def analyzeCut(scaleImage, edgeImage, cut): """Extract the interesting features respecting the cut""" # Set up constraints constraints = regionSelector.Constraints(cv.cvGetSize(scaleImage), cut, margin, superMargin, 0.002, 0.25) # Create temporary images blurImage = cv.cvCreateImage(cv.cvGetSize(scaleImage), 8, 3) workImage = cv.cvCreateImage(cv.cvGetSize(scaleImage), 8, 3) # Create a blurred copy of the original cv.cvSmooth(scaleImage, blurImage, cv.CV_BLUR, 3, 3, 0) # Superimpose the edges onto the blured image cv.cvNot(edgeImage, edgeImage) cv.cvCopy(blurImage, workImage, edgeImage) # Get the edges back to white cv.cvNot(edgeImage, edgeImage) # We're done with the blurred image now cv.cvReleaseImage(blurImage) # Retrive the regions touching the cut component_dictionary = featureDetector.ribbonFloodFill(scaleImage, edgeImage, workImage, cut, margin, lo, up) # Clean up cv.cvReleaseImage(workImage) # Prune components newComponents = regionSelector.pruneRegions(component_dictionary, constraints) # Return the dictionary of accepted components #transformer.translateBoundingBoxes(newComponents, 1) return newComponents
def findEdges(original, out, threshold1 = 100, threshold2 = None):
"""Return a new edge detected image with a specified threshold"""
warnings.warn("Use findBWEdges instead unless you really need colored edges.", DeprecationWarning)
#Define threshold2
if threshold2 == None:
threshold2 = threshold1 * 3
# Create two pictures with only one channel for a b/w copy
# and one for storring the edges found in the b/w picture
gray = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
edge = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
# Create the b/w copy of the original
cv.cvCvtColor(original, gray, cv.CV_BGR2GRAY)
# Blur the b/w copy, but put the result into edge pic
cv.cvSmooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
# Negate the b/w copy of original with newly blurred
# b/w copy. This will make egdes stand out
cv.cvNot(gray, edge)
# Run an edge-finding algorithm called 'Canny'
# It will analyse the first argument and store the
# resulting picture in the second argument
cv.cvCanny(gray, edge, threshold1, threshold2)
# We initialize our out-image to black
cv.cvSetZero(out)
# Finally, we use the found edges, which are b/w, as
# a mask for copying the colored edges from the original
# to the out-image
cv.cvCopy(original, out, edge)
def analyzeCut(original, edgeImage, cut, settings, showBlobs=False): """Extract the interesting features in the vicinity of a given cut""" # Get all data from the settings lo = settings.lo up = settings.up # Set up the margin with respect to the cut margin = marginCalculator.getPixels(original, cut, settings.marginPercentage) superMargin = 0 # ^^ We don't use superMargin # Set up constraints constraints = regionSelector.Constraints(cv.cvGetSize(original), cut, margin, superMargin, 0.002, 0.25) # Create temporary images blurImage = cv.cvCreateImage(cv.cvGetSize(original), 8, 3) workImage = cv.cvCreateImage(cv.cvGetSize(original), 8, 3) # Create a blurred copy of the original cv.cvSmooth(original, blurImage, cv.CV_BLUR, 3, 3, 0) # Superimpose the edges onto the blured image cv.cvNot(edgeImage, edgeImage) cv.cvCopy(blurImage, workImage, edgeImage) # We're done with the blurred image now cv.cvReleaseImage(blurImage) # Get