def testRotate():
import cv
import optparse
import imgutil
# handle command line arguments
parser = optparse.OptionParser()
parser.add_option("-f", "--filename", help="input image file")
parser.add_option("-a", "--angle", help="rotation angle in degrees", default=0, type="float")
options, remain = parser.parse_args()
if options.filename is None:
parser.print_help()
exit(0)
# load image
cvimg = cv.LoadImage(options.filename)
npimg = imgutil.cv2array(cvimg)
# rotate image
h,w = npimg.shape[0:2]
print h, w
A = makeCenteredRotation(options.angle, (w/2.0, h/2.0))
nprot = transformImage(npimg, A, "auto")
imgutil.imageShow(npimg, "original")
imgutil.imageShow(nprot, "rotate")
cv.WaitKey(0)
def montage(self, descriptors, numCols=32): ''' Displays all of the descriptors in a montage. @descriptors: a numpy array of feature descriptors with size w x w x N @numCols: the number of descriptors to display @return: a numpy array (the montage) ''' numPatches = descriptors.shape[2] numRows = numPatches/numCols sideLength = descriptors.shape[0] #resize descriptors into a montage montage = numpy.zeros((sideLength*numRows,sideLength*numCols)) #place the descriptors on a canvas count = 0 for i in range(numRows): for j in range(numCols): montage[i*sideLength:(i+1)*sideLength,j*sideLength:(j+1)*sideLength] = descriptors[...,count] count += 1 #show the image imgutil.imageShow(montage, title="montage", norm=True) #cv.WaitKey(0) return montage
def displayDescriptors(self, descriptorArray):
'''
Displays the first twenty five prominent features in a grid from the
descriptorArray
Returns the image
'''
wAndH = descriptorArray.shape[0]
numDesc = descriptorArray.shape[2]
#sets up the numpy image where the descriptors will be put
img = numpy.zeros((5*wAndH,5*wAndH))
for j in range(5):
for i in range(5):
#checks to make sure you dont go over the number of descriptors
if i*5+j <= numDesc:
startY = i * wAndH
endY = (i + 1) * wAndH
startX = j * wAndH
endX = (j + 1) * wAndH
img[startY:endY, startX:endX] = descriptorArray[:,:,i*5+j]
imgutil.imageShow(img, "descriptor")
return img
def settleAutoExposure(self, numFrames=30, display=True):
'''
Run image acquisition for a while in auto exposure mode.
'''
self.setAutoExposure(True)
self.startTransmission()
for i in range(numFrames):
npimg = self.acquireFrame()
if display:
imgutil.imageShow(npimg, "Auto", False, wait=10)
self.setAutoExposure(False)
def settleAutoExposure(self, numFrames=30, display=True):
'''
Run image acquisition for a while in auto exposure mode.
'''
self.setAutoExposure(True)
self.startTransmission()
for i in range(numFrames):
npimg = self.acquireFrame()
if display:
imgutil.imageShow(npimg, "Auto", False, wait=10)
self.setAutoExposure(False)
def testStart():
# connect to firewire camera, select frame format
fwcam = FirewireVideo(0, DC1394_ISO_SPEED_800)
fwcam.setVideoMode(DC1394_VIDEO_MODE_1024x768_RGB8, DC1394_FRAMERATE_15)
# set up camera parameters
fwcam.setExposureAbsolute(brightness=0, gamma=1.0)
fwcam.setAutoExposure(True)
fwcam.setColorAbsolute(whiteBlue=1023, whiteRed=276)
# start grabbing video frames
fwcam.startTransmission()
# display frames forever
index = 0
poll = 100
t0 = time.time()
key = None
while key != 27:
# grab a frame
frame = fwcam.acquireFrame()
key = imgutil.imageShow(frame, "fwvideo", False, 10)
index += 1
t1 = time.time()
if index % poll == 0:
fwcam.printFeatures(False)
print "{0:8}: {1:8.3f} fps".format(index, float(poll) / (t1-t0))
t0 = t1
