Introduction

Rationale

The goal of this package is to simplify the expression of computer vision algorithms in Python. Images can be represented as 2D or 3D arrays which are the domain of NumPy but many powerful image and point cloud specific operations are provided by other popular packages such as OpenCV, Pillow, SciPy, scikit-image, and Open3D. OpenCV does an adequate job of displaying images but is nowhere nearly as powerful matplotlib which can display a wide range of 2D graphics, but for 3D graphics Open3D is the go-to.

In practice, using these various packages together, to exploit their individual strengths, is complex – each have their own way of working, similar options are accessed differently and some function require image pixels to have particular types. None of them consider the image as an object with a set of useful image and vision processing methods and operators.

For example, to read an image using OpenCV, smooth it, and display it is:

import cv2
import numpy

# read image
src = cv2.imread(".../flowers1.png", cv2.IMREAD_UNCHANGED)

# apply Gaussian blur on src image
dst = cv2.GaussianBlur(src, (5,5), cv2.BORDER_DEFAULT)

# display input and output image
cv2.imshow("Gaussian Smoothing",numpy.hstack((src, dst)))
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows() # destroys the window showing image

Using this toolbox we would write instead:

from machinevisiontoolbox import Image

img = Image.Read("flowers1.png") # read the image
smooth = img.smooth(hw=2)  # apply a Gaussian blur
smooth.disp(block=True)  # display and block until window dismissed

or even:

from machinevisiontoolbox import Image

img = Image.Read("flowers1.png").smooth(hw=2).disp(block=True)

which exploits the power of Python’s method chaining – allowing a processing pipeline to be expressed in a single line of very readable code.

While the merits (or demerits) of these different approaches is subjective, you get the idea that the Toolbox allows succinct coding without the need for lots of OpenCV flags like cv2.IMREAD_UNCHANGED in the example above.

In summary, the Machine Vision Toolbox for Python (MVTB-P):

• provides many functions that are useful in machine vision and vision-based control.

• provides a simple, yet powerful and consistent, object-oriented wrapper of OpenCV functions. It supports operator overloading and handles the gnarly details of OpenCV-like conversion to/from float32 and the BGR color order.

• leverages the power of NumPy and OpenCV, and inherits their efficiency, portability and maturity.

• has similar, but not identical, functionality to the older Machine Vision Toolbox for MATLAB.

• includes over 100 functions such as image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration and color space conversion. With input from a web camera and output to a robot (not provided) it would be possible to implement a visual servo system entirely in Python.

• includes functionality spanning photometry, photogrammetry, colorimetry; while also being sufficient to support the book Robotics, Vision & Control.

Image objects

The key element of the Toolbox is the Image class. This sections provides some examples, but full details are given in The Image object for image operations and processing. The remainder of this section provides a brief overview of the key features of the Image class with examples.

Firstly, there are lots of ways to create an image. We can read an image from a file:

img = Image.Read("street.png")

or create it from code:

img = Image.Zeros(100, dtype="uint8")

Under the hood the Image object contains some image parameters, a lot of methods, and a reference to a 2D or 3D NumPy ndarray containing the pixel data.

Image object methods generally consider pixel coordinates with the horizontal coordinate first and the vertical coordinate second – consistent with the way we write about algorithms but the opposite to the way that NumPy indexes an array.

An image object has a lot of useful attributes that describe the image, including:

• img.width, the width of the image in pixels

• img.height, the height of the image in pixels

• img.size, the size of the image (width, height) in pixels

• img.nplanes, the number of planes in the image

as well as a number of useful predicates including:

• img.iscolor, is the image multichannel?

• img.ismono, is the image single channel?

• img.isfloat, does the image have floating point pixels?

Accessing the pixel array

We can access the array of pixel values by either the A or image attribute, or by using the object as if it were a NumPy array, for example:

np.mean(img.A)
np.mean(img.image)
np.mean(img)

We can slice the image using the same syntax as a NumPy array:

img[10:20, 30:40]

but only for reading, not for assignment. The result is another Image object.

