步骤1-6的代码实现如下实现: #image chromaticity values fruits_R = fruits[:,:,0]*1.0/fruits.sum(axis=2) fruits_G = fruits[:,:,1]*1.0/fruits.sum(axis=2) #patch patch_strw = fruits[60:90,50:90,:] #patch chromaticity 3, figsize=(20, 7)) ax[0,0].scatter(fruits_R.flatten(),fruits_G.flatten()) ax[0,0].set_title('RG Chromaticity Patch', size=14) ax[1,0].scatter(patch_R.flatten(),patch_G.flatten()) ax[1,0].set_title('Patch RG Chromaticity
usual, it has the same value of DateTimeOriginal(0x9003)0x013eWhitePointunsigned rational2Defines chromaticity daylight'), the values are '3127/10000,3290/10000'.0x013fPrimaryChromaticitiesunsigned rational6Defines chromaticity
" -> "Boolean" "0x2AE3" -> "Chromatic Distance from Planckian" "0x2AE4" -> "Chromaticity Coordinates" "0x2AE5" -> "Chromaticity in CCT and Duv Values" "0x2AE6" -> "Chromaticity "0x2B1A" -> "Voltage Statistics" "0x2B1B" -> "Volume Flow" "0x2B1C" -> "Chromaticity
还有另外一个术语色度(chromaticity)通常是说明『饱和度』和『色调』这两种特征的综合表现。 以上便是我们的视觉感知到图像及其颜色的原因。
return "Chromatic Distance from Planckian"; case "0x2AE4": return "Chromaticity Coordinates"; case "0x2AE5": return "Chromaticity in CCT and Duv Values" ; case "0x2AE6": return "Chromaticity Tolerance"; case "0x2AE7 "0x2B1B": return "Volume Flow"; case "0x2B1C": return "Chromaticity
还有一个色度(chromaticity)通常是说明『饱和度』和『色调』这两种特征的综合表现。 参见:《图像的表示(1)》第 2.2 节 日常音视频开发处理的视频数据对应的颜色模型是什么? 忽略颜色的亮度(brightness)特征,只关注色度(chromaticity)时,使用二维的颜色坐标系来表示颜色模型平面图示法。 参见:《图像的表示(2)》第 3.2 节 色度图的基准是什么?
Another possible way of representing a color image is to separate the color information (chromaticity
CIE xyY表示法中的xy平面代表传统的色度(Chromaticity),CIE u’v’Y表示法中的u’v’平面也表示色度,但是当Y一定时,相对于xy平面来说,u’v’表示法下的色度更加的一致。