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
# capture the 1st frame to get some propertie on it
frame = highgui.cvQueryFrame (capture)
# get some properties of the frame
frame_size = cv.cvGetSize (frame)
# compute which selection of the frame we want to monitor
selection = cv.cvRect (0, 0, frame.width, frame.height)
# create some images usefull later
hue = cv.cvCreateImage (frame_size, 8, 1)
mask = cv.cvCreateImage (frame_size, 8, 1)
hsv = cv.cvCreateImage (frame_size, 8, 3 )
# create the histogram
hist = cv.cvCreateHist ([hdims], cv.CV_HIST_ARRAY, hranges, 1)
while 1:
# do forever
# 1. capture the current image
frame = highgui.cvQueryFrame (capture)
if frame is None:
# no image captured... end the processing
break
# mirror the captured image
cv.cvFlip (frame, None, 1)
# compute the hsv version of the image
cv.cvCvtColor (frame, hsv, cv.CV_BGR2HSV)
frame = highgui.cvQueryFrame(capture)
# get some properties of the frame
frame_size = cv.cvGetSize(frame)
# compute which selection of the frame we want to monitor
selection = cv.cvRect(0, 0, frame.width, frame.height)
# create some images usefull later
hue = cv.cvCreateImage(frame_size, 8, 1)
mask = cv.cvCreateImage(frame_size, 8, 1)
hsv = cv.cvCreateImage(frame_size, 8, 3)
backproject = cv.cvCreateImage(frame_size, 8, 1)
# create the histogram
hist = cv.cvCreateHist([hdims], cv.CV_HIST_ARRAY, hranges, 1)
obj_hist = cv.cvCreateHist([hdims], cv.CV_HIST_ARRAY, hranges, 1)
while 1:
# do forever
# 1. capture the current image
frame = highgui.cvQueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
# mirror the captured image
#cv.cvFlip (frame, None, 1)
# compute the hsv version of the image
cv.cvCvtColor(frame, hsv, cv.CV_BGR2HSV)
sample_pixels = 0.0
for x in xrange(size.width):
for y in xrange(size.height):
if cv.cvGetReal2D(mask_bw, y, x) > 0:
sample_pixels = sample_pixels + 1
print "Sample region: %f%%" % (100 * sample_pixels / total_pixels)
del (tmp)
h_bins = 20
h_limit = 180
s_bins = 32
v_bins = 32
v_limit = 255
# create histogram with 30 bins
h_hue = cv.cvCreateHist([h_bins], cv.CV_HIST_ARRAY, [[0, h_limit]], 1)
h_sat = cv.cvCreateHist([s_bins], cv.CV_HIST_ARRAY, [[0, 255]], 1)
h_val = cv.cvCreateHist([v_bins], cv.CV_HIST_ARRAY, [[0, v_limit]], 1)
# histogram limits
hsv_min = cv.cvScalar(0, 30, 10, 0)
hsv_max = cv.cvScalar(180, 256, 256, 0)
scalewidth = 20
scaleheight = 400.0
# object classification boundries
# if the bin value a given pixel falls into is lower than one of these cutoffs,
# that pixel is classified as an obstacle
hue_cutoff = 0.01 * sample_pixels # 1.0%
val_cutoff = 0.015 * sample_pixels # 1.5%
