def show_video(fileorid):
cv.namedWindow(str(fileorid), cv.CV_WINDOW_AUTOSIZE)
video = cv.VideoCapture(fileorid)
img = cv.Mat()
img2 = cv.Mat()
while video.grab():
video.retrieve(img, 0)
#cv.cvtColor(img, img2, cv.CV_GBR2RGB)
cv.imshow(str(fileorid), img)
cv.waitKey(5)
def show_video(fileorid):
cv.namedWindow(str(fileorid), cv.CV_WINDOW_AUTOSIZE)
video = cv.VideoCapture(fileorid)
img = cv.Mat()
img2 = cv.Mat()
while video.grab():
video.retrieve(img, 0)
#cv.cvtColor(img, img2, cv.CV_GBR2RGB)
cv.imshow(str(fileorid), img)
cv.waitKey(5)
show_img = cv.Mat()
cv.cvtColor(edge_img, show_img, cv.CV_GRAY2BGR)
else:
show_img = self.img.clone()
4 ###
# 线段检测
theta = self.theta / 180.0 * np.pi
lines = cv.HoughLinesP(edge_img, self.rho, theta, self.hough_th,
self.minlen, self.maxgap)
for line in lines:
cv.line(show_img, cv.asPoint(line[:2]), cv.asPoint(line[2:]),
cv.CV_RGB(255, 0, 0), 2)
5 ###
# 圆形检测
circles = cv.HoughCircles(self.img_smooth,
3,
self.dp,
self.mindist,
param1=self.param1,
param2=self.param2)
for circle in circles:
cv.circle(show_img, cv.Point(int(circle[0]), int(circle[1])),
int(circle[2]), cv.CV_RGB(0, 255, 0), 2)
cv.imshow("Hough Demo", show_img)
cv.namedWindow("Hough Demo")
demo = HoughDemo()
demo.configure_traits()
def redraw(self):
def func(x, y):
return eval(self.surf_func, globals(), locals())
try:
self.map1[:], self.map2[:] = make_surf_map(func, self.range,
self.w, self.h,
self.view_height)
except SyntaxError:
return
if self.grid:
img = self.gridimg
else:
img = self.img
cv.remap(img, self.dstimg, self.map1, self.map2, cv.INTER_LINEAR)
cv.imshow("Remap Demo", self.dstimg)
def make_grid_img(self):
img = self.img.clone()
for i in range(0, self.w, 30):
cv.line(img, cv.Point(i, 0), cv.Point(i, self.h),
cv.CV_RGB(0, 0, 0), 1)
for i in range(0, self.h, 30):
cv.line(img, cv.Point(0, i), cv.Point(self.w, i),
cv.CV_RGB(0, 0, 0), 1)
return img
cv.namedWindow("Remap Demo", cv.CV_WINDOW_AUTOSIZE)
demo = RemapDemo()
demo.configure_traits()
class PerspectiveDemo(HasTraits):
src = Array(shape=(4, 2), dtype=np.float32)
dst = Array(shape=(4, 2), dtype=np.float32)
View = View(Item("dst", label=u"变换后坐标"), title=u"Perspective Demo控制面板")
def __init__(self, **traits):
super(PerspectiveDemo, self).__init__(**traits)
self.img = cv.imread("lena.jpg")
w = self.img.size().width
h = self.img.size().height
self.src = np.array([[0, 0], [w, 0], [0, h], [w, h]], dtype=np.float32)
self.dst = np.array([[0, 0], [w, 0], [0, h], [w, h]], dtype=np.float32)
self.on_trait_change(self.redraw, "src,dst")
self.redraw()
def redraw(self):
src = cv.asvector_Point2f(self.src)
dst = cv.asvector_Point2f(self.dst)
m = cv.getPerspectiveTransform(src, dst)
print m
img2 = cv.Mat()
cv.warpPerspective(self.img, img2, m, self.img.size())
cv.imshow("Perspective Demo", img2)
cv.namedWindow("Perspective Demo")
demo = PerspectiveDemo()
demo.configure_traits()
m = Array(np.float, (2, 3))
size = Array(np.int, (1, 2))
view = View(Item("m", label="变换矩阵"),
Item("size", label="图像大小"),
title="Affine Demo控制面板")
def __init__(self, **traits):
super(AffineDemo, self).__init__(**traits)
self.img = cv.imread("lena.jpg")
self.m = np.array([[0.5, -0.3, 100], [0.3, 0.5, 0]])
