我正在尝试拍摄图像并将其转换为灰度，为该图像添加一些高斯模糊，并检测边缘。我无法使用matplotlib的{{1}}显示图片。
pyplot在import cv2
import matplotlib.pyplot as plt
def read_image_and_print_dims(image_path):
"""Reads and returns image.
Helper function to examine ow an image is represented"""
#reading an image
image=cv2.imread(image_path)
#printing out some stats and plottin
print('This image is ',type(image),' with dinmesions',image.shape)
plt.subplot(2,2,3)
plt.imshow(image)
return image
image_path='fall-leaves.png'
img=read_image_and_print_dims(image_path)
#Make a blurred/smoothed version
def gaussian_blur(img,kernel_size):
"""Applies a Gaussian Noise Kernel"""
print ('Inside Gaussian')
return cv2.GaussianBlur(img,(kernel_size,kernel_size),4)
#Gray Scale Image
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
you should call plimshow(gray, cmap='gray')"""
print ('Inside gray sale')
return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# gray scale it
greyscaled_image = grayscale(img)
plt.subplot(2, 2, 1)
plt.imshow(greyscaled_image, cmap='gray')
# smooth it a bit with Gaussian blur
kernal_size = 11
blur_gray = gaussian_blur(img, kernal_size)
plt.subplot(2, 2, 2)
plt.imshow(blur_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
上运行代码时，会生成以下消息：
Pycharm但它没有绘制图像。
我使用('This image is ', <type 'numpy.ndarray'>, ' with dinmesions', (320L, 400L, 3L))
Inside gray sale
Inside Gaussian
显示它。但是，现在我遇到了另一个问题。我从plt.show获得this figure，但使用pyplot，我得到了这些：top two images，bottom two images
这是我cv2.imshow的代码：
plt.show这是我使用#REad Image
import numpy as np
import cv2
import matplotlib.pyplot as plt
def read_image_and_print_dims(image_path):
"""Reads and returns image.
Helper function to examine ow an image is represented"""
#reading an image
image=cv2.imread(image_path)
#printing out some stats and plottin
print('This image is ',type(image),' with dinmesions',image.shape)
plt.subplot(2,2,1)
#cv2.imshow('Original Image',image)
plt.imshow(image)
return image
image_path='fall-leaves.png'
img=read_image_and_print_dims(image_path)
#Make a blurred/smoothed version
def gaussian_blur(img,kernel_size):
"""Applies a Gaussian Noise Kernel"""
print ('Inside Gaussian')
return cv2.GaussianBlur(img,(kernel_size,kernel_size),4)
#Gray Scale Image
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
you should call plimshow(gray, cmap='gray')"""
print ('Inside gray sale')
gray_image=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return gray_image
def canny(img,low_threshold,high_threshold):
"""Applies the Canny Transform"""
return cv2.Canny(img,low_threshold,high_threshold)
# gray scale it
greyscaled_image = grayscale(img)
plt.subplot(2, 2, 2)
plt.imshow(greyscaled_image)
#cv2.imshow('grey scale',greyscaled_image)
# smooth it a bit with Gaussian blur
kernal_size = 11
blur_gray = gaussian_blur(img, kernal_size)
plt.subplot(2, 2, 3)
plt.imshow(blur_gray)
#cv2.imshow('gaussian ',blur_gray)
#Canny image detection
edges_image=canny(blur_gray,50,150)
plt.subplot(2, 2, 4)
plt.imshow(edges_image)
plt.show()
#cv2.imshow('Canny image detection',edges_image)
#
# cv2.waitKey(0)
# cv2.destroyAllWindows()
的代码：
cv2.imshow使用#REad Image
import numpy as np
import cv2
import matplotlib.pyplot as plt
def read_image_and_print_dims(image_path):
"""Reads and returns image.
Helper function to examine ow an image is represented"""
#reading an image
image=cv2.imread(image_path)
#printing out some stats and plottin
print('This image is ',type(image),' with dinmesions',image.shape)
#plt.subplot(2,2,3)
cv2.imshow('Original Image',image)
return image
image_path='fall-leaves.png'
img=read_image_and_print_dims(image_path)
#Make a blurred/smoothed version
def gaussian_blur(img,kernel_size):
"""Applies a Gaussian Noise Kernel"""
print ('Inside Gaussian')
return cv2.GaussianBlur(img,(kernel_size,kernel_size),4)
#Gray Scale Image
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
you should call plimshow(gray, cmap='gray')"""
print ('Inside gray sale')
gray_image=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return gray_image
def canny(img,low_threshold,high_threshold):
"""Applies the Canny Transform"""
return cv2.Canny(img,low_threshold,high_threshold)
# gray scale it
greyscaled_image = grayscale(img)
#plt.subplot(2, 2, 1)
cv2.imshow('grey scale',greyscaled_image)
# smooth it a bit with Gaussian blur
kernal_size = 11
blur_gray = gaussian_blur(img, kernal_size)
#plt.subplot(2, 2, 2)
cv2.imshow('gaussian ',blur_gray)
#Canny image detection
edges_image=canny(blur_gray,50,150)
cv2.imshow('Canny image detection',edges_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
和pyplot获取不同的图像。不应该获得相同的图像吗？
答案 0 :(得分：1)
创建plt.show()后，您应该使用subplots来显示要显示的情节。
Matplotlib假设RGB顺序，而OpenCV使用BGR排序。要使Matplotlib图像具有正确的颜色，您需要交换第一个和最后一个通道。您可以使用内置的OpenCV方法rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)在显示它们之前更改它们。
plt.imshow()右侧的图像也不使用灰色色彩图，即使它们是灰色图像。您需要使用plt.imshow(blur_gray, cmap='gray')和plt.imshow(edges_image, cmap='gray')来使用灰度色彩图。当只有一个频道时，cv2.imshow()始终显示灰度。您的顶级代码集使用正确的色彩映射。