def change_photo_size(photo, new_photo, x_size, y_size):
if os.path.lexists(photo):
if os.path.getsize(photo) > 0:
foto_orig = Image.open(photo)
foto_mod = foto_orig.resize((x_size, y_size))
foto_mod.save(new_photo, quality=90)
im = pyopencv.imread(new_photo)
if pyopencv.imwrite(new_photo + '_new', im):
os.remove(new_photo)
os.rename(new_photo + '_new', new_photo)
if os.path.lexists(new_photo):
os.remove(photo)
else:
print 'Nao foi possivel modificar a foto.'
else:
print 'A foto esta vazia.'
else:
print 'A foto nao foi achada.'
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 __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(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 load_image(path, swap=False):
'''
'''
frame = cv.imread(path, 1)
if swap:
cv.convertImage(frame, frame, cv.CV_CVTIMG_SWAP_RB)
return frame
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 __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(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 __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 __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 __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 __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, *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, *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 __init__(self, *args, **kwargs):
self.lo_diff.fill(5)
self.hi_diff.fill(5)
self.img = cv.imread("lena.jpg")
self.data = ArrayPlotData(img = self.img[:,:,::-1])
w = self.img.size().width
h = self.img.size().height
self.plot = Plot(self.data, padding=10, aspect_ratio=float(w)/h)
self.plot.x_axis.visible = False
self.plot.y_axis.visible = False
self.imgplot = self.plot.img_plot("img", origin="top left")[0]
self.imgplot.interpolation = "nearest"
self.imgplot.overlays.append(PointPicker(application=self, component=self.imgplot))
self.on_trait_change(self.redraw, "point,lo_diff,hi_diff,option")
def __init__(self, *args, **kwargs):
self.lo_diff.fill(5)
self.hi_diff.fill(5)
self.img = cv.imread("lena.jpg")
self.data = ArrayPlotData(img=self.img[:, :, ::-1])
w = self.img.size().width
h = self.img.size().height
self.plot = Plot(self.data, padding=10, aspect_ratio=float(w) / h)
self.plot.x_axis.visible = False
self.plot.y_axis.visible = False
self.imgplot = self.plot.img_plot("img", origin="top left")[0]
self.imgplot.interpolation = "nearest"
self.imgplot.overlays.append(
PointPicker(application=self, component=self.imgplot))
self.on_trait_change(self.redraw, "point,lo_diff,hi_diff,option")
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 __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):
#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)
# -*- 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###
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
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)
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()
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 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
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)
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 __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 __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 __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()
# -*- 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 matplotlib.pyplot as plt
img = cv.imread("fruits.jpg")
#srs = [20, 40, 80]
srs = [20]
for i, sr in enumerate(srs):
img2 = img.clone()
result = cv.pyrMeanShiftFiltering(img, img2, 20, sr, 1)
plt.subplot(100+len(srs)*10+i+1)
plt.imshow(img2[:,:,::-1])
plt.gca().set_axis_off()
plt.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)
plt.show()
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 __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()
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
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)
# -*- 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 matplotlib.pyplot as plt
img = cv.imread("fruits.jpg")
threshold2 = [10, 30, 60]
for i, th2 in enumerate(threshold2):
img2 = img.clone()
storage = cv.createMemStorage(0)
result = cv.pyrSegmentation(img, img2, storage, 4, 200, th2)
plt.subplot(100 + len(threshold2) * 10 + i + 1)
plt.imshow(img2[:, :, ::-1])
plt.gca().set_axis_off()
plt.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)
plt.show()
# -*- 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
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 ###
