def main(argv=None):
if argv is None:
argv = sys.argv
desc = """%prog is a tool performs mean shift clustering and a phase symmetry transfromation\n
on a given input image, in that order."""
parser = optparse.OptionParser(description=desc,
usage='Usage: ex) %prog imageFile.png')
(opts, args) = parser.parse_args(argv)
args = args[1:]
#check to see if the user provided an output directory
if len(args) == 0:
print "\nNo input file provided"
parser.print_help()
sys.exit(-1)
#check to see if the file system image is provided
if len(args) > 1:
print "\n invalid argument(s) %s provided" % (str(args))
parser.print_help()
sys.exit(-1)
#begin transform
filename = args[0]
img = meanShift(filename)
cv2.imwrite("MS_" + filename, img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
pha, ori, tot, T = phasesym(img)
pha *= 255 / (np.max(np.max(pha)))
cv2.imwrite("MSPS_Serial_" + filename, pha)
pha = np.uint8(pha)
blobDetect(pha)
def main(argv=None):
if argv is None:
argv = sys.argv
desc = """%prog is a tool performs mean shift clustering and a phase symmetry transfromation\n
on a given input image, in that order."""
parser = optparse.OptionParser(description=desc, usage='Usage: ex) %prog imageFile.png')
(opts, args) = parser.parse_args(argv)
args = args[1:]
#check to see if the user provided an output directory
if len(args) == 0:
print "\nNo input file provided"
parser.print_help()
sys.exit(-1)
#check to see if the file system image is provided
if len(args) > 1:
print "\n invalid argument(s) %s provided" % (str(args))
parser.print_help()
sys.exit(-1)
#begin transform
filename = args[0]
img = meanShift(filename)
cv2.imwrite("MS_" + filename, img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
pha, ori, tot, T = phasesym(img)
pha *= 255/(np.max(np.max(pha)))
cv2.imwrite("MSPS_Serial_" + filename, pha)
pha = np.uint8(pha)
blobDetect(pha)
def phaseTuning(filename,
scaleMin=5,
scaleMax=10,
orientationMin=6,
orientationMax=16,
kMax=30):
#Performs phase symmetry transformation on a give for a given range across all parameters
img = cv2.imread(filename)
#img = meanShift(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sigmaMult = [(0.85, 1.3), (0.75, 1.6), (0.65, 2.1), (0.55, 3), (0.45, 3.4)]
#so ugly
startTime = time.time()
for scale in range(scaleMin, scaleMax):
#scales loop
for orient in range(orientationMin, orientationMax):
#orientations loop
for thisTuple in sigmaMult:
sigOnF = thisTuple[0]
mul = thisTuple[1]
for kVal in range(1, kMax):
#k loop
itStartTime = time.time()
print "scale:" + str(scale) + " orient:" + str(orient) + " mult:" + str(mul) + \
" sigmaOnF:" + str(sigOnF) + " k:" + str(kVal)
pha, ori, tot, T = phasesym(img,
nscale=scale,
norient=orient,
mult=mul,
sigmaOnf=sigOnF,
k=kVal)
cv2.normalize(pha, pha, 0, 255, cv2.NORM_MINMAX)
pha = np.uint8(pha)
cv2.imwrite(
filename + "_phase_symmetry_nscale:" + str(scale) +
"_norient:" + str(orient) + "_mult:" + str(mul) +
"_sigmaOnF:" + str(sigOnF) + "_k:" + str(kVal) +
"_.png", pha)
itEndTime = time.time()
print "Loop Time: " + str(itEndTime - itStartTime)
endTime = time.time()
print "Finished. \nTotal runtime: " + str(endTime - startTime)
def phaseTuning(filename, scaleMin=5, scaleMax=10, orientationMin=6,orientationMax=16, kMax=30):
#Performs phase symmetry transformation on a give for a given range across all parameters
img = cv2.imread(filename)
#img = meanShift(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sigmaMult = [(0.85, 1.3),
(0.75, 1.6),
(0.65, 2.1),
(0.55, 3),
(0.45, 3.4)]
#so ugly
startTime = time.time()
for scale in range(scaleMin, scaleMax):
#scales loop
for orient in range(orientationMin, orientationMax):
#orientations loop
for thisTuple in sigmaMult:
sigOnF = thisTuple[0]
mul = thisTuple[1]
for kVal in range(1, kMax):
#k loop
itStartTime = time.time()
print "scale:" + str(scale) + " orient:" + str(orient) + " mult:" + str(mul) + \
" sigmaOnF:" + str(sigOnF) + " k:" + str(kVal)
pha, ori, tot, T = phasesym(img, nscale=scale, norient=orient, mult=mul, sigmaOnf=sigOnF, k=kVal)
cv2.normalize(pha, pha, 0, 255, cv2.NORM_MINMAX)
pha = np.uint8(pha)
cv2.imwrite(filename + "_phase_symmetry_nscale:" + str(scale) + "_norient:" + str(orient)
+ "_mult:" + str(mul) +"_sigmaOnF:" + str(sigOnF) + "_k:" + str(kVal) + "_.png", pha)
itEndTime = time.time()
print "Loop Time: " + str(itEndTime - itStartTime)
endTime = time.time()
print "Finished. \nTotal runtime: " + str(endTime - startTime)
def main(argv=None):
if argv is None:
argv = sys.argv
desc = """%prog is a tool performs mean shift clustering and a phase symmetry transfromation\n
on a given input image, in that order."""
