def covR(data):
#Calculation of image covariance matrix as a function of mask radius R
#Find the image center-of-mass
xbar, ybar, cov = image_raw_moments(data)
center = [xbar, ybar]
p2x, p2y = imtl.ImageFit(data, 1.0)
print "Image center is at: ", p2x[0], p2y[0]
center = [p2x[0], p2y[0]]
#Define image size
h, w = data.shape[:2]
cov_array = np.zeros((m, 4))
#Loop over radius size (program core loop)
for i in range(0, m):
factor = int(max(semi_a0, semi_b0) / min(semi_a0, semi_b0))
semi_a = semi_a0 + i * step
semi_b = semi_b0 + i * step
mask = create_elliptical_mask(h, w, center, semi_a, semi_b, skew)
temp = data.copy()
#zero all the points outside of region of interest
temp[~mask] = 0.0
xbart, ybart, covt = image_raw_moments(temp)
cov_array[i, :] = np.ndarray.flatten(covt)
print semi_a, semi_b, i
#store calculated moments as a function of radius size for analysis
return cov_array
def __init__(self, word_list):
frame_xrc.xrcWordList.__init__(self, None)
# load some images into an image list
#创建图像列表
self.word_list = word_list
self.il = wx.ImageList(200, 200, True)
for im, info in word_list:
try:
img = ImageTool.pilImage_to_wxImage(im)
except:
print im, im.size, info
continue
bmp = wx.BitmapFromImage(img)
self.il.Add(bmp)
# create the list control
#创建列表窗口部件
self.list = xrc.XRCCTRL(self, "img_list_ctrl")
# assign the image list to it
self.list.AssignImageList(self.il, wx.IMAGE_LIST_NORMAL)
# create some items for the list
#为列表创建一些项目
for x in range(len(word_list)):
img_index = x
name = str(word_list[x][1])
self.list.InsertImageStringItem(x, name, img_index)
self.BindEvent()
def __init__(self, word_list):
wx.Frame.__init__(self, None, -1, "PyOCR", size=(600, 400))
# load some images into an image list
# 创建图像列表
w, h = word_list[0][0].size
il = wx.ImageList(w, h, True)
for im, _x, _y in word_list:
# im.save(str(_x)+str(_y)+".png")
img = ImageTool.pilImage_to_wxImage(im)
bmp = wx.BitmapFromImage(img)
il_max = il.Add(bmp)
# create the list control
# 创建列表窗口部件
self.list = wx.ListCtrl(self, -1, style=wx.LC_ICON | wx.LC_AUTOARRANGE)
# assign the image list to it
self.list.AssignImageList(il, wx.IMAGE_LIST_NORMAL)
# create some items for the list
# 为列表创建一些项目
for x in range(25):
img = x % (il_max + 1)
self.list.InsertImageStringItem(x, "%02d" % x, img)
def LoadBackground(self):
global Background
print('background')
fnametmp = QFileDialog.getOpenFileName(self, 'Open file', './')
fname = fnametmp[0]
print("filename", fname)
Background = imgtl.Load(fname)
Refresh()
def LoadImage(self):
global Image
print('loading image...')
