Get started with OpenPIV in 5 minutes! This guide will help you install and run your first PIV analysis.

Install OpenPIV

Choose your preferred installation method:

pip install openpiv

Requires Python 3.8 or higher

conda install -c conda-forge openpiv

Recommended for scientific Python users

Get Sample PIV Images

Download example images or use your own PIV image pairs:

from openpiv import tools
frame_a, frame_b = tools.imread('exp1_001_a.bmp'), tools.imread('exp1_001_b.bmp')

Sample Data: You can download test images from the OpenPIV GitHub repository

Run Your First Analysis

Use this simple script to perform PIV analysis:

import openpiv.tools
import openpiv.process
import openpiv.scaling

# Load images
frame_a = openpiv.tools.imread('exp1_001_a.bmp')
frame_b = openpiv.tools.imread('exp1_001_b.bmp')

# Run PIV analysis
u, v, sig2noise = openpiv.process.extended_search_area_piv(
    frame_a, frame_b, 
    window_size=32, 
    overlap=16, 
    dt=0.02, 
    search_area_size=64
)

# Scale to physical units (example: 1 pixel = 0.1 mm, dt = 0.02 s)
x, y, u, v = openpiv.scaling.uniform(
    u, v, 
    scaling_factor=0.1/0.02  # 5 mm/s per pixel/frame
)

# Save results
openpiv.tools.save(x, y, u, v, 'output.txt')

print('PIV analysis complete! Results saved to output.txt')

Visualize Your Results

Create beautiful velocity field plots:

import matplotlib.pyplot as plt
import openpiv.tools

# Load results
x, y, u, v = openpiv.tools.load('output.txt')

# Create quiver plot
fig, ax = plt.subplots(figsize=(10, 8))
ax.quiver(x, y, u, v, scale=50)
ax.set_xlabel('X [mm]')
ax.set_ylabel('Y [mm]')
ax.set_title('PIV Velocity Field')
plt.savefig('velocity_field.png', dpi=150)
plt.show()