imshow

This section describes various options available for imshow plots in fivecentplots

See the full API

Setup

Import packages:



import fivecentplots as fcp
import pandas as pd
from pathlib import Path
import imageio

Read a ridiculous image from the world wide web that illustrates a crime against the felis genus:

e581a5049f914b01b170e6f49ec31e80



url = 'https://imagesvc.meredithcorp.io/v3/mm/image?q=85&c=sc&rect=0%2C214%2C2000%2C1214&poi=%5B920%2C546%5D&w=2000&h=1000&url=https%3A%2F%2Fstatic.onecms.io%2Fwp-content%2Fuploads%2Fsites%2F47%2F2020%2F10%2F07%2Fcat-in-pirate-costume-380541532-2000.jpg'
imgr = imageio.imread(url)
imgr

Warning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.



Array([[[220, 221, 223],
        [220, 221, 223],
        [220, 221, 223],
        ...,
        [219, 220, 222],
        [219, 220, 222],
        [219, 220, 222]],

       [[220, 221, 223],
        [220, 221, 223],
        [220, 221, 223],
        ...,
        [219, 220, 222],
        [219, 220, 222],
        [219, 220, 222]],

       [[220, 221, 223],
        [220, 221, 223],
        [220, 221, 223],
        ...,
        [219, 220, 222],
        [219, 220, 222],
        [219, 220, 222]],

       ...,

       [[108,  95,  86],
        [106,  93,  84],
        [103,  90,  81],
        ...,
        [209, 211, 210],
        [209, 211, 210],
        [209, 211, 210]],

       [[107,  94,  85],
        [105,  92,  83],
        [103,  90,  81],
        ...,
        [210, 212, 211],
        [210, 212, 211],
        [210, 212, 211]],

       [[106,  93,  84],
        [105,  92,  83],
        [103,  90,  81],
        ...,
        [210, 212, 211],
        [210, 212, 211],
        [210, 212, 211]]], dtype=uint8)

Convert this image from RGB to a grayscale DataFrame using a utility function provided by fivecentplots:



img = fcp.utilities.img_grayscale(imgr)
img.head()
0 1 2 3 4 5 6 7 8 9 ... 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
0 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.9070 220.9070 ... 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071
1 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.907 219.9071 219.9071 ... 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071
2 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.907 219.9071 219.9071 ... 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071
3 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.907 219.9071 219.9071 ... 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071
4 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.907 220.9070 220.9070 ... 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071 219.9071

5 rows × 2000 columns

Optionally set the design theme (skipping here and using default):



#fcp.set_theme('gray')
#fcp.set_theme('white')

Basic image display

Display our grayscale image depicting one of many problems with human-feline interactions.

Notes:

  1. the original ratio of the image height and width are preserved regardless of the values of ax_size, which will just apply to the largest of the two dimensions

  2. tick labels are turned off by default (use tick_labels_major=True to enable)

  3. imshow uses the “gray” colormap by default



fcp.imshow(img, ax_size=[600, 600])
_images/imshow_15_0.png

Colors

Color map

We can add any standard color map from matplotlib to an imshow using keyword cmap:



fcp.imshow(img, cmap='inferno', ax_size=[600, 600])
_images/imshow_19_0.png

Colorbar

We can add a colorbar to the image showing the z-range with the keyword cbar=True



fcp.imshow(img, cmap='inferno', ax_size=[600, 600], cbar=True)
_images/imshow_22_0.png

Zoom

We can zoom in on this stupid kitten by changing the x and y limits:



fcp.imshow(img, cmap='inferno', cbar=True, ax_size=[600, 600], xmin=700, xmax=1100, ymin=300, ymax=400, tick_labels_major=True)
_images/imshow_25_0.png

private eyes are watching you…

Contrast stretching

In some cases (such as raw image sensor data analysis) it is helpful to adjust the colormap limits in order to “stretch” the contrast. This can be done via the z axis limits. In this example, we stretch +/-3 standard deviations from the mean pixel value.



uu = img.stack().mean()
ss = img.stack().std()
fcp.imshow(img, cmap='inferno', cbar=True, ax_size=[600, 600], zmin=uu-3*ss, zmax=uu+3*ss)
_images/imshow_29_0.png

imshow plots in fivecentplots also provides a convenient kwarg called stretch which calculates a numerical multiplier of the standard deviation above and below the mean to set new z-limits (essentially the same thing as done above manually). stretch can be a single value of std dev which is interpreted as +/- that value or a 2-value list with the lower and higher std deviation respectively. First, we consider a +/- 4 sigma stretch as above:



fcp.imshow(img, cmap='inferno', cbar=True, ax_size=[600, 600], stretch=4)
_images/imshow_31_0.png

Now we show a single-sided stretch that applies a 3 * std dev increase to the upper z-limit:



fcp.imshow(img, cmap='inferno', cbar=True, ax_size=[600, 600], stretch=[0, 3])
_images/imshow_33_0.png

Split color planes

When analyzing Bayer-type images, it is often useful to split the image data based on the color-filter array pattern used on the image sensor. fivecentplots provides a simple utility function to do this and imshow can be used to display the result. Consider the following image:

9f8f683613f74d8db5b5bb6b6121152e

First we read the image from the world-wide web and convert this random RGB image into a Bayer-like image (rough hack for the purposes of this example):



url = 'https://upload.wikimedia.org/wikipedia/commons/2/28/RGB_illumination.jpg'
imgr = imageio.imread(url)
raw = fcp.utilities.rgb2bayer(imgr)
raw

Warning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
0 1 2 3 4 5 6 7 8 9 ... 390 391 392 393 394 395 396 397 398 399
0 47 47 47 48 46 47 45 45 48 46 ... 47 103 49 103 48 104 47 96 69 0
1 47 95 47 96 48 85 47 90 46 95 ... 102 46 103 51 100 46 100 42 121 0
2 46 46 47 47 47 48 46 48 48 47 ... 49 102 47 105 47 102 50 98 73 0
3 47 104 46 113 47 102 48 89 47 101 ... 103 47 106 49 102 44 100 44 122 2
4 47 47 48 46 48 48 46 48 48 48 ... 47 103 45 107 48 107 48 101 72 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
295 47 47 47 47 47 47 47 47 47 47 ... 47 47 47 47 47 46 48 44 75 0
296 47 47 47 47 47 47 47 47 47 47 ... 47 47 48 47 47 47 48 43 77 0
297 47 47 47 47 47 47 47 47 47 47 ... 47 47 48 47 47 47 48 43 77 0
298 47 47 47 47 47 47 47 47 47 47 ... 47 47 48 47 47 47 48 43 77 0
299 47 47 47 47 47 47 47 47 47 47 ... 47 47 48 47 47 47 48 43 77 0

300 rows × 400 columns

Now we plot with imshow using the keyword cfa with an “RGGB” pattern using a wrap-style plot. Notice that the primary colors from the original image are split into separate subplots.



fcp.imshow(raw, cmap='inferno', ax_size=[300, 300], cfa='rggb', wrap='Plane')
_images/imshow_39_0.png

Sneaky input

imshow is one of two plot types (hist and imshow) that allows us to pass a 2D numpy array instead of a DataFrame. When plotting a single image, we don’t need column names so we don’t technically need a DataFrame. Why do we allow this? This is a sneaky, under-the-table trick just to make life easier. Numpy arrays are converted into DataFrames behind the scenes so you don’t have to take an extra step. This can be our dirty little secret…