parser = optparse.OptionParser(description=desc, usage='Usage: ex) %prog imageFile.png')
parser.add_option('--scale', help='specify number of phasesym scales', dest='NSCALE', default=4, type="int")
parser.add_option('--ori', help='specify number of phasesym orientations', dest='NORIENT', default=6, type="int")
parser.add_option('--mult', help='specify multiplier for phasesym', dest='MULT', default=3.0, type="float")
parser.add_option('--sig', help='specify sigma on frequncy for phasesym', dest='SIGMAONF', default=0.55, type="float")
parser.add_option('--k', help='specify k value for phasesym', dest='K', default=1, type="int")
parser.add_option('--blur', help='specify blur width value N for NxN blur operation', dest='BLUR', default=3, type="int")
parser.add_option('--srad', help='specify spatial radius for mean shift', dest='SRAD', default=5, type="int")
parser.add_option('--rrad', help='specify radiometric radius for mean shift', dest='RRAD', default=6, type="int")
parser.add_option('--den', help='specify pixel density value for mean shift', dest='DEN', default=10, type="int")
parser.add_option('--amin', help='specify blob minimum area for boxing', dest='AMIN', default=5, type="int")
parser.add_option('--amax', help='specify blob maximum area for boxing', dest='AMAX', default=400, type="int")
parser.add_option('--wmin', help='specify box minimum width acceptance', dest='WMIN', default=3, type="int")
parser.add_option('--wmax', help='specify box maximum width acceptance', dest='WMAX', default=35, type="int")
parser.add_option('--hmin', help='specify box minimum height acceptance', dest='HMIN', default=2, type="int")
parser.add_option('--hmax', help='specify box maximum height acceptance', dest='HMAX', default=55, type="int")
parser.add_option('--arat', help='specify minimum box aspect ratio for acceptance', dest='ARATIO', default=0.25, type="float")
parser.add_option('--edgeMin', help='specify minimum hysteresis value for edge detection', dest='EDGEMIN', default=100, type="int")
parser.add_option('--edgeMax', help='specify maximum hysteresis value for edge detection', dest='EDGEMAX', default=200, type="int")
parser.add_option('--ref', help='specify reference car image', dest='MASTERIMG', default="")
parser.add_option('--win', help='specify search window size', dest='WINSZ', default=55, type="int")
parser.add_option('--tol', help='specify shape description tolerance', dest='TOL', default=0.07, type="float")
parser.add_option('--eps', help='specify maximum epsilon value for DBSCAN clustering algorithm', dest='EPS', default=15, type="int")
parser.add_option('--min_samples', help='specify the numer fo minimum samples that constitute a cluster during DBSCAN',
dest='MINSAMPLES', default=1, type="int")
(opts, args) = parser.parse_args(argv)
args = args[1:]
#check to see if the user provided an output directory
if len(args) == 0:
print "\nNo input file provided"
parser.print_help()
sys.exit(-1)
#check to see if the file system image is provided
if len(args) > 1:
print "\n invalid argument(s) %s provided" % (str(args))
parser.print_help()
sys.exit(-1)
NSCALE = opts.NSCALE
NORIENT = opts.NORIENT
MULT = opts.MULT
SIGMAONF = opts.SIGMAONF
K = opts.K
BLUR = (opts.BLUR, opts.BLUR)
SRAD = opts.SRAD
RRAD = opts.RRAD
DEN = opts.DEN
AMIN = opts.AMIN
AMAX = opts.AMAX
WMAX = opts.WMAX
WMIN = opts.WMIN
HMAX = opts.HMAX
HMIN = opts.HMIN
ARATIO = opts.ARATIO
EDGEMIN = opts.EDGEMIN
EDGEMAX = opts.EDGEMAX
WINSZ = opts.WINSZ
TOL = opts.TOL
EPS = opts.EPS
MINSAMPLES = opts.MINSAMPLES
masterImg = ""
masterHist = None
if len(opts.MASTERIMG) > 0:
masterImg = cv2.imread(opts.MASTERIMG)
h, w, z = masterImg.shape
if w%2:
x = int(np.ceil(w/float(2)))
else:
x = w/2
if h%2:
y = int(np.ceil(h/float(2)))
else:
