def main():
img = cv.LoadImage("sample/image3114.png")
h, s, v = get_hsv_planes(img)
hist = get_gray_histogram(h, 180, 180)
clear_hist_in_range(hist, *h2cv_values(50, 340))
hist_img = get_hist_image(hist, 180, 500)
h_mask = image_empty_clone(h)
cv.CalcBackProject((h, ), h_mask, hist)
show_images({"img": img, "h": h, "h_mask": h_mask, "hist_img": hist_img})
def hs_histogram(src, patch):
# Convert to HSV
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
hsv_patch= cv.CreateImage(cv.GetSize(patch), 8, 3)
# Extract the H and S planes
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
h_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
h_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
s_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
v_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
cv.Split(hsv, h_plane, s_plane, v_plane, None)
cv.Split(hsv, h_plane_img, s_plane_img, None, None)
cv.Split(hsv_patch, h_plane_patch, s_plane_patch, None, None)
#cv.Split(src, h_plane, s_plane, v_plane, None)
planes = [h_plane_patch, s_plane_patch]#, s_plane, v_plane]
h_bins = 30
s_bins = 32
v_bins = 30
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]
v_ranges = [0, 255]
ranges = [h_ranges, s_ranges]#, s_ranges, v_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)
back_proj_img = cv.CreateImage(cv.GetSize(src), 8, 1)
cv.CalcBackProject([h_plane_img, s_plane_img], back_proj_img, 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 / 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 back_proj_img, hist
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 back_project_hs(self, img):
self.check_for_hist()
hsv = cv.CreateImage(cv.GetSize(img), 8, 3)
scratch = cv.CreateImage(cv.GetSize(img), 8, 1)
back_proj_img = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.CvtColor(img, hsv, cv.CV_BGR2HSV)
h_plane_img = cv.CreateImage(cv.GetSize(img), 8, 1)
s_plane_img = cv.CreateImage(cv.GetSize(img), 8, 1)
cv.Split(hsv, h_plane_img, s_plane_img, None, None)
cv.CalcBackProject([h_plane_img, s_plane_img], back_proj_img, self.hist)
#cv.Threshold(back_proj_img, back_proj_img, 200, 255, cv.CV_THRESH_BINARY)
#cv.MorphologyEx(back_proj_img, back_proj_img, None, None, cv.CV_MOP_OPEN, 1)
#cv.MorphologyEx(back_proj_img, back_proj_img, None, None, cv.CV_MOP_CLOSE, 2)
return back_proj_img, self.hist
def back_project_hs(src, patch):
# Convert to HSV
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
hsv_patch= cv.CreateImage(cv.GetSize(patch), 8, 3)
cv.CvtColor(patch, hsv_patch, cv.CV_BGR2HSV)
# Extract the H and S planes
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
h_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
h_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane_img = cv.CreateImage(cv.GetSize(src), 8, 1)
s_plane_patch = cv.CreateMat(patch.rows, patch.cols, cv.CV_8UC1)
v_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
cv.Split(hsv, h_plane, s_plane, v_plane, None)
cv.Split(hsv, h_plane_img, s_plane_img, None, None)
cv.Split(hsv_patch, h_plane_patch, s_plane_patch, None, None)
#cv.Split(src, h_plane, s_plane, v_plane, None)
planes = [h_plane_patch, s_plane_patch]#, s_plane, v_plane]
# planes = [s_plane_patch]#, s_plane, v_plane]
h_bins = 30
s_bins = 32
hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again */
h_ranges = [0, 180]
s_ranges = [0, 255]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
ranges = [h_ranges, s_ranges]#, s_ranges, v_ranges]
#ranges = [s_ranges]#, s_ranges, v_ranges]
scale = 1
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
#hist = cv.CreateHist([s_bins], cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
(min_value, max_value, _, _) = cv.GetMinMaxHistValue(hist)
#cv.NormalizeHist(hist, 20*250.0)
print "min hist value is :", min_value
print "max hist value is :", max_value
back_proj_img = cv.CreateImage(cv.GetSize(src), 8, 1)
#cv.NormalizeHist(hist, 2000)
cv.CalcBackProject([h_plane_img, s_plane_img], back_proj_img, hist)
back_modified = cv.CreateImage(cv.GetSize(src), 8, 1)
back_modified2 = cv.CreateImage(cv.GetSize(src), 8, 1)
# cv.Dilate(back_proj_img, back_proj_img)
# cv.Erode(back_proj_img, back_proj_img)
#cv.Smooth(back_proj_img, back_modified)
#cv.AdaptiveThreshold(back_proj_img, back_modified, 255, adaptive_method=cv.CV_ADAPTIVE_THRESH_GAUSSIAN_C)
#cv.Threshold(back_proj_img, back_modified, 250, 255, cv.CV_THRESH_BINARY)
#cv.MorphologyEx(back_modified,back_modified2, None, None, cv.CV_MOP_CLOSE, 3)
#cv.MorphologyEx(back_modified,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
# cv.MorphologyEx(back_proj_img,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
#cv.MorphologyEx(back_modified2,back_modified2, None, None, cv.CV_MOP_OPEN, 2)
cv.MorphologyEx(back_proj_img,back_modified, None, None, cv.CV_MOP_OPEN, 1)
cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_CLOSE, 2)
cv.Threshold(back_modified, back_modified, 250, 255, cv.CV_THRESH_BINARY)
# cv.MorphologyEx(back_proj_img,back_modified2, None, None, cv.CV_MOP_CLOSE, 1)
# cv.MorphologyEx(back_modified2,back_modified2, None, None, cv.CV_MOP_OPEN, 2)
#cv.FloodFill(back_modified, (320, 240), cv.Scalar(255), cv.Scalar(30), cv.Scalar(30), flags=8)
# for i in xrange (10):
# cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_OPEN, 3)
# cv.MorphologyEx(back_modified,back_modified, None, None, cv.CV_MOP_CLOSE, 1)
#cv.SubRS(back_modified, 255, back_modified)
# cv.CalcBackProject([s_plane_img], back_proj_img, hist)
# cv.Scale(back_proj_img, back_proj_img, 30000)
cv.ShowImage("back_projection", back_proj_img)
cv.ShowImage("back_modified", back_modified)
cv.ShowImage("back_modified2", back_modified2)
cv.WaitKey(0)
#return back_proj_img, hist
return back_modified, hist