the edges back to white cv.cvNot(edgeImage, edgeImage) # Retrive the regions touching the cut component_dictionary = featureDetector.ribbonFloodFill(original, edgeImage, workImage, cut, margin, lo, up) #start expanded # Prune components BEFORE we delete the workImage tmpnewComponents = regionSelector.pruneExpandedRegions(component_dictionary, constraints) newComponents = regionSelector.pruneExpandedRagionsto(tmpnewComponents, constraints, cut, workImage) # Clean up only if we do not return the image if not showBlobs: cv.cvReleaseImage(workImage) # Return the dictionary of accepted components or both if not showBlobs: return newComponents else: return (workImage, newComponents)
def on_trackbar (position):
cv.cvSmooth (gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.cvNot (gray, edge)
# run the edge dector on gray scale
cv.cvCanny (gray, edge, position, position * 3, 3)
# reset
cv.cvSetZero (col_edge)
# copy edge points
cv.cvCopy (image, col_edge, edge)
# show the image
highgui.cvShowImage (win_name, col_edge)
def on_trackbar(position):
cv.cvSmooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.cvNot(gray, edge)
# run the edge dector on gray scale
cv.cvCanny(gray, edge, position, position * 3, 3)
# reset
cv.cvSetZero(col_edge)
# copy edge points
cv.cvCopy(image, col_edge, edge)
# show the image
highgui.cvShowImage(win_name, col_edge)
def on_trackbar (position):
#下面两句应该是没什么用的
cv.cvSmooth (gray, edge, cv.CV_BLUR, 3, 3, 0) #图像平滑
cv.cvNot (gray, edge) #计算数组元素的按位取反
# run the edge dector on gray scale
cv.cvCanny (gray, edge, position, position * 3, 3) #采用 Canny 算法做边缘检测
# reset
cv.cvSetZero (col_edge) #清空数组
# copy edge points
cv.cvCopy (image, col_edge, edge) #参数edge影响拷贝的结果
# show the image
highgui.cvShowImage (win_name, col_edge)
def findBWEdges(original, out, threshold1, threshold2): """Identical with findEdges except that this returns white edges on a black background. We really don't need colored edges any longer. This also makes it easy to to a manual merge of edge and blur picture.""" if threshold2 == None: threshold2 = threshold1 * 3 gray = cv.cvCreateImage(cv.cvGetSize(original), 8, 1) cv.cvCvtColor(original, gray, cv.CV_BGR2GRAY) cv.cvSmooth(gray, out, cv.CV_BLUR, 3, 3, 0) cv.cvNot(gray, out) cv.cvCanny(gray, out, threshold1, threshold2) return out
def main():
ct1 = CurvePoint()
ct2 = CurvePoint()
sc = CntSC()
ang = CntAngle()
usage = "%prog [options] <imgfile1> <imgfile2>"
version = "%prog 0.2\nLongbin Chen, [email protected]"
oparser = optparse.OptionParser(usage=usage, version=version)
oparser.add_option('-d', '--display', action="store_true", dest = 'display', default = False, help = 'display the image')
oparser.add_option('-n', '--number', dest = 'num', type="int", default = 200 , help = 'the number of feature points')
oparser.add_option('-s', '--save', dest = 'save', default = None, help = 'save the img file')
oparser.add_option('-o', '--output', dest = 'output', default = None, help = 'output file')
(options, args) = oparser.parse_args(sys.argv)
if len(args) != 3:
oparser.parse_args([sys.argv[0], "--help"])
sys.exit(1)
ct1.GetContour(args[1], options.num)
allkeys = []
for c in ct1.allselected:
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
ang.ExtractFeature(allkeys,0);
allkeys = []
ct2.GetContour(args[2], options.num)
