def minimalExample(path):
import cv2
import matplotlib.pyplot as plt
img = cv2.imread(path+'Check\\Img_0.jpg',0)
print(img)
#plt.imshow(img, cmap='Greys')
# cv2.imshow('Original', img)
edges = cv2.Canny(img,50,150)
cv2.imshow('Canny', edges)
cv2.imwrite(path+'Check\\Img_0_Cannied_50-150.jpg', edges)
contours, hierarchy = cv2.findContours(edges,cv2.cv.CV_RETR_EXTERNAL,cv2.cv.CV_CHAIN_APPROX_NONE)
contours = tr.selectContour(contours)
counter=-1
for cnt in contours:
counter += 1
measureCuravture = tr.findMaxCurvatureInContour(img, cnt)
measureCuravture = tr.smooth(measureCuravture)
print('%d: max Curvature = %f \t' %(counter, max(measureCuravture)), end="")
M = cv2.moments(cnt)
print('Area = %f \t' %M['m00'], end="")
cntHull = cv2.convexHull(cnt, returnPoints=True)
cntPoly=cv2.approxPolyDP(cnt, epsilon=1, closed=True)
MHull = cv2.moments(cntHull)
MPoly = cv2.moments(cntPoly)
print('Area after Convec Hull = %f \t Area after apporxPoly = %f \t' %(MHull['m00'], MPoly['m00']), end="")
x, y =img.shape
size = (w, h, channels) = (x, y, 1)
canvas = np.zeros(size, np.uint8)
cv2.drawContours(canvas, cnt, -1, 255)
cv2.imwrite(path+'Check\\Img_0_ContoursCnt.jpg', canvas)
canvas = np.zeros(size, np.uint8)
cv2.drawContours(canvas, cntHull, -1, 255)
cv2.imwrite(path+'Check\\Img_0_ContoursCntHull.jpg', canvas)
canvas = np.zeros(size, np.uint8)
cv2.drawContours(canvas, cntPoly, -1, 255)
cv2.imwrite(path+'Check\\Img_0_ContoursCntPoly.jpg', canvas)
def runSym(path, geoTJ, plot=False, show=False):
SMOOTHING_WINDOW=11
daugtherChannelMiddel = (geoTJ[0]+ geoTJ[1]+ 0.0)/2.0
filenames, leng = sortPhotosPCO(path, prefixleng=10)
symmetry = np.zeros(leng, dtype=float)
symmetry.fill(SYM_ERROR_NR)
centroid_y = np.zeros(leng, dtype=float)
for ii in range(leng):
readPath=path+filenames[ii].fn
symmetry[ii], centroid_y[ii] = symmetryOfOutline(readPath, plot=False)
xarr = range(leng)
for ii in range(len(symmetry)-1, -1, -1 ):
if symmetry[ii] == SYM_ERROR_NR:
del symmetry[ii]
del xarr[ii]
if plot:
fig = plt.figure(figsize=(8, 6), dpi=200,); ax = fig.add_subplot(111)
plt.plot(xarr, np.abs(symmetry), 's', linestyle='None')
smooth_sym = tr.smooth(np.abs(symmetry),window_len=11,window='hanning')
plt.plot(xarr, smooth_sym[:-1], 'r', linestyle='--')
fig = plt.figure(figsize=(8, 6), dpi=200,); ax = fig.add_subplot(111)
plt.plot(xarr, centroid_y - daugtherChannelMiddel, 'or', linestyle='None')
smooth_cent = tr.smooth((centroid_y - daugtherChannelMiddel),window_len=11,window='hanning')
plt.plot(xarr, smooth_cent[:-1], 'b', linestyle='--')
plt.xlabel('Picture #')
plt.ylabel('Difference Centreline and y-centroid')
plt.xlim([100, 220])
print('length actual = %d and images %d' %(len(centroid_y), leng))
# dx1, count = forwardDifferenc(centroid_y - daugtherChannelMiddel)
dx2, count = centralDifferenc(centroid_y - daugtherChannelMiddel)
# smooth_centdx1 = tr.smooth((dx1),window_len=11,window='hanning')
smooth_centdx2 = tr.smooth((dx2),window_len=SMOOTHING_WINDOW,window='hanning')
#look a differential and define threshold above which it is not in SS
threshSSLost=0.1 #TODO: adjust this to take into account units?
