def __init__(self, **traits):
super(HistDemo, self).__init__(**traits)
img = cv.imread("lena.jpg")
gray_img = cv.Mat()
cv.cvtColor(img, gray_img, cv.CV_BGR2GRAY)
self.img = gray_img
self.img2 = self.img.clone()
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(
cv.vector_Mat([self.img]),
channels=cv.vector_int([0, 1]),
mask=cv.Mat(),
hist=result,
histSize=cv.vector_int([256]),
ranges=ranges,
)
data = ArrayPlotData(x=np.arange(0, len(result[:])), y=result[:])
self.plot = Plot(data, padding=10)
line = self.plot.plot(("x", "y"))[0]
self.select_tool = RangeSelection(line, left_button_selects=True)
line.tools.append(self.select_tool)
self.select_tool.on_trait_change(self._selection_changed, "selection")
line.overlays.append(RangeSelectionOverlay(component=line))
cv.imshow("Hist Demo", self.img)
self.timer = Timer(50, self.on_timer)
def __init__(self, **traits):
super(HistDemo, self).__init__(**traits)
img = cv.imread("lena.jpg")
gray_img = cv.Mat()
cv.cvtColor(img, gray_img, cv.CV_BGR2GRAY)
self.img = gray_img
self.img2 = self.img.clone()
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(cv.vector_Mat([self.img]),
channels=cv.vector_int([0, 1]),
mask=cv.Mat(),
hist=result,
histSize=cv.vector_int([256]),
ranges=ranges)
data = ArrayPlotData(x=np.arange(0, len(result[:])), y=result[:])
self.plot = Plot(data, padding=10)
line = self.plot.plot(("x", "y"))[0]
self.select_tool = RangeSelection(line, left_button_selects=True)
line.tools.append(self.select_tool)
self.select_tool.on_trait_change(self._selection_changed, "selection")
line.overlays.append(RangeSelectionOverlay(component=line))
cv.imshow("Hist Demo", self.img)
self.timer = Timer(50, self.on_timer)
def get_focus_measure(src, kind):
# from numpy import r_
# from scipy import fft
# from pylab import plot, show
# w = 100
# h = 100
# x = (640 - w) / 2
# y = (480 - h) / 2
# src = crop(src, x, y, w, h)
# src = grayspace(src)
# d = src.ndarray
#
# # print d[0]
# # print d[-1]
# fftsig = fft(d)
# d = abs(fftsig)
# print d.shape
# dst = src.clone()
# cv.convertScaleAbs(cv.asMat(d), dst, 1, 0)
# return dst
# xs = xrange(len(ys))
# plot(xs, ys)
# show()
# N = len(d)
# f = 50000 * r_[0:(N / 2)] / N
# n = len(f)
# # print f
# d = d.transpose()
# d = abs(fftsig[:n]) / N
# print d
# # plot(f, d[0], 'b', f, d[1], 'g', f, d[2], 'r')
# plot(f, d)
# show()
planes = cv.vector_Mat()
src = cv.asMat(src)
laplace = cv.Mat(src.size(), cv.CV_16SC1)
colorlaplace = cv.Mat(src.size(), cv.CV_8UC3)
cv.split(src, planes)
for plane in planes:
cv.Laplacian(plane, laplace, 3)
cv.convertScaleAbs(laplace, plane, 1, 0)
cv.merge(planes, colorlaplace)
f = colorlaplace.ndarray.flatten()
# f.sort()
# print f[-int(len(f) * 0.1):], int(len(f) * 0.1), len(f)
# len(f)
return f[-int(len(f) * 0.1):].mean()
def isolate_color(src, channel):
planes = cv.vector_Mat()
cv.split(src, planes)
w, h = src.size()
for i in range(3):
if i == channel:
continue
planes[i] = cv.Mat(src.size(), cv.CV_8UC1)
dst = cv.Mat(src.size(), cv.CV_8UC3)
cv.merge(planes,
dst)
print dst
return dst
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
img = cv.imread("fruits_section.jpg")
img_hsv = cv.Mat()
cv.cvtColor(img, img_hsv, cv.CV_BGR2HSV)
channels = cv.vector_int([0, 1])
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(cv.vector_Mat([img_hsv]), channels, cv.Mat(), result,
cv.vector_int([40, 40]), ranges)
result[:] /= np.max(result[:]) / 255
2 ###
img2 = cv.imread("fruits.jpg")
img_hsv2 = cv.Mat()
cv.cvtColor(img2, img_hsv2, cv.CV_BGR2HSV)
img_bp = cv.Mat()
cv.calcBackProject(cv.vector_Mat([img_hsv2]),
channels=channels,
hist=result,
backProject=img_bp,
ranges=ranges)
3 ###
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
img = cv.imread("fruits_section.jpg")
img_hsv = cv.Mat()
cv.cvtColor(img, img_hsv, cv.CV_BGR2HSV)
channels = cv.vector_int([0, 1])
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(cv.vector_Mat([img_hsv]), channels, cv.Mat(),
result, cv.vector_int([40, 40]), ranges)
result[:] /= np.max(result[:]) / 255
2###
img2 = cv.imread("fruits.jpg")
img_hsv2 = cv.Mat()
cv.cvtColor(img2, img_hsv2, cv.CV_BGR2HSV)
img_bp = cv.Mat()
cv.calcBackProject(cv.vector_Mat([img_hsv2]),
channels=channels,
hist=result,
backProject=img_bp,
ranges = ranges)
3###
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
img = cv.imread("lena.jpg")
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(cv.vector_Mat([img]),
channels=cv.vector_int([0, 1]),
mask=cv.Mat(),
hist=result,
histSize=cv.vector_int([30, 20]),
ranges=ranges)
hist, _x, _y = np.histogram2d(img[:, :, 0].flatten(),
img[:, :, 1].flatten(),
bins=(30, 20),
range=[(0, 256), (0, 256)])
print np.all(hist == result[:])
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
img = cv.imread("lena.jpg")
result = cv.MatND()
r = cv.vector_float32([0, 256])
ranges = cv.vector_vector_float32([r, r])
cv.calcHist(
cv.vector_Mat([img]),
channels=cv.vector_int([0, 1]),
mask=cv.Mat(),
hist=result,
histSize=cv.vector_int([30, 20]),
ranges=ranges,
)
hist, _x, _y = np.histogram2d(img[:, :, 0].flatten(), img[:, :, 1].flatten(), bins=(30, 20), range=[(0, 256), (0, 256)])
print np.all(hist == result[:])