# disconnect from camera
fwcam.setAutoExposure(False)
fwcam.stopTransmission()
fwcam.closeVideoDevice()
def testStart():
# connect to firewire camera, select frame format
fwcam = FirewireVideo(0, DC1394_ISO_SPEED_800)
fwcam.setVideoMode(DC1394_VIDEO_MODE_640x480_RGB8, DC1394_FRAMERATE_30)
# set up camera parameters
fwcam.setExposureAbsolute(brightness=0, gamma=1.0)
fwcam.setAutoExposure(True)
fwcam.setColorAbsolute(whiteBlue=1023, whiteRed=276)
# start grabbing video frames
fwcam.startTransmission()
# display frames forever
index = 0
poll = 100
t0 = time.time()
key = None
while key != 27:
# grab a frame
frame = fwcam.acquireFrame()
key = imgutil.imageShow(frame, "fwvideo", False, 10)
index += 1
t1 = time.time()
if index % poll == 0:
fwcam.printFeatures(False)
print "{0:8}: {1:8.3} fps".format(index, poll / (t1-t0))
t0 = t1
# disconnect from camera
fwcam.setAutoExposure(False)
fwcam.stopTransmission()
fwcam.closeVideoDevice()
def testRectifying(self):
'''
This function tests the rectification of an image
it is structured so you have to manually manipulate
the four corners that the points you choose to go to
'''
cv.NamedWindow("grab", cv.CV_WINDOW_NORMAL)
grab = ginput.Grab("grab", 4)
key = None
while key != 27:
# grab frame
cvimg = cv.LoadImage("../perspective.jpg")
# draw points
pts = grab.getPoints()
for pxy in pts:
cv.Rectangle(cvimg, (pxy[0]-15, pxy[1]-15), (pxy[0]+15, pxy[1]+15), (0, 0, 255))
# display
cv.ShowImage("grab", cvimg)
# handle keys
key = cv.WaitKey(100)
if key >= 0 and chr(key) == 'c':
grab.clear(4)
if pts.shape[0] != 4:
return
npimg = imgutil.cv2array(cvimg)
#The points that the point you choose are transformed to
squarePts = numpy.array([[0,0],
[100, 0],
[0,200],
[100,200]])
#Finds the homography based on the points
homography = transform.homography(pts, squarePts)
output = transform.transformImage(npimg, homography, "auto")
imgutil.imageShow(output, "Homography")
# handle keys
cv.WaitKey(0)
def createPanorama(self, display = False):
'''
builds the entire panorama
'''
#adding the identity matrix as the first matrix
self.homographies.append(self.identity)
self.constPanObjects()
if self.pick == False:
self.setHomographies()
else:
self.pickPoints()
if len(self.panImages) == 0:
print "You don't have any images"
exit()
#setting the np Image of the last picture
self.panImages[-1].setNpImg( imgutil.cv2array(self.panImages[-1].getImg()))
self.removeSquares()
self.panImages[0].setHomography(self.identity)
self.MapHomography()
#gets all the corners of the Panorama
corners = self.panCorners()
#transforms all the images based on there homographies
for panImg in self.panImages:
npimg = panImg.getNpImg()
hom = panImg.getHomography()
output = transform.transformImage(npimg, hom, corners)
panImg.setOutput(output)
if display == True:
for panImg in self.panImages:
imgutil.imageShow(panImg.getOutput(), "Panorama")
average = self.combineImage()
imgutil.imageShow(average)
cv.WaitKey(0)
def showMatches(self, img1, img2, matches1, matches2): ''' Draws lines between matching features @img: a cvimg @harrisCorners: an array of matches ''' npimg1 = imgutil.cv2array(img1) npimg2 = imgutil.cv2array(img2) #create a new window combined = numpy.zeros((max(npimg1.shape[0],npimg2.shape[0]),npimg1.shape[1]+npimg2.shape[1],3)) combined[0:npimg1.shape[0],0:npimg1.shape[1],...] = npimg1 combined[0:npimg2.shape[0],npimg1.shape[1]:npimg1.shape[1]+npimg2.shape[1],...] = npimg2 combined = imgutil.array2cv(combined) #draw lines for i in range(matches1.shape[0]): cv.Line(combined, (int(matches1[i,0]),int(matches1[i,1])), (int(matches2[i,0]+npimg1.shape[1]),int(matches2[i,1])), (0, 255, 0)) combined = imgutil.cv2array(combined) #show the image imgutil.imageShow(combined, "combined")