size = self.img.size()
self.on_trait_change(self.redraw, "m,size")
self.size = np.array([[size.width, size.height]])
def redraw(self):
M = cv.asMat(self.m, force_single_channel=True)
size = cv.Size(int(self.size[0, 0]), int(self.size[0, 1]))
img2 = cv.Mat()
if size.width > 0 and size.height > 0:
cv.warpAffine(self.img,
img2,
M,
size,
borderValue=cv.CV_RGB(255, 255, 255))
cv.imshow("Affine Demo", img2)
cv.namedWindow("Affine Demo")
demo = AffineDemo()
demo.configure_traits()
Item("iter", label=u"迭代次数"),
title=u"Morphology Demo控制面板")
def __init__(self, *args, **kwargs):
super(MorphologyDemo, self).__init__(*args, **kwargs)
self.structing_element = np.ones((3, 3), dtype=np.uint8)
self.img = cv.imread("lena.jpg")
self.on_trait_change(self.redraw,
"structing_element,process_type,iter")
self.redraw()
def redraw(self):
img2 = cv.Mat()
element = cv.asMat(self.structing_element, force_single_channel=True)
if self.process_type.startswith("MORPH_"):
type = getattr(cv, self.process_type)
cv.morphologyEx(self.img,
img2,
type,
element,
iterations=self.iter)
else:
func = getattr(cv, self.process_type)
func(self.img, img2, element, iterations=self.iter)
cv.imshow("Morphology Demo", img2)
cv.namedWindow("Morphology Demo", cv.CV_WINDOW_AUTOSIZE)
demo = MorphologyDemo()
demo.configure_traits()
class PerspectiveDemo(HasTraits):
src = Array(shape=(4,2), dtype=np.float32)
dst = Array(shape=(4,2), dtype=np.float32)
View = View(
Item("dst", label=u"变换后坐标"),
title = u"Perspective Demo控制面板"
)
def __init__(self, **traits):
super(PerspectiveDemo, self).__init__(**traits)
self.img = cv.imread("lena.jpg")
w = self.img.size().width
h = self.img.size().height
self.src = np.array([[0,0],[w,0],[0,h],[w,h]],dtype=np.float32)
self.dst = np.array([[0,0],[w,0],[0,h],[w,h]],dtype=np.float32)
self.on_trait_change(self.redraw, "src,dst")
self.redraw()
def redraw(self):
src = cv.asvector_Point2f(self.src)
dst = cv.asvector_Point2f(self.dst)
m = cv.getPerspectiveTransform(src, dst)
print m
img2 = cv.Mat()
cv.warpPerspective(self.img, img2, m, self.img.size())
cv.imshow("Perspective Demo", img2)
cv.namedWindow("Perspective Demo")
demo = PerspectiveDemo()
demo.configure_traits()
if __name__ == '__main__':
winName = 'PyOpenCV fun'
cascade = pyopencv.CascadeClassifier()
cascade.load("haarcascades/haarcascade_mcs_eyepair_big.xml")
fallbackCascade = pyopencv.CascadeClassifier()
fallbackCascade.load("haarcascades/haarcascade_mcs_eyepair_small.xml")
glasses = Image.open('darkkamina.png')
glasses.load()
capture = pyopencv.VideoCapture()
frame = pyopencv.Mat()
capture.open(1)
pyopencv.namedWindow(winName, pyopencv.CV_WINDOW_AUTOSIZE&1)
if capture.isOpened():
while True:
capture.retrieve(frame)
if frame.empty():
break
eyes = detect(frame, cascade, fallbackCascade)
final = draw(frame, eyes, glasses)
pyopencv.imshow(winName, final)
if pyopencv.waitKey(25) >= 0:
break
def new_video_writer(path, fps, size, use_color=False):
print cv.CV_FOURCC('x', '2', '6', '4')
return cv.VideoWriter(path,
# cv.CV_FOURCC('D', 'I', 'V', 'X'),
cv.CV_FOURCC('x', '2', '6', '4'),
# cv.CV_FOURCC('I', 'Y', 'U', 'V'),
# cv.CV_FOURCC('F', 'L', 'V', '1'),
fps, cv.Size(*size), use_color)