y = h/2
print y, x
masterHist = RadAngleHist(masterImg, 0, y, x,BLUR)
else:
#masterImg = cv2.imread('singleCar.png')
#masterHist = RadAngleHist(masterImg, 0, 27, 29)
masterImg, cen = genReferenceCar(WINSZ)
print cen
print masterImg
masterHist = RadAngleHist(masterImg, 0, cen[0], cen[1],BLUR)
print str(masterHist)
#begin transform
filename = args[0]
basename = os.path.splitext(filename)[0]
img = cv2.imread(filename)
grayImg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imwrite(basename+"_gray.png", grayImg)
pha, ori, tot, T = phasesym(grayImg, nscale=NSCALE, norient=NORIENT, minWaveLength=3, mult=MULT, sigmaOnf=SIGMAONF, k=K, polarity=0)
pha = cv2.normalize(pha, pha, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
pha = np.uint8(pha)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K), pha)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_ori.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K), ori)
np.savetxt('python_ori.txt',ori)
pha = cv2.blur(pha, BLUR)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1]), pha)
pha = cv2.cvtColor(pha, cv2.COLOR_GRAY2RGB)
pha = cv2.cvtColor(pha, cv2.COLOR_RGB2LUV)
(segmented_image, labels_image, number_regions) = pms.segment(pha, spatial_radius=SRAD, range_radius=RRAD, min_density=DEN)
segmented_image=cv2.normalize(segmented_image, segmented_image, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
segmented_image = np.uint8(segmented_image)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_LUV2RGB)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2GRAY)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), segmented_image)
#ori = np.uint8(ori)
#(ori, labels_image, number_regions) = pms.segment(ori, spatial_radius=SRAD, range_radius=RRAD, min_density=DEN)
#ori=cv2.normalize(ori, ori, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
#ori = np.uint8(ori)
#cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_ori.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), ori)
thresh_img = cv2.adaptiveThreshold(segmented_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 7, 0)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_T.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), thresh_img)
#get centroids
contours = getContours(thresh_img, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO)
centroids = getCentroids(contours)
#cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_A_%d_%d_W_%d_%d_H_%d_%d_R_%.2f.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO), img)
histograms = []
dirName = 'windowTiles'
if not (os.path.isdir(dirName)):
#create subdirectory if it does not already exist
os.mkdir(dirName)
outputImg = img.copy()
centroidsImg = img.copy()
drawCentroids(centroidsImg,centroids)
#drawCentroids(img,centroids)
np.set_printoptions(formatter={'float': '{: 0.2f}'.format})
print str(masterHist) + "\n"
shapedCentroids = []
for cen,cnt in zip(centroids,contours):
print cen
#win = getImageWindow(img, cen[0],cen[1],26,52)
#if cen[0]==100 and cen[1] == 310:
# import pdb; pdb.set_trace()
win = getImageWindow(img, cen[0],cen[1],WINSZ,WINSZ) #correct
filename = "win_%d_%d.jpg" % (cen[0], cen[1])
cv2.imwrite(os.path.join(dirName, filename), win)
#histogram = RadAngleHist(win, 90+ori[cen[0], cen[1]],cen[0],cen[1])
histogram = RadAngleHist(win, 90+ori[cen[0], cen[1]],cen[0],cen[1],BLUR)
#print ori[cen[0], cen[1]]
print str(histogram) + "\n"
if histogram.getHistSum() < 0.1:
continue
#if cen == (11,144):
# import pdb;pdb.set_trace()
#print masterHist.compare(histogram)
if masterHist.compare(histogram) < TOL:
drawBox(outputImg, cnt)
shapedCentroids.append(cen)
dirName = ""
