def mask_image( im, mask ): if mask.depth == 8: bim = cv.cvCreateImage( cv.cvSize(mask.width, mask.height), cv.IPL_DEPTH_32F, mask.nChannels ) cv.cvConvertScale( mask, bim, 1.0/255.0) if im.depth == 8: newim = cv.cvCreateImage( cv.cvSize(im.width, im.height), cv.IPL_DEPTH_32F, im.nChannels ) cv.cvConvertScale( im, newim, 1.0/255.0) print 'newim.depth = ',newim.depth print 'newim.nChannels = ',newim.nChannels print 'bim.depth = ',bim.depth print 'bim.nChannels = ',bim.nChannels if newim.nChannels == 3 and newim.depth == 32 and bim.nChannels == 3 and bim.depth == 32: outputIm = cv.cvCloneImage( bim ) cv.cvMul( bim, newim, outputIm, 1 ) return outputIm else: print 'oops problem with formats' return mask
def mask_image(im, mask):
if mask.depth == 8:
bim = cv.cvCreateImage(cv.cvSize(mask.width, mask.height),
cv.IPL_DEPTH_32F, mask.nChannels)
cv.cvConvertScale(mask, bim, 1.0 / 255.0)
if im.depth == 8:
newim = cv.cvCreateImage(cv.cvSize(im.width, im.height),
cv.IPL_DEPTH_32F, im.nChannels)
cv.cvConvertScale(im, newim, 1.0 / 255.0)
print 'newim.depth = ', newim.depth
print 'newim.nChannels = ', newim.nChannels
print 'bim.depth = ', bim.depth
print 'bim.nChannels = ', bim.nChannels
if newim.nChannels == 3 and newim.depth == 32 and bim.nChannels == 3 and bim.depth == 32:
outputIm = cv.cvCloneImage(bim)
cv.cvMul(bim, newim, outputIm, 1)
return outputIm
else:
print 'oops problem with formats'
return mask
def display_images(image_list, max_x=1200, max_y=1000, save_images=False):
"""
Display a list of OpenCV images tiled across the screen
with maximum width of max_x and maximum height of max_y
save_images - will save the images(with timestamp)
"""
curtime = time.localtime()
date_name = time.strftime('%Y_%m_%d_%I%M%S', curtime)
loc_x, loc_y = 0, 0
wins = []
for i, im in enumerate(image_list):
if save_images:
if im.nChannels == 1 and im.depth == cv.IPL_DEPTH_32F:
clr = cv.cvCreateImage(cv.cvSize(im.width, im.height),
cv.IPL_DEPTH_8U, 1)
cv.cvConvertScale(im, clr, 255.0)
im = clr
highgui.cvSaveImage('image%d_' % i + date_name + '.png', im)
window_name = 'image %d' % i
wins.append((window_name, im))
highgui.cvNamedWindow(window_name, highgui.CV_WINDOW_AUTOSIZE)
highgui.cvMoveWindow(window_name, loc_x, loc_y)
loc_x = loc_x + im.width
if loc_x > max_x:
loc_x = 0
loc_y = loc_y + im.height
if loc_y > max_y:
loc_y = 0
while True:
for name, im in wins:
highgui.cvShowImage(name, im)
keypress = highgui.cvWaitKey(10)
if keypress == '\x1b':
break
def display_images(image_list, max_x = 1200, max_y = 1000, save_images=False):
"""
Display a list of OpenCV images tiled across the screen
with maximum width of max_x and maximum height of max_y
save_images - will save the images(with timestamp)
"""
curtime=time.localtime()
date_name = time.strftime('%Y_%m_%d_%I%M%S', curtime)
loc_x, loc_y = 0, 0
wins = []
for i, im in enumerate(image_list):
if save_images:
if im.nChannels == 1 and im.depth == cv.IPL_DEPTH_32F:
clr = cv.cvCreateImage(cv.cvSize(im.width, im.height), cv.IPL_DEPTH_8U, 1)
cv.cvConvertScale(im, clr, 255.0)
im = clr
highgui.cvSaveImage('image%d_'%i+date_name+'.png', im)
window_name = 'image %d' % i
wins.append((window_name, im))
highgui.cvNamedWindow(window_name, highgui.CV_WINDOW_AUTOSIZE)
highgui.cvMoveWindow(window_name, loc_x, loc_y)
loc_x = loc_x + im.width
if loc_x > max_x:
loc_x = 0
loc_y = loc_y + im.height
if loc_y > max_y:
loc_y = 0
while True:
for name, im in wins:
highgui.cvShowImage(name, im)
keypress = highgui.cvWaitKey(10)
if keypress == '\x1b':
break
mask_roi = cv.cvGetSubRect (mask, selection)
# it's time to compute the histogram
cv.cvCalcHist (hue_roi, hist, 0, mask_roi)
# extract the min and max value of the histogram
min_val, max_val = cv.cvGetMinMaxHistValue (hist, None, None)
# compute the scale factor
if max_val > 0:
scale = 255. / max_val
else:
scale = 0.
