1.2. Usage

This package provides the handy Fringes class, which handles all the required parameters for configuring the fringe pattern sequence and provides methods for encoding and decoding them.

1.2.1. Script

You instantiate, parameterize and deploy the Fringes class:

from fringes import Fringes  # import the Fringes class

f = Fringes()                # instantiate Fringes object

1.2.1.1. Configure

All parameters are accessible by the respective attributes of the Fringes instance. They are implemented as properties (managed attributes), which are parsed when set. Note that some attributes have sub-dependencies, hence dependent attributes might change as well. Circular dependencies are resolved automatically.

f.X = 1920                   # set width of fringe patterns to screen width
f.Y = 1080                   # set height of fringe patterns to screen height
f.N = 4                      # set number of shifts
f.v = [9, 10]                # set spatial frequencies

T = f.T                      # get number of frames

1.2.1.2. Encode

For generating the fringe pattern sequence I, use the method encode(). It returns a Numpy array in vshape() (frames T, width X, height Y, color channels C).

I = f.encode()               # encode fringe patterns

1.2.1.3. Record

Now display each frame of the fringe pattern sequence on a screen and capture the scene with a camera according to the following pseudocode (a minimal working example is given in the example below):

# allocate image stack
Irec = []

for t in range(f.T):
    # display frame on screen
    frame = I[t]
    ...

    # capture scene with camera
    image = ...

    # append to image stack
    Irec.append(image)

1.2.1.4. Decode

For analyzing (recorded) fringe patterns Irec, use the method decode(). It returns the Numpy arrays brightness a, modulation b and (screen) coordinate x.

a, b, x = f.decode(Irec)     # decode fringe patterns

Note

For the computationally expensive decode()-function we make use of the just-in-time compiler Numba. During the first execution, an initial compilation is executed. This can take several tens of seconds up to single digit minutes, depending on your CPU and energy settings. However, for any subsequent execution, the compiled code is cached and the code of the function runs much faster, approaching the speeds of code written in C.

1.2.1.5. Example

The only hardware you need is a screen and a camera. The setup can be as simple as only using your notebook’s screen and integrated camera, cf. Fig. 1.1:

Make sure the reflected fringe pattern is visible by the camera. Also, make sure to focus onto the test object and adjust the exposure settings of the camera. Then you can use the following code snipped to record and decode your own dataset:

"""Use the `Fringes` package to encode, record and decode data."""

import cv2
from fringes import Fringes
import numpy as np

# prepare window in which the fringe pattern sequence will be shown in fullscreen mode
cv2.namedWindow("Fringes", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("Fringes", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
left, top, width, height = cv2.getWindowImageRect("Fringes")

# prepare camera
camera = cv2.VideoCapture(0)

camera.set(cv2.CAP_PROP_AUTOFOCUS, 1)  # turn on autofocus
camera.set(cv2.CAP_PROP_AUTO_WB, 1)  # turn on whitebalance
camera.set(cv2.CAP_PROP_AUTO_EXPOSURE, 1)  # turn on autoexposure

white = np.full((height, width), 255, np.uint8)  # white image
cv2.imshow("Fringes", white)  # display white image
key = cv2.waitKey(250)  # delay time of the screen for displaying the image

for _ in range(10):
    # let camera automatically set focus, whitebalance and exposure
    ret, image = camera.read()

camera.set(cv2.CAP_PROP_AUTOFOCUS, 0)  # turn off autofocus
camera.set(cv2.CAP_PROP_AUTO_WB, 0)  # turn off whitebalance
camera.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0)  # turn off autoexposure

# configure and create fringe pattern sequence
f = Fringes()
f.X = width
f.Y = height
I = f.encode()

# allocate empty image stack
Irec = np.empty((f.T,) + image.shape, image.dtype)

# record
try:
    # record fringe patterns in a loop
    for t in range(f.T):
        # display fringe pattern with index 't' in fullscreen mode
        cv2.imshow("Fringes", I[t])
        key = cv2.waitKey(250)  # delay time of the screen for displaying the image

        # capture reflected fringe pattern; ensure the fringe pattern is not overexposed!
        ret, image = camera.read()  # note: OpenCV has color order 'BGR' (instead of 'RGB') for color images

        if ret:
            # save captured fringe pattern to image stack
            Irec[t] = image
finally:
    # close window
    cv2.destroyWindow("Fringes")

    # release camera resources
    camera.release()

If the results look strange or wrong, please check out the troubleshooting. More complete code examples can be found in the examples directory.

1.2.2. CLI

You can run Fringes directly from the command-line interface with option flags.

If infile -i is not specified, Fringes encodes the fringe pattern sequence. (If outfile -o is not specified, the result is saved to outfile.npy respective outfile.npz per default.)

fringes

fringes -o pattern.npy

If infile -i is specified, Fringes decodes the given data and saves it to .

fringes -i pattern.npy -o decoded.npz

To list all options, call Fringes with the help flag -h:

fringes -h