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):
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 findPairs(surf, objectKeypoints, objectDescriptors, imageKeypoints,
imageDescriptors):
ptpairs = []
N = len(objectKeypoints)
K = 2 # K as in K nearest neighbors
dim = surf.descriptorSize()
m_object = cv.asMat(objectDescriptors).reshape(1, N)
m_image = cv.asMat(imageDescriptors).reshape(1, len(imageKeypoints))
flann = cv.Index(m_image, cv.KDTreeIndexParams(4))
indices = cv.Mat(N, K, cv.CV_32S)
dists = cv.Mat(N, K, cv.CV_32F)
flann.knnSearch(m_object, indices, dists, K, cv.SearchParams(250))
indices = indices[:, 0].ravel()
dists = dists.ndarray
for i in xrange(N):
if dists[i, 0] < 0.6 * dists[i, 1]:
ptpairs.append(
(objectKeypoints[i].pt, imageKeypoints[int(indices[i])].pt))
return ptpairs
def __init__(self, *args, **kwargs):
super(RemapDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("lena.jpg")
self.size = self.img.size()
self.w, self.h = self.size.width, self.size.height
self.dstimg = cv.Mat()
self.map1 = cv.Mat(self.size, cv.CV_32FC1)
self.map2 = cv.Mat(self.size, cv.CV_32FC1)
self.gridimg = self.make_grid_img()
self.on_trait_change(self.redraw, "surf_func,range,view_height,grid")
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):
# 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)
def affine(self):
self.img2 = cv.Mat()
M = cv.asMat(self.m, force_single_channel=True)
cv.warpAffine(self.img1,
self.img2,
M,
self.img1.size(),
borderValue=cv.CV_RGB(255, 255, 255))
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):
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):
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 __init__(self, *args, **kwargs):
super(SURFDemo, self).__init__(*args, **kwargs)
img = cv.imread("lena_small.jpg")
self.m = np.array([[0.8, -0.6, 60], [0.6, 0.7, -20]])
self.img1 = cv.Mat()
cv.cvtColor(img, self.img1, cv.CV_BGR2GRAY)
self.affine()
self.on_trait_change(self.redraw, "max_distance,draw_circle")
self.on_trait_change(self.recalculate, "m,hessian_th,octaves,layers")
self.recalculate()
self.redraw()
def __init__(self):
#读入图像
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
self.fimg = fft.fft2(img[:]) # 图像的频域信号
mag_img = np.log10(np.abs(self.fimg))
# 创建计算用图像
filtered_img = np.zeros((N, N), dtype=np.float)
self.mask = np.zeros((N, N), dtype=np.float)
self.mask_img = cv.asMat(self.mask) # 在self.mask上绘制多边形用的图像
# 创建数据源
self.data = ArrayPlotData(mag_img=fft.fftshift(mag_img),
filtered_img=filtered_img,
mask_img=self.mask)
# 创建三个图像绘制框以及容器
meg_plot, img = self.make_image_plot("mag_img")
mask_plot, _ = self.make_image_plot("mask_img")
filtered_plot, _ = self.make_image_plot("filtered_img")
self.plot = HPlotContainer(meg_plot, mask_plot, filtered_plot)
# 创建套索工具
lasso_selection = LassoSelection(component=img)
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=img,
selection_alpha=0.3)
img.tools.append(lasso_selection)
img.overlays.append(lasso_overlay)
self.lasso_selection = lasso_selection
# 监听套索工具的事件、开启时钟事件
lasso_selection.on_trait_change(self.lasso_updated,
"disjoint_selections")
self.timer = Timer(50, self.on_timer)
def __init__(self, *args, **kwargs):
super(HoughDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("stuff.jpg")
self.img_gray = cv.Mat()
cv.cvtColor(self.img, self.img_gray, cv.CV_BGR2GRAY)
self.img_smooth = self.img_gray.clone()
cv.smooth(self.img_gray, self.img_smooth, cv.CV_GAUSSIAN, 7, 7, 0, 0)
self.redraw()
self.on_trait_change(self.redraw,
"th1,th2,show_canny,rho,theta,hough_th,minlen,maxgap,dp,mindist,param1,param2")
def __init__(self, *args, **kwargs):
super(InPaintDemo, self).__init__(*args, **kwargs)
self.img = cv.imread("stuff.jpg") # 原始图像
self.img2 = self.img.clone() # inpaint效果预览图像
self.mask = cv.Mat(self.img.size(), cv.CV_8UC1) # 储存选区的图像
self.mask[:] = 0
self.data = ArrayPlotData(img=self.img[:, :, ::-1])
self.plot = Plot(self.data,
padding=10,
aspect_ratio=float(self.img.size().width) /
self.img.size().height)
self.plot.x_axis.visible = False
self.plot.y_axis.visible = False
imgplot = self.plot.img_plot("img", origin="top left")[0]
self.painter = CirclePainter(component=imgplot)
imgplot.overlays.append(self.painter)
