scikit-image库-- 圆形和椭圆形霍夫变换（十七）

╰+哭是因爲堅強的太久メ 2021-10-15 16:51 573阅读 0赞

最简单形式的霍夫变换是一种检测直线的方法，但它也可用于检测圆或椭圆。该算法假设检测到边缘并且它对噪声或缺失点具有鲁棒性。

#### 圆检测 ####

在以下示例中，Hough变换用于检测硬币位置并匹配其边缘。我们提供一系列合理的半径。对于每个半径，提取两个圆圈，我们最终保留五个最突出的候选者。结果表明，硬币位置被很好地检测到。

#### 算法概述 ####

给定白色背景上的黑色圆圈，我们首先猜测其半径（或半径范围）以构建新圆。该圆圈应用于原始图像的每个黑色像素，并且该圆圈的坐标在累加器中进行投票。从这种几何结构，原始圆心位置获得最高分。请注意，累加器大小构建为大于原始图像，以便检测帧外的中心。它的尺寸扩大了半径更大的两倍。

import numpy as np
    import matplotlib.pyplot as plt
    
    from skimage import data, color
    from skimage.transform import hough_circle, hough_circle_peaks
    from skimage.feature import canny
    from skimage.draw import circle_perimeter
    from skimage.util import img_as_ubyte
    %matplotlib inline
    
    # Load picture and detect edges
    image = img_as_ubyte(data.coins()[160:230, 70:270]) #将图像转换为8位无符号整数格式。
    
    edges = canny(image, sigma=3, low_threshold=10, high_threshold=50)
    
    
    # Detect two radii
    hough_radii = np.arange(20, 35, 2)
    hough_res = hough_circle(edges, hough_radii)
    
    # Select the most prominent 5 circles
    accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
                                               total_num_peaks=3)
    
    # Draw them
    fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(20, 10))
    image = color.gray2rgb(image)
    for center_y, center_x, radius in zip(cy, cx, radii):
        circy, circx = circle_perimeter(center_y, center_x, radius)
        image[circy, circx] = (220, 20, 20)
    
    ax.imshow(image, cmap=plt.cm.gray)
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]

#### data.coins()\[160:230, 70:270\]改为data.coins()，total\_num\_peaks取15 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]

--------------------

#### 椭圆检测 ####

在该第二示例中，目的是检测咖啡杯的边缘。 基本上，这是圆的投影，即椭圆。 要解决的问题要困难得多，因为必须确定五个参数，而不是三个参数。

#### 算法概述 ####

该算法采用属于椭圆的两个不同点。 它假定它是主轴。 所有其他点上的循环确定椭圆传递给它们的程度。 良好的匹配对应于高累加器值。

import matplotlib.pyplot as plt
    
    from skimage import data, color, img_as_ubyte
    from skimage.feature import canny
    from skimage.transform import hough_ellipse
    from skimage.draw import ellipse_perimeter
    
    # Load picture, convert to grayscale and detect edges
    image_rgb = data.coffee()[0:220, 160:420]
    image_gray = color.rgb2gray(image_rgb)
    edges = canny(image_gray, sigma=2.0,
                  low_threshold=0.55, high_threshold=0.8)
    
    # Perform a Hough Transform
    # The accuracy corresponds to the bin size of a major axis.
    # The value is chosen in order to get a single high accumulator.
    # The threshold eliminates low accumulators
    result = hough_ellipse(edges, accuracy=20, threshold=250,
                           min_size=100, max_size=120)
    result.sort(order='accumulator')
    
    # Estimated parameters for the ellipse
    best = list(result[-1])
    yc, xc, a, b = [int(round(x)) for x in best[1:5]]
    orientation = best[5]
    
    # Draw the ellipse on the original image
    cy, cx = ellipse_perimeter(yc, xc, a, b, orientation) #生成椭圆周长坐标。返回属于椭圆周长的像素指数。 可用于直接索引到数组
    image_rgb[cy, cx] = (0, 0, 255) #蓝色
    # Draw the edge (white) and the resulting ellipse (red)
    edges = color.gray2rgb(img_as_ubyte(edges))
    edges[cy, cx] = (250, 0, 0)  #红色
    
    fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4),
                                    sharex=True, sharey=True)
    
    ax1.set_title('Original picture')
    ax1.imshow(image_rgb)
    
    ax2.set_title('Edge (white) and result (red)')
    ax2.imshow(edges)
    
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 2]

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