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)
# -*- 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[:])