cv2.imwrite(os.path.join(dirName, "Boxes_Centroids.jpg"), outputImg)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_A_%d_%d_W_%d_%d_H_%d_%d_R_%.2f_E_%d_%d_W_%d_T_%.2f.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO,EDGEMIN,EDGEMAX,WINSZ,TOL), outputImg)
cv2.imwrite(os.path.join(dirName, "Centroids.jpg"), centroidsImg)
dbResult = scanBlobs(shapedCentroids, img.copy(), EPS, MINSAMPLES)
drawClusterColors(dbResult, img.copy(), shapedCentroids)
def main(argv=None):
if argv is None:
argv = sys.argv
desc = """%prog is a tool performs mean shift clustering and a phase symmetry transfromation\n
on a given input image, in that order."""
parser = optparse.OptionParser(description=desc, usage='Usage: ex) %prog imageFile.png')
parser.add_option('--scale', help='specify number of phasesym scales', dest='NSCALE', default=4, type="int")
parser.add_option('--ori', help='specify number of phasesym orientations', dest='NORIENT', default=6, type="int")
parser.add_option('--mult', help='specify multiplier for phasesym', dest='MULT', default=3.0, type="float")
parser.add_option('--sig', help='specify sigma on frequncy for phasesym', dest='SIGMAONF', default=0.55, type="float")
parser.add_option('--k', help='specify k value for phasesym', dest='K', default=1, type="int")
parser.add_option('--blur', help='specify blur width value N for NxN blur operation', dest='BLUR', default=3, type="int")
parser.add_option('--srad', help='specify spatial radius for mean shift', dest='SRAD', default=5, type="int")
parser.add_option('--rrad', help='specify radiometric radius for mean shift', dest='RRAD', default=6, type="int")
parser.add_option('--den', help='specify pixel density value for mean shift', dest='DEN', default=10, type="int")
parser.add_option('--amin', help='specify blob minimum area for boxing', dest='AMIN', default=5, type="int")
parser.add_option('--amax', help='specify blob maximum area for boxing', dest='AMAX', default=400, type="int")
parser.add_option('--wmin', help='specify box minimum width acceptance', dest='WMIN', default=3, type="int")
parser.add_option('--wmax', help='specify box maximum width acceptance', dest='WMAX', default=35, type="int")
parser.add_option('--hmin', help='specify box minimum height acceptance', dest='HMIN', default=2, type="int")
parser.add_option('--hmax', help='specify box maximum height acceptance', dest='HMAX', default=55, type="int")
parser.add_option('--arat', help='specify minimum box aspect ratio for acceptance', dest='ARATIO', default=0.25, type="float")
parser.add_option('--edgeMin', help='specify minimum hysteresis value for edge detection', dest='EDGEMIN', default=100, type="int")
parser.add_option('--edgeMax', help='specify maximum hysteresis value for edge detection', dest='EDGEMAX', default=200, type="int")
parser.add_option('--eps', help='specify maximum epsilon value for DBSCAN clustering algorithm', dest='EPS', default=15, type="int")
parser.add_option('--min_samples', help='specify the numer fo minimum samples that constitute a cluster during DBSCAN',
dest='MINSAMPLES', default=1, type="int")
(opts, args) = parser.parse_args(argv)
args = args[1:]
#check to see if the user provided an output directory
if len(args) == 0:
print "\nNo input file provided"
parser.print_help()
sys.exit(-1)
#check to see if the file system image is provided
if len(args) > 1:
print "\n invalid argument(s) %s provided" % (str(args))
parser.print_help()
sys.exit(-1)
NSCALE = opts.NSCALE
NORIENT = opts.NORIENT
MULT = opts.MULT
SIGMAONF = opts.SIGMAONF
K = opts.K
BLUR = (opts.BLUR, opts.BLUR)
SRAD = opts.SRAD
RRAD = opts.RRAD
DEN = opts.DEN
AMIN = opts.AMIN
AMAX = opts.AMAX
WMAX = opts.WMAX
WMIN = opts.WMIN
HMAX = opts.HMAX
HMIN = opts.HMIN
ARATIO = opts.ARATIO
EDGEMIN = opts.EDGEMIN
EDGEMAX = opts.EDGEMAX
EPS = opts.EPS
MINSAMPLES = opts.MINSAMPLES
#begin transform
filename = args[0]