Multi-plane images

Color images are handled a bit more sensibly than raw OpenCV. A multi-channel or multi-plane image is a NumPy ndarray with an arbitrary number of planes and a dictionary that maps channel names to an integer index. For instance, to create multi-plane images we can write any of the following:

img = Image.Zeros(100, colororder="RGB")
img = Image.Zeros(100, colororder="XYZ")
img = Image.Zeros(100, colororder="red:green:blue")
img = Image.Zeros(100, colororder="PQRST")  # 5 channel image

which create 100x100 images with 3, 3, 3 and 5 planes respectively, with all pixel values set to zero. Rather than have the meaning of the plane implicit (ie. plane 0 is red), it is explicit, for example:

img.plane("R")
img.plane("Y")
img.plane("blue")

A more common example is to read a color image:

img = Image.Read("flowers1.png")
img.red().disp()  # display the red plane of the image, whether RGB or BGR format
img.colorspace("hsv").plane("h").disp()  # display the hue plane of an HSV image

Image iterators

Frequently we want to use images that form a seqeuence – consecutive frames from a camera or a video file, a web camera, image files in a folder or zip file. Rather than build this capability into the Image object we provide a number of iterator objects:

for img in ZipArchive("holidaypix.zip"):
        # process the image

Getting started

Using pip

Install a snapshot from PyPI:

% pip install machinevision-toolbox-python

From GitHub source

Install the current code base from GitHub and pip install a link to that cloned copy:

% git clone https://github.com/petercorke/machinevision-toolbox-python.git
% cd machinevision-toolbox-python
% pip install -e .

Examples

MVTB tool

An interactive IPython session with all the MVTB tools loaded. Start a session from the shell:

% mvtbtool
_  _ ____ ____ _  _ _ _  _ ____    _  _ _ ____ _ ____ _  _
|\/| |__| |    |__| | |\ | |___    |  | | [__  | |  | |\ |
|  | |  | |___ |  | | | \| |___     \/  | ___] | |__| | \|

___ ____ ____ _    ___  ____ _  _
|  |  | |  | |    |__] |  |  \/
|  |__| |__| |___ |__] |__| _/\_

for Python

You're running: MVTB==0.9.7, SMTB==1.1.13, NumPy==1.26.4, SciPy==1.14.1,
            Matplotlib==3.10.0, OpenCV==4.10.0, Open3D==0.18.0

from machinevisiontoolbox import *
from spatialmath import *

matplotlib interactive mode on

func/object?       - show brief help
help(func/object)  - show detailed help
func/object??      - show source code


Python 3.10.16 (main, Dec 11 2024, 10:22:29) [Clang 14.0.6 ]
Type 'copyright', 'credits' or 'license' for more information
IPython 8.31.0 -- An enhanced Interactive Python. Type '?' for help.
Using matplotlib backend: macosx

>>> im = Image.Read("monalisa.png")

>>> im.disp()
Out[2]: <matplotlib.image.AxesImage at 0x1690e9720>

It has the advantage of command history, tab completion, and inline help.

Binary blobs

We load a binary image of two sharks and find the blobs in the image. We then display the image with the blobs marked by bounding boxes and centroids.

import machinevisiontoolbox as mvtb
import matplotlib.pyplot as plt
im = mvtb.Image("shark2.png")   # read a binary image of two sharks
fig = im.disp();   # display it with interactive viewing tool
f = im.blobs()  # find all the white blobs
print(f)

which will display:

┌───┬────────┬──────────────┬──────────┬───────┬───────┬─────────────┬────────┬────────┐
│id │ parent │     centroid │     area │ touch │ perim │ circularity │ orient │ aspect │
├───┼────────┼──────────────┼──────────┼───────┼───────┼─────────────┼────────┼────────┤
│ 0 │     -1 │ 371.2, 355.2 │ 7.59e+03 │ False │ 557.6 │       0.341 │  82.9° │  0.976 │
│ 1 │     -1 │ 171.2, 155.2 │ 7.59e+03 │ False │ 557.6 │       0.341 │  82.9° │  0.976 │
└───┴────────┴──────────────┴──────────┴───────┴───────┴─────────────┴────────┴────────┘

f.plot_box(fig, color='g')  # put a green bounding box on each blob
f.plot_centroid(fig, 'o', color='y')  # put a circle+cross on the centroid of each blob
f.plot_centroid(fig, 'x', color='y')
plt.show(block=True)  # display the result


