#!/usr/bin/env python
# coding: utf-8

# ## geom_imshow()
# 
# `geom_imshow()` displays an image specified by 2D or 3D Numpy array.
# 
# Whether the image is grayscale or color depends on the shape of the image array:
# - (M, N) - grey-scale image
# - (M, N, 3) - color RGB image
# - (M, N, 4) - color RGB image with alpha channel
# 
# The array's `dtype` can be int, uint or float.
# 
# By default, all values in the image array will be transformed to the range [0-255] using a linear scaler.

# In[1]:


import numpy as np
from lets_plot import *

LetsPlot.setup_html()


# ### Grayscale image
# 

# In[2]:


A2x3 = np.array([
    [50, 150 ,200],
    [200,100,50]
    ])

ggplot() + geom_imshow(A2x3)


# #### Grayscale image without normalization

# In[3]:


ggplot() + geom_imshow(A2x3, norm=False)


# #### Grayscale image in "Viridis" colors

# In[4]:


ggplot() + geom_imshow(A2x3, cmap="viridis")


# ### RGB image
# 
# M x N x 3 array

# In[5]:


A2x3x3 = np.array([
    [[255, 0, 0], [0, 255, 0], [0, 0, 255]],
    [[0, 255, 0], [0, 0, 255], [255, 0, 0]]
    ])
ggplot() + geom_imshow(A2x3x3)


# ### RGB image with alpha channel
# 
# M x N x 4 array

# In[6]:


A2x3x4 = np.array([
    [[1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1]],
    [[0, 1, 0, 0.3], [0, 0, 1, 0.3], [1, 0, 0, 0.3]]
    ])

ggplot() + geom_imshow(A2x3x4)


# ### Just a random image

# In[7]:


np.random.seed(42)
image = np.random.choice([0.0, 1.0], [10, 100, 3])
ggplot() + geom_imshow(image) + coord_cartesian()