def displayMatches(self, matchPts, inliers):
'''
a display is shown of the matches that connect the two images
'''
matchOne = matchPts[:,:2]
matchTwo = matchPts[:,2:]
self.drawSquare(self.imageOne, matchOne, self.matchColor)
self.drawSquare(self.imageTwo, matchTwo, self.matchColor)
inlierMatchOne = matchOne[inliers]
inlierMatchTwo = matchTwo[inliers]
self.drawSquare(self.imageOne, inlierMatchOne, self.inliersColor)
self.drawSquare(self.imageTwo, inlierMatchTwo, self.inliersColor)
npimg = imgutil.cv2array(self.imageOne)
npimg2 = imgutil.cv2array(self.imageTwo)
displayPic = numpy.hstack((npimg,npimg2))
#add the width of image one to image two
#inlierMatchTwo[:,0] = inlierMatchTwo[:,0] + npimg.shape[1]
cvimg = imgutil.array2cv(displayPic)
for i in range(inlierMatchOne.shape[0]):
x1, y1 = int(inlierMatchOne[i,0]), int(inlierMatchOne[i,1])
x2, y2 = int(inlierMatchTwo[i,0]+ npimg.shape[1]), int(inlierMatchTwo[i,1])
cv.Line(cvimg, (x1,y1), ( x2, y2), self.inliersColor )
imgutil.imageShow(cvimg, "matchImg")
cv.WaitKey(0)
def testScale():
# pipeSource = source.CameraFW(0,
# FirewireVideo.DC1394_VIDEO_MODE_640x480_RGB8,
# FirewireVideo.DC1394_FRAMERATE_15)
# pipeSource.getCamera().setColorAbsolute(whiteBlue=1023, whiteRed=276)
pipeSource = source.CameraCV()
#pipeSource = source.FileReader("../hw3/leukemia_40x.png")
gainBias = GainBias(pipeSource.getOutput())
key = None
frame = 0
t0 = time.time()
span = 30
while key != 27:
pipeSource.updatePlayMode()
gainBias.update()
key = imgutil.imageShow(
gainBias.getOutput().getData(), "GB", False, 10)
if key >= 0:
key &= 255 # Convert Unicode key to ASCII
char = chr(key)
print "Key: ", key, char
if char == "-":
gainBias.setScale(gainBias.getScale() * 0.95)
elif char == "=":
gainBias.setScale(gainBias.getScale() * 1.05)
elif char == "[":
gainBias.setOffset(gainBias.getOffset() - 5.0)
elif char == "]":
gainBias.setOffset(gainBias.getOffset() + 5.0)
elif char == "r":
gainBias.setScale(1.0)
gainBias.setOffset(0.0)
elif char == "a":
gainBias.setAutoContrast(not gainBias.getAutoContrast())
elif char == "p":
gainBias.setPreserveIntensity(not gainBias.getPreserveIntensity())
frame += 1
if frame % span == 0:
t1 = time.time()
print "{0:8.5f} fps".format(span / (t1 - t0))
t0 = t1
def testCameraFW():
# acquire images from the firewire camera
camfw = CameraFW(0,
FirewireVideo.DC1394_VIDEO_MODE_800x600_RGB8,
FirewireVideo.DC1394_FRAMERATE_15)
# cycle through images until escape is pressed
key = None
i = 0
while key != 27:
camfw.updatePlayMode()
camfw.update()
key = imgutil.imageShow(camfw.getOutput(0).getData(), "pipeline", False, 10)
i += 1
if i % 20 == 0:
print "{0}: {1:8.3f} fps".format(i, camfw.getCamera().computeFramerate())
def testCameraFW():
# acquire images from the firewire camera
camfw = CameraFW(0, FirewireVideo.DC1394_VIDEO_MODE_800x600_RGB8,
FirewireVideo.DC1394_FRAMERATE_15)
# cycle through images until escape is pressed
key = None
i = 0
while key != 27:
camfw.updatePlayMode()
camfw.update()
key = imgutil.imageShow(
camfw.getOutput(0).getData(), "pipeline", False, 10)
i += 1
if i % 20 == 0:
print "{0}: {1:8.3f} fps".format(
i,
camfw.getCamera().computeFramerate())
def test_scale_offset():
'''
Displays video and tests the scale_offset operator.