for c in ct2.allselected:
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
ang.ExtractFeature(allkeys,0);
sumscore = []
matcher = SmithWaterman()
ct1.bDrawNumber = 0
ct2.bDrawNumber = 0
if (options.display):
ct1.DrawKeyPoints()
ct2.DrawKeyPoints()
myfont = cv.cvInitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5)
idx = -1
putoriginal(args[1], ct1.drawimg)
putoriginal(args[2], ct2.drawimg)
cv.cvNot(ct1.drawimg, ct1.drawimg)
cv.cvNot(ct2.drawimg, ct2.drawimg)
for c1 in ct1.allselected:
idx += 1
cscore = -100000000
cpt1 = getdata(c1)
bX = []
bY = []
bestcurve = None
for c2 in ct2.allselected:
cpt2 = getdata(c2)
cost,align,X,Y = matcher.Align(cpt1, cpt2)
normalized_score = cost - log10(len(c2) + 1) * 1000
print len(c1), len(c2),cost, normalized_score, cscore
if (normalized_score > cscore):
cscore = normalized_score
bX = X[:]
bY = Y[:]
bestcurve = c2
if (options.display):
ptcount = 0
for i in range(len(bX)):
xi = bX[i]
yi = bY[i]
#if (xi == -1):
#cv.cvDrawCircle(ct2.drawimg, cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),4, cv.cvScalar(255,0,0,0))
#cv.cvPutText(ct2.drawimg, 'O', cv.cvPoint(int(c2[yi].x), int(c2[yi].y)), myfont, cv.cvScalar(255, 0, 0,0))
#if (yi == -1):
#cv.cvDrawCircle(ct1.drawimg, cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),4, cv.cvScalar(255,0,0,0))
#cv.cvPutText(ct1.drawimg, 'O', cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), myfont, cv.cvScalar(255, 0, 0,0))
if (xi != -1 and yi != -1):
ptcount += 1
cv.cvDrawCircle(ct1.drawimg, cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),2, clrs[idx])
cv.cvPutText(ct1.drawimg, str(ptcount), cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), myfont, clrs[idx])
cv.cvDrawCircle(ct2.drawimg, cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),2, clrs[idx])
cv.cvPutText(ct2.drawimg, str(ptcount), cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)), myfont, clrs[idx])
sumscore.append(cscore)
print sumscore
if (options.display):
highgui.cvNamedWindow ("contour1", 1)
highgui.cvNamedWindow ("contour2", 1)
highgui.cvShowImage ("contour1", ct1.drawimg)
highgui.cvShowImage ("contour2", ct2.drawimg)
highgui.cvWaitKey (0)
if (options.save):
mergeimg = mergeimage_83(ct1.drawimg, ct2.drawimg)
highgui.cvSaveImage("_sw_result.bmp", mergeimg)
def main():
ct1 = CurvePoint()
ct2 = CurvePoint()
agl = CntAngle()
sc = CntSC()
try:
opts, args = getopt.getopt(sys.argv[1:], "ho:dn:es", ["help", "output=", "draw", "num=", "even", "save"])
except getopt.GetoptError:
usage()
sys.exit(2)
output = None
bDraw = 0
bSave = 0
bOriginal = 0
npoint = 100
for o, a in opts:
if o == "-v":
ct1.verbose = 1
ct2.verbose = 1
if o in ("-h", "--help"):
usage()
sys.exit()
if o in ("-o", "--output"):
output = a
if o in ("-d", "--draw"):
bDraw = 1
if o in ("-s", "--save"):
bSave = 1
if o in ("-r", "--original"):
bOriginal = 1
if o in ("-n", "--num"):
npoint = string.atoi(a)
if o in ("-e", "--even"):
ct1.bEven = 1
ct2.bEven = 1
if (len(args)) != 2:
usage()
sys.exit(2)
ct1.GetContour(args[0], npoint)
allkeys = []
for c in ct1.allselected:
# agl.ExtractFeature(c, ct1.drawimg)
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
allkeys = []
ct2.GetContour(args[1], npoint)
for c in ct2.allselected:
# agl.ExtractFeature(c, ct2.drawimg)
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
