Image input/output

These methods read, write and display images.

class machinevisiontoolbox.ImageIO.ImageIOMixin[source]
classmethod Read(filename, alpha=False, rgb=True, **kwargs)[source]

Read image from file

Parameters:

  • filename (str) – image file name

  • alpha (bool, optional) – include alpha plane if present, defaults to False

  • rgb (bool, optional) – force color image to be in RGB order, defaults to True

  • kwargs – options applied to image frames, see convert

Raises:

ValueError – file not found

Returns:

image from file

Return type:

Image

Load monochrome or color image from file, many common formats are supported. A number of transformations can be applied to the image loaded from the file before it is returned.

Example:

>>> from machinevisiontoolbox import Image
>>> Image.Read('street.png')
Image: 1280 x 851 (uint8) [.../images/street.png]
>>> Image.Read('flowers1.png')
Image: 640 x 426 (uint8), R:G:B [.../images/flowers1.png]
>>> Image.Read('flowers1.png', grey=True)
Image: 640 x 426 (uint8) [.../images/flowers1.png]
>>> Image.Read('flowers1.png', dtype='float16')
Image: 640 x 426 (float16), R:G:B [.../images/flowers1.png]
>>> Image.Read('flowers1.png', reduce=4)
Image: 160 x 107 (uint8), R:G:B [.../images/flowers1.png]
>>> Image.Read('flowers1.png', gamma='sRGB') # linear tristimulus values
Image: 640 x 426 (uint8), R:G:B [.../images/flowers1.png]
Note:

If the path is not absolute it is first searched for relative to the current directory, and if not found, it is searched for in the images folder of the mvtb_data package.

Seealso:

iread convert cv2.imread

disp(title=None, **kwargs)[source]

Display image

Parameters:

  • title (bool) – named of window, defaults to image name

  • kwargs – options, see idisp

Display an image using either Matplotlib (default) or OpenCV.

Example:

>>> from machinevisiontoolbox import Image
>>> img = Image.Read('flowers1.png')
>>> img.disp()
<matplotlib.image.AxesImage object at 0x7fba586292b0>

(Source code, png, hires.png, pdf)

_images/image_io-1.png
Seealso:

idisp

write(filename, dtype='uint8', **kwargs)[source]

Write image to file

Parameters:

  • filename (str) – filename to write to

  • dtype (str) – data type to convert to, before writing

  • kwargs – options for iwrite

Write image data to a file. The file format is taken from the extension of the filename.

Example:

>>> from machinevisiontoolbox import Image
>>> img = Image.Read('flowers1.png')
>>> img.write('flowers.jpg')
True
Seealso:

iwrite cv2.imwrite

metadata(key=None)[source]

Get image EXIF metadata

Parameters:

key (str, optional) – metadata key

Returns:

image metadata

Return type:

dict, int, float, str

Get image metadata from EXIF headers.

Example:

>>> from machinevisiontoolbox import Image
>>> img = Image.Read('church.jpg')
>>> meta = img.metadata()  # get all metadata as a dict
>>> len(meta)
38
>>> meta
{'ResolutionUnit': 2, 'ExifOffset': 188, 'Make': 'Panasonic', 'Model': 'DMC-FZ30', 'Software': 'Ver.1.0  ', 'Orientation': 1, 'DateTime': '2009:08:22 15:50:06', 'XResolution': 72.0, 'YResolution': 72.0, 'ExifVersion': b'0220', 'FlashPixVersion': b'0100', 'CompressedBitsPerPixel': 4.0, 'DateTimeOriginal': '2009:08:22 15:50:06', 'DateTimeDigitized': '2009:08:22 15:50:06', 'ExposureBiasValue': 0.0, 'MaxApertureValue': 3.0, 'MeteringMode': 5, 'LightSource': 0, 'Flash': 16, 'FocalLength': 7.4, 'ColorSpace': 1, 'ExifImageWidth': 1280, 'DigitalZoomRatio': 0.0, 'FocalLengthIn35mmFilm': 37, 'SceneCaptureType': 0, 'ExifImageHeight': 851, 'Contrast': 0, 'Saturation': 0, 'Sharpness': 0, 'SensingMethod': 2, 'ExposureTime': 0.0025, 'FNumber': 8.0, 'ExposureProgram': 2, 'CustomRendered': 0, 'ISOSpeedRatings': 80, 'ExposureMode': 0, 'WhiteBalance': 0, 'GainControl': 0}
>>> meta['FocalLength']
7.4
>>> img.metadata('FocalLength')  # get specific metadata item
7.4
Note:

