#!/usr/bin/env python3
"""Create real-data GIFs of the promoted CPU student's detector and tracker."""
from __future__ import annotations

import hashlib
import json
import pickle
import sys
from dataclasses import dataclass
from pathlib import Path

import numpy as np
from PIL import Image, ImageDraw, ImageFont

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))

from orbitsight.eval.metrics import iou_xywh  # noqa: E402
from orbitsight.ingestion import discover_sequences, iter_windows, load_events, load_gt_boxes  # noqa: E402
from orbitsight.postprocessing import finalize_detections, rank_detections  # noqa: E402
from orbitsight.tracking import get_tracking_policy  # noqa: E402
from orbitsight.tracking.tracker import tracks_to_detections  # noqa: E402
from orbitsight.viz.visualize import _event_image  # noqa: E402

CACHE = ROOT / "artifacts/cpu_student/post_training_latency_neutral/current_student_top1_cache.pkl"
OUT = ROOT / "competition_ready_improved_cpu_student/visualizations/animations"

BG = "#07111f"
PANEL = "#0e2035"
TEXT = "#e9f2f9"
MUTED = "#91a7ba"
DETECTOR = "#ff5a5f"
TRACKER = "#39e6ff"
GT = "#ffd43b"
GOOD = "#54e38e"


@dataclass(frozen=True)
class Clip:
    slug: str
    sequence: str
    start: int
    end: int
    title: str
    subtitle: str


CLIPS = (
    Clip(
        "davis_saocom_tracker_smoothing",
        "DAVIS_SAOCOM1B_46265_2024-12-04-18-21-37",
        3388,
        3416,
        "DAVIS SAOCOM-1B: detector + tracker",
        "Native 346×260 events; cyan smoothing stabilizes the raw red localization",
    ),
    Clip(
        "evk4_bright_track",
        "2025_12_23_20_53_46_EVK4_mag7.3",
        82,
        113,
        "EVK4: detector + recurrent tracker",
        "Large-sensor event field; cyan Kalman state follows the raw red proposal",
    ),
    Clip(
        "dvx_stars_tracker_smoothing",
        "DVX_Filtered_Stars3_2025-01-20-20-22-53",
        4136,
        4165,
        "DVX Stars3: tracker smoothing in clutter",
        "A real interval where tracking materially improves several localization overlaps",
    ),
    Clip(
        "dvx_thuraya_low_snr",
        "DVX_Filtered_Thuraya3_32404_2025-01-20-20-02-43",
        2445,
        2477,
        "DVX Thuraya3: low-SNR target",
        "The hardest regime: tiny boxes, weak event evidence, and changing confidence",
    ),
)


def font(size: int, bold: bool = False):
    name = "DejaVuSans-Bold.ttf" if bold else "DejaVuSans.ttf"
    path = Path("/usr/share/fonts/truetype/dejavu") / name
    return ImageFont.truetype(str(path), size) if path.exists() else ImageFont.load_default()


def dashed_rectangle(draw: ImageDraw.ImageDraw, box, fill, width=3, dash=8):
    x1, y1, x2, y2 = [int(round(v)) for v in box]
    for x in range(x1, x2, dash * 2):
        draw.line((x, y1, min(x + dash, x2), y1), fill=fill, width=width)
        draw.line((x, y2, min(x + dash, x2), y2), fill=fill, width=width)
    for y in range(y1, y2, dash * 2):
        draw.line((x1, y, x1, min(y + dash, y2)), fill=fill, width=width)
        draw.line((x2, y, x2, min(y + dash, y2)), fill=fill, width=width)


def xyxy(box):
    cx, cy, w, h = box[:4]
    return cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2


def replay_diagnostics(row: dict, end: int) -> dict[int, dict]:
    policy = get_tracking_policy("cpu_student")
    tracker = policy.build_tracker()
    records = {}
    for index, detections in enumerate(row["detections"][: end + 1]):
        fed = rank_detections(
            [tuple(float(v) for v in det) for det in detections
             if float(det[4]) >= policy.detector_threshold],
            policy.pre_max_det,
        )
        tracks = tracker.update(fed)
        emitted = tracks_to_detections(
            tracks,
            emit_coasting=policy.emit_coasting,
            max_coast_age=policy.max_coast_age,
            coast_decay=policy.coast_decay,
        )
        emitted = finalize_detections(emitted, row["sensor_profile"], policy.post_max_det)
        emitted = [det for det in emitted if det[4] >= row["emit_gate"]]
        chosen_id = None
        chosen_age = None
        if emitted:
            target = emitted[0]
            nearest = min(
                tracks,
                key=lambda track: (track.box[0] - target[0]) ** 2 + (track.box[1] - target[1]) ** 2,
            )
            chosen_id, chosen_age = nearest.id, nearest.age
        records[index] = {
            "detector": fed[:1],
            "tracker": emitted[:1],
            "track_id": chosen_id,
            "track_age": chosen_age,
        }
    return records