fnametmp = QFileDialog.getOpenFileName(self, 'Open file', './')
fname = fnametmp[0]
print("filename", fname)
Image = imgtl.Load(fname)
Refresh()
def getData(inteIma, blockInfo, getRange, features):
#inteIma 是积分图
#blockInfo是上一帧目标位置第一块的信息
#getRange是搜索范围信息
#features是在每个块的哪个位置建立的特征值。这是个二维数组，第一维是每个随机蕨，第二位是随机蕨中每个特征值
originlenth = blockInfo['lenth']
originWidth = blockInfo['width']
blocks = []
tureFeats = []
dataMats = [] #每个检测块的数据
for magnification in range(-10, 11): #对于块的浮动大小。长宽从缩小10%到放大10%
lenth = int(originlenth * (1 + (magnification / 100))) # 块的长计算
width = int(originWidth * (1 + (magnification / 100))) # 块的宽计算
#print(originlenth,lenth)
if magnification > -10: #第一取样（第一次循环）时不判断
if lenth == blocks[-1]['lenth'] and width == blocks[-1]['width']:
continue #前后两次取样长宽差别太小（相差不足1），跳过
#print("长要搜索",len(range(getRange[0], getRange[1]-lenth+1)))
x = getRange[0]
while x <= getRange[1] - lenth + 1:
#for x in range(getRange[0], getRange[1]-lenth+1): # 对于起点横坐标从开始到结束
#print("宽要搜索",len(range(getRange[2], getRange[3]-width+1)))
y = getRange[2]
while y <= getRange[3] - width + 1:
#for y in range(getRange[2], getRange[3]-width+1): # 对于起始位置纵坐标，从开始到结束。块的起点位置确定完毕。
dataMat = [] # 一个块里建多个蕨。里面装的是data
#print("搜索随机蕨的个数：",len(features))
block = {}
block['x'] = x
block['y'] = y
block['lenth'] = lenth
block['width'] = width #块的基本信息
blocks.append(block)
for featurelist in features: #对于同一个块上不同的随机蕨.
# 从特征的相对位置到实际位置转换（不完全）
tureFeat = lt.getFeature(featurelist, block)
#计算特征值
#print(tureFeat)
#print()
data = [] # 每个蕨里的特征数据，按顺序排列，顺序与特征位置数组相同
#print("搜索每个蕨特征的个数：",len(tureFeat))
for feature in tureFeat: # 对于一个随机蕨的不同特征
value = it.towBitBP(inteIma, block['x'] + feature['x'],
block['y'] + feature['y'],
feature['lenth'], feature['width'])
data.append(value)
dataMat.append(data)
dataMats.append(dataMat)
#此处dataMat第一维是每个块（此处应理解为示例），第二维是每个随机蕨，第三维是蕨内容
y += int(width * 0.1)
x += int(lenth * 0.1)
#print(dataMats)
return dataMats, blocks
def getDataTem(inteIma, features, blocksInfo):
dataMats = []
for blockInfo in blocksInfo: #对于每个块
dataMat = []
for featurelist in features: #下面这四行我自己看着都晕，慢慢琢磨吧。
tureFeat = lt.getFeature(featurelist, blockInfo)
data = []
for feature in tureFeat:
value = it.towBitBP(inteIma, blockInfo['x'] + feature['x'],
blockInfo['y'] + feature['y'],
feature['lenth'], feature['width'])
data.append(value)
dataMat.append(data)
dataMats.append(dataArray)
return dataMats
def update_figure(self):
global ImageDisplayed
self.ax0.cla()
self.axx.cla()
self.axy.cla()
self.ax0.imshow(ImageDisplayed,
aspect='auto',
cmap=beam_map,
origin='lower')
projx, projy = ImageDisplayed.sum(axis=0), ImageDisplayed.sum(axis=1)
x = np.arange(len(projx))
y = np.arange(len(projy))
if FLAG_fit > 0:
p2X = imgtl.FitProfile(projx, x)
p2Y = imgtl.FitProfile(projy, y)
# # top plot
self.axx.plot(projx)
if FLAG_fit > 0:
self.axx.plot(x, imgtl.dg(x, p2X), 'r--', linewidth=3)
