$"]
deoffsetters = [_deoffset_rawdata, _deoffset_rawdata, _deoffset_lam_data]
fs = [fit_1d_doeverything, fit_1d_doeverything, fit_1d_doeverything_for_lam]
for xx,yy,f in zip(xdata,ydata,fs):
xx2,yy2 = f(xx,yy)
xx2,yy2 = take_positive_half(xx2,yy2)
xout.append(xx2)
yout.append(yy2)
# plt.figure(1, figsize=(9,6))
# plt.clf()
# x,y = _deoffset_rawdata(B,H)
# plt.plot(x, y, color='#d0d0d0', linestyle='solid')
# plt.plot(xx, yy, color='#909090', linestyle='solid')
# plt.plot(xx, yy3, color='#909090', linestyle='dashed') # DEBUG: pos/neg parts swapped
# plt.plot(xx, yy2, color='k', linestyle='solid')
# plt.xlabel(r"$B$")
# plt.ylabel(r"$H$")
plt.figure(1, figsize=(14,6))
plt.clf()
nplots = len(ydata)
for i,deof,xx_raw,xx_filt,xlabel,yy_raw,yy_filt,ylabel in \
zip(range(nplots), deoffsetters, xdata, xout, xlabels, ydata, yout, ylabels):
ax = plt.subplot(1,nplots, i+1)
x,y= deof(xx_raw,yy_raw)
ax.plot(x, y, color='#d0d0d0', linestyle='solid')
ax.plot(xx_filt, yy_filt, color='#909090', linestyle='solid')
ax.axis( [np.min(xx_filt), np.max(xx_filt), np.min(yy_filt), np.max(yy_filt)] )
axis_marginize(ax, 0.02, 0.02)
ax.grid(b=True, which='both')
ax.set_title(r"%s(%s)" % (ylabel, xlabel))
break
# We have no guarantees that x starts from 0. Fix this.
#
tol = 1e-8
if xout[0][0] < tol:
xout[0][0] = 0
if xout[1][0] < tol:
xout[1][0] = 0
# HACK: the lambda curve is fitted with the axes swapped.
if yout[2][0] < tol:
yout[2][0] = 0
else:
raise ValueError("something went wrong, lambda curve does not start from zero (got %g)" % (yout[2][0]))
# Clip data to the smallest common max(H)
#
xs = [xout[0], xout[1], yout[2]]
ys = [yout[0], yout[1], xout[2]]
fs = []
max_minx = -np.inf
min_maxx = +np.inf
for x,y in zip(xs,ys):
min_maxx = min(np.max(x), min_maxx)
max_minx = max(np.min(x), max_minx)
fs.append( scipy.interpolate.interp1d(x,y) )
# Interpolate to a common grid on the H axis
#
xx = np.linspace(0, min_maxx, 10001)
yout2 = []
for f in fs:
yout2.append( f(xx) )
# Save.
#
A = np.empty( (xx.shape[0],4), dtype=np.float64 )
A[:,0] = xx
A[:,1] = yout2[0]
A[:,2] = yout2[1]
A[:,3] = yout2[2]
scipy.io.savemat( output_filename, mdict={ 'A' : A } )
print( "All done." )
#!/usr/bin/python3.4
# -*- coding: utf-8 -*-
# 本脚本为切割图片变成训练样本
from PIL import Image
import time
import random
import os
from util import listfiles
if __name__ == '__main__':
path = "../jpg/img/"
os.mkdir("../jpg/letter")
jpgname = listfiles(path, "jpg")
for item in jpgname:
try:
jpgpath = item
im = Image.open(jpgpath)
# jpg不是最低像素，gif才是，所以要转换像素
im = im.convert("P")
# 打印像素直方图
his = im.histogram()
values = {}
for i in range(0, 256):
values[i] = his[i]
# 排序，x:x[1]是按照括号内第二个字段进行排序,x:x[0]是按照第一个字段
if __name__ == '__main__':
numset = ['0','1','2','3','4','5','6','7','8','9']
symbol_set = []
iconset = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k',
'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
imageset = []
for letter in iconset:
for img in os.listdir('../iconset1/%s/' % (letter)):
temp = []
if img != "Thumbs.db" and img != ".DS_Store":
temp.append(buildvector(Image.open("../iconset1/%s/%s" % (letter, img))))
imageset.append({letter: temp})
path = "../jpg/letter/"
jpgname = listfiles(path, "gif")
# ../jpg/letter/20161210145303813.gif
for item in jpgname:
print(item)
try:
# 加载训练集
v = VectorCompare()
guess = []
# 这样子写是为了close文件
# 不然报错：[WinError 32] 另一个程序正在使用此文件，进程无法访问。: '../jpg/letter/201612101452081010.gif'
im3 = Image.open(item)
# 将切割得到的验证码小片段与每个训练片段进行比较
for image in imageset:
for x, y in image.items():