'''
self.add_operator(Scale_Offset)
#TODO: might want to have display code separate from this function
# to avoid duplication (definately wnat something like this though)
key = None
frame = 0
t0 = time.time()
span = 30
# while we're not pressing escape...
while key != 27:
# update the pipeline's source and the operator
self.source.updatePlayMode()
self.update_pipeline()
key = imgutil.imageShow(
self.last_operator.getOutput().getData(), "pipeline", False, 10)
if key >= 0:
char = chr(key)
print "Key: ", key, char
if char == "-":
scale_offset.setScale(scale_offset.getScale() * 0.95)
elif char == "=":
scale_offset.setScale(scale_offset.getScale() * 1.05)
elif char == "[":
scale_offset.setOffset(scale_offset.getOffset() - 5.0)
elif char == "]":
scale_offset.setOffset(scale_offset.getOffset() + 5.0)
elif char == "r":
scale_offset.setScale(1.0)
scale_offset.setOffset(0.0)
frame += 1
if frame % span == 0:
t1 = time.time()
print "{0:8.5f} fps".format(span / (t1 - t0))
t0 = t1
def testScale():
# pipeSource = source.CameraFW(0,
# FirewireVideo.DC1394_VIDEO_MODE_640x480_RGB8,
# FirewireVideo.DC1394_FRAMERATE_15)
# pipeSource.getCamera().setColorAbsolute(whiteBlue=1023, whiteRed=276)
pipeSource = source.CameraCV()
scale = Scale(pipeSource.getOutput())
offset = Offset(scale.getOutput())
key = None
frame = 0
t0 = time.time()
span = 30
while key != 27:
pipeSource.updatePlayMode()
offset.update()
key = imgutil.imageShow(
offset.getOutput().getData(), "pipeline", False, 10)
if key >= 0:
char = chr(key)
print "Key: ", key, char
if char == "-":
scale.setScale(scale.getScale() * 0.95)
elif char == "=":
scale.setScale(scale.getScale() * 1.05)
elif char == "[":
offset.setOffset(offset.getOffset() - 5.0)
elif char == "]":
offset.setOffset(offset.getOffset() + 5.0)
elif char == "r":
scale.setScale(1.0)
offset.setOffset(0.0)
frame += 1
if frame % span == 0:
t1 = time.time()
print "{0:8.5f} fps".format(span / (t1 - t0))
t0 = t1
def panorama(self, sigma): ''' Creates a panorama with alpha stitching and displays ''' #print "\n--------------------------------" #print "Panorama " #print "--------------------------------\n" #list to hold the homographies inlierL = [] homography = [numpy.matrix(numpy.identity(3))] # find the homography between each set of pictures for i in range(len(self.files)-1): #get everything for image 1 img1 = cv.LoadImage(self.files[i]) npimg1 = imgutil.cv2array(img1) npimg1 = self.grayscale(npimg1) pts1 = feature.harris(npimg1,count=512) desc1 = self.extract(npimg1, pts1) #get everything for image 2 img2 = cv.LoadImage(self.files[i+1]) npimg2 = imgutil.cv2array(img2) npimg2 = self.grayscale(npimg2) pts2 = feature.harris(npimg2,count=512) desc2 = self.extract(npimg2, pts2) matches = self.matching(desc1,desc2) self.showHarris(img1, pts1[matches[:,0]]) self.showHarris(img2, pts2[matches[:,1]]) """ montagePts = feature.harris(npimg1,count=20) montageDesc = self.extract(npimg1, montagePts) montage = self.montage(montageDesc, numCols=5) imgutil.imageShow(montage, "montage") """ imgutil.imageShow(img1,"image1") imgutil.imageShow(img2,"image2") #cv.WaitKey(0) matches1 = pts1[matches[:,0],0:2] matches2 = pts2[matches[:,1],0:2] data = numpy.hstack((matches1,matches2)) h = self.ransac(data,0.5) self.showMatches(img1, img2, data[h[1]][:,0,0:2], data[h[1]][:,0,2:]) homography.append(numpy.linalg.inv(h[0])) inlierL.append(h[1]) #print "List of homographies: " #print homography midHomographyL = [] #map all the homographies to image 1 for i in