imshow doesn't normalize the color range in RGB images

Bug summary
When single channel float image is passed toimshow it gets normalized to range [0,1] before display. This does not happen for RGB images.

Code for reproduction

img=np.arange(0,0.6,.1).reshape(6,1)plt.subplot(141)plt.title('grayscale')plt.imshow(img)plt.colorbar()img=np.arange(0,0.6,.1).repeat(3).reshape(6,1,3)plt.subplot(142)plt.title('RGB')plt.imshow(img)plt.colorbar()img=np.arange(0,1.6,.2).reshape(8,1)plt.subplot(143)plt.title('grayscale')plt.imshow(img)plt.colorbar()img=np.arange(0,1.6,.2).repeat(3).reshape(8,1,3)plt.subplot(144)plt.title('RGB')plt.imshow(img)plt.colorbar()

Actual outcome

Please also note how the colorbars are misleading.

Expected outcome
I'd expect the RGB images to look the same as grayscale.

Real life example
This is how I encountered this issue. What is the value in all three channels at [3,18]? It is actually around 1.2!

Matplotlib version: 2.0.2

Discussion
The same issue:#5382 ,on SO, andanother SO.

In#5382@tacaswell explains that and RGB image "goes through a different code path which do not get passed through the normalize/colormap framework". Colormapping of course makes no sense for color images but I believe that normalization should be consistently applied to RGB images.

I got used to matplotlib normalization of single channel float images and the need of usingvmin andvmax to avoid this normalization and intuitively expected RGB images to be treated consistently. As I noticed subsequently the docstrigs say that "the value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0." But I believe that to simply overflow without raising error or warning is not correct.