endIndex=np.where(smooth_centdx2[:-SMOOTHING_WINDOW] > threshSSLost)
if not endIndex: #if condition is never satisfied
endIndex = ERROR_CODE-1
steadyStateReachedOnFrame =endIndex[0][-1] +1
s = "Steady state reached on frame %d" %steadyStateReachedOnFrame
fig = plt.figure(figsize=(8, 6), dpi=200,); ax = fig.add_subplot(111)
# plt.plot(xarr, dx1, 'or', linestyle='None', label='Result Forward Difference')
plt.plot(xarr, dx2, 'sb', linestyle='None', label='Result Central Difference')
# plt.plot(xarr, smooth_centdx1[:-1], 'r', linestyle='--', label = 'Smoothed Forward Difference')
plt.plot(xarr, smooth_centdx2[:-1], 'r',linewidth=3, linestyle='--', label = 'Smoothed Central Difference')
xmin, xmax = plt.xlim()
dif = xmax - steadyStateReachedOnFrame
# plt.xlim([int(xmax - 2.5* dif),len(width1) xmax])
plt.xlim([100, 220])
ymin, ymax = plt.ylim()
# plt.ylim([-0.2, ymax])
plt.plot([steadyStateReachedOnFrame, steadyStateReachedOnFrame], [-0.2, ymax], 'y',linewidth=2, linestyle='-', label = 'SS reached (on # %d)' %steadyStateReachedOnFrame)
plt.xlabel('Picture #')
plt.ylabel('Gradient of Difference Centreline and y-centroid')
plt.legend(loc='best')
ax.text(0.05, 0.2, s, fontsize=12, horizontalalignment='left', verticalalignment='center', transform = ax.transAxes)
sp1=path.rfind('\\')
sp2=path[:sp1].rfind('\\')
sp3=path[:sp2].rfind('\\')
directory=path[:sp2]
# print(directory)
fullpath=directory + "\\%s_GradientCentreline.jpg" %path[sp3+1:sp2]
if not os.path.exists(path[:sp3] + "\\Symmetry"): os.makedirs(path[:sp3] + "\\Symmetry")
fullpath2=path[:sp3] + "\\Symmetry\\%s_GradientCentreline.jpg" %path[sp3+1:sp2]
plt.title(path[sp3+1:sp2] + ' Difference Centreline')
# print(fullpath)
plt.savefig(fullpath, dpi=300)
plt.savefig(fullpath2, dpi=300)
if show:
plt.show()
else:
plt.close(fig)
return steadyStateReachedOnFrame
def findSymmetryByCentreline(path, geoTJ, plot=False, show=False):
'''
Evaluate the point when capsule has reached a steady state from distance of
centroid from centreline of daugther channel.
The centreline is found from top and bottom of daugther channel, stored in
geoTJ.
-Input-
path path to folder with result file from run (folder that stores images)
geoTJ 4 int values in pixels, related to the geometry:
-top daugther channel
-bottom daugther channel
-left side of main channel
-right side of main channel
plot plot results
show show plots
-Output-
framesToSS number of frames from max width to steady state
distanceToSS distance (in pixelse) from max width to steady state
frameMaxWidth Frame on which max width was reached
frameSS Frame on which steady state was reached
TODO: This still assumes that there are no gaps in capsules tracking. This
can be checked using the array "number".
