def createHist(img):
#cv.CvtColor(img,img,cv.CV_BGR2HSV)
b_plane = cv.CreateImage((img.width,img.height), 8, 1)
g_plane = cv.CreateImage((img.width,img.height), 8, 1)
r_plane = cv.CreateImage((img.width,img.height), 8, 1)
cv.Split(img,b_plane,g_plane,r_plane,None)
planes = [b_plane, g_plane, r_plane]
bins = 4
b_bins = bins
g_bins = bins
r_bins = bins
hist_size = [b_bins,g_bins,r_bins]
b_range = [0,255]
g_range = [0,255]
r_range = [0,255]
ranges = [b_range,g_range,r_range]
hist = cv.CreateHist(hist_size, cv.CV_HIST_ARRAY, ranges, 1)
cv.CalcHist([cv.GetImage(i) for i in planes], hist)
cv.NormalizeHist(hist,1)
return hist
def hs_histogram(src, mask=None):
'''Takes a cvMat and computes a hue-saturation histogram (value is dropped)'''
# Convert to HSV
# Allocate the 3 HSV 8 bit channels
hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
# Convert from the default BGR representation to HSV
cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
# Extract the H and S planes
h_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
s_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
v_plane = cv.CreateMat(src.rows, src.cols, cv.CV_8UC1)
# Copy out omitting the values we don't want
cv.Split(hsv, h_plane, s_plane, v_plane, None)
planes = [h_plane, s_plane]
if 0:
pplane('H plane', h_plane, prefix=' ')
pplane('S plane', s_plane, prefix=' ')
h_bins = 8
s_bins = 8
#hist_size = [h_bins, s_bins]
# hue varies from 0 (~0 deg red) to 180 (~360 deg red again
# ??? My values give a hue of 0-255
h_ranges = [0, 255]
# saturation varies from 0 (black-gray-white) to
# 255 (pure spectrum color)
s_ranges = [0, 255]
ranges = [h_ranges, s_ranges]
# Allocate bins
# note that we haven't put any data in yet
#1: uniform
hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1)
# Convert the array planes back into images since thats what CalcHist needs?
# Doc seems to indcate that cvMat will work as well
# Doesn't this cross the hue and saturate values together? That doesn't seem to make sense, like comparing red to blue
# Guess its a 2D histogram so it deals with where they overlap
cv.CalcHist([cv.GetImage(i) for i in planes], hist, 0, mask)
cv.NormalizeHist(hist, 1.0)
return hist
def compute_histogram(src, bins=255):
hist = cv.CreateHist([255], cv.CV_HIST_ARRAY, ranges=[(0, 256)])
cv.CalcHist([src], hist) #compute histogram
cv.NormalizeHist(hist, 1.0) #normalize hist
return hist
import cv2.cv as cv
im = cv.LoadImage("meinv.jpg", cv.CV_8U)
cv.SetImageROI(im, (1, 1, 30, 30))
histsize = 256 #Because we are working on grayscale pictures
hist = cv.CreateHist([histsize], cv.CV_HIST_ARRAY, [[0, histsize]], 1)
cv.CalcHist([im], hist)
cv.NormalizeHist(
hist, 1) # The factor rescale values by multiplying values by the factor
_, max_value, _, _ = cv.GetMinMaxHistValue(hist)
if max_value == 0:
max_value = 1.0
cv.NormalizeHist(hist, 256 / max_value)
cv.ResetImageROI(im)
res = cv.CreateMat(im.height, im.width, cv.CV_8U)
cv.CalcBackProject([im], res, hist)
cv.Rectangle(im, (1, 1), (30, 30), (0, 0, 255), 2, cv.CV_FILLED)
cv.ShowImage("Original Image", im)
cv.ShowImage("BackProjected", res)
cv.WaitKey(0)