self.axx.set_xlim(self.ax0.get_xlim())
# # Right plot
self.axy.plot(projy, range(len(projy)))
if FLAG_fit > 0:
self.axy.plot(imgtl.dg(y, p2Y), y, 'r--', linewidth=3)
self.axy.set_ylim(self.ax0.get_ylim())
# Remove tick labels
nullfmt = ticker.NullFormatter()
self.axx.xaxis.set_major_formatter(nullfmt)
self.axx.yaxis.set_major_formatter(nullfmt)
self.axy.xaxis.set_major_formatter(nullfmt)
self.axy.yaxis.set_major_formatter(nullfmt)
self.draw()
eclick.ydata, erelease.ydata = erelease.ydata, eclick.ydata
if eclick.xdata > erelease.xdata:
eclick.xdata, erelease.xdata = erelease.xdata, eclick.xdata
print(eclick.xdata, eclick.ydata, erelease.xdata, erelease.ydata)
# fig.canvas.draw()
bbox = np.array([
int(eclick.xdata),
int(eclick.ydata),
int(erelease.xdata),
int(erelease.ydata)
])
return (bbox)
Filename = "./data_samples/vc.dat"
X, dx, dy, Nframes = imtl.LoadAWA(Filename)
print("Dx,Dy,NFrames= ", dx, dy, Nframes)
# sum m all the frame
arr = imtl.DesInterlace(np.sum(X, axis=2))
fig = plt.figure()
ax = fig.add_subplot(111)
print("select bounding box for signal and press Q")
plt_image = plt.imshow(arr)
toggle_selector.RS = widgets.RectangleSelector(ax,
onselect_getroi,
drawtype='box',
rectprops=dict(facecolor='red',
edgecolor='red',
#以下是循环和循环要用的变量的初始化了
StanPosition =ut.getPosition(path) #从标准位置文件中获取目标真实位置
CenterError =[]
blockSorted = [] # 块按概率排序的列表，八个块都在里面
targetPosition = [] #记录追踪过程中目标位置的序列
targetPosition.append(target)
blockClassifier = [] # 每个块已训练好的的强分类器（adaboost分类器）
offsetInfo = [] # 维护分块的偏移信息,所有示例的值都相同，并且不需要重置
features = []
imaIndex=0
centerErr=[]
for image in images: #对于每一帧
imaIndex+=1
blocksInfos = [] # 维护示例的分块信息，每帧需要重置。第一维是示例，第二维是块
imaMat = it.image2Mat(imagePath+'/'+image,color) #图像转矩阵
inteIma = it.getInteIma(imaMat) #积分图
if image == '0001.jpg': #第一帧只学习，不分类，单独拿出来
originIndex = 0
posBag = it.getPosBag(X,Y,lenth,width) #正包
lables =list(np.ones(len(posBag))) #正包标签
negBag = it.getNegBag(X,Y,lenth,width,2,4) #负包
lables.extend(np.zeros(len(negBag))) #整个标签
#上面两个都是二位列表，blocksInfos[0][0][x]是第一个示例的第一个块的起始坐标的x值
for instance in posBag: #对于每个正示例分块并存储。这个循环只能干这么点事
# 分块,第一帧就用target了，没用targetPosition[-1]
blocksInfo, offsetInfo = it.imageFrag(
instance['x'], instance['y'], instance['lenth'], instance['width'], 2, 4)
plt.axis('off')
# compute profiles
histx, histy, x, y = imgtl.GetImageProjection(IMGt,cal)
# plt.figure()
plt.subplot(2,2,4)
x=x-x[np.argmax(histx)]
y=y-y[np.argmax(histy)]
plt.plot (x,histx,'-')
plt.plot (y,histy,'-')
plt.xlabel ('distance')
plt.ylabel ('population')
plt.title('profiles::'+FilenameBeam,fontsize=FTsize)
plt.show()
# RMS calculations:
imgtl.stats1d(x, histx)
imgtl.stats1d(y, histy)
imgtl.stats2d(x,y,IMGt)
norm0, meanx0, meany0, meanI0, stdx0, stdy0, stdI0, correl0, Wx0, Wy0, averImage0, IMGf = \
imgtl.window_scan2dthreshold(IMGt, 1., 50, 0.00)
plt.figure()
plt.subplot (2,2,1)