def save_image(src, path):
cv.imwrite(path, src)
if __name__ == '__main__':
cv.namedWindow('foo', 1)
# src = load_image('/Users/ross/Programming/demo/lena.jpg', swap=True)
src = load_image('/Users/ross/Programming/demo/lena.jpg', swap=True)
# draw_point(src, (100, 100))
gsrc = grayspace(src)
tsrc = threshold(gsrc, 100)
csrc = colorspace(tsrc)
# gsrc = src
# conts, hierarchy = contour(tsrc)
# #
# print get_polygons(conts)
draw_lines(csrc, [[(0, 0), (100, 100)]])
cv.imshow('foo', csrc)
c = cv.waitKey(0)
# if(c & 255 == 27):
cv.imshow("Watershed Demo", img3)
# 区域的颜色列表
marks_color = [
cv.CV_RGB(0, 0, 0),
cv.CV_RGB(255, 0, 0),
cv.CV_RGB(0, 255, 0),
cv.CV_RGB(0, 0, 255),
cv.CV_RGB(255, 255, 0),
cv.CV_RGB(0, 255, 255),
cv.CV_RGB(255, 0, 255),
cv.CV_RGB(255, 255, 255)
]
# 将颜色列表转换为调色板数组，只取前三个通道的值
palette = np.array([c.ndarray[:-1] for c in marks_color], dtype=np.uint8)
seed = 1 # 从序号1开始设置区域颜色
mask_opacity = 0.5 # 绘制区域颜色的透明度
img = cv.imread("fruits.jpg")
img2 = img.clone() # 绘制初始区域用
markers = cv.Mat(img2.size(), cv.CV_32S) # 储存初始区域的数组
markers[:] = 0
cv.namedWindow("Watershed Demo")
cv.imshow("Watershed Demo", img2)
cv.setMouseCallback("Watershed Demo", mouse_call_back)
cv.waitKey(0)
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
img = cv.imread("lena.jpg")
size = img.size()
w, h = size
img2 = cv.Mat()
map1, map2 = np.meshgrid(
np.linspace(0,w*2,w).astype(np.float32),
np.linspace(0,h*2,h).astype(np.float32),
)
map1 = cv.asMat(map1)
map2 = cv.asMat(map2)
cv.remap(img, img2, map1, map2, cv.INTER_LINEAR)
cv.namedWindow( "Remap Resize", cv.CV_WINDOW_AUTOSIZE )
cv.imshow("Remap Resize", img2)
cv.waitKey(0)
# 同时显示两幅图像
w = self.img1.size().width
h = self.img1.size().height
show_img = cv.Mat(cv.Size(w*2, h), cv.CV_8UC3)
for i in xrange(3):
show_img[:,:w,i] = self.img1[:]
show_img[:,w:,i] = self.img2[:]
# 绘制特征线条
if self.draw_circle:
self.draw_keypoints(show_img, self.keypoints1, 0)
self.draw_keypoints(show_img, self.keypoints2, w)
# 绘制直线连接距离小于阈值的两个特征点
for idx1 in np.where(self.mindist < self.max_distance)[0]:
idx2 = self.idx_mindist[idx1]
pos1 = self.keypoints1[int(idx1)].pt
pos2 = self.keypoints2[int(idx2)].pt
p1 = cv.Point(int(pos1.x), int(pos1.y))
p2 = cv.Point(int(pos2.x)+w, int(pos2.y))
cv.line(show_img, p1, p2, cv.CV_RGB(0,255,255), lineType=16)
cv.imshow("SURF Demo",show_img)
cv.namedWindow("SURF Demo")
demo = SURFDemo()
demo.configure_traits()
# 计算结果图
if self.show_canny:
show_img = cv.Mat()
cv.cvtColor(edge_img, show_img, cv.CV_GRAY2BGR)
else:
show_img = self.img.clone()
4###
# 线段检测
theta = self.theta / 180.0 * np.pi
lines = cv.HoughLinesP(edge_img,
self.rho, theta, self.hough_th, self.minlen, self.maxgap)
for line in lines:
cv.line(show_img,
cv.asPoint(line[:2]),
cv.asPoint(line[2:]),
cv.CV_RGB(255, 0, 0), 2)
5###
# 圆形检测
circles = cv.HoughCircles(self.img_smooth, 3,
self.dp, self.mindist, param1=self.param1, param2=self.param2)
for circle in circles:
cv.circle(show_img,
cv.Point(int(circle[0]), int(circle[1])), int(circle[2]),
cv.CV_RGB(0, 255, 0), 2)
cv.imshow("Hough Demo", show_img)
cv.namedWindow("Hough Demo")
demo = HoughDemo()
demo.configure_traits()
iter = Int(1)
view = View(
Item("structing_element", label=u"结构元素"),