Metadata items will be converted, where possible, to int or float values.

showpixels(textcolors=['yellow', 'blue'], fmt=None, ax=None, windowsize=0, **kwargs)[source]

Display image with pixel values

Parameters:

  • textcolors (list, optional) – text color, defaults to [‘yellow’, ‘blue’]

  • fmt (str, optional) – format string for displaying pixel values, defaults to None

  • ax (axes, optional) – Matplotlb axes to draw into, defaults to None

  • windowsize (int, optional) – half side length of superimposed moving window, defaults to 0

Returns:

a moving window

Return type:

Window instance

Display a monochrome image with the pixel values overlaid. This is suitable for small images, of order 10x10, used for pedagogical purposes. For example it can be used to animate the operation of sliding window operations like convolution or morphology.

The first color in textcolors is used for pixels below 50% intensity and the second color for those above 50%.

If windowsize is given then a translucent colored window is superimposed and a Window instance returned. This allows the window position, color and opacity to be changed.

Example:

>>> from machinevisiontoolbox import Image
>>> img = Image.Random(10)
>>> window = img.showpixels(windowsize=1) # with 3x3 window
>>> window.move(2,3) # position window at (2,3)
>>> window.move(4,5, color='blue', alpha=0.7)

(Source code, png, hires.png, pdf)

_images/image_io-2.png

(Source code, png, hires.png, pdf)

_images/image_io-3.png
Seealso:

print

anaglyph(right, colors='rc', disp=0)[source]

Convert stereo images to an anaglyph image

Parameters:

  • right (Image instance) – right image

  • colors (str, optional) – lens colors (left, right), defaults to ‘rc’

  • disp (int, optional) – disparity, defaults to 0

Raises:

ValueErrror – images are not the same size

Returns:

anaglyph image

Return type:

Image

Returns an anaglyph image which combines the two images of a stereo pair by coding them in two different colors. By default the left image is red, and the right image is cyan.

colors describes the lens color coding as a string with 2 letters, the first for left, the second for right, and each is one of:

code

color

‘r’

red

‘g’

green

‘b’

green

‘c’

cyan

‘m’

magenta

If disp is positive the disparity is increased by shifting the right image to the right. If negative disparity is reduced by shifting the right image to the left. These adjustments are achieved by trimming the images. Use this option to make the images more natural/comfortable to view, useful if the images were captured with a stereo baseline significantly different to the human eye separation (typically 65mm).

Example:

>>> from machinevisiontoolbox import Image
>>> left = Image.Read("rocks2-l.png", reduce=2)
>>> right = Image.Read("rocks2-r.png", reduce=2)
>>> left.anaglyph(right).disp()
<matplotlib.image.AxesImage object at 0x7fc192808550>

(Source code, png, hires.png, pdf)

_images/image_io-4.png
Reference:

  • Robotics, Vision & Control for Python, Section 14.4, P. Corke, Springer 2023.

Seealso:

stdisp Overlay

stdisp(right)[source]

Interactive display of stereo image pair

Parameters:

right (Image) – right image

The left and right images are displayed, stacked horizontally. Clicking in the left-hand image sets a crosshair cursor in the right-hand image. Clicking the corresponding point in the right-hand image will display the disparity at the top of the right-hand image.

Example:

>>> from machinevisiontoolbox import Image
>>> left = Image.Read("rocks2-l.png", reduce=2)
>>> right = Image.Read("rocks2-r.png", reduce=2)
>>> left.stdisp(right)

(Source code, png, hires.png, pdf)

_images/image_io-5.png
Note:

The images are assumed to be epipolar aligned.

Reference:

  • Robotics, Vision & Control for Python, Section 14.4, P. Corke, Springer 2023.

Seealso:

anaglyph