scikit-image库-- RGB to HSV &GRAY（十三）

小灰灰 2021-10-15 15:34 240阅读 0赞

此示例说明如何使用RGB到HSV（色调、饱和度、值）转换\[来促进分割过程。  
通常，图像中的对象具有不同的颜色（色调）和亮度，因此这些特征可用于分隔图像的不同区域。在RGB表示中，色调和亮度表示为R、G、B通道的线性组合，而它们对应于HSV图像的单通道（色调和值通道）。仅仅通过对HSV通道进行阈值分割就可以有效地对图像进行简单的分割。

import matplotlib.pyplot as plt
    %matplotlib inline
    from skimage import data
    from skimage.color import rgb2hsv
    
    rgb_img = data.coffee()
    hsv_img = rgb2hsv(rgb_img)
    hue_img = hsv_img[:, :, 0]
    value_img = hsv_img[:, :, 2]
    
    fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, figsize=(8, 2))
    
    ax0.imshow(rgb_img)
    ax0.set_title("RGB image")
    ax0.axis('off')
    ax1.imshow(hue_img, cmap='hsv')
    ax1.set_title("Hue channel")
    ax1.axis('off')
    ax2.imshow(value_img)
    ax2.set_title("Value channel")
    ax2.axis('off')
    
    fig.tight_layout()

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

hue_threshold = 0.04
    binary_img = hue_img > hue_threshold
    
    fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(13, 6))
    
    ax0.hist(hue_img.ravel(),512) #512 为直方图条形的个数 自己设定
    ax0.set_title("Histogram of the Hue channel with threshold")
    ax0.axvline(x=hue_threshold, color='r', linestyle='dashed', linewidth=2) #画竖线
    ax0.set_xbound(0, 0.12) #设置X轴范围
    ax1.imshow(binary_img)
    ax1.set_title("Hue-thresholded image")
    ax1.axis('off')
    
    fig.tight_layout()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]  
最后，我们在值通道上执行一个额外的阈值，以部分消除杯子的阴影(箭头区域)。

fig, ax0 = plt.subplots(figsize=(4, 3))
    
    value_threshold = 0.10
    binary_img = (hue_img > hue_threshold) | (value_img < value_threshold)
    
    ax0.imshow(binary_img)
    ax0.set_title("Hue and value thresholded image")
    ax0.axis('off')
    
    fig.tight_layout()
    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]

#### RGB to grayscale ####

import numpy as np
    import matplotlib.pyplot as plt
    
    from skimage import data
    from skimage.color import rgb2gray
    
    original = data.astronaut()
    grayscale = rgb2gray(original)   #Y = 0.2125 R + 0.7154 G + 0.0721 B
    
    fig, axes = plt.subplots(1, 2, figsize=(8, 4))
    ax = axes.ravel()
    
    ax[0].imshow(original)
    ax[0].set_title("Original")
    ax[1].imshow(grayscale, cmap=plt.cm.gray)
    ax[1].set_title("Grayscale")
    
    fig.tight_layout()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 3]

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