feat(feature-compressor): add DINOv2 feature extraction and compression pipeline

mini-nav/feature_compressor/core/visualizer.py

@@ -0,0 +1,178 @@
+"""Feature visualization using Plotly."""
+
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+import torch
+import yaml
+from plotly.graph_objs import Figure
+
+from ..utils.plot_utils import (
+    apply_theme,
+    create_comparison_plot,
+    create_histogram,
+    create_pca_scatter_2d,
+    save_figure,
+)
+
+
+class FeatureVisualizer:
+    """Visualize DINOv2 features with interactive Plotly charts.
+
+    Supports histograms, PCA projections, and feature comparisons
+    with multiple export formats.
+
+    Args:
+        config_path: Path to YAML configuration file
+    """
+
+    def __init__(self, config_path: Optional[str] = None):
+        self.config = self._load_config(config_path)
+
+    def _load_config(self, config_path: Optional[str] = None) -> dict:
+        """Load configuration from YAML file.
+
+        Args:
+            config_path: Path to config file, or None for default
+
+        Returns:
+            Configuration dictionary
+        """
+        if config_path is None:
+            config_path = (
+                Path(__file__).parent.parent.parent
+                / "configs"
+                / "feature_compressor.yaml"
+            )
+
+        with open(config_path) as f:
+            return yaml.safe_load(f)
+
+    def plot_histogram(self, features: torch.Tensor, title: str = None) -> object:
+        """Plot histogram of feature values.
+
+        Args:
+            features: Feature tensor [batch, dim]
+            title: Plot title
+
+        Returns:
+            Plotly Figure object
+        """
+        features_np = features.cpu().numpy()
+        fig = create_histogram(features_np, title=title)
+
+        viz_config = self.config.get("visualization", {})
+        fig = apply_theme(fig, viz_config.get("plot_theme", "plotly_white"))
+        fig.update_layout(
+            width=viz_config.get("fig_width", 900),
+            height=viz_config.get("fig_height", 600),
+        )
+
+        return fig
+
+    def plot_pca_2d(self, features: torch.Tensor, labels: List = None) -> Figure:
+        """Plot 2D PCA projection of features.
+
+        Args:
+            features: Feature tensor [n_samples, dim]
+            labels: Optional labels for coloring
+
+        Returns:
+            Plotly Figure object
+        """
+        features_np = features.cpu().numpy()
+        viz_config = self.config.get("visualization", {})
+
+        fig = create_pca_scatter_2d(features_np, labels=labels)
+        fig = apply_theme(fig, viz_config.get("plot_theme", "plotly_white"))
+        fig.update_traces(
+            marker=dict(
+                size=viz_config.get("point_size", 8),
+                colorscale=viz_config.get("color_scale", "viridis"),
+            )
+        )
+        fig.update_layout(
+            width=viz_config.get("fig_width", 900),
+            height=viz_config.get("fig_height", 600),
+        )
+
+        return fig
+
+    def plot_comparison(
+        self, features_list: List[torch.Tensor], names: List[str]
+    ) -> object:
+        """Plot comparison of multiple feature sets.
+
+        Args:
+            features_list: List of feature tensors
+            names: Names for each feature set
+
+        Returns:
+            Plotly Figure object
+        """
+        features_np_list = [f.cpu().numpy() for f in features_list]
+
+        fig = create_comparison_plot(features_np_list, names)
+
+        viz_config = self.config.get("visualization", {})
+        fig = apply_theme(fig, viz_config.get("plot_theme", "plotly_white"))
+        fig.update_layout(
+            width=viz_config.get("fig_width", 900) * len(features_list),
+            height=viz_config.get("fig_height", 600),
+        )
+
+        return fig
+
+    def generate_report(self, results: List[dict], output_dir: str) -> List[str]:
+        """Generate full feature analysis report.
+
+        Args:
+            results: List of extractor results
+            output_dir: Directory to save visualizations
+
+        Returns:
+            List of generated file paths
+        """
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        generated_files = []
+
+        # Extract all compressed features
+        all_features = torch.cat([r["compressed_features"] for r in results], dim=0)
+
+        # Create histogram
+        hist_fig = self.plot_histogram(all_features, "Compressed Feature Distribution")
+        hist_path = output_dir / "feature_histogram"
+        self.save(hist_fig, str(hist_path), formats=["html"])
+        generated_files.append(str(hist_path) + ".html")
+
+        # Create PCA
+        pca_fig = self.plot_pca_2d(all_features)
+        pca_path = output_dir / "feature_pca_2d"
+        self.save(pca_fig, str(pca_path), formats=["html", "png"])
+        generated_files.append(str(pca_path) + ".html")
+        generated_files.append(str(pca_path) + ".png")
+
+        return generated_files
+
+    def save(self, fig: object, path: str, formats: List[str] = None) -> None:
+        """Save figure in multiple formats.
+
+        Args:
+            fig: Plotly Figure object
+            path: Output file path (without extension)
+            formats: List of formats to export
+        """
+        if formats is None:
+            formats = ["html"]
+
+        output_config = self.config.get("output", {})
+
+        for fmt in formats:
+            if fmt == "png":
+                save_figure(fig, path, format="png")
+            else:
+                save_figure(fig, path, format=fmt)