range(1,len(homography)): homography[i] = homography[i-1] * homography[i] middle = len(self.files)/2 for i in range(len(homography)): #warp mid, Him = Hm0^-1 * Hi0 where m is middle image inverse = numpy.linalg.inv(homography[middle]) midHomography = inverse * homography[i] midHomographyL.append(midHomography) #find bounds of global extent and original picture warpedL = [] output_range = self.corners(midHomographyL)[0] midCorners = self.corners(midHomographyL)[1] # warp the images for i in range(len(self.files)): #convert the file cvimg = cv.LoadImage(self.files[i]) npimg = imgutil.cv2array(cvimg) #compute the gaussian weight h = npimg.shape[0] w = npimg.shape[1] yy,xx = numpy.mgrid[0:h,0:w] dist = (yy - h/2)**2 + (xx - w/2)**2 gwt = numpy.exp(-dist/(2.0*sigma**2)) #add the gaussian weight as the 4th channel npimg = numpy.dstack((npimg,gwt)) #append the warped image to the list warpedImg = transform.transformImage(npimg,midHomographyL[i], output_range) warpedL.append(warpedImg) imgutil.imageShow(warpedImg, "test") #stich the images top = numpy.zeros(warpedL[0].shape,dtype=float) bot = numpy.zeros(warpedL[0].shape,dtype=float) bot[:,:,3]=1.0 for i in range(len(warpedL)): top[:,:,0] += warpedL[i][:,:,3] * warpedL[i][:,:,0] top[:,:,1] += warpedL[i][:,:,3] * warpedL[i][:,:,1] top[:,:,2] += warpedL[i][:,:,3] * warpedL[i][:,:,2] top[:,:,3] += warpedL[i][:,:,3] bot[:,:,0] += warpedL[i][:,:,3] bot[:,:,1] += warpedL[i][:,:,3] bot[:,:,2] += warpedL[i][:,:,3] bot[bot == 0] = 1 output = top/bot #autoCrop if it is on if self.autoCrop: output = self.crop(output, output_range, midCorners[0:2,...]) #show the panorama print "showing panorama" imgutil.imageShow(output, "final") cv.WaitKey(0)
def main():
# set up command line parameters
parser = optparse.OptionParser()
parser.add_option("-p", "--path", help="the path of the image folder", default="./bacteria/")
parser.add_option("-r", "--rect", help="rectangle to crop", default="95,925,40,1315")
parser.add_option("-n", "--num", help="number of background frames", default=60)
parser.add_option("-f", "--ff", help="flatfield file to write/read from", default=None)
parser.add_option("-w", "--write", help="write to ff and bg file", default=False, action="store_true")
parser.add_option("-b", "--bg", help="bg file to write/read from", default=None)
parser.add_option("-a", "--alpha", help="number of standard deviations to use in segmentation", default=4)
parser.add_option("-s", "--save", help="save displays to disk", default=False, action="store_true")
options, remain = parser.parse_args()
# parse command line parameters
rawFlat = options.path + "/flat*.raw"
rawBact = options.path + "/bact*.raw"
bgFrames = int(options.num)
coords = options.rect
coords.strip("()")
coords = tuple(int(x) for x in coords[:].split(","))
# check if fffile exists
try:
if options.ff:
fffile = open(options.ff, "w" if options.write else "r")
else:
fffile = None
except IOError:
print "Can't read from ff file"
return 0
# check if bgfile exists
try:
if options.bg:
bgfile = open(options.bg, "w" if options.write else "r")
else:
bgfile = None
except IOError:
print "Can't read from bg file"
return 0
# crop and convert the images to proper
ic = ImageConversion(None, numpy.float64, coords)
# determine if the fffile needs to be created or read from file
if fffile and not options.write:
ffimg = numpy.load(fffile)
else:
# acquire flat field images from file
files = glob.glob(rawFlat)
ffs = source.FileStackReader(files)
# convert images and apply flat field correction
ic.setInput(ffs.getOutput())
