"""
explainability/spectral_heatmap.py
------------------------------------
Spectral Heatmap Visualization for FFT/DCT Branch.
STATUS: COMPLETE

Converts the FFT log-magnitude spectrum from the spectral branch
into a colorized heatmap suitable for display in the web UI.

Also generates a side-by-side comparison of:
  - Log-magnitude spectrum
  - High-frequency anomaly regions highlighted

Output:
  - heatmap_b64 : base64-encoded JPEG of the spectral heatmap
  - annotated_b64 : base64-encoded JPEG with anomaly rings annotated
"""

import numpy as np
import cv2
from utils.image_utils import encode_image_to_base64


def render_spectral_heatmap(
    spectrum_map: np.ndarray,
    img: np.ndarray,
) -> dict:
    """
    Render the FFT spectrum as a colored heatmap and annotate anomaly peaks.

    Args:
        spectrum_map : (H, W) float32 in [0, 1] — from spectral_branch
        img          : (H, W, 3) float32 in [0, 1] — original image

    Returns:
        dict with:
            "spectrum_b64"  : base64 JPEG of raw spectrum heatmap
            "annotated_b64" : base64 JPEG with anomaly rings drawn
    """
    H, W = spectrum_map.shape

    # ── 1. Colorize spectrum ───────────────────────────────────────
    spec_u8 = (spectrum_map * 255).astype(np.uint8)
    spec_colored = cv2.applyColorMap(spec_u8, cv2.COLORMAP_INFERNO)
    spec_colored_rgb = cv2.cvtColor(spec_colored, cv2.COLOR_BGR2RGB)
    spectrum_b64 = encode_image_to_base64(spec_colored_rgb)

    # ── 2. Annotate high-frequency anomaly rings ───────────────────
    annotated = spec_colored_rgb.copy()
    cy, cx = H // 2, W // 2

    # Draw reference rings at 10%, 25%, 45% of half-diagonal
    half_diag = int(min(H, W) * 0.5)
    radii = [
        ("LF",  int(half_diag * 0.10), (0, 200, 0)),    # Low freq (green)
        ("MF",  int(half_diag * 0.25), (255, 200, 0)),   # Mid freq (yellow)
        ("HF",  int(half_diag * 0.45), (255, 50, 50)),   # High freq (red)
    ]

    for label, r, color in radii:
        cv2.circle(annotated, (cx, cy), r, color, 1)
        cv2.putText(
            annotated, label,
            (cx + r + 3, cy),
            cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1, cv2.LINE_AA
        )

    # Mark DC component (center)
    cv2.circle(annotated, (cx, cy), 3, (255, 255, 255), -1)

    # Detect and mark anomalous peaks in high-frequency band
    y_idx, x_idx = np.ogrid[:H, :W]
    dist = np.sqrt((y_idx - cy) ** 2 + (x_idx - cx) ** 2)
    hf_mask = (dist > half_diag * 0.15) & (dist < half_diag * 0.45)
    hf_spectrum = spectrum_map * hf_mask

    # Find top-5 peak locations
    flat_idx = np.argsort(hf_spectrum.ravel())[::-1][:5]
    peak_coords = np.unravel_index(flat_idx, hf_spectrum.shape)

    for py, px in zip(peak_coords[0], peak_coords[1]):
        if hf_spectrum[py, px] > 0.5:  # Only mark significant peaks
            cv2.drawMarker(
                annotated, (px, py), (0, 255, 255),
                cv2.MARKER_CROSS, 8, 1, cv2.LINE_AA
            )

    annotated_b64 = encode_image_to_base64(annotated)

    return {
        "spectrum_b64":  spectrum_b64,
        "annotated_b64": annotated_b64,
    }


def render_noise_map(noise_map: np.ndarray) -> str:
    """
    Render the diffusion branch's residual noise map as a colorized heatmap.

    Args:
        noise_map : (H, W) float32 in [0, 1]

    Returns:
        base64-encoded JPEG string
    """
    noise_u8 = (np.clip(noise_map, 0, 1) * 255).astype(np.uint8)
    colored = cv2.applyColorMap(noise_u8, cv2.COLORMAP_VIRIDIS)
    colored_rgb = cv2.cvtColor(colored, cv2.COLOR_BGR2RGB)
    return encode_image_to_base64(colored_rgb)


def render_edge_map(edge_map: np.ndarray) -> str:
    """
    Render the edge branch's edge magnitude map as a colorized heatmap.

    Args:
        edge_map : (H, W) float32 in [0, 1]

    Returns:
        base64-encoded JPEG string
    """
    edge_u8 = (np.clip(edge_map, 0, 1) * 255).astype(np.uint8)
    colored = cv2.applyColorMap(edge_u8, cv2.COLORMAP_BONE)
    colored_rgb = cv2.cvtColor(colored, cv2.COLOR_BGR2RGB)
    return encode_image_to_base64(colored_rgb)