def rgbBackProjectHist(im, fg_hist, bg_hist=None):
'''
Compute the hue saturation histogram of an image. (Based on OpenCV example code).
@param im: the image
@type im: pv.Image
@param fg_hist: the histogram
@type fg_hist: pv.Histogram
@param bg_hist:
@type bg_hist: pv.Histogram
@return: an OpenCV histogram
@rtype: pv.Image
'''
w, h = im.size
bgr = im.asOpenCV()
if bg_hist != None:
# set the histogram size
hist_size = [fg_hist.nbins1, fg_hist.nbins2, fg_hist.nbins3]
# pixel value ranges
b_ranges = [0, 255]
g_ranges = [0, 255]
r_ranges = [0, 255]
ranges = [b_ranges, g_ranges, r_ranges]
# Calculate the histogram
prob_hist = cv.CreateHist(hist_size, cv.CV_HIST_ARRAY, ranges, 1)
fg_hist.rescaleMax(255)
bg_hist.rescaleMax(255)
cv.CalcProbDensity(bg_hist.hist, fg_hist.hist, prob_hist, scale=255)
fg_hist = pv.Histogram(prob_hist, pv.HIST_HS, fg_hist.nbins1,
fg_hist.nbins2, None)
# Extract the H and S planes
b_plane = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
g_plane = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
r_plane = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
cv.Split(bgr, b_plane, g_plane, r_plane, None)
planes = [b_plane, g_plane, r_plane]
output = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
# Normalize the histogram
fg_hist.rescaleMax(255)
cv.CalcBackProject(planes, output, fg_hist.hist)
return pv.Image(output)
def hsBackProjectHist(im, fg_hist, bg_hist=None):
'''
Compute the hue saturation histogram of an image. (Based on OpenCV example code).
@param im: the image
@type im: pv.Image
@param fg_hist: the histogram
@type fg_hist: pv.Histogram
@param bg_hist:
@type bg_hist: pv.Histogram
@return: an OpenCV histogram
@rtype: pv.Image
'''
w, h = im.size
hsv = im.asHSV()
h_ranges = [0, 180]
s_ranges = [0, 255]
ranges = h_ranges + s_ranges
if bg_hist != None:
# set the histogram size
hist_size = [fg_hist.nbins1, fg_hist.nbins2]
# pixel value ranges
h_ranges = [0, 180]
s_ranges = [0, 255]
ranges = h_ranges + s_ranges
fg_hist.rescaleMax(255)
bg_hist.rescaleMax(255)
#fg_hist =
#cv2.calcProbDensity(bg_hist.hist, fg_hist.hist, prob_hist, scale=255)
fg_hist = pv.Histogram(fg_hist.hist / bg_hist.hist, pv.HIST_HS,
fg_hist.nbins1, fg_hist.nbins2, None)
#output = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
# Normalize the histogram
fg_hist.rescaleMax(255)
#cv.CalcBackProject(planes,output,fg_hist.hist)
#calcBackProject(images, channels, hist, ranges, scale[, dst]) -> dst
output = cv2.calcBackProject([hsv], [0, 1], fg_hist.hist, ranges, 1.0)
return pv.Image(output)
def RGBHist(im,r_bins=8,g_bins=8,b_bins=8,mask=None,normalize=True):
'''
Compute the rgb saturation histogram of an image. (Based on OpenCV example code).
@param im: the image
@type im: pv.Image
@param r_bins: the number of bins for hue.
@type r_bins: int
@param g_bins: the number of bins for hue.
@type g_bins: int
@param b_bins: the number of bins for hue.
@type b_bins: int
@param mask: an image containing a mask
@type mask: cv.Image or np.array(dtype=np.bool)
@return: an OpenCV histogram
@rtype: pv.Histogram
'''
w,h = im.size
bgr = im.asOpenCV()
# Extract the H and S planes
b_plane = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
g_plane = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
r_plane = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
cv.Split(bgr, b_plane, g_plane, r_plane, None)
planes = [b_plane, g_plane, r_plane]
# set the histogram size
hist_size = [b_bins, g_bins, r_bins]
# pixel value ranges
b_ranges = [0, 255]
g_ranges = [0, 255]
r_ranges = [0, 255]
ranges = [b_ranges, g_ranges, r_ranges]
# Calculate the histogram
hist = cv.CreateHist(hist_size, cv.CV_HIST_ARRAY, ranges, 1)
if mask != None:
mask = mask.asOpenCVBW()
cv.CalcHist(planes, hist, mask=mask)
return pv.Histogram(hist,HIST_RGB,b_bins,g_bins,r_bins)
def HSHist(im,h_bins=32,s_bins=32,mask=None,normalize=True):
'''
Compute the hue saturation histogram of an image. (Based on OpenCV example code).
@param im: the image
@type im: pv.Image
@param h_bins: the number of bins for hue.
@type h_bins: int
@param s_bins: the number of bins for saturation.
@type s_bins: int
@param mask: an image containing a mask
@type mask: cv.Image or np.array(dtype=np.bool)
@return: an OpenCV histogram
@rtype: pv.Histogram
'''
w,h = im.size
hsv = im.asHSV()
# Extract the H and S planes
h_plane = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
s_plane = cv.CreateImage((w,h), cv.IPL_DEPTH_8U, 1)
cv.Split(hsv, h_plane, s_plane, None, None)
planes = [h_plane, s_plane]
# set the histogram size
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
# Calculate the histogram
hist = cv.CreateHist(hist_size, cv.CV_HIST_ARRAY, ranges, 1)
if mask != None:
mask = mask.asOpenCVBW()
cv.CalcHist(planes, hist,mask=mask)
return pv.Histogram(hist,HIST_HS,h_bins,s_bins,None)
def HSHist(im, h_bins=32, s_bins=32, mask=None, normalize=True):
'''
Compute the hue saturation histogram of an image. (Based on OpenCV example code).
@param im: the image
@type im: pv.Image
@param h_bins: the number of bins for hue.
@type h_bins: int
@param s_bins: the number of bins for saturation.
@type s_bins: int
@param mask: an image containing a mask
@type mask: cv.Image or np.array(dtype=np.bool)
@return: an OpenCV histogram
@rtype: pv.Histogram
'''
w, h = im.size
hsv = im.asHSV()
# set the histogram size
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = h_ranges + s_ranges
channels = (0, 1)
# Calculate the histogram
if mask != None:
mask = mask.asOpenCV2BW()
hist = cv2.calcHist([hsv], channels, None, hist_size, ranges)
return pv.Histogram(hist, HIST_HS, h_bins, s_bins, None)