Item("process_type", label=u"处理类型"),
Item("iter", label=u"迭代次数"),
title = u"Morphology Demo控制面板"
)
def __init__(self, *args, **kwargs):
super(MorphologyDemo, self).__init__(*args, **kwargs)
self.structing_element = np.ones((3,3), dtype=np.uint8)
self.img = cv.imread("lena.jpg")
self.on_trait_change(self.redraw, "structing_element,process_type,iter")
self.redraw()
def redraw(self):
img2 = cv.Mat()
element = cv.asMat(self.structing_element, force_single_channel=True)
if self.process_type.startswith("MORPH_"):
type = getattr(cv, self.process_type)
cv.morphologyEx(self.img, img2, type, element, iterations=self.iter)
else:
func = getattr(cv, self.process_type)
func(self.img, img2, element, iterations=self.iter)
cv.imshow("Morphology Demo", img2)
cv.namedWindow( "Morphology Demo", cv.CV_WINDOW_AUTOSIZE )
demo = MorphologyDemo()
demo.configure_traits()
Z = 1./(h[2,0]*x + h[2,1]*y + h[2,2])
X = (h[0,0]*x + h[0,1]*y + h[0,2])*Z
Y = (h[1,0]*x + h[1,1]*y + h[1,2])*Z
dst_corners[i] = cv.Point(int(X), int(Y))
return 1
if __name__ == '__main__':
if len(sys.argv) == 3:
object_filename = sys.argv[1]
scene_filename = sys.argv[2]
else:
object_filename = "box.png"
scene_filename = "box_in_scene.png"
cv.namedWindow("Object", 1)
cv.namedWindow("Object Correspond", 1)
colors = [
cv.Scalar(0,0,255),
cv.Scalar(0,128,255),
cv.Scalar(0,255,255),
cv.Scalar(0,255,0),
cv.Scalar(255,128,0),
cv.Scalar(255,255,0),
cv.Scalar(255,0,0),
cv.Scalar(255,0,255),
cv.Scalar(255,255,255),
]
# read the two images
def redraw(self):
# 同时显示两幅图像
w = self.img1.size().width
h = self.img1.size().height
show_img = cv.Mat(cv.Size(w * 2, h), cv.CV_8UC3)
for i in range(3):
show_img[:, :w, i] = self.img1[:]
show_img[:, w:, i] = self.img2[:]
# 绘制特征线条
if self.draw_circle:
self.draw_keypoints(show_img, self.keypoints1, 0)
self.draw_keypoints(show_img, self.keypoints2, w)
# 绘制直线连接距离小于阈值的两个特征点
for idx1 in np.where(self.mindist < self.max_distance)[0]:
idx2 = self.idx_mindist[idx1]
pos1 = self.keypoints1[int(idx1)].pt
pos2 = self.keypoints2[int(idx2)].pt
p1 = cv.Point(int(pos1.x), int(pos1.y))
p2 = cv.Point(int(pos2.x) + w, int(pos2.y))
cv.line(show_img, p1, p2, cv.CV_RGB(0, 255, 255), lineType=16)
cv.imshow("SURF Demo", show_img)
cv.namedWindow("SURF Demo")
demo = SURFDemo()
demo.configure_traits()
cv.erode(image, dest, element)
cv.imshow("Opening&Closing window",dest);
def Erosion(pos, user_data):
element = cv.asMat(np.ones((pos*2+1, pos*2+1), 'uint8'), True)
cv.erode(src, dest, element)
cv.imshow("Erosion&Dilation window",dest);
def Dilation(pos, user_data):
element = cv.asMat(np.ones((pos*2+1, pos*2+1), 'uint8'), True)
cv.dilate(src, dest, element)
cv.imshow("Erosion&Dilation window",dest);
if __name__ == "__main__":
filename = "baboon.jpg"
if len(sys.argv)==2:
filename = sys.argv[1]
src = cv.imread(filename,1)
if src.empty():
sys.exit(-1)
image = src.clone()
dest = src.clone()
cv.namedWindow("Opening&Closing window",1)
cv.namedWindow("Erosion&Dilation window",1)
cv.imshow("Opening&Closing window",src)
cv.imshow("Erosion&Dilation window",src)
cv.createTrackbar("Open","Opening&Closing window",0,10,Opening)
cv.createTrackbar("Close","Opening&Closing window",0,10,Closing)