def selected_windows(ref, start: int, end: int):
    selected = {}
    events = load_events(ref, mmap=True)
    gt = load_gt_boxes(ref)
    for window in iter_windows(events, ref.name, gt):
        if start <= window.index <= end:
            selected[window.index] = window
        if window.index >= end:
            break
    expected = set(range(start, end + 1))
    if set(selected) != expected:
        raise RuntimeError(f"missing animation windows for {ref.name}: {sorted(expected-set(selected))}")
    return selected


def crop_bounds(windows, records, sensor, start, end):
    points = []
    for index in range(start, end + 1):
        window = windows[index]
        for gt in window.gt_boxes:
            points.append((gt.cx, gt.cy))
        for key in ("detector", "tracker"):
            for box in records[index][key]:
                points.append((box[0], box[1]))
    if not points:
        return 0, 0, sensor.width, sensor.height
    xs, ys = zip(*points)
    span = max(max(xs) - min(xs), max(ys) - min(ys), 95 if sensor.name == "DVX" else 155)
    cx, cy = (min(xs) + max(xs)) / 2, (min(ys) + max(ys)) / 2
    half = span * 0.72
    x1, x2 = max(0, cx - half), min(sensor.width, cx + half)
    y1, y2 = max(0, cy - half), min(sensor.height, cy + half)
    return x1, y1, x2, y2


def fit_rect(src_w, src_h, dst):
    x, y, w, h = dst
    scale = min(w / src_w, h / src_h)
    rw, rh = src_w * scale, src_h * scale
    return x + (w - rw) / 2, y + (h - rh) / 2, rw, rh


def map_box(box, source_bounds, target_rect):
    sx1, sy1, sx2, sy2 = source_bounds
    tx, ty, tw, th = target_rect
    x1, y1, x2, y2 = xyxy(box)
    return (
        tx + (x1 - sx1) / (sx2 - sx1) * tw,
        ty + (y1 - sy1) / (sy2 - sy1) * th,
        tx + (x2 - sx1) / (sx2 - sx1) * tw,
        ty + (y2 - sy1) / (sy2 - sy1) * th,
    )


def render_clip(clip: Clip, ref, row: dict) -> tuple[Path, Path, dict]:
    sensor = ref.sensor
    windows = selected_windows(ref, clip.start, clip.end)
    records = replay_diagnostics(row, clip.end)
    crop = crop_bounds(windows, records, sensor, clip.start, clip.end)
    canvas_size = (960, 540)
    full_slot = (20, 72, 565, 440)
    zoom_slot = (610, 72, 330, 330)
    full_rect = fit_rect(sensor.width, sensor.height, full_slot)
    zoom_rect = fit_rect(crop[2] - crop[0], crop[3] - crop[1], zoom_slot)
    detector_trail, tracker_trail = [], []
    frames = []
    stats = {"frames": clip.end - clip.start + 1, "tracker_iou_better": 0, "gt_frames": 0}

    for index in range(clip.start, clip.end + 1):
        window = windows[index]
        record = records[index]
        gt = [(g.cx, g.cy, g.w, g.h) for g in window.gt_boxes]
        detector = record["detector"]
        tracked = record["tracker"]
        if detector:
            detector_trail.append((detector[0][0], detector[0][1]))
        if tracked:
            tracker_trail.append((tracked[0][0], tracked[0][1]))

        base = Image.fromarray(_event_image(np.asarray(window.events), sensor)).convert("RGB")
        canvas = Image.new("RGB", canvas_size, BG)
        draw = ImageDraw.Draw(canvas)
        draw.text((20, 13), clip.title, fill=TEXT, font=font(22, True))
        draw.text((20, 42), clip.subtitle, fill=MUTED, font=font(12))

        full = base.resize((int(full_rect[2]), int(full_rect[3])), Image.Resampling.BILINEAR)
        canvas.paste(full, (int(full_rect[0]), int(full_rect[1])))
        crop_img = base.crop(tuple(int(round(v)) for v in crop))
        crop_img = crop_img.resize((int(zoom_rect[2]), int(zoom_rect[3])), Image.Resampling.NEAREST)
        canvas.paste(crop_img, (int(zoom_rect[0]), int(zoom_rect[1])))
        draw = ImageDraw.Draw(canvas)
        draw.rectangle((full_rect[0], full_rect[1], full_rect[0]+full_rect[2], full_rect[1]+full_rect[3]), outline="#29445e", width=2)
        draw.rectangle((zoom_rect[0], zoom_rect[1], zoom_rect[0]+zoom_rect[2], zoom_rect[1]+zoom_rect[3]), outline="#29445e", width=2)
        draw.text((zoom_rect[0]+8, zoom_rect[1]+7), "TARGET ZOOM", fill=TEXT, font=font(12, True), stroke_width=2, stroke_fill=BG)

        all_bounds = (0, 0, sensor.width, sensor.height)
        for trail, color in ((detector_trail[-18:], DETECTOR), (tracker_trail[-18:], TRACKER)):
            for bounds, rect in ((all_bounds, full_rect), (crop, zoom_rect)):
                mapped = [(
                    rect[0] + (x-bounds[0])/(bounds[2]-bounds[0])*rect[2],
                    rect[1] + (y-bounds[1])/(bounds[3]-bounds[1])*rect[3],
                ) for x, y in trail if bounds[0] <= x <= bounds[2] and bounds[1] <= y <= bounds[3]]
                if len(mapped) > 1:
                    draw.line(mapped, fill=color, width=2)

