def process(self, videofile, progress):
progress(0, _("Extracting histogram"))
video = hg.cvCreateFileCapture(str(videofile).encode(sys.getfilesystemencoding()))
if not video:
raise Exception("Could not open video file")
histo = cv.cvCreateHist([256],cv.CV_HIST_ARRAY,[[0,256]], 1)
frame = hg.cvQueryFrame(video)
frame_gray = cv.cvCreateImage(cv.cvGetSize(frame), frame.depth, 1);
hists = []
nbframes = 0
fps = hg.cvGetCaptureProperty(video, hg.CV_CAP_PROP_FPS)
while frame :
if not progress(hg.cvGetCaptureProperty(video, hg.CV_CAP_PROP_POS_AVI_RATIO)):
break
hg.cvConvertImage(frame,frame_gray)
cv.cvCalcHist(frame_gray,histo,0,None)
h = [cv.cvGetReal1D(histo.bins,i) for i in range(255) ]
h = numpy.array(h,dtype='int32')
hists.append(h)
frame = hg.cvQueryFrame(video)
nbframes += 1
hists = numpy.array(hists)
return hists.reshape(nbframes, -1), fps
# compute the hsv version of the image
cv.cvCvtColor (frame, hsv, cv.CV_BGR2HSV)
# compute which pixels are in the wanted range
cv.cvInRangeS (hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.cvSplit (hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
hue_roi = cv.cvGetSubRect (hue, selection)
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 hsv version of the image
cv.cvCvtColor(frame, hsv, cv.CV_BGR2HSV)
# compute which pixels are in the wanted range
cv.cvInRangeS(hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.cvSplit(hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
hue_roi = cv.cvGetSubRect(hue, selection)
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 hsv version of the image
cv.cvCvtColor (frame, hsv, cv.CV_BGR2HSV)
# compute which pixels are in the wanted range
cv.cvInRangeS (hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.cvSplit (hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
hue_roi = cv.cvGetSubRect (hue, selection)
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)
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
if track_object:
_vmin = vmin
_vmax = vmax
cv.cvInRangeS( hsv,
cv.cvScalar( 0, smin,min(_vmin,_vmax),0),
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 )
# compute the hsv version of the image
cv.cvCvtColor (frame, hsv, cv.CV_BGR2HSV)
# compute which pixels are in the wanted range
cv.cvInRangeS (hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.cvSplit (hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
hue_roi = cv.cvGetSubRect (hue, selection)
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)
cv.CV_RGB(0, 255, 0), 2, 8, 0)
if track_object:
_vmin = vmin
_vmax = vmax
cv.cvInRangeS(hsv, cv.cvScalar(0, smin, min(_vmin, _vmax), 0),
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.cvZero(thresh_mask)
highgui.cvShowImage("Input", img)
# 5x5 Gaussian Blur
cv.cvSmooth(img, img, cv.CV_GAUSSIAN, 5, 5)
# convert to HSV
cv.cvCvtColor(img, img, cv.CV_BGR2HSV)
# threshold bad values
cv.cvInRangeS(img, hsv_min, hsv_max, thresh_mask)
cv.cvAnd(thresh_mask, mask_bw, thresh_mask)
# Hue(0,180), Saturation(0,255), Value(0,255)
cv.cvSplit(img, img_h, img_s, img_v, 0)
# calculate histogram
cv.cvCalcHist(img_h, h_hue, 0, thresh_mask)
cv.cvCalcHist(img_s, h_sat, 0, thresh_mask)
cv.cvCalcHist(img_v, h_val, 0, thresh_mask)
# Don't normalize, use total mask pixels to calculate relative importance
# cv.cvNormalizeHist(h_hue, 180)
# cv.cvNormalizeHist(h_sat, 255)
# cv.cvNormalizeHist(h_val, 255)
# minv,maxv,minp,maxp = cv.cvMinMaxLoc(img_h)
# print minv,maxv
cv.cvZero(hist_hue_img)
# hue_min,hue_max,min_loc,max_loc = cv.cvGetMinMaxHistValue(h_hue)
for h in xrange(h_bins):
hue = cv.cvGetReal1D(h_hue.bins, h)
color = hsv2rgb(h * h_limit / h_bins)