cv.createTrackbar("Dilate","Erosion&Dilation window",0,10,Dilation)
cv.createTrackbar("Erode","Erosion&Dilation window",0,10,Erosion)
cv.waitKey(0)
self.gridimg = self.make_grid_img()
self.on_trait_change(self.redraw, "surf_func,range,view_height,grid")
def redraw(self):
def func(x, y):
return eval(self.surf_func, globals(), locals())
try:
self.map1[:], self.map2[:] = make_surf_map(func, self.range, self.w, self.h, self.view_height)
except SyntaxError:
return
if self.grid:
img = self.gridimg
else:
img = self.img
cv.remap(img, self.dstimg, self.map1, self.map2, cv.INTER_LINEAR)
cv.imshow("Remap Demo", self.dstimg)
def make_grid_img(self):
img = self.img.clone()
for i in xrange(0, self.w, 30):
cv.line(img, cv.Point(i, 0), cv.Point(i, self.h), cv.CV_RGB(0, 0, 0), 1)
for i in xrange(0, self.h, 30):
cv.line(img, cv.Point(0, i), cv.Point(self.w, i), cv.CV_RGB(0, 0, 0), 1)
return img
cv.namedWindow("Remap Demo", cv.CV_WINDOW_AUTOSIZE)
demo = RemapDemo()
demo.configure_traits()
cv.imshow("Watershed Demo", img3)
# 区域的颜色列表
marks_color = [
cv.CV_RGB(0, 0, 0) ,cv.CV_RGB(255, 0, 0),
cv.CV_RGB(0, 255, 0) ,cv.CV_RGB(0, 0, 255),
cv.CV_RGB(255, 255, 0),cv.CV_RGB(0, 255, 255),
cv.CV_RGB(255, 0, 255),cv.CV_RGB(255, 255, 255)
]
# 将颜色列表转换为调色板数组，只取前三个通道的值
palette = np.array([c.ndarray[:-1] for c in marks_color], dtype=np.uint8)
seed = 1 # 从序号1开始设置区域颜色
mask_opacity = 0.5 # 绘制区域颜色的透明度
img = cv.imread("fruits.jpg")
img2 = img.clone() # 绘制初始区域用
markers = cv.Mat(img2.size(), cv.CV_32S) # 储存初始区域的数组
markers[:] = 0
cv.namedWindow("Watershed Demo")
cv.imshow("Watershed Demo", img2)
cv.setMouseCallback("Watershed Demo", mouse_call_back)
cv.waitKey(0)
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 _selection_changed(self):
if self.select_tool.selection != None:
self.need_update = True
def on_timer(self):
if self.need_update:
x0, x1 = self.select_tool.selection
self.img2[:] = self.img[:]
np.clip(self.img2[:], x0, x1, out=self.img2[:])
self.img2[:] -= x0
self.img2[:] *= 256.0 / (x1 - x0)
cv.imshow("Hist Demo", self.img2)
self.need_update = False
cv.namedWindow("Hist Demo", cv.CV_WINDOW_AUTOSIZE)
if __name__ == "__main__":
p = HistDemo()
p.configure_traits()
# -*- coding: utf-8 -*-
import pyopencv as cv
import sys
try:
filename = sys.argv[1]
except:
filename = "lena.jpg"
img = cv.imread( filename )
cv.namedWindow("demo1")
cv.imshow("demo1", img)
cv.waitKey(0)
from enthought.traits.ui.api import View, Item
class MeanShiftDemo(HasTraits):
spatial_radius = Range(1, 40, 20)
color_radius = Range(1, 100, 40)
max_level = Range(1, 4, 2)
do_button = Button(u"计算")
view = View(
Item("spatial_radius", label=u"空间半径"),
Item("color_radius", label=u"颜色半径"),
Item("max_level", label=u"最大层数"),
Item("do_button", show_label=False),
title = u"Mean Shift Demo控制面板"
)
def __init__(self, *args, **kwargs):
super(MeanShiftDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("fruits.jpg")
self.img2 = self.img.clone()
self.on_trait_change(self.redraw, "do_button")
self.redraw()
def redraw(self):
cv.pyrMeanShiftFiltering(self.img, self.img2,
self.spatial_radius, self.color_radius, self.max_level)
cv.imshow("Mean Shift Demo", self.img2)
cv.namedWindow("Mean Shift Demo")