basename = os.path.splitext(filename)[0]
img = cv2.imread(filename)
grayImg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imwrite(basename+"_gray.png", grayImg)
pha, ori, tot, T = phasesym(grayImg, nscale=NSCALE, norient=NORIENT, minWaveLength=3, mult=MULT, sigmaOnf=SIGMAONF, k=K, polarity=0)
pha = cv2.normalize(pha, pha, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
pha = np.uint8(pha)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K), pha)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_ori.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K), ori)
np.savetxt('python_ori.txt',ori)
pha = cv2.blur(pha, BLUR)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1]), pha)
pha = cv2.cvtColor(pha, cv2.COLOR_GRAY2RGB)
pha = cv2.cvtColor(pha, cv2.COLOR_RGB2LUV)
(segmented_image, labels_image, number_regions) = pms.segment(pha, spatial_radius=SRAD, range_radius=RRAD, min_density=DEN)
segmented_image=cv2.normalize(segmented_image, segmented_image, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
segmented_image = np.uint8(segmented_image)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_LUV2RGB)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2GRAY)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), segmented_image)
#ori = np.uint8(ori)
#(ori, labels_image, number_regions) = pms.segment(ori, spatial_radius=SRAD, range_radius=RRAD, min_density=DEN)
#ori=cv2.normalize(ori, ori, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
#ori = np.uint8(ori)
#cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_ori.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), ori)
thresh_img = cv2.adaptiveThreshold(segmented_image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 7, 0)
cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_T.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN), thresh_img)
#get centroids
contours = getContours(thresh_img, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO)
centroids = getCentroids(contours)
#cv2.imwrite(basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_A_%d_%d_W_%d_%d_H_%d_%d_R_%.2f.png" % (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO), img)
histograms = []
f = open('masterHist.txt')
h = np.loadtxt(f)
masterImg = cv2.imread('singleCar.png')
masterHist = RadAngleHist(masterImg, 180, 27, 29)
dirName = 'windowTiles'
if not (os.path.isdir(dirName)):
#create subdirectory if it does not already exist
os.mkdir(dirName)
outputImg = img.copy()
#ori = np.loadtxt('matlab_ori.txt',delimiter=',')
shapedCentroids = []
for cen,cnt in zip(centroids,contours):
#print cen
#win = getImageWindow(img, cen[0],cen[1],26,52)
#import pdb; pdb.set_trace()
win = getImageWindow(img, cen[0],cen[1],55,55) #correct
#win = getImageWindow(img, cen[0],cen[1],51,51)
filename = "win_%d_%d.jpg" % (cen[0], cen[1])
cv2.imwrite(os.path.join(dirName, filename), win)
histogram = RadAngleHist(win, ori[cen[0], cen[1]],cen[0],cen[1])
#print ori[cen[0], cen[1]]
#print masterHist.compare(histogram)
# if masterHist.compare(histogram, dist=60) == 0:
# histograms.append(histogram)
if masterHist.compare(histogram) < 0.07:
drawBox(outputImg, cnt)
shapedCentroids.append(cen)
# boxedImg = img.copy()
# drawBox(boxedImg,cnt)
# filename = "win_box_%d_%d.jpg" % (cen[0], cen[1])
# cv2.imwrite(os.path.join(dirName, filename), boxedImg)
dirName = ""
filename = "Boxes_+_Centroids.jpg"
cv2.imwrite(os.path.join(dirName, filename), outputImg)
dbResult = scanBlobs(shapedCentroids, img.copy(), EPS, MINSAMPLES)
drawClusterColors(dbResult, img.copy(), shapedCentroids)
def main(argv=None):
if argv is None:
argv = sys.argv
desc = """%prog is a tool performs mean shift clustering and a phase symmetry transfromation\n
on a given input image, in that order."""