plt.plot (Wx0, averImage0,'o')
plt.subplot (2,2,2)
plt.plot (Wx0, stdx0,'ro')
plt.plot (Wx0, stdy0,'go')
plt.subplot (2,2,3)
plt.plot (Wx0, correl0,'ro')
filenameBk = sys.argv[2]
print("background file: "+filenameBk)
Xi = pyl.imread(filenameIm)
Xb = pyl.imread(filenameBk)
X=Xi-Xb
Xc=imgtl.RemoveEdge(X, 0)
plt.imshow(Xc)
norm0, meanx0, meany0, meanI0, stdx0, stdy0, stdI0, correl0, Wx0, Wy0, averImage0, IMGf = \
imgtl.window_scan2dthreshold(Xc, 1., 50, 0.00)
plt.figure()
plt.subplot (2,2,1)
plt.plot (Wx0, averImage0,'o')
plt.subplot (2,2,2)
plt.plot (Wx0, stdx0,'ro')
plt.plot (Wx0, stdy0,'go')
plt.subplot (2,2,3)
plt.plot (Wx0, correl0,'ro')
plt.subplot (2,2,4)
width = 98
target = {} # 记录一帧中目标位置，放在track里
target['x'] = X
target['y'] = Y
target['lenth'] = lenth
target['width'] = width
targetPosition = [] # 记录追踪过程中目标位置的序列
targetPosition.append(target)
numFeat = 4
numFern = 10
testX = 121
testY = 61
testLenth = 77
testWidth = 93
imaMat = it.image2Mat(imagePath + '/' + "0001.jpg", 1)
inteIma = it.getInteIma(imaMat)
posBag = it.getPosBag(X, Y, lenth, width) # 正包
lables = list(np.ones(len(posBag))) # 正包标签
negBag = it.getNegBag(X, Y, lenth, width, 1, 1) # 负包
lables.extend(np.zeros(len(negBag))) # 整个标签
offsetInfo = [] # 这个是块偏移信息，放外面
#print(allInstance)
blocksInfos = [] # 每个块的信息，这里一幅图就是一个块。
blockClassifier = [] # 每个块已训练好的的强分类器（adaboost分类器）
blockInfos = posBag.extend(negBag)
blocksInfos.append(blockInfos) #为了使用以前写的randomFen函数，而这么搞的
randomFerns, dataMats, features = lt.randomFern(
import ImageTool as imtl
import numpy as np
import pylab as pyl
import matplotlib.pyplot as plt
import pydefaults
# directory
upperfile = "/Users/piot/ASTA_commissioning/quadscan/X121_20150601//tight_focus/"
filename = "nml-2015-06-01-2205-23-13076.png"
# in um/pixel
cal = 9.
thres = 0.02
A = imtl.Load(upperfile + filename)
B = imtl.AutoCrop(A, 100)
C = imtl.Threshold(B, thres)
#imtl.DisplayCalibrated(B, cal)
imtl.DisplayCalibratedProj(B, cal, 0.3)
plt.xlabel('x ($\mu$m)', fontsize=24)
plt.ylabel('y ($\mu$m)', fontsize=24)
plt.title(filename, fontsize=24)
plt.tight_layout()
plt.show()
import numpy as np
import time
import os
import scipy.optimize
import math
import matplotlib.pyplot as plt
import ImageTool as imtl
import AWA_tool as awa
Filename = "./data_samples/vc.dat"
cal = 0.039 # mm per pixel
X, dx, dy, Nframes = imtl.LoadAWA(Filename)
print("Dx,Dy,NFrames= ", dx, dy, Nframes)
# sum m all the frame
X_all_frame = imtl.DesInterlace(np.sum(X, axis=2))
TT = imtl.MouseCrop(X_all_frame)
centers = imtl.ImageCenter(TT)
print('center=', centers)
plt.figure()
#imtl.DisplayImage(X_all_frame)
#plt.subplot (2,2,1)
#imtl.DisplayCalibratedProj(X_all_frame, cal, 0.3)
#
#plt.subplot (2,2,2)
#imtl.DisplayCalibratedProj(imtl.Crop(X_all_frame,centers, [200,200]), cal, 0.3)
#plt.subplot (2,2,3)
# parameters
bbox = 200
cal = 1
fudge = 0.3
threshold = 0.