        for bounds, rect, width in ((all_bounds, full_rect, 2), (crop, zoom_rect, 4)):
            for box in gt:
                dashed_rectangle(draw, map_box(box, bounds, rect), GT, width=width, dash=8)
            for box in detector:
                draw.rectangle(map_box(box, bounds, rect), outline=DETECTOR, width=width)
            for box in tracked:
                draw.rectangle(map_box(box, bounds, rect), outline=TRACKER, width=width)

        det_iou = tracker_iou = None
        if gt:
            stats["gt_frames"] += 1
            if detector:
                det_iou = iou_xywh(detector[0][:4], gt[0])
            if tracked:
                tracker_iou = iou_xywh(tracked[0][:4], gt[0])
            if det_iou is not None and tracker_iou is not None and tracker_iou > det_iou:
                stats["tracker_iou_better"] += 1

        info_y = 418
        draw.rounded_rectangle((610, info_y, 940, 520), radius=9, fill=PANEL, outline="#29445e")
        elapsed = (window.t_start_us - windows[clip.start].t_start_us) / 1e6
        draw.text((625, info_y+10), f"window {index}   +{elapsed:0.2f}s   events {len(window.events):,}", fill=TEXT, font=font(12, True))
        det_text = "none" if not detector else f"{detector[0][4]:.3f}  IoU {det_iou:.2f}" if det_iou is not None else f"{detector[0][4]:.3f}"
        trk_text = "none" if not tracked else f"#{record['track_id']} age {record['track_age']}  IoU {tracker_iou:.2f}" if tracker_iou is not None else f"#{record['track_id']} age {record['track_age']}"
        draw.text((625, info_y+36), f"DETECTOR  {det_text}", fill=DETECTOR, font=font(12, True))
        draw.text((625, info_y+58), f"TRACKER   {trk_text}", fill=TRACKER, font=font(12, True))
        draw.text((625, info_y+80), "GT dashed yellow", fill=GT, font=font(11))
        progress = (index - clip.start + 1) / (clip.end - clip.start + 1)
        draw.rectangle((20, 526, 920, 532), fill="#1b334a")
        draw.rectangle((20, 526, 20 + int(900 * progress), 532), fill=GOOD)
        frames.append(canvas.quantize(colors=128, method=Image.Quantize.FASTOCTREE))

    OUT.mkdir(parents=True, exist_ok=True)
    gif = OUT / f"{clip.slug}.gif"
    poster = OUT / f"{clip.slug}_poster.png"
    frames[0].convert("RGB").save(poster, optimize=True)
    frames[0].save(gif, save_all=True, append_images=frames[1:], duration=125,
                   loop=0, optimize=True, disposal=2)
    stats.update({"sequence": clip.sequence, "start_window": clip.start,
                  "end_window": clip.end, "gif": gif.name, "poster": poster.name})
    return gif, poster, stats


def refresh_checksums(bundle: Path) -> None:
    lines = []
    for path in sorted(p for p in bundle.rglob("*") if p.is_file() and p.name != "CHECKSUMS.sha256"):
        lines.append(f"{hashlib.sha256(path.read_bytes()).hexdigest()}  {path.relative_to(bundle)}")
    (bundle / "CHECKSUMS.sha256").write_text("\n".join(lines) + "\n")


def main() -> int:
    with CACHE.open("rb") as stream:
        cache = pickle.load(stream)
    refs = {ref.name: ref for ref in discover_sequences(str(ROOT / "data"))}
    outputs = []
    for clip in CLIPS:
        gif, _poster, stats = render_clip(clip, refs[clip.sequence], cache[clip.sequence])
        stats["sha256"] = hashlib.sha256(gif.read_bytes()).hexdigest()
        outputs.append(stats)
        print(f"rendered {gif}")
    manifest = {
        "source_cache": str(CACHE.relative_to(ROOT)),
        "policy": "cpu_student",
        "scope": "Exact promoted cached top-1 detector proposals replayed through the final Kalman/coast policy; GT is visualization-only.",
        "legend": {"detector": DETECTOR, "tracker": TRACKER, "ground_truth": GT},
        "outputs": outputs,
    }
    (OUT / "animation_manifest.json").write_text(json.dumps(manifest, indent=2) + "\n")
    (OUT / "README.md").write_text(
        "# OrbitSight detector + tracker animations\n\n"
        "These GIFs use real ChallengeON recordings and the exact promoted CPU-student top-one cache. "
        "Red is the raw detector proposal, cyan is the Kalman-smoothed emitted box, yellow dashed is "
        "human ground truth, and the colored trails show recent centers. Ground truth is used only for "
        "annotation and IoU display.\n\n" +
        "\n".join(f"- `{row['gif']}` — `{row['sequence']}`, windows {row['start_window']}–{row['end_window']}" for row in outputs) + "\n"
    )
    refresh_checksums(OUT.parents[1])
    print(json.dumps(manifest, indent=2))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())