demo = MeanShiftDemo()
demo.configure_traits()
# -*- coding: utf-8 -*-
import pyopencv as cv
import sys
try:
filename = sys.argv[1]
except:
filename = "lena.jpg"
img = cv.imread(filename)
cv.namedWindow("demo1")
cv.imshow("demo1", img)
cv.waitKey(0)
from enthought.traits.ui.api import View, Item
class AffineDemo(HasTraits):
m = Array(np.float, (2,3))
size = Array(np.int, (1,2))
view = View(
Item("m", label=u"变换矩阵"),
Item("size", label=u"图像大小"),
title = u"Affine Demo控制面板"
)
def __init__(self, **traits):
super(AffineDemo, self).__init__(**traits)
self.img = cv.imread("lena.jpg")
self.m = np.array([[0.5,-0.3,100],[0.3,0.5,0]])
size = self.img.size()
self.on_trait_change(self.redraw, "m,size")
self.size = np.array([[size.width, size.height]])
def redraw(self):
M = cv.asMat(self.m, force_single_channel=True)
size = cv.Size(int(self.size[0,0]), int(self.size[0,1]))
img2 = cv.Mat()
if size.width > 0 and size.height > 0:
cv.warpAffine(self.img, img2, M, size, borderValue=cv.CV_RGB(255,255,255))
cv.imshow("Affine Demo", img2)
cv.namedWindow("Affine Demo")
demo = AffineDemo()
demo.configure_traits()
import pyopencv as cv
import numpy as np
from enthought.traits.api import HasTraits, Array, Float
from enthought.traits.ui.api import View, Item
class FilterDemo(HasTraits):
kernel = Array(shape=(3,3),dtype=np.float)
scale = Float
view = View(
Item("kernel", label=u"卷积核"),
Item("scale", label=u"乘积因子"),
title = u"Filter Demo控制面板"
)
def __init__(self, *args, **kwargs):
super(FilterDemo, self).__init__(*args, **kwargs)
self.kernel = np.ones((3,3))
self.img = cv.imread("lena.jpg")
self.on_trait_change(self.redraw, "kernel,scale")
self.scale = 1.0 / 9.0
def redraw(self):
img2 = cv.Mat()
kernel = cv.asMat(self.kernel*self.scale, force_single_channel=True)
cv.filter2D(self.img, img2, -1, kernel)
cv.imshow("Filter Demo", img2)
cv.namedWindow( "Filter Demo", cv.CV_WINDOW_AUTOSIZE )
demo = FilterDemo()
demo.configure_traits()
import pyopencv as cv
import numpy as np
from enthought.traits.api import HasTraits, Array, Float
from enthought.traits.ui.api import View, Item
class FilterDemo(HasTraits):
kernel = Array(shape=(3, 3), dtype=np.float)
scale = Float
view = View(Item("kernel", label=u"卷积核"),
Item("scale", label=u"乘积因子"),
title=u"Filter Demo控制面板")
def __init__(self, *args, **kwargs):
super(FilterDemo, self).__init__(*args, **kwargs)
self.kernel = np.ones((3, 3))
self.img = cv.imread("lena.jpg")
self.on_trait_change(self.redraw, "kernel,scale")
self.scale = 1.0 / 9.0
def redraw(self):
img2 = cv.Mat()
kernel = cv.asMat(self.kernel * self.scale, force_single_channel=True)
cv.filter2D(self.img, img2, -1, kernel)
cv.imshow("Filter Demo", img2)
cv.namedWindow("Filter Demo", cv.CV_WINDOW_AUTOSIZE)
demo = FilterDemo()
demo.configure_traits()
X = (h[0, 0] * x + h[0, 1] * y + h[0, 2]) * Z
Y = (h[1, 0] * x + h[1, 1] * y + h[1, 2]) * Z
dst_corners[i] = cv.Point(int(X), int(Y))
return 1
if __name__ == '__main__':
if len(sys.argv) == 3:
object_filename = sys.argv[1]
scene_filename = sys.argv[2]
else:
object_filename = "box.png"
scene_filename = "box_in_scene.png"
cv.namedWindow("Object", 1)
cv.namedWindow("Object Correspond", 1)
