def redraw(self):
edge_img = cv.Mat()
# 边缘检测
cv.Canny(self.img_gray, edge_img, self.th1, self.th2)
3###
# 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)
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 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)
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 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)
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)
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)
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
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
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)
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)
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)
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)
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 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 mouse_call_back(event, x, y, flags, user_data):
global seed
# 右键松开时，初始化种子图像
if event == cv.CV_EVENT_RBUTTONUP:
img2[:] = img[:]
markers[:] = 0
seed = 1
cv.imshow("Watershed Demo", img2)
if seed == len(marks_color): return
# 左键按下时，在种子图像上添加种子
if flags == cv.CV_EVENT_FLAG_LBUTTON:
pt = cv.Point(x, y)
cv.circle(markers, pt, 5, cv.Scalar(seed, seed, seed, seed),
cv.CV_FILLED)
cv.circle(img2, pt, 5, marks_color[seed], cv.CV_FILLED)
cv.imshow("Watershed Demo", img2)
# 左键松开时，使用watershed进行图像分割
if event == cv.CV_EVENT_LBUTTONUP:
seed += 1
tmp_markers = markers.clone()
cv.watershed(img, tmp_markers)
color_map = tmp_markers[:].astype(np.int)
img3 = img2.clone()
img4 = cv.asMat(palette[color_map])
cv.addWeighted(img3, 1.0, img4, mask_opacity, 0, img3)
cv.imshow("Watershed Demo", img3)
def mouse_call_back(event, x, y, flags, user_data):
global seed
# 右键松开时，初始化种子图像
if event == cv.CV_EVENT_RBUTTONUP:
img2[:] = img[:]
markers[:] = 0
seed = 1
cv.imshow("Watershed Demo", img2)
if seed == len(marks_color): return
# 左键按下时，在种子图像上添加种子
if flags == cv.CV_EVENT_FLAG_LBUTTON:
pt = cv.Point(x, y)
cv.circle(markers, pt, 5, cv.Scalar(seed,seed,seed,seed), cv.CV_FILLED)
cv.circle(img2, pt, 5, marks_color[seed], cv.CV_FILLED)
cv.imshow("Watershed Demo", img2)
# 左键松开时，使用watershed进行图像分割
if event == cv.CV_EVENT_LBUTTONUP:
seed += 1
tmp_markers = markers.clone()
cv.watershed(img, tmp_markers)
color_map = tmp_markers[:].astype(np.int)
img3 = img2.clone()
img4 = cv.asMat( palette[color_map] )
cv.addWeighted(img3, 1.0, img4, mask_opacity, 0, img3)
cv.imshow("Watershed Demo", img3)
import numpy as np
import pyopencv as cv2
# Load an color image in grayscale
img = cv2.imread('IMG.jpeg',0)
cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('1.png', img)
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
y, x = np.ogrid[-1:1:250j, -1:1:250j]
z = np.sin(10 * np.sqrt(x * x + y * y)) * 0.5 + 0.5
np.round(z, decimals=1, out=z)
img = cv.asMat(z)
cv.namedWindow("demo1")
cv.imshow("demo1", img)
img2 = cv.Mat()
cv.Laplacian(img, img2, img.depth(), ksize=3)
cv.namedWindow("demo2")
cv.imshow("demo2", img2)
cv.waitKey(0)
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
y, x = np.ogrid[-1:1:250j,-1:1:250j]
z = np.sin(10*np.sqrt(x*x+y*y))*0.5 + 0.5
np.round(z, decimals=1, out=z)
img = cv.asMat(z)
cv.namedWindow("demo1")
cv.imshow("demo1", img)
img2 = cv.Mat()
cv.Laplacian(img, img2, img.depth(), ksize=3)
cv.namedWindow("demo2")
cv.imshow("demo2", img2)
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)
# create a correspond Mat
correspond = cv.Mat(image.rows+object.rows, image.cols, cv.CV_8UC1, cv.Scalar(0))
# copy the images to correspond -- numpy way
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
# find pairs
ptpairs = findPairs(surf, objectKeypoints, objectDescriptors, imageKeypoints, imageDescriptors)
for pair in ptpairs:
cv.line( correspond, cv.asPoint(pair[0]), cv.Point(int(pair[1].x), int(pair[1].y+object.rows)), colors[8] )
# locate planar object
if locatePlanarObject( ptpairs, src_corners, dst_corners ):
for i in range(4):
r1 = dst_corners[i]
r2 = dst_corners[(i+1)%4]
cv.line( correspond, cv.Point(r1.x, r1.y+object.rows ), cv.Point(r2.x, r2.y+object.rows ), colors[8] )
# show the object correspondents
cv.imshow("Object Correspond", correspond)
# draw circles
for keypt in objectKeypoints:
cv.circle(object_color, cv.asPoint(keypt.pt), int(keypt.size*1.2/9.*2), colors[0], 1, 8, 0)
cv.imshow("Object", object_color)
cv.waitKey(0)
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)
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)
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)
correspond[:object.rows, :object.cols] = object[:]
correspond[object.rows:, :image.cols] = image[:]
# find pairs
ptpairs = findPairs(surf, objectKeypoints, objectDescriptors,
imageKeypoints, imageDescriptors)
for pair in ptpairs:
cv.line(correspond, cv.asPoint(pair[0]),
cv.Point(int(pair[1].x), int(pair[1].y + object.rows)),
colors[8])
# locate planar object
if locatePlanarObject(ptpairs, src_corners, dst_corners):
for i in range(4):
r1 = dst_corners[i]
r2 = dst_corners[(i + 1) % 4]
cv.line(correspond, cv.Point(r1.x, r1.y + object.rows),
cv.Point(r2.x, r2.y + object.rows), colors[8])
# show the object correspondents
cv.imshow("Object Correspond", correspond)
# draw circles
for keypt in objectKeypoints:
cv.circle(object_color, cv.asPoint(keypt.pt),
int(keypt.size * 1.2 / 9. * 2), colors[0], 1, 8, 0)
cv.imshow("Object", object_color)
cv.waitKey(0)
# -*- 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)
# -*- 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)
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 Closing(pos, user_data):
element = cv.asMat(np.ones((pos * 2 + 1, pos * 2 + 1), 'uint8'), True)
cv.dilate(src, image, element)
cv.erode(image, dest, element)
cv.imshow("Opening&Closing window", dest)
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)
import numpy as np
import pyopencv as cv2
# Load an color image in grayscale
img = cv2.imread('IMG.jpeg', 0)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('1.png', img)
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):
# break
#======== EOF ===================================================
# def new_dst(src, zero=False, width=None,
# height=None, nchannels=None, size=None):
# '''
# '''
#
# # print nchannels
# if width is not None and height is not None:
# size = cv.Size(width, height)
# elif size is None:
# size = cv.getSize(src)
#
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.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)
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);
def Closing(pos, user_data):
element = cv.asMat(np.ones((pos*2+1, pos*2+1), 'uint8'), True)
cv.dilate(src, image, element)
cv.erode(image, dest, element)
cv.imshow("Opening&Closing window",dest);
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.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)
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)