# scan over the number of data point
# fileonly = rootname+"-"+str(1+i)+".png"
# filename = UpperDir+"/"+SubDir+"/"+fileonly
filenameBeam = FileDir + FilenameBeam
filenameBkgd = FileDir + FilenameBkgd
# load the image
if test == 0:
IMGbeam = imgtl.Load(filenameBeam)
IMGbkgd = imgtl.Load(filenameBkgd)
IMG = 1. * IMGbeam - IMGbkgd
# IMG=ndimage.gaussian_filter(IMGT, 2)
# ndimage.gaussian_filter(IMGT, sigma=3)
if test == 1:
# this makes a test image
# a gaussian curve
# IMG = 10.+np.random.rand((1296,1606))
IMGT = np.zeros((1296, 1606))
BKGD = np.zeros((1296, 1606))
IMG = np.zeros((1296, 1606))
s = np.shape(IMG)
v = np.linspace(0, s[0], s[0])
h = np.linspace(0, s[1], s[1])
# parameters
bbox = 200
cal = 1
fudge = 0.3
threshold = 0.
# scan over the number of data point
# fileonly = rootname+"-"+str(1+i)+".png"
# filename = UpperDir+"/"+SubDir+"/"+fileonly
filenameBeam = FileDir + FilenameBeam
filenameBkgd = FileDir + FilenameBkgd
# load the image
if test == 0:
IMGbeam = imgtl.Load(filenameBeam)
IMGbkgd = imgtl.Load(filenameBkgd)
IMG = 1. * IMGbeam - IMGbkgd
# IMG=ndimage.gaussian_filter(IMGT, 2)
# ndimage.gaussian_filter(IMGT, sigma=3)
if test == 1:
# this makes a test image
# a gaussian curve
# IMG = 10.+np.random.rand((1296,1606))
IMGT = np.zeros((1296, 1606))
BKGD = np.zeros((1296, 1606))
IMG = np.zeros((1296, 1606))
s = np.shape(IMG)
v = np.linspace(0, s[0], s[0])
h = np.linspace(0, s[1], s[1])
width = 98
target = {} #记录一帧中目标位置，放在track里
target['x'] = X
target['y'] = Y
target['lenth'] = lenth
target['width'] = width
targetPosition = [] #记录追踪过程中目标位置的序列
targetPosition.append(target)
numFeat = 4
numFern = 12
testX = 121
testY = 61
testLenth = 77
testWidth = 93
imaMat = it.image2Mat(imagePath + '/' + "0001.jpg", 1)
inteIma = it.getInteIma(imaMat)
posBag = it.getPosBag(X, Y, lenth, width) #正包
lables = list(np.ones(len(posBag))) #正包标签
negBag = it.getNegBag(X, Y, lenth, width, 2, 4) #负包
lables.extend(np.zeros(len(negBag))) #整个标签
#负包并不是以整个图片出现的，而是以同一个块的组合出现，使每个块都学习到相同的
offsetInfo = [] #这个是块偏移信息，放外面
#print(allInstance)
blocksInfos = []
blockClassifier = [] # 每个块已训练好的的强分类器（adaboost分类器）
for instance in posBag: #对于每个正示例分块并存储。这个循环只能干这么点事。
# 分块,第一帧就用target了，没用targetPosition[-1]
filenameBk = sys.argv[2]
print("background file: "+filenameBk)
Xi = pyl.imread(filenameIm)
Xb = pyl.imread(filenameBk)
X=Xi-Xb
Xc=imgtl.RemoveEdge(X, 0)
plt.imshow(Xc)
IMGf=imgtl.AutoCrop(Xc, 2000)
histx, histy, x, y = imgtl.GetImageProjection(IMGf,cal)
x=x-x[np.argmax(histx)]
y=y-y[np.argmax(histy)]
p2X= imgtl.FitProfile(histx, x)
p2Y= imgtl.FitProfile(histy, y)
print("fitX: ", p2X)
print("fitY: ", p2Y)
plt.figure()
def image_moments(data, semiaxis_a, semiaxis_b):
global radius0
radius0 = (semiaxis_a + semiaxis_b) / 2
global semi_a0, semi_b0
semi_a0 = int(2 * semiaxis_a)
semi_b0 = int(2 * semiaxis_b)
print "Inital mask radius: ", radius0
h, v = data.shape[:2]
global step
step = int((h / 2 - max(semi_a0, semi_b0)) / m)
print "Step size is: ", step
#Retrieve cov(R) data
cov_array = covR(data)
#Find the image center-of-mass
xbar, ybar, cov = image_raw_moments(data)
