python cv code GaussianBlur

在Python中，使用OpenCV库进行高斯模糊处理可以使用以下代码：

import cv2
import numpy as np

# 读取图像
img = cv2.imread('image.jpg')

# 对图像进行高斯模糊处理
blurred = cv2.GaussianBlur(img, (5, 5), 0)

# 显示原图和处理后的图像
cv2.imshow('Original Image', img)
cv2.imshow('Blurred Image', blurred)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码首先使用`cv2.imread`函数读取图像文件。然后，使用`cv2.GaussianBlur`函数对图像进行高斯模糊处理。该函数的第一个参数是要处理的图像，第二个参数是一个元组，指定了高斯核的大小，通常是奇数大小（例如(5,5)表示一个5x5的核）。第三个参数是标准差，它决定了高斯核的宽度。标准差越大，模糊效果越明显。在这个例子中，标准差被设置为0，这意味着高斯核的大小是固定的。

最后，使用`cv2.imshow`函数显示原始图像和处理后的图像。请注意，您需要安装OpenCV库才能运行此代码。如果您尚未安装OpenCV，可以使用pip安装：`pip install opencv-python`。

相关问题

opencv with cuda GaussianBlur on a video in python

To apply GaussianBlur on a video using OpenCV with CUDA in Python, you can use the following code:

import cv2

# Load the video
cap = cv2.VideoCapture('input_video.mp4')

# Create a CUDA enabled GaussianBlur object
blur = cv2.cuda.createGaussianFilter(cap.get(cv2.CAP_PROP_FRAME_HEIGHT), cap.get(cv2.CAP_PROP_FRAME_WIDTH), (3, 3), 0, cv2.CV_32F)

# Process each frame of the video
while True:
    # Capture a frame
    ret, frame = cap.read()
    if not ret:
        break
    
    # Convert the frame to a CUDA enabled matrix
    src = cv2.cuda_GpuMat(frame)
    
    # Apply GaussianBlur on the frame
    dst = blur.apply(src)
    
    # Convert the CUDA enabled matrix to a numpy array
    result = dst.download()
    
    # Display the resulting frame
    cv2.imshow('frame', result)
    
    # Wait for key press
    if cv2.waitKey(1) == ord('q'):
        break

# Release the video and cleanup
cap.release()
cv2.destroyAllWindows()

In this code, we first load the video using `cv2.VideoCapture()`. We then create a CUDA enabled GaussianBlur object using `cv2.cuda.createGaussianFilter()`. We then process each frame of the video in a loop, where we first convert the frame to a CUDA enabled matrix using `cv2.cuda_GpuMat()`. We then apply GaussianBlur on the CUDA enabled matrix using `blur.apply()`. We then convert the CUDA enabled matrix to a numpy array using `dst.download()`. Finally, we display the resulting frame using `cv2.imshow()` and wait for a key press using `cv2.waitKey()`.

import cv2 import numpy as np def getContours(img): contours, hierarchy = cv2.findContours( img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE ) for cnt in contours: area = cv2.contourArea(cnt) print(area) if area > 500: cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3) path = "/home/robot/code.py/shape.jpg" img = cv2.imread(path) imgContour = img.copy() imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) imgCanny = cv2.Canny(imgBlur, 50, 50) getContours(imgCanny) imgBlank = np.zeros_like(img) cv2.imshow("Original", img) cv2.imshow("Gray", imgGray) cv2.imshow("Blur", imgBlur) cv2.imshow("Canny", imgCanny) cv2.imshow("Blank", imgContour) cv2.waitKey(0)有什么bug

代码中存在两个问题：

1. 在导入 cv2 模块时，应该使用以下方式：

   import cv2
   import numpy as np

2. 在调用 ``cv2.drawContours()`` 函数时，需要将第一个参数改为 ``imgContour``，因为 ``imgContour`` 是我们要绘制轮廓的图像。

修改后的代码如下：

import cv2
import numpy as np

def getContours(img):
    contours, hierarchy = cv2.findContours(
        img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
    )
    for cnt in contours:
        area = cv2.contourArea(cnt)
        print(area)
        if area > 500:
            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)

path = "/home/robot/code.py/shape.jpg"
img = cv2.imread(path)
imgContour = img.copy()

imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1)
imgCanny = cv2.Canny(imgBlur, 50, 50)

getContours(imgCanny)

imgBlank = np.zeros_like(img)

cv2.imshow("Original", img)
cv2.imshow("Gray", imgGray)
cv2.imshow("Blur", imgBlur)
cv2.imshow("Canny", imgCanny)
cv2.imshow("Contour", imgContour)

cv2.waitKey(0)