sumscore = []
matcher = SmithWaterman()
ct1.bDrawNumber = 0
ct2.bDrawNumber = 0
if bDraw:
ct1.DrawKeyPoints()
ct2.DrawKeyPoints()
myfont = cv.cvInitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5)
idx = -1
putoriginal(args[0], ct1.drawimg)
putoriginal(args[1], ct2.drawimg)
cv.cvNot(ct1.drawimg, ct1.drawimg)
cv.cvNot(ct2.drawimg, ct2.drawimg)
for c1 in ct1.allselected:
idx += 1
cscore = -100000000
cpt1 = getdata(c1)
bX = []
bY = []
bestcurve = None
for c2 in ct2.allselected:
cpt2 = getdata(c2)
cost, align, X, Y = matcher.Align(cpt1, cpt2)
normalized_score = cost - log10(len(c2) + 1) * 1000
print len(c1), len(c2), cost, normalized_score, cscore
if normalized_score > cscore:
cscore = normalized_score
bX = X[:]
bY = Y[:]
bestcurve = c2
if bDraw:
ptcount = 0
for i in range(len(bX)):
xi = bX[i]
yi = bY[i]
# if (xi == -1):
# cv.cvDrawCircle(ct2.drawimg, cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),4, cv.cvScalar(255,0,0,0))
# cv.cvPutText(ct2.drawimg, 'O', cv.cvPoint(int(c2[yi].x), int(c2[yi].y)), myfont, cv.cvScalar(255, 0, 0,0))
# if (yi == -1):
# cv.cvDrawCircle(ct1.drawimg, cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),4, cv.cvScalar(255,0,0,0))
# cv.cvPutText(ct1.drawimg, 'O', cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), myfont, cv.cvScalar(255, 0, 0,0))
if xi != -1 and yi != -1:
ptcount += 1
cv.cvDrawCircle(ct1.drawimg, cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), 2, clrs[idx])
cv.cvPutText(ct1.drawimg, str(ptcount), cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), myfont, clrs[idx])
cv.cvDrawCircle(ct2.drawimg, cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)), 2, clrs[idx])
cv.cvPutText(
ct2.drawimg,
str(ptcount),
cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),
myfont,
clrs[idx],
)
sumscore.append(cscore)
print sumscore
if bDraw:
highgui.cvNamedWindow("contour1", 1)
highgui.cvNamedWindow("contour2", 1)
highgui.cvShowImage("contour1", ct1.drawimg)
highgui.cvShowImage("contour2", ct2.drawimg)
highgui.cvWaitKey(0)
if bSave:
mergeimg = mergeimage_83(ct1.drawimg, ct2.drawimg)
highgui.cvSaveImage("_sw_result.bmp", mergeimg)
def main():
ct1 = CurvePoint()
ct2 = CurvePoint()
sc = CntSC()
ang = CntAngle()
usage = "%prog [options] <imgfile1> <imgfile2>"
version = "%prog 0.2\nLongbin Chen, [email protected]"
oparser = optparse.OptionParser(usage=usage, version=version)
oparser.add_option('-d',
'--display',
action="store_true",
dest='display',
default=False,
help='display the image')
oparser.add_option('-n',
'--number',
dest='num',
type="int",
default=200,
help='the number of feature points')
oparser.add_option('-s',
'--save',
dest='save',
default=None,
help='save the img file')
oparser.add_option('-o',
'--output',
dest='output',
default=None,
help='output file')
(options, args) = oparser.parse_args(sys.argv)
if len(args) != 3:
oparser.parse_args([sys.argv[0], "--help"])
sys.exit(1)
ct1.GetContour(args[1], options.num)
allkeys = []
for c in ct1.allselected:
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
ang.ExtractFeature(allkeys, 0)
allkeys = []
ct2.GetContour(args[2], options.num)
for c in ct2.allselected:
allkeys = allkeys + c
sc.ExtractFeature(allkeys)
ang.ExtractFeature(allkeys, 0)
sumscore = []
matcher = SmithWaterman()
ct1.bDrawNumber = 0
ct2.bDrawNumber = 0
if (options.display):
