panorai 2.0.17

Panoramic image projection and blending using Gnomonic and other spherical projections.

PanorAi: Spherical Image Processing & Projection

PanorAi is a framework for working with spherical (equirectangular) images, enabling efficient transformation into Gnomonic projections and back to equirectangular format. It provides flexible samplers and blenders to optimize projection and reconstruction processes.

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🚀 Quick Start

Installation

pip install panorai

1️⃣ Load an Equirectangular Image

Convert an image to an EquirectangularImage object.

from panorai import PanoraiData

eq_image = PanoraiData.from_file("path/to/image.png", data_type="equirectangular")

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📌 Core Functions

2️⃣ Convert to Gnomonic Projection

Extract a rectilinear (Gnomonic) face from the equirectangular image.

face = eq_image.to_gnomonic(lat=45, lon=90, fov=60)
face.show()

3️⃣ Convert Back to Equirectangular

Reproject a gnomonic face back to equirectangular.

eq_reprojected = face.to_equirectangular(eq_shape=(512, 1024))
eq_reprojected.show()

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🛠️ Advanced Usage

4️⃣ Convert to Multiple Gnomonic Faces

Use sampling strategies (e.g., "cube", "fibonacci") to extract multiple faces.

face_set = eq_image.to_gnomonic_face_set(fov=60, sampling_method="cube")
face_set[0].show()  # View first face

5️⃣ Reconstruct Using a Blender

Back-project multiple faces using different blending methods ("closest", "average").

eq_reconstructed = face_set.to_equirectangular(eq_shape=(512, 1024), blender_name="closest")
eq_reconstructed.show()

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🔧 Configuring Samplers & Blenders

You can fine-tune sampling & blending strategies using ConfigManager.

Set Custom Sampler

from panorai.pipelines.sampler.config import SamplerConfig

sampler_config = SamplerConfig(n_points=5)

Select Blender

from panorai.pipelines.blender.registry import BlenderRegistry

blender = BlenderRegistry.get("average")  # Options: "closest", "average", etc.

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⚡ End-to-End Workflow with PanoraiPipeline

For streamlined processing, use the PanoraiPipeline.

from panorai.pipelines.panorai_pipeline import PanoraiPipeline

pipeline = PanoraiPipeline(sampler_name="cube", blender_name="average")

# Forward projection (Equirectangular → Gnomonic Faces)
faces = pipeline.forward_pass(data=eq_image.data, fov=85, lat=0, lon=0)

# Back-projection (Faces → Equirectangular)
eq_final = pipeline.backward_pass(data=faces, eq_shape=(512, 1024))
eq_final.show()

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📌 Summary

Feature	Function
Load Image	PanoraiData.from_file()
Convert to Gnomonic	to_gnomonic(lat, lon, fov)
Convert to Face Set	to_gnomonic_face_set(fov, sampling_method)
Convert Back to EQ	to_equirectangular(eq_shape, blender_name)
Use Samplers & Blenders	ConfigManager, BlenderRegistry
Pipeline Processing	PanoraiPipeline.forward_pass(), backward_pass()

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📚 Next Steps

• Experiment with different samplers ("cube", "fibonacci").
• Try blenders ("closest", "average") for optimal reconstructions.
• Use Torch tensors for deep learning integration.

🔗 PanorAi Documentation (Link to full API reference)

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