flatField = FlatFieldAcquisition(ic.getOutput(), len(files))
for _ in range(len(files)):
ffs.increment()
flatField.update()
ffimg = flatField.getFlatField()
# write the image if flagged
if fffile and options.write:
numpy.save(fffile, ffimg)
# grab the bacteria images from the directory and sort them
files = glob.glob(rawBact)
files.sort()
# calibrate the converted images
calibrated = FlatFieldCalibration(ic.getOutput(),ff=ffimg)
# determine if the bgfile needs to be created or read from file
if bgfile and not options.write:
bgImg, stdDevImg = pickle.load(bgfile)
else:
# grab background images
ffs = source.FileStackReader(files[:bgFrames])
# convert images to correct size and type
ic.setInput(ffs.getOutput())
calibrated = FlatFieldCalibration(ic.getOutput(),ff=ffimg)
bgMean = StdDevImageAcquisition(calibrated.getOutput(), bgFrames)
for _ in range(bgFrames):
ffs.increment()
bgMean.update()
# calculate the background and standard deviation images
bgImg = bgMean.getMean()
stdDevImg = bgMean.getStdDev()
if bgfile and options.write:
pickle.dump((bgImg, stdDevImg), bgfile)
# grab the bacteria frames
ffs = source.FileStackReader(files[bgFrames:])
ffs.setLoop(True)
ic.setInput(ffs.getOutput())
#segmented = BinarySegmentationOld(calibrated.getOutput(), bgImg, -5)
segmented = BinarySegmentation(calibrated.getOutput(), bgImg, stdDevImg, alpha=float(options.alpha))
# open -> accumulate -> open
opened = Opening(segmented.getOutput(), iterations=1)
acc = OrAccumulating(opened.getOutput())
opened2 = Opening(acc.getOutput(), iterations=6)
# find labels
labels = ConnectedComponents(opened2.getOutput())
# gather information
rp = RegionProperties(labels.getOutput())
wk = 0
count = 0
while wk != 27:
count += 1
ffs.increment()
rp.update()
print ffs.getIndex()
b=str("b-%03d.png" %(count))
s=str("s-%03d.png" %(count))
l=str("l-%03d.png" %(count))
p=str("p-%03d.png" %(count))
# display windows
wk = imgutil.imageShow(segmented.getOutput().getData(), wait = 10, title="Binary Segmentation", file=b, flag=options.flag)&255
wk = imgutil.imageShow(ic.getOutput().getData(), wait = 10, title="Source", file=s, flag=options.flag)&255
wk = imgutil.imageShow(labels.getOutput().getData(), wait = 10, title="Labels", file=l, flag=options.flag)&255
wk = imgutil.imageShow(rp.getOutput().getData(), wait = 10, title="Perimeter", file=p, flag=options.flag)&255
# display windows
wk = imgutil.imageShow(segmented.getOutput().getData(), wait = 10, title="Binary Segmentation")&255
wk = imgutil.imageShow(ic.getOutput().getData(), wait = 10, title="Source")&255
wk = imgutil.imageShow(labels.getOutput().getData(), wait = 10, title="Labels")&255
wk = imgutil.imageShow(rp.getOutput().getData(), wait = 10, title="Perimeter")&255
# waits for the user to reset the image loop
if ffs.getIndex() == ffs.getLength()-1:
pickle.dump((labels.getOutput().getData(), rp.com, rp.area, rp.circularity), file("data.p","w"))
growth(rp.com, rp.area, rp.circularity)
raw_input("Press enter to reset")
acc.reset()
rp.reset()
# keys for controlling the number of standard deviations
# up arrow: increases standard deviations
# down arrow: decreases standard deviations
if wk==82:
segmented.setAlpha(segmented.getAlpha()+0.05)
print segmented.getAlpha()
acc.reset()
rp.reset()
if wk==84:
print segmented.getAlpha()
segmented.setAlpha(segmented.getAlpha()-0.05)
acc.reset()
rp.reset()
def display_avg(self):
avg_image = self.avg.get_avg_image()
if avg_image != None:
imgutil.imageShow(avg_image, "Averaged image", False, 10)