ct1.DrawKeyPoints()
ct2.DrawKeyPoints()
myfont = cv.cvInitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5)
idx = -1
putoriginal(args[1], ct1.drawimg)
putoriginal(args[2], ct2.drawimg)
cv.cvNot(ct1.drawimg, ct1.drawimg)
cv.cvNot(ct2.drawimg, ct2.drawimg)
for c1 in ct1.allselected:
idx += 1
cscore = -100000000
cpt1 = getdata(c1)
bX = []
bY = []
bestcurve = None
for c2 in ct2.allselected:
cpt2 = getdata(c2)
cost, align, X, Y = matcher.Align(cpt1, cpt2)
normalized_score = cost - log10(len(c2) + 1) * 1000
print len(c1), len(c2), cost, normalized_score, cscore
if (normalized_score > cscore):
cscore = normalized_score
bX = X[:]
bY = Y[:]
bestcurve = c2
if (options.display):
ptcount = 0
for i in range(len(bX)):
xi = bX[i]
yi = bY[i]
#if (xi == -1):
#cv.cvDrawCircle(ct2.drawimg, cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),4, cv.cvScalar(255,0,0,0))
#cv.cvPutText(ct2.drawimg, 'O', cv.cvPoint(int(c2[yi].x), int(c2[yi].y)), myfont, cv.cvScalar(255, 0, 0,0))
#if (yi == -1):
#cv.cvDrawCircle(ct1.drawimg, cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),4, cv.cvScalar(255,0,0,0))
#cv.cvPutText(ct1.drawimg, 'O', cv.cvPoint(int(c1[xi].x), int(c1[xi].y)), myfont, cv.cvScalar(255, 0, 0,0))
if (xi != -1 and yi != -1):
ptcount += 1
cv.cvDrawCircle(ct1.drawimg,
cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),
2, clrs[idx])
cv.cvPutText(ct1.drawimg, str(ptcount),
cv.cvPoint(int(c1[xi].x), int(c1[xi].y)),
myfont, clrs[idx])
cv.cvDrawCircle(
ct2.drawimg,
cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),
2, clrs[idx])
cv.cvPutText(
ct2.drawimg, str(ptcount),
cv.cvPoint(int(bestcurve[yi].x), int(bestcurve[yi].y)),
myfont, clrs[idx])
sumscore.append(cscore)
print sumscore
if (options.display):
highgui.cvNamedWindow("contour1", 1)
highgui.cvNamedWindow("contour2", 1)
highgui.cvShowImage("contour1", ct1.drawimg)
highgui.cvShowImage("contour2", ct2.drawimg)
highgui.cvWaitKey(0)
if (options.save):
mergeimg = mergeimage_83(ct1.drawimg, ct2.drawimg)
highgui.cvSaveImage("_sw_result.bmp", mergeimg)
def SaveImage(self, filename):
cv.cvNot(self.drawimg, self.drawimg)
highgui.cvSaveImage(filename, self.drawimg)
def SaveImage(self, filename):
cv.cvNot(self.drawimg, self.drawimg)
highgui.cvSaveImage(filename, self.drawimg)
print "Finding the golden means in the picture"
lines = lib.findGoldenMeans(cv.cvGetSize(image))
#lines = lib.findMeans(cv.cvGetSize(image), lib.PHI)
print "Test plot and line scanner methods"
points = lineScanner.naiveBWLineScanner(edges, lines[0])
#cv.cvSmooth(out, out, cv.CV_MEDIAN, 7, 7, 0)
cv.cvSmooth(image, blurImage, cv.CV_BLUR, 3, 3, 0)
#cv.cvSmooth(out, out, cv.CV_GAUSSIAN, 7, 7, 0)
#out = blurImage
# Superimpose the edges onto the blured image
cv.cvNot(edges, edges)
cv.cvCopy(blurImage, out, edges)
# We're done with the blurred image now
#cv.cvReleaseImage(blurImage)
#print points[:0]
cut = lines[1]
margin = marginCalculator.getPixels(image, cut, 0.024)
component_dictionary = featureDetector.ribbonFloodFill(image, edges, out, cut, margin, lo, up)
#featureDetector.floodFillLine(image, out, points, cut, lo, up, {})
#flags = cv.CV_FLOODFILL_FIXED_RANGE
#flags = 4
color = lib.getRandomColor()
comp = cv.CvConnectedComp()
#cv.cvFloodFill(out, cv.cvPoint(x,y), color, cv.CV_RGB(lo,lo,lo), cv.CV_RGB(up,up,up),comp ,flags)#, None);