Binary blob hierarchy

We load a binary image with nested objects

im = mvtb.Image("multiblobs.png")
im.disp()

f  = im.blobs()
print(f)

which will display:

┌───┬────────┬───────────────┬──────────┬───────┬────────┬─────────────┬────────┬────────┐
│id │ parent │      centroid │     area │ touch │  perim │ circularity │ orient │ aspect │
├───┼────────┼───────────────┼──────────┼───────┼────────┼─────────────┼────────┼────────┤
│ 0 │      1 │  898.8, 725.3 │ 1.65e+05 │ False │ 2220.0 │       0.467 │  86.7° │  0.754 │
│ 1 │      2 │ 1025.0, 813.7 │ 1.06e+05 │ False │ 1387.9 │       0.769 │ -88.9° │  0.739 │
│ 2 │     -1 │  938.1, 855.2 │ 1.72e+04 │ False │  490.7 │       1.001 │  88.7° │  0.862 │
│ 3 │     -1 │  988.1, 697.2 │ 1.21e+04 │ False │  412.5 │       0.994 │ -87.8° │  0.809 │
│ 4 │     -1 │  846.0, 511.7 │ 1.75e+04 │ False │  496.9 │       0.992 │ -90.0° │  0.778 │
│ 5 │      6 │  291.7, 377.8 │  1.7e+05 │ False │ 1712.6 │       0.810 │ -85.3° │  0.767 │
│ 6 │     -1 │  312.7, 472.1 │ 1.75e+04 │ False │  495.5 │       0.997 │ -89.9° │  0.777 │
│ 7 │     -1 │  241.9, 245.0 │ 1.75e+04 │ False │  496.9 │       0.992 │ -90.0° │  0.777 │
│ 8 │      9 │ 1228.0, 254.3 │ 8.14e+04 │ False │ 1215.2 │       0.771 │ -77.2° │  0.713 │
│ 9 │     -1 │ 1225.2, 220.0 │ 1.75e+04 │ False │  496.9 │       0.992 │ -90.0° │  0.777 │
└───┴────────┴───────────────┴──────────┴───────┴────────┴─────────────┴────────┴────────┘

We can display a label image, where the value of each pixel is the label of the blob that the pixel belongs to

out = f.labelImage(im)
out.stats()
out.disp(block=True, colormap="jet", cbar=True, vrange=[0,len(f)-1])

and request the blob label image which we then display

Camera modelling

cam = mvtb.CentralCamera(f=0.015, rho=10e-6, imagesize=[1280, 1024], pp=[640, 512], name="mycamera")
print(cam)
                        Name: mycamera [CentralCamera]
        focal length: (array([0.015]), array([0.015]))
          pixel size: 1e-05 x 1e-05
        principal pt: (640.0, 512.0)
          image size: 1280.0 x 1024.0
        focal length: (array([0.015]), array([0.015]))
                        pose: t = 0, 0, 0; rpy/zyx = 0°, 0°, 0°

and its intrinsic parameters are

print(cam.K)
        [[1.50e+03 0.00e+00 6.40e+02]
        [0.00e+00 1.50e+03 5.12e+02]
        [0.00e+00 0.00e+00 1.00e+00]]

We can define an arbitrary point in the world

P = [0.3, 0.4, 3.0]

and then project it into the camera

p = cam.project(P)
print(p)
        [790. 712.]

which is the corresponding coordinate in pixels. If we shift the camera slightly the image plane coordinate will also change

p = cam.project(P, T=SE3(0.1, 0, 0) )
print(p)
[740. 712.]

We can define an edge-based cube model and project it into the camera’s image plane

X, Y, Z = mkcube(0.2, pose=SE3(0, 0, 1), edge=True)
cam.mesh(X, Y, Z)

Color space

Plot the CIE chromaticity space

showcolorspace("xy")

Load the spectrum of sunlight at the Earth’s surface and compute the CIE xy chromaticity coordinates

nm = 1e-9
lam = np.linspace(400, 701, 5) * nm # visible light
sun_at_ground = loadspectrum(lam, 'solar')
xy = lambda2xy(lambda, sun_at_ground)
print(xy)
        [[0.33272798 0.3454013 ]]
print(colorname(xy, 'xy'))
        khaki