center = [xbar, ybar]
p2x, p2y = imtl.ImageFit(data, 1.0)
print "Image center is at: ", p2x[0], p2y[0]
center = [p2x[0], p2y[0]]
# ellipse = data.copy()
# mask_ellipse = create_elliptical_mask(h,v,center,radius0,3*radius0,45.0)
# ellipse[~mask_ellipse] = 0
# plt.figure()
# plt.imshow(ellipse)
# plt.show()
#Calculate the correct radii for <xx>,<yy> and <xy> moments. The condition is min(d covR(R) / dR)
# m = len(cov_array)
#Retrieve the positions of max(d covR(R) / dR)
# nx, ny, nxy = dcov_array_max(cov_array)
#Radius is calculated as the location of derivative minimum
# radiusX = radius0 + step*np.argmin(np.gradient(cov_array[nx:m-1,0])) + step*nx
# radiusY = radius0 + step*np.argmin(np.gradient(cov_array[ny:m-1,3])) + step*ny
# radiusXY = radius0 + step*np.argmin(np.abs(np.gradient(cov_array[nxy:m-1,1]))) + step*nxy
minx, miny, minxy = dcov_array_min(cov_array)
semi_aX = semi_a0 + step * minx
semi_bX = semi_b0 + step * minx
semi_aY = semi_a0 + step * miny
semi_bY = semi_b0 + step * miny
semi_aXY = semi_a0 + step * minxy
semi_bXY = semi_b0 + step * minxy
print "Mask ellipse X: ", semi_aX, semi_bX
print "Mask ellipse Y: ", semi_aY, semi_bY
print "Mask ellipse XY: ", semi_aXY, semi_bXY
#Plotting covariance matrix elements for debugging
plot_covarray(cov_array)
#Create masks for <xx>,<yy> and <xy> moments
maskX = create_elliptical_mask(h, v, center, semi_aX, semi_bX, skew)
maskY = create_elliptical_mask(h, v, center, semi_aY, semi_bY, skew)
maskXY = create_elliptical_mask(h, v, center, semi_aXY, semi_bXY, skew)
#Prepare three copies of masked data for final moments calculation
tempX = data.copy()
tempY = data.copy()
tempXY = data.copy()
tempX[~maskX] = 0
tempY[~maskY] = 0
tempXY[~maskXY] = 0
# plt.figure()
fig, ax = plt.subplots(1)
extent = (0, len(tempXY[:, 0]), 0, len(tempXY[:, 1]))
tempXY = np.flip(tempXY, 0)
ax.imshow(imtl.Normalize(tempXY), extent=extent)
cx = patches.Ellipse(center,
2 * semi_aX,
2 * semi_bX,
skew,
color='y',
linewidth=3,
fill=False)
cy = patches.Ellipse(center,
2 * semi_aY,
2 * semi_bY,
skew,
color='g',
linewidth=3,
fill=False)
cxy = patches.Ellipse(center,
2 * semi_aXY,
2 * semi_bXY,
skew,
color='m',
linewidth=3,
fill=False)
ax.add_patch(cx)
ax.add_patch(cy)
ax.add_patch(cxy)
plt.legend((cx, cy, cxy),
(r'Mask $\langle xx \rangle$', r'Mask $\langle yy\rangle$',
r'Mask $\langle xy\rangle$'))
plt.xlabel('x (px)')
plt.ylabel('y (px)')
plt.tight_layout()
plt.show()
#Final calculation
xxt, yyt, covx = image_raw_moments(tempX)
xxt, yyt, covy = image_raw_moments(tempY)
xxt, yyt, covxy = image_raw_moments(tempXY)
return covx[0, 0], covy[1, 1], covxy[0, 1]
for i in range(NPoints):
filenameX = UpperDir + "/" + SubDir + "/" + rootname + "_" + str(i) + "_x"
filenameY = UpperDir + "/" + SubDir + "/" + rootname + "_" + str(i) + "_y"
# load the save histograms
histX = np.loadtxt(filenameX)
histY = np.loadtxt(filenameY)
# create the horizontal coordinate
axisX = np.arange(len(histX))
axisY = np.arange(len(histY))
# center the histogram and select a region of interest (ROI) around the peak
window = 1000.