parser = optparse.OptionParser(description=desc,
usage='Usage: ex) %prog imageFile.png')
parser.add_option('--scale',
help='specify number of phasesym scales',
dest='NSCALE',
default=4,
type="int")
parser.add_option('--ori',
help='specify number of phasesym orientations',
dest='NORIENT',
default=6,
type="int")
parser.add_option('--mult',
help='specify multiplier for phasesym',
dest='MULT',
default=3.0,
type="float")
parser.add_option('--sig',
help='specify sigma on frequncy for phasesym',
dest='SIGMAONF',
default=0.55,
type="float")
parser.add_option('--k',
help='specify k value for phasesym',
dest='K',
default=1,
type="int")
parser.add_option('--blur',
help='specify blur width value N for NxN blur operation',
dest='BLUR',
default=3,
type="int")
parser.add_option('--srad',
help='specify spatial radius for mean shift',
dest='SRAD',
default=5,
type="int")
parser.add_option('--rrad',
help='specify radiometric radius for mean shift',
dest='RRAD',
default=6,
type="int")
parser.add_option('--den',
help='specify pixel density value for mean shift',
dest='DEN',
default=10,
type="int")
parser.add_option('--amin',
help='specify blob minimum area for boxing',
dest='AMIN',
default=5,
type="int")
parser.add_option('--amax',
help='specify blob maximum area for boxing',
dest='AMAX',
default=400,
type="int")
parser.add_option('--wmin',
help='specify box minimum width acceptance',
dest='WMIN',
default=3,
type="int")
parser.add_option('--wmax',
help='specify box maximum width acceptance',
dest='WMAX',
default=35,
type="int")
parser.add_option('--hmin',
help='specify box minimum height acceptance',
dest='HMIN',
default=2,
type="int")
parser.add_option('--hmax',
help='specify box maximum height acceptance',
dest='HMAX',
default=55,
type="int")
parser.add_option('--arat',
help='specify minimum box aspect ratio for acceptance',
dest='ARATIO',
default=0.25,
type="float")
(opts, args) = parser.parse_args(argv)
args = args[1:]
#check to see if the user provided an output directory
if len(args) == 0:
print "\nNo input file provided"
parser.print_help()
sys.exit(-1)
#check to see if the file system image is provided
if len(args) > 1:
print "\n invalid argument(s) %s provided" % (str(args))
parser.print_help()
sys.exit(-1)
NSCALE = opts.NSCALE
NORIENT = opts.NORIENT
MULT = opts.MULT
SIGMAONF = opts.SIGMAONF
K = opts.K
BLUR = (opts.BLUR, opts.BLUR)
SRAD = opts.SRAD
RRAD = opts.RRAD
DEN = opts.DEN
AMIN = opts.AMIN
AMAX = opts.AMAX
WMAX = opts.WMAX
WMIN = opts.WMIN
HMAX = opts.HMAX
HMIN = opts.HMIN
ARATIO = opts.ARATIO
THRESH = 0
#begin transform
filename = args[0]
basename = os.path.splitext(filename)[0]
img = cv2.imread(filename)
#img = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
#(segmented_image, labels_image, number_regions) = pms.segment(img, spatial_radius=3, range_radius=11, min_density=10)
#img = cv2.cvtColor(img, cv2.COLOR_LUV2RGB)
#cv2.imwrite("MS_" + basename+".png", img)
#img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#pha, ori, tot, T = phasesym(img, nscale=5, norient=6, minWaveLength=3, mult=3.4, sigmaOnf = 0.45, k=29)
#pha=cv2.normalize(pha,pha,0,255,cv2.NORM_MINMAX,cv2.CV_8UC1)
#pha = np.uint8(pha)
#cv2.imwrite("MSPS_Serial_" + basename + ".png", pha)
#blobDetect(pha)
#NSCALE = 4
