def hs_histogram(src):
# Convert to HSV
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
# Extract the H and S plane
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
cv.Split(hsv, h_plane, s_plane, None, None)
planes = [h_plane, s_plane]
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 to 180
h_ranges = [0, 180]
# saturation varies from 0 to 255
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
scale = 10
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
(_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
hist_img = cv.CreateImage((h_bins * scale, s_bins * scale), 8, 3)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv.QueryHistValue_2D(hist, h, s)
intensity = cv.Round(bin_val * 255 / max_value)
cv.Rectangle(hist_img, (h * scale, s * scale),
((h + 1) * scale - 1, (s + 1) * scale - 1),
cv.RGB(intensity, intensity, intensity), cv.CV_FILLED)
return hist_img
def calcEM(hist1, hist2, h_bins, s_bins):
#Define number of rows
numRows = h_bins * s_bins
sig1 = cv.CreateMat(numRows, 3, cv.CV_32FC1)
sig2 = cv.CreateMat(numRows, 3, cv.CV_32FC1)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv.QueryHistValue_2D(hist1, h, s)
cv.Set2D(sig1, h * s_bins + s, 0, cv.Scalar(bin_val))
cv.Set2D(sig1, h * s_bins + s, 1, cv.Scalar(h))
cv.Set2D(sig1, h * s_bins + s, 2, cv.Scalar(s))
bin_val = cv.QueryHistValue_2D(hist2, h, s)
cv.Set2D(sig2, h * s_bins + s, 0, cv.Scalar(bin_val))
cv.Set2D(sig2, h * s_bins + s, 1, cv.Scalar(h))
cv.Set2D(sig2, h * s_bins + s, 2, cv.Scalar(s))
#This is the important line were the OpenCV EM algorithm is called
return cv.CalcEMD2(sig1, sig2, cv.CV_DIST_L2)
def asMatrix(self):
if self.nbins2 == None:
result = np.zeros([self.nbins1])
for i in range(self.nbins1):
result[i] = cv.QueryHistValue_1D(self.hist,i)
return result
elif self.nbins3 == None:
result = np.zeros([self.nbins1,self.nbins2])
for i in range(self.nbins1):
for j in range(self.nbins2):
result[i,j] = cv.QueryHistValue_2D(self.hist,i,j)
return result
else:
result = np.zeros([self.nbins1,self.nbins2,self.nbins3])
for i in range(self.nbins1):
for j in range(self.nbins2):
for k in range(self.nbins3):
result[i,j,k] = cv.QueryHistValue_3D(self.hist,i,j,k)
return result
def get_2d_hist_img(x_bins=30, y_bins=32, scale=10, hist=None, img=None):
if not hist:
if img:
hist = get_hs_2d_hist(img, x_bins, y_bins)
else:
raise Exception("Histogram or image should be given")
(_, max_value, _, _) = cv.GetMinMaxHistValue(hist)
hist_img = cv.CreateImage((x_bins * scale, y_bins * scale), 8, 3)
for h in range(x_bins):
for s in range(y_bins):
bin_val = cv.QueryHistValue_2D(hist, h, s)
intensity = cv.Round(bin_val * 255 / max_value)
cv.Rectangle(
hist_img, (h * scale, s * scale),
((h + 1) * scale - 1, (s + 1) * scale - 1),
cv.RGB(255 - intensity, 255 - intensity,
255 - intensity), cv.CV_FILLED)
return hist_img
hueImg = cv.CreateMat(frameSmallHSV.height,
frameSmallHSV.width, cv.CV_8UC1)
satImg = cv.CreateMat(frameSmallHSV.height,
frameSmallHSV.width, cv.CV_8UC1)
cv.Split(frameSmallHSV, hueImg, satImg, None, None)
binSum = face.height * face.width
maxProbInt = 0
for x in range(0, frameSmallHSV.height):
for y in range(0, frameSmallHSV.width):
hue = int(hueImg[x, y] / (180 / hBins))
if hue == hBins:
hue = hBins - 1
sat = int(satImg[x, y] / (256 / sBins))
binVal = cv.QueryHistValue_2D(myHist, hue, sat)
binProb = binVal / binSum
probIntensity = int(binProb * 255 /
(maxValue / binSum))
skinProbImg[x, y] = probIntensity
if probIntensity > maxProbInt:
maxProbInt = probIntensity
cv.ShowImage("skinProb",
skinProbImg) #Original skin probability image
#threshold
cv.InRangeS(skinProbImg, 100, 255, skinProbImg)
cv.ShowImage(
"skinProbThresholded1", skinProbImg
) #Original skin probability image after thresholding
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, [(0, 180),
(0, 255)], 1)
cv.CalcHist(planes, hist, 0, None)
cv.NormalizeHist(hist, 1.0)
# Create an image to use to visualize our histogram.
scale = 10
hist_img = cv.CreateImage((h_bins * scale, s_bins * scale), 8, 3)
cv.Zero(hist_img)
max_value = 0
(minvalue, maxvalue, minidx, maxidx) = cv.GetMinMaxHistValue(hist)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv.QueryHistValue_2D(hist, h, s)
intensity = cv.Round(bin_val * 255 / maxvalue)
cv.Rectangle(hist_img, (h * scale, s * scale),
((h + 1) * scale - 1, (s + 1) * scale - 1),
(intensity, intensity, intensity), cv.CV_FILLED)
print intensity
cv.ShowImage("src", src)
cv.ShowImage("hsv", hist_img)
cv.WaitKey(0)
else:
print "usage : python example_7_1.py <image>"