histXc, axisXc = imgtl.center_and_ROI(histX, axisX, window)
histYc, axisYc = imgtl.center_and_ROI(histY, axisY, window)
# remove background (there are many way of doing this)
window = 10
histXc, axisXc = imgtl.removebackground(histXc, axisXc, window)
histYc, axisYc = imgtl.removebackground(histYc, axisYc, window)
p2X = imgtl.FitProfile(histXc, axisXc)
p2Y = imgtl.FitProfile(histYc, axisYc)
print("fitX: ", p2X)
print("fitY: ", p2Y)
plt.figure()
plt.plot(axisXc, histXc, 'ob', alpha=0.1)
plt.axis('off')
# compute profiles
histx, histy, x, y = imgtl.GetImageProjection(IMGt,cal)
# plt.figure()
plt.subplot(2,2,4)
x=x-x[np.argmax(histx)]
y=y-y[np.argmax(histy)]
plt.plot (x,histx,'-')
plt.plot (y,histy,'-')
plt.xlabel ('distance')
plt.ylabel ('population')
plt.title('profiles::'+FilenameBeam,fontsize=FTsize)
plt.show()
# RMS calculations:
imgtl.stats1d(x, histx)
imgtl.stats1d(y, histy)
imgtl.stats2d(x,y,IMGt)
norm0, meanx0, meany0, meanI0, stdx0, stdy0, stdI0, correl0, Wx0, Wy0, averImage0, IMGf = \
imgtl.window_scan2dthreshold(IMGt, 1., 50, 0.00)
plt.figure()
plt.subplot (2,2,1)
plt.plot (Wx0, averImage0,'o')
plt.subplot (2,2,2)
plt.plot (Wx0, stdx0,'ro')
plt.plot (Wx0, stdy0,'go')
plt.subplot (2,2,3)
plt.plot (Wx0, correl0,'ro')
for i in range (NPoints):
filenameX= UpperDir+"/"+SubDir+"/"+rootname+"_"+str(i)+"_x"
filenameY= UpperDir+"/"+SubDir+"/"+rootname+"_"+str(i)+"_y"
# load the save histograms
histX=np.loadtxt(filenameX)
histY=np.loadtxt(filenameY)
# create the horizontal coordinate
axisX=np.arange(len(histX))
axisY=np.arange(len(histY))
# center the histogram and select a region of interest (ROI) around the peak
window=1000.
histXc, axisXc = imgtl.center_and_ROI(histX, axisX, window)
histYc, axisYc = imgtl.center_and_ROI(histY, axisY, window)
# remove background (there are many way of doing this)
window = 10
histXc, axisXc = imgtl.removebackground(histXc, axisXc, window)
histYc, axisYc = imgtl.removebackground(histYc, axisYc, window)
p2X= imgtl.FitProfile(histXc, axisXc)
p2Y= imgtl.FitProfile(histYc, axisYc)
print "fitX: ", p2X
print "fitY: ", p2Y
#A. Halavanau. DeKalb, IL (2017) / Menlo Park, CA (2018) import numpy as np import matplotlib.pyplot as plt from scipy.ndimage import rotate import ImageTool as imtl from statistical import * from parameters import * i = 1 pwd = 'examples/Normal_quad_scan_L2/' filename = pwd + 'X111_YAG_' + str(i) + '_bkg_0.png' filenamebg = pwd + 'X111_YAG_' + str(i) + '_img_0.png' data = imtl.Load(filename) bg = imtl.Load(filenamebg) data = data - bg data = data[0:2000, 0:2000] data = imtl.Normalize(data) #experimental data_rot = data.copy() data_rot = rotate(data, skew, reshape=False) x, y = imtl.ImageFit(data_rot, 1.0, True) print "Image center is at: ", int(x[0]), int(y[0]) print "Gaussian fit sizes (px): ", int(x[2]), int(y[2]) print "Gaussian fit sizes (m): ", cal * x[2], cal * y[2] sigma_max = int(np.max((x[2], y[2]))) sigma_min = int(np.min((x[2], y[2])))