'''
#Constants
SMOOTHING_WINDOW=11
CUTOFF_LEFT = 0.2
CUTOFF_RIGHT = 0.8
#look a differential and define threshold above which capsule is not in Steady state
THRESH_SS_LOST=0.1 #TODO: adjust this to take into account units?
daugtherChannelMiddel = (geoTJ[0]+ geoTJ[1]+ 0.0)/2.0
centroid_x_fromFile, centroid_y_fromFile, _, _, _, width1, _, _ = rof.readResultsFile(path)
readCentroidsFromOutlines = True
#check whether file centroid positions contains only whole numbers
for ii in range(len(centroid_x_fromFile)):
if not np.equal(np.mod(centroid_x_fromFile[ii], 1), 0) or not np.equal(np.mod(centroid_y_fromFile[ii], 1), 0):
readCentroidsFromOutlines=False
centroid_x, centroid_y = centroid_x_fromFile, centroid_y_fromFile
break;
if readCentroidsFromOutlines:
#Older versions of the script analysising the images (track_capsule_TJ)
#rounded centroid positions to an integer, so need to find centroid position from
#re-anaylising contour. For new result files, this is unnessecary and centroid
#position can be read from the results file
centroid_x,centroid_y, number = findCentroidFromOutline(path)
#find the point of maximum widht of capsule
index=np.arange(len(width1))
widthShort1=width1[int(CUTOFF_LEFT*len(width1)):int(CUTOFF_RIGHT*len(width1))]
indexShort=index[int(CUTOFF_LEFT*len(width1)):int(CUTOFF_RIGHT*len(width1))]
#TODO: ensure its that last index where max width is reached!
maxwidth1=np.max(widthShort1)
indS = np.argmax(widthShort1)
indexMaxWidth = indexShort[indS]
dx2, count = centralDifferenc(centroid_y - daugtherChannelMiddel)
smooth_centdx2 = tr.smooth((dx2),window_len=SMOOTHING_WINDOW,window='hanning')
endIndex=np.where(smooth_centdx2[:-SMOOTHING_WINDOW] > THRESH_SS_LOST)
if not endIndex: #if condition is never satisfied
endIndex = ERROR_CODE-1
steadyStateReachedOnFrame =endIndex[0][-1] +1
s = "Steady state reached on frame %d" %(steadyStateReachedOnFrame)
#distance traveled to reach SS
distanceToSS = np.sqrt( (centroid_x[steadyStateReachedOnFrame] - centroid_x[indexMaxWidth])**2 + (centroid_y[steadyStateReachedOnFrame] - centroid_y[indexMaxWidth])**2 )
if plot: #mainly for testing purposes
xarr = range(len(centroid_y))
fig = plt.figure(figsize=(8, 6), dpi=200,); ax = fig.add_subplot(111)
plt.plot(xarr, centroid_y - daugtherChannelMiddel, 'or', linestyle='None')
smooth_cent = tr.smooth(np.array(centroid_y - daugtherChannelMiddel).flatten(),window_len=11,window='hanning')
plt.plot(xarr, smooth_cent[:-1], 'b', linestyle='--')
plt.xlabel('Picture #')
plt.ylabel('Difference Centreline and y-centroid')
plt.xlim([0, 220])
fig = plt.figure(figsize=(8, 6), dpi=200,); ax = fig.add_subplot(111)
plt.plot(xarr, dx2, 'sb', linestyle='None', label='Result Central Difference')
plt.plot(xarr, smooth_centdx2[:-1], 'r',linewidth=3, linestyle='--', label = 'Smoothed Central Difference')
# xmin, xmax = plt.xlim(); dif = xmax - steadyStateReachedOnFrame; plt.xlim([int(xmax - 2.5* dif), xmax])
plt.xlim([0, 220])
ymin, ymax = plt.ylim()
# plt.ylim([-0.2, ymax])
plt.plot([steadyStateReachedOnFrame, steadyStateReachedOnFrame], [-0.2, ymax], 'y',linewidth=2, linestyle='-', label = 'SS reached (on # %d)' %steadyStateReachedOnFrame)
plt.xlabel('Picture #')
plt.ylabel('Gradient of Difference Centreline and y-centroid')
plt.legend(loc='best')
ax.text(0.05, 0.2, s, fontsize=12, horizontalalignment='left', verticalalignment='center', transform = ax.transAxes)
if show:
plt.show()
else:
plt.close(fig)
#framesToSS, distanceToSS, frameMaxWidth, frameSS
return steadyStateReachedOnFrame - indexMaxWidth +0.0, distanceToSS, indexMaxWidth, steadyStateReachedOnFrame