#NORIENT = 6
#MULT = 3
#SIGMAONF = 0.55
#K = 1
#BLUR = (3,3)
#SRAD = 5
#RRAD = 6
#DEN = 10
#THRESH = 127
#AMIN = 5
#AMAX = 400
#WMAX = 35
#WMIN = 3
#HMAX = 55
#HMIN = 2
#ARATIO = 0.25
grayImg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imwrite(basename + "_gray.png", grayImg)
#pha, ori, tot, T = phasesym(grayImg, nscale=5, norient=6, minWaveLength=3, mult=3.4, sigmaOnf = 0.45, k=29)
#pha, ori, tot, T = phasesym(grayImg, nscale=6, norient=6, minWaveLength=3, mult=3, sigmaOnf = 0.55, k=1, polarity=0)
pha, ori, tot, T = phasesym(grayImg,
nscale=NSCALE,
norient=NORIENT,
minWaveLength=3,
mult=MULT,
sigmaOnf=SIGMAONF,
k=K,
polarity=0)
pha = cv2.normalize(pha, pha, 0, 255, cv2.NORM_MINMAX, cv2.CV_8UC1)
pha = np.uint8(pha)
cv2.imwrite(
basename + "_PS" + "_%d_%d_%.2f_%.2f_%d.png" %
(NSCALE, NORIENT, MULT, SIGMAONF, K), pha)
pha = cv2.blur(pha, BLUR)
cv2.imwrite(
basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d.png" %
(NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1]), pha)
pha = cv2.cvtColor(pha, cv2.COLOR_GRAY2RGB)
pha = cv2.cvtColor(pha, cv2.COLOR_RGB2LUV)
#(segmented_image, labels_image, number_regions) = pms.segment(pha, spatial_radius=3, range_radius=11, min_density=10)
#(segmented_image, labels_image, number_regions) = pms.segment(pha, spatial_radius=15, range_radius=11, min_density=10)
(segmented_image, labels_image,
number_regions) = pms.segment(pha,
spatial_radius=SRAD,
range_radius=RRAD,
min_density=DEN)
segmented_image = cv2.normalize(segmented_image, segmented_image, 0, 255,
cv2.NORM_MINMAX, cv2.CV_8UC1)
segmented_image = np.uint8(segmented_image)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_LUV2RGB)
segmented_image = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2GRAY)
cv2.imwrite(
basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d.png" %
(NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD,
DEN), segmented_image)
#ret,thresh_img = cv2.threshold(segmented_image,THRESH,255, cv2.THRESH_BINARY| cv2.THRESH_OTSU)
thresh_img = cv2.adaptiveThreshold(segmented_image, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 7, 0)
cv2.imwrite(
basename + "_PS" + "_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_T_%d.png" %
(NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD, DEN,
THRESH), thresh_img)
#find contours in black and white image
contours, hierarchy = cv2.findContours(thresh_img, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
#if area >= 200 and area <= 2000:
#if area >= 10 and area <= 1200:
if area >= AMIN and area <= AMAX:
x, y, w, h = cv2.boundingRect(cnt)
#if w < WMAX and h < HMAX and w > 3 and h > 2:
if w < WMAX and h < HMAX and w > WMIN and h > HMIN:
aspectRatio = 0
if w == min(w, h):
aspectRatio = float(w) / h
else:
aspectRatio = float(h) / w
if aspectRatio > ARATIO:
print "x=%d y=%d w=%d h=%d" % (x, y, w, h)
rect = cv2.minAreaRect(cnt)
box = cv2.cv.BoxPoints(rect)
box = np.int0(box)
cv2.drawContours(img, [box], 0, (255, 0, 0), 2)
#cv2.imwrite('Boxes.jpg', img)
cv2.imwrite(
basename + "_PS" +
"_%d_%d_%.2f_%.2f_%d_B_%d_%d_MS_%d_%d_%d_T_%d_A_%d_%d_W_%d_%d_H_%d_%d_R_%.2f.png"
% (NSCALE, NORIENT, MULT, SIGMAONF, K, BLUR[0], BLUR[1], SRAD, RRAD,
DEN, THRESH, AMIN, AMAX, WMIN, WMAX, HMIN, HMAX, ARATIO), img)