# scale the histograms
cv.cvConvertScale (hist.bins, hist.bins, scale, 0)
# clear the histogram image
cv.cvSetZero (histimg)
# compute the width for each bin do display
bin_w = histimg.width / hdims
for i in range (hdims):
# for all the bins
# get the value, and scale to the size of the hist image
val = cv.cvRound (cv.cvGetReal1D (hist.bins, i)
* histimg.height / 255)
# compute the color
def Process(inFile, outFile): image1 = highgui.cvLoadImage(inFile,0) sz = cv.cvGetSize(image1) image2 = cv.cvCreateImage(sz, 8, 1) cv.cvConvertScale(image1, image2, 1.2, 0) highgui.cvSaveImage(outFile, image2)
cv.cvScalar(180, 256, max(_vmin,_vmax),0),
mask );
cv.cvSplit( hsv, hue, None, None, None)
if track_object < 0:
max_val = 0.0
subhue = cv.cvGetSubRect(hue, selection)
submask = cv.cvGetSubRect(mask, selection)
cv.cvCalcHist( subhue, hist, 0, submask )
# extract the min and max value of the histogram
min_val, max_val, min_idx, max_idx = cv.cvGetMinMaxHistValue (hist)
if (max_val):
cv.cvConvertScale( hist.bins, hist.bins, 255.0 / max_val, 0)
else:
cv.cvConvertScale( hist.bins, hist.bins, 0.0, 0 )
track_window = selection
track_object = 1
cv.cvCalcArrBackProject( hue, backproject, hist )
cv.cvAnd( backproject, mask, backproject, 0 )
cv.cvCamShift( backproject, track_window,
cv.cvTermCriteria( cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1 ),
track_comp, track_box )
track_window = track_comp.rect
cv.cvScalar(180, 256, max(_vmin, _vmax), 0), mask)
cv.cvSplit(hsv, hue, None, None, None)
if track_object < 0:
max_val = 0.0
subhue = cv.cvGetSubRect(hue, selection)
submask = cv.cvGetSubRect(mask, selection)
cv.cvCalcHist(subhue, hist, 0, submask)
# extract the min and max value of the histogram
min_val, max_val, min_idx, max_idx = cv.cvGetMinMaxHistValue(
hist)
if (max_val):
cv.cvConvertScale(hist.bins, hist.bins, 255.0 / max_val, 0)
else:
cv.cvConvertScale(hist.bins, hist.bins, 0.0, 0)
track_window = selection
track_object = 1
cv.cvCalcArrBackProject(hue, backproject, hist)
cv.cvAnd(backproject, mask, backproject, 0)
cv.cvCamShift(
backproject, track_window,
cv.cvTermCriteria(cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10,
1), track_comp, track_box)
track_window = track_comp.rect
mask_roi = cv.cvGetSubRect(mask, selection)
# it's time to compute the histogram
cv.cvCalcHist(hue_roi, hist, 0, mask_roi)
# extract the min and max value of the histogram
min_val, max_val, min_idx, max_idx = cv.cvGetMinMaxHistValue(hist)
# compute the scale factor
if max_val > 0:
scale = 255. / max_val
else:
scale = 0.
# scale the histograms
cv.cvConvertScale(hist.bins, hist.bins, scale, 0)
# clear the histogram image
cv.cvSetZero(histimg)
# compute the width for each bin do display
bin_w = histimg.width / hdims
for i in range(hdims):
# for all the bins
# get the value, and scale to the size of the hist image
val = cv.cvRound(
cv.cvGetReal1D(hist.bins, i) * histimg.height / 255)
# compute the color
