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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
Fabry-Perot Interference Pattern
================================
This example demonstrates image processing techniques for analyzing Fabry-Perot
interference patterns. It shows how to load experimental images, define regions
of interest, detect circular contours, and extract intensity profiles for
quantitative analysis of interference fringes.
Usage:
python fabry_perot_example.py
The script demonstrates optical analysis workflows commonly used in
interferometry, optical metrology, and precision measurements.
"""
# %%
# Importing necessary modules
# --------------------------------
# We start by importing all the required modules for image processing
# and visualization. To run this example, ensure you have all the required
# dependencies installed.
import sigima.enums
import sigima.objects
import sigima.proc.image
from sigima.tests import vistools
from sigima.tests.data import get_test_image
# %%
# Load Fabry-Perot test images
# --------------------------------
# We load two sample image of Fabry-Perot interference patterns.
# These imags is included in the Sigima test data. We will analyze the
# interference fringes present in these images.
# Load the first Fabry-Perot test image
img1 = get_test_image("fabry-perot1.jpg")
print("✓ Successfully loaded fabry-perot1.jpg")
print(f"Image dimensions: {img1.data.shape}")
print(f"Data type: {img1.data.dtype}")
print(f"Intensity range: {img1.data.min()} - {img1.data.max()}")
# Visualize the original interference pattern
vistools.view_images(img1, title="Fabry-Perot Interference Pattern #1")
# %%
# Define circular ROI for fringe analysis
# -------------------------------------------
# We define a circular region of interest (ROI) centered on the image
# to focus our analysis on the central interference fringes.
# Calculate image center
center_x, center_y = 601, 559
roi_radius = 460 # Radius to capture the first few interference rings
print(f"\n✓ Image center: ({center_x}, {center_y})")
print(f"ROI radius: {roi_radius} pixels")
print("This ROI focuses analysis on the central interference pattern")
# Create circular ROI coordinates:
roi_coords = [center_x, center_y, roi_radius]
# Apply circular ROI to the image
img1.roi = sigima.objects.create_image_roi("circle", roi_coords, indices=True)
print("✓ Circular ROI applied to image")
# Visualize image with ROI
vistools.view_images(img1, title="Fabry-Perot with Circular ROI")
# %%
# Configure contour detection for circular fringes
# ---------------------------------------------------
# We can now detect circular contours in the defined ROI. This will help us identify
# the interference rings. To perform this, the first step is to set up the contour
# detection parameter.
# Set up contour shape detection parameter for circles
contour_param = sigima.proc.image.ContourShapeParam()
contour_param.shape = sigima.enums.ContourShape.CIRCLE
contour_param.threshold = 0.5 # Threshold for fringe detection
print("\n✓ Contour detection configured:")
print(f"Shape: {contour_param.shape}")
print(f"Threshold: {contour_param.threshold}")
print("This will detect circular interference fringes")
# %%
# We can now perform the contour detection on the image using the
# configured parameters.
contour_results = sigima.proc.image.contour_shape(img1, contour_param)
print("\n✓ Contour detection completed for first image")
# %%
# We can now print the detected circular contours and their properties
print(f"Number of circular contours detected: {len(contour_results.coords)}")
contour_df = contour_results.to_dataframe()
print("\nDetected contours data frame:")
print(contour_df)
# %%
# Extract horizontal intensity profile
# ----------------------------------------
# We can extract an intensity profile along a horizontal line through the center
# of the image. This profile will show the intensity variations across the
# interference fringes. As before, we need to set up the line profile extraction
# parameter.
# Configure line profile extraction
profile_param = sigima.proc.image.LineProfileParam()
profile_param.direction = "horizontal"
profile_param.row = center_y # Extract profile through image center
print("\n✓ Horizontal profile configured:")
print(f"Direction: {profile_param.direction}")
print(f"Row: {profile_param.row} (image center)")
# Extract intensity profile
profile_signal1 = sigima.proc.image.line_profile(img1, profile_param)
print(f"✓ Profile extracted: {len(profile_signal1.y)} data points")
print(f"Intensity range: {profile_signal1.y.min():.1f} - {profile_signal1.y.max():.1f}")
# Visualize the intensity profile
vistools.view_curves(
[profile_signal1],
title="Horizontal Intensity Profile - Image 1",
xlabel="Position (pixels)",
ylabel="Intensity",
)
# %%
# Load the second Fabry-Perot image
# ----------------------------------------
# We want now to load the Analyze second Fabry-Perot image
try:
# Load second test image
img2 = get_test_image("fabry-perot2.jpg")
print("\n✓ Successfully loaded fabry-perot2.jpg")
print(f"Image dimensions: {img2.data.shape}")
# Copy ROI settings from first image
img2.metadata = img1.metadata # This includes the ROI information
# Visualize second image
vistools.view_images([img2], title="Fabry-Perot Interference Pattern #2")
except Exception as exc:
raise RuntimeError("Failed to load second Fabry-Perot test image.") from exc
# %%
# Contour detection on second image
# ----------------------------------------
# To perform the contour detection on the second image, we can
# reuse the same contour detection parameters defined earlier.
# This technique, applied to multiple images, allow to perform the same analysis on
# each of them and make comparisons easier.
# Apply the same contour detection to the second image
contour_results2 = sigima.proc.image.contour_shape(img2, contour_param)
print("✓ Contour detection completed for second image")
contour_df2 = contour_results2.to_dataframe()
print("\nDetected contours data frame (Image 2):")
print(contour_df2)
# %%
# Extract profile from second image and compare both profiles
# ----------------------------------------------------------------
# We can extract the horizontal intensity profile from the second image
# using the same profile extraction parameters defined earlier.
# Extract horizontal profile from second image
profile_signal2 = sigima.proc.image.line_profile(img2, profile_param)
print(f"\n✓ Profile extracted from second image: {len(profile_signal2.y)} points")
print(f"Intensity range: {profile_signal2.y.min():.1f} - {profile_signal2.y.max():.1f}")
# Compare profiles from both images
vistools.view_curves(
[profile_signal1, profile_signal2],
title="Intensity Profile Comparison",
xlabel="Position (pixels)",
ylabel="Intensity",
)
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