不调用python库中成熟的算法编写hough变换算法

Hough变换算法是一种图像处理算法，可以用于检测图像中的直线和圆。它的基本思想是将图像中的每个像素点都映射到一个参数空间中，在这个参数空间中，每个参数都代表一条直线或一个圆。通过对参数空间进行统计分析，可以找到在原始图像中的直线或圆。

以下是一种简单的Hough变换算法的实现：

1. 将图像转换为二值图像

2. 对于每个白色像素点，在参数空间中枚举所有可能的直线或圆，并将其投影到参数空间中

3. 统计参数空间中每个参数的投票数，找到投票数最高的参数，即为最有可能的直线或圆

4. 在原始图像中绘制检测到的直线或圆

以下是Python代码实现：

import numpy as np
import cv2

def hough_line(img):
    # Convert the image to grayscale
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    
    # Apply Canny edge detection
    edges = cv2.Canny(gray,50,200,apertureSize = 3)
    
    # Define the Hough transform parameters
    rho = 1 # distance resolution in pixels of the Hough grid
    theta = np.pi/180 # angular resolution in radians of the Hough grid
    threshold = 50 # minimum number of votes (intersections in Hough grid cell)
    min_line_length = 100 # minimum number of pixels making up a line
    max_line_gap = 10 # maximum gap in pixels between connectable line segments
    
    # Run Hough on edge detected image
    lines = cv2.HoughLinesP(edges,rho,theta,threshold,np.array([]),min_line_length,max_line_gap)
    
    # Draw lines on the original image
    for line in lines:
        x1,y1,x2,y2 = line[0]
        cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
    
    # Return the original image with the detected lines
    return img

def hough_circle(img):
    # Convert the image to grayscale
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    
    # Apply Gaussian blur to reduce noise
    blur = cv2.GaussianBlur(gray,(5,5),0)
    
    # Apply HoughCircles transform
    circles = cv2.HoughCircles(blur,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=0)
    
    # Draw circles on the original image
    if circles is not None:
        circles = np.round(circles[0,:]).astype("int")
        for (x,y,r) in circles:
            cv2.circle(img,(x,y),r,(0,255,0),2)
    
    # Return the original image with the detected circles
    return img

# Load an image
img = cv2.imread('image.jpg')

# Apply Hough line transform
img_lines = hough_line(img)

# Apply Hough circle transform
img_circles = hough_circle(img)

# Display the original image and the detected lines and circles
cv2.imshow('Original',img)
cv2.imshow('Lines',img_lines)
cv2.imshow('Circles',img_circles)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码实现了对图像中的直线和圆的检测，并在原始图像中绘制了检测到的直线和圆。这个实现虽然简单，但是对于复杂的图像，可能无法得到准确的检测结果。因此，在实际应用中，通常需要使用更加复杂的算法来实现Hough变换。