colors = [
cv.Scalar(0, 0, 255),
cv.Scalar(0, 128, 255),
cv.Scalar(0, 255, 255),
cv.Scalar(0, 255, 0),
cv.Scalar(255, 128, 0),
cv.Scalar(255, 255, 0),
cv.Scalar(255, 0, 0),
cv.Scalar(255, 0, 255),
cv.Scalar(255, 255, 255),
]
# read the two images
from enthought.traits.ui.api import View, Item
class MeanShiftDemo(HasTraits):
spatial_radius = Range(1, 40, 20)
color_radius = Range(1, 100, 40)
max_level = Range(1, 4, 2)
do_button = Button("计算")
view = View(Item("spatial_radius", label="空间半径"),
Item("color_radius", label="颜色半径"),
Item("max_level", label="最大层数"),
Item("do_button", show_label=False),
title="Mean Shift Demo控制面板")
def __init__(self, *args, **kwargs):
super(MeanShiftDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("fruits.jpg")
self.img2 = self.img.clone()
self.on_trait_change(self.redraw, "do_button")
self.redraw()
def redraw(self):
cv.pyrMeanShiftFiltering(self.img, self.img2, self.spatial_radius,
self.color_radius, self.max_level)
cv.imshow("Mean Shift Demo", self.img2)
cv.namedWindow("Mean Shift Demo")
demo = MeanShiftDemo()
demo.configure_traits()
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 _selection_changed(self):
if self.select_tool.selection != None:
self.need_update = True
def on_timer(self):
if self.need_update:
x0, x1 = self.select_tool.selection
self.img2[:] = self.img[:]
np.clip(self.img2[:], x0, x1, out=self.img2[:])
self.img2[:] -= x0
self.img2[:] *= 256.0 / (x1 - x0)
cv.imshow("Hist Demo", self.img2)
self.need_update = False
cv.namedWindow("Hist Demo", cv.CV_WINDOW_AUTOSIZE)
if __name__ == "__main__":
p = HistDemo()
p.configure_traits()
w, h = img.size().width, img.size().height
def blend(img, img2):
"""
混合两幅图像, 其中img2有4个通道
"""
#使用alpha通道计算img2的混和值
b = img2[:,:,3:] / 255.0
a = 1 - b # img的混合值
#混合两幅图像
img[:,:,:3] *= a
img[:,:,:3] += b * img2[:,:,:3]
img2[:] = 0
for i in xrange(0, w, w/10):
cv.line(img2, cv.Point(i,0), cv.Point(i, h),
cv.Scalar(0, 0, 255, i*255/w), 5)
blend(img, img2)
img2[:] = 0
for i in xrange(0, h, h/10):
cv.line(img2, cv.Point(0,i), cv.Point(w, i),
cv.Scalar(0, 255, 0, i*255/h), 5)
blend(img, img2)
cv.namedWindow("Draw Demo")
cv.imshow("Draw Demo", img)
cv.waitKey(0)
def Erosion(pos, user_data):
element = cv.asMat(np.ones((pos * 2 + 1, pos * 2 + 1), 'uint8'), True)
cv.erode(src, dest, element)
cv.imshow("Erosion&Dilation window", dest)
def Dilation(pos, user_data):
element = cv.asMat(np.ones((pos * 2 + 1, pos * 2 + 1), 'uint8'), True)
cv.dilate(src, dest, element)
cv.imshow("Erosion&Dilation window", dest)
if __name__ == "__main__":
filename = "baboon.jpg"
if len(sys.argv) == 2:
filename = sys.argv[1]
src = cv.imread(filename, 1)
if src.empty():
sys.exit(-1)
image = src.clone()
dest = src.clone()
cv.namedWindow("Opening&Closing window", 1)
cv.namedWindow("Erosion&Dilation window", 1)
cv.imshow("Opening&Closing window", src)
cv.imshow("Erosion&Dilation window", src)
cv.createTrackbar("Open", "Opening&Closing window", 0, 10, Opening)
cv.createTrackbar("Close", "Opening&Closing window", 0, 10, Closing)
cv.createTrackbar("Dilate", "Erosion&Dilation window", 0, 10, Dilation)
cv.createTrackbar("Erode", "Erosion&Dilation window", 0, 10, Erosion)
cv.waitKey(0)
