Imageio usage examples

Some of these examples use Visvis to visualize the image data, but one can also use Matplotlib to show the images.

Imageio provides a range of example images, which can be used by using a URI like 'imageio:chelsea.png'. The images are automatically downloaded if not already present on your system. Therefore most examples below should just work.

Read an image of a cat

Probably the most important thing you ever need.

import imageio

im = imageio.imread('imageio:chelsea.png')
print(im.shape)

Read from fancy sources

Imageio can read from filenames, file objects, http, zipfiles and bytes.

import imageio
import visvis as vv

im = imageio.imread('http://upload.wikimedia.org/wikipedia/commons/d/de/Wikipedia_Logo_1.0.png')
vv.imshow(im)

Note: reading from HTTP and zipfiles works for many formats including png and jpeg, but may not work for all formats (some plugins “seek” the file object, which HTTP/zip streams do not support). In such a case one can download/extract the file first. For HTTP one can use something like imageio.imread(imageio.core.urlopen(url).read(), '.gif').

Iterate over frames in a movie

import imageio

reader = imageio.get_reader('imageio:cockatoo.mp4')
for i, im in enumerate(reader):
    print('Mean of frame %i is %1.1f' % (i, im.mean()))

Grab screenshot or image from the clipboard

(Screenshots are supported on Windows and OS X, clipboard on Windows only.)

import imageio

im_screen = imageio.imread('<screen>')
im_clipboard = imageio.imread('<clipboard>')

Grab frames from your webcam

Use the special <video0> uri to read frames from your webcam (via the ffmpeg plugin). You can replace the zero with another index in case you have multiple cameras attached. You need to pip install imageio-ffmpeg in order to use this plugin.

import imageio
import visvis as vv

reader = imageio.get_reader('<video0>')
t = vv.imshow(reader.get_next_data(), clim=(0, 255))
for im in reader:
    vv.processEvents()
    t.SetData(im)

Convert a movie

Here we take a movie and convert it to gray colors. Of course, you can apply any kind of (image) processing to the image here … You need to pip install imageio-ffmpeg in order to use the ffmpeg plugin.

import imageio

reader = imageio.get_reader('imageio:cockatoo.mp4')
fps = reader.get_meta_data()['fps']

writer = imageio.get_writer('~/cockatoo_gray.mp4', fps=fps)

for im in reader:
    writer.append_data(im[:, :, 1])
writer.close()

Read medical data (DICOM)

import imageio
dirname = 'path/to/dicom/files'

# Read as loose images
ims = imageio.mimread(dirname, 'DICOM')
# Read as volume
vol = imageio.volread(dirname, 'DICOM')
# Read multiple volumes (multiple DICOM series)
vols = imageio.mvolread(dirname, 'DICOM')

Volume data

import imageio
import visvis as vv

vol = imageio.volread('imageio:stent.npz')
vv.volshow(vol)

Writing videos with FFMPEG and vaapi

Using vaapi (on Linux only) (intel only?) can help free up resources on your laptop while you are encoding videos. One notable difference between vaapi and x264 is that vaapi doesn’t support the color format yuv420p.

Note, you will need ffmpeg compiled with vaapi for this to work.

import imageio
import numpy as np

# All images must be of the same size
image1 = np.stack([imageio.imread('imageio:camera.png')] * 3, 2)
image2 = imageio.imread('imageio:astronaut.png')
image3 = imageio.imread('imageio:immunohistochemistry.png')

w = imageio.get_writer('my_video.mp4', format='FFMPEG', mode='I', fps=1,
                       codec='h264_vaapi',
                       output_params=['-vaapi_device',
                                      '/dev/dri/renderD128',
                                      '-vf',
                                      'format=gray|nv12,hwupload'],
                       pixelformat='vaapi_vld')
w.append_data(image1)
w.append_data(image2)
w.append_data(image3)
w.close()

A little bit of explanation:

  • output_params
    • vaapi_device speficifies the encoding device that will be used.
    • vf and format tell ffmpeg that it must upload to the dedicated hardware. Since vaapi only supports a subset of color formats, we ensure that the video is in either gray or nv12 before uploading it. The or operation is acheived with |.
  • pixelformat: set to 'vaapi_vld' to avoid a warning in ffmpeg.
  • codec: the code you wish to use to encode the video. Make sure your hardware supports the chosen codec. If your hardware supports h265, you may be able to encode using 'hevc_vaapi'

Optimizing a GIF using pygifsicle

When creating a GIF using imageio the resulting images can get quite heavy, as the created GIF is not optimized. This can be useful when the elaboration process for the GIF is not finished yet (for instance if some elaboration on specific frames stills need to happen), but it can be an issue when the process is finished and the GIF is unexpectedly big.

GIF files can be compressed in several ways, the most common one method (the one used here) is saving just the differences between the following frames. In this example, we apply the described method to a given GIF my_gif using pygifsicle, a porting of the general-purpose GIF editing command-line library gifsicle. To install pygifsicle and gifsicle, read the setup on the project page: it boils down to installing the package using pip and following the console instructions:

pip install pygifsicle

Now, let’s start by creating a gif using imageio:

import imageio
import matplotlib.pyplot as plt

n = 100
gif_path = "test.gif"
frames_path = "{i}.jpg"

n = 100
plt.figure(figsize=(4,4))
for i, x in enumerate(range(n)):
    plt.scatter(x/n, x/n)
    plt.xlim(0, 1)
    plt.ylim(0, 1)
    plt.savefig("{i}.jpg".format(i=i))

with imageio.get_writer(gif_path, mode='I') as writer:
    for i in range(n):
        writer.append_data(imageio.imread(frames_path.format(i=i)))

This way we obtain a 2.5MB gif.

We now want to compress the created GIF. We can either overwrite the initial one or create a new optimized one: We start by importing the library method:

from pygifsicle import optimize

optimize(gif_path, "optimized.gif") # For creating a new one
optimize(gif_path) # For overwriting the original one

The new optimized GIF now weights 870KB, almost 3 times less.

Putting everything together:

import imageio
import matplotlib.pyplot as plt
from pygifsicle import optimize

n = 100
gif_path = "test.gif"
frames_path = "{i}.jpg"

n = 100
plt.figure(figsize=(4,4))
for i, x in enumerate(range(n)):
    plt.scatter(x/n, x/n)
    plt.xlim(0, 1)
    plt.ylim(0, 1)
    plt.savefig("{i}.jpg".format(i=i))

with imageio.get_writer(gif_path, mode='I') as writer:
    for i in range(n):
        writer.append_data(imageio.imread(frames_path.format(i=i)))

optimize(gif_path)