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Tools for working with optical flow

Project description

flowpy 💾 - A python package for working with optical flows

Optical flow is the displacement map of pixels between two frames. It is a low-level analysis used in many computer vision programs.

Working with optical flow may be cumbersome:

  • It is quite hard to represent it in a comprehensible manner.
  • Multiple formats exist for storing it.

Flowpy provides tools to work with optical flow more easily in python.


We recommend using pip:

pip install flowpy


The main features of flowpy are:

  • Reading and writing optical flows in two formats:
    • .flo (as defined here)
    • .png (as defined here)
  • Visualizing optical flows with matplotlib
  • Backward and forward warp


A simple RGB plot

This is the simplest example of how to use flowpy, it:

  • Reads a file using flowpy.flow_read.
  • Transforms the flow as an rgb image with flowpy.flow_to_rgb and shows it with matplotlib


import flowpy
import matplotlib.pyplot as plt

flow = flowpy.flow_read("tests/data/kitti_occ_000010_10.flo")

fig, ax = plt.subplots()



Sample image from the KITTI dataset

Plotting arrows, showing flow values and a calibration pattern

Flowpy comes with more than just RGB plots, the main features here are: - Arrows to quickly visualize the flow - The flow values below cursor showing in the tooltips - A calibration pattern side by side as a legend for your graph


flow = flowpy.flow_read("tests/data/Dimetrodon.flo")
height, width, _ = flow.shape

image_ratio = height / width
max_radius = flowpy.get_flow_max_radius(flow)

fig, (ax_1, ax_2) = plt.subplots(
    1, 2, gridspec_kw={"width_ratios": [1, image_ratio]}

flowpy.attach_arrows(ax_1, flow)
flowpy.attach_coord(ax_1, flow)

flowpy.attach_calibration_pattern(ax_2, flow_max_radius=max_radius)



Sample image from the Middlebury dataset

Warping images (backward):

If you know the flow (first_image -> second_image), you can backward warp the second_image back to first_image.

flow = flowpy.flow_read("static/kitti_occ_000010_10.png")
first_image = np.asarray("static/kitti_000010_10.png"))
second_image = np.asarray("static/kitti_000010_11.png"))

flow[np.isnan(flow)] = 0
warped_first_image = flowpy.backward_warp(second_image, flow)

fig, axes = plt.subplots(3, 1)
for ax, image, title in zip(axes, (first_image, second_image, warped_first_image),
                            ("First Image", "Second Image", "Second image warped to first image")):



Note that the artifacts in the warp are normal, they are caused by unknown flows and occlusions.

Warping images (forward):

Forward warp is often less used as it is quite more complex. It relies on a k-nearest neighbor search instead of direct bi-linear interpolation.

forward_warp is about 10x slower than backward_warp but you still may find it useful.

flow = flowpy.flow_read("static/kitti_occ_000010_10.png")
first_image = np.asarray("static/kitti_000010_10.png"))
second_image = np.asarray("static/kitti_000010_11.png"))

flow[np.isnan(flow)] = 0
warped_second_image = flowpy.forward_warp(first_image, flow)

fig, ax = plt.subplots()

ax.set_title( "First image warped to the second")




You can find the above examples in the examples folder. You can also look in tests. If you encounter a bug or have an idea for a new feature, feel free to open an issue.

Most of the visualization and io handling has been translated from matlab and c code from the Middlebury flow code. Credits to thank Simon Baker, Daniel Scharste, J. P. Lewis, Stefan Roth, Michael J. Black and Richard Szeliski.

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