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)
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
import time
img = cv.asMat(np.random.rand(1000, 1000))
row = cv.getGaussianKernel(7, -1)
col = cv.getGaussianKernel(5, -1)
kernel = cv.asMat(np.dot(col[:], row[:].T), force_single_channel=True)
img2 = cv.Mat()
img3 = cv.Mat()
start = time.clock()
cv.filter2D(img, img2, -1, kernel)
print(("filter2D:", time.clock() - start))
start = time.clock()
cv.sepFilter2D(img, img3, -1, row, col)
print(("sepFilter3D:", time.clock() - start))
print(("error=", np.max(np.abs(img2[:] - img3[:]))))
# -*- 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[:])
import pyopencv as cv
y, x = np.ogrid[-2:2:300j, -2:2:300j]
z = (x**2 + y**2 - 1)**3 - x**2 * y**3
fig = plt.figure(figsize=(4, 4))
w, h = fig.bbox.width, fig.bbox.height
video = None
for level in np.linspace(-0.2, 0.2, 101):
fig.clear()
axe = fig.add_subplot(111, aspect=1)
plt.contour(x.ravel(), y.ravel(), z, levels=[level])
plt.title("level=%5.3f" % level)
axe.xaxis.set_ticks([])
axe.yaxis.set_ticks([])
fig.canvas.draw()
buf = fig.canvas.buffer_rgba(0, 0)
array = np.frombuffer(buf, np.uint8)
array.shape = h, w, 4
if not video:
video = cv.VideoWriter()
size = cv.Size2i(int(w), int(h))
#video.open("contour.avi", cv.CV_FOURCC(*"DIB "), 30, size)
video.open("contour.avi", cv.CV_FOURCC(*"ffds"), 30, size)
image = cv.Mat(size, cv.CV_8UC3)
image[:] = array[:, :, 2::-1]
video << image
del video
# -*- 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)
def get_features(self, img):
surf = cv.SURF(self.hessian_th, self.octaves, self.layers, True)
keypoints = cv.vector_KeyPoint()
features = surf(img, cv.Mat(), keypoints)
return keypoints, np.array(features)
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("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
import matplotlib.pyplot as plt
# 读入图片并缩小为1/2
img0 = cv.imread("lena.jpg")
size = img0.size()
w, h = size.width, size.height
img1 = cv.Mat()
cv.resize(img0, img1, cv.Size(w // 2, h // 2))
# 各种卷积核
kernels = [("低通滤波器", np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]]) * 0.1),
("高通滤波器", np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])),
("边缘检测", np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]))]
index = 0
for name, kernel in kernels:
plt.subplot(131 + index)
# 将卷积核转换为Mat对象
kmat = cv.asMat(kernel.astype(np.float), force_single_channel=True)
img2 = cv.Mat()
cv.filter2D(img1, img2, -1, kmat)
# 由于matplotlib的颜色顺序和OpenCV的顺序相反
plt.imshow(img2[:, :, ::-1])
plt.title(name)
index += 1
plt.gca().set_axis_off()
plt.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)
plt.show()
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
object_color = cv.imread(object_filename, cv.CV_LOAD_IMAGE_COLOR)
image = cv.imread(scene_filename, cv.CV_LOAD_IMAGE_GRAYSCALE)
if not object_color or not image:
print("Can not load %s and/or %s\n" \
"Usage: find_obj [<object_filename> <scene_filename>]\n" \
% (object_filename, scene_filename))
exit(-1)
object = cv.Mat(object_color.size(), cv.CV_8UC1)
cv.cvtColor(object_color, object, cv.CV_BGR2GRAY)
# corners
src_corners = [
cv.Point(0, 0),
cv.Point(object.cols, 0),
cv.Point(object.cols, object.rows),
cv.Point(0, object.rows)
]
dst_corners = [cv.Point()] * 4
# find keypoints on both images
surf = cv.SURF(500, 4, 2, True)
mask = cv.Mat()
tt = float(cv.getTickCount())
# -*- coding: utf-8 -*-
import pyopencv as cv
img = cv.imread("lena.jpg")
img2 = cv.Mat(img.size(), cv.CV_8UC4)
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)
# -*- coding: utf-8 -*-
import numpy as np
from numpy import fft
import pyopencv as cv
import matplotlib.pyplot as plt
N = 256
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
fimg = fft.fft2(img[:])
mag_img = np.log10(np.abs(fimg))
shift_mag_img = fft.fftshift(mag_img)
rects = [(80, 125, 85, 130), (90, 90, 95, 95), (150, 10, 250, 250),
(110, 110, 146, 146)]
filtered_results = []
for i, (x0, y0, x1, y1) in enumerate(rects):
mask = np.zeros((N, N), dtype=np.bool)
mask[x0:x1 + 1, y0:y1 + 1] = True
mask[N - x1:N - x0 + 1, N - y1:N - y0 + 1] = True
mask = fft.fftshift(mask)
fimg2 = fimg * mask
filtered_img = fft.ifft2(fimg2).real
filtered_results.append(filtered_img)
### 绘图部分 ###
