"""Interactive target picker for offline tracking (matplotlib/Tk GUI).

Loops through videos that need tracking and lets the user click 3 reference
points per video in L-shape order:

    1) TOP target (above the corner)
    2) CORNER target (the right-angle vertex)
    3) LEFT target (to the left of the corner)

These three points are the same reference layout used by ethoscope's
`TargetGridROIBuilder`: dst_points = [(0, -1), (0, 0), (-1, 0)] in unit
coordinates. Saving them as a JSON sidecar lets the offline tracker build the
6-ROI HD mating arena grid without needing auto-target detection.

Output JSON sidecar: TARGETS_DIR/<video_basename>.json
    {
      "video_path": "/mnt/.../*.mp4",
      "frame_index": <int>,
      "reference_points": [[x0, y0], [x1, y1], [x2, y2]],
      "order": ["top", "corner", "left"],
      "picked_at": "<isoformat>"
    }

Keys (in the picker window):
    LEFT-CLICK  add a point (top → corner → left)
    r           reset clicks for current video
    d           skip this video for THIS run only (no JSON written)
    u           mark this video unusable (FOV wrong etc.); skipped forever
    .  /  ,     advance / rewind by 25 frames (≈ 1 s @ 25 fps)
    ]  /  [     advance / rewind by 5% of the video (~3 min in a 1 h video)
    #           jump to the middle of the video
    enter       save the 3 points and move on
    q / ESC     quit picker

After the 3rd click, the 6 ROI rectangles are drawn over the frame so you
can sanity-check the geometry before pressing ENTER.

With --redo, if a JSON sidecar exists, its points are pre-loaded so you can
nudge them rather than restart from scratch.

Why matplotlib instead of cv2.imshow:
    OpenCV's bundled GUI uses Qt, which needs XKeyboard + a fonts directory and
    is fragile over SSH X11-forwarding. matplotlib's TkAgg backend uses pure
    Tk/X11 and works out of the box on any DISPLAY (and gives free pan/zoom
    via the toolbar — useful for clicking small targets precisely).
"""

from __future__ import annotations

import argparse
import datetime as dt
import json
import os
import sys
from pathlib import Path

# Force TkAgg BEFORE importing matplotlib. We override even if MPLBACKEND is
# already set, because the script is unusable with a non-interactive backend.
os.environ["MPLBACKEND"] = "TkAgg"

import cv2  # noqa: E402
import matplotlib  # noqa: E402
import matplotlib.pyplot as plt  # noqa: E402
import numpy as np  # noqa: E402
import pandas as pd  # noqa: E402

# matplotlib.backend_bases exposes the cursor identifiers under different
# names depending on version: `Cursors` enum on 3.5+, lowercase `cursors`
# instance on older releases. Both have the same integer attributes.
try:
    from matplotlib.backend_bases import Cursors as _Cursors  # 3.5+
except ImportError:
    try:
        from matplotlib.backend_bases import cursors as _Cursors  # older
    except ImportError:
        _Cursors = None

# Verify we ended up on an interactive backend; bail loud (with a concrete
# explanation) if not. matplotlib silently falls back to 'agg' when its
# requested backend can't load, which is hard to debug without help.
_backend = matplotlib.get_backend()
if _backend.lower() in ("agg", "headless", "template", "pdf", "svg", "ps"):
    diag = []
    try:
        import tkinter as _tk
        try:
            _tk.Tk().destroy()
            diag.append("tkinter import + Tk() instantiation: OK")
        except Exception as e:
            diag.append(f"tkinter imported but Tk() failed: {e!r}")
    except Exception as e:
        diag.append(f"tkinter import FAILED: {e!r}")
        diag.append("  → on Manjaro/Arch, run:  sudo pacman -S tk")
    print(
        f"ERROR: matplotlib loaded the non-interactive backend {_backend!r}.\n"
        f"  Expected 'TkAgg'. Diagnostic info:\n"
        f"    DISPLAY        = {os.environ.get('DISPLAY')!r}\n"
        f"    MPLBACKEND     = {os.environ.get('MPLBACKEND')!r}\n"
        f"    matplotlib ver = {matplotlib.__version__}\n"
        + "\n".join(f"    {d}" for d in diag),
        file=sys.stderr,
    )
    sys.exit(2)

from config import INVENTORY_CSV, TARGETS_DIR  # noqa: E402
from tracking_geometry import compute_roi_polygons  # noqa: E402

# Strip default matplotlib keybindings that would conflict with ours.
for k in ("keymap.home", "keymap.save", "keymap.quit", "keymap.fullscreen",
          "keymap.pan", "keymap.zoom", "keymap.back", "keymap.forward"):
    try:
        plt.rcParams[k] = []
    except KeyError:
        pass

CLICK_LABELS = ("TOP", "CORNER", "LEFT")
CLICK_COLORS = ("red", "lime", "deepskyblue")


def grab_frame(
    video_path: Path, frame_idx: int
) -> tuple[np.ndarray, int, int] | None:
    """Return (RGB frame, actual_frame_idx, n_frames) from the video, or None.

    Clamps frame_idx to [0, n_frames-1] so callers can step blindly.
    """
    cap = cv2.VideoCapture(str(video_path))
    if not cap.isOpened():
        return None
    n = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    if n > 0:
        frame_idx = max(0, min(frame_idx, n - 1))
    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
    ok, frame = cap.read()
    cap.release()
    if not ok or frame is None:
        return None
    return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), frame_idx, n


def pick_one(
    video_path: Path,
    frame_idx: int,
    status_prefix: str,
    initial_points: list[tuple[float, float]] | None = None,
) -> dict | None:
    """Show the picker UI for a single video; return the result dict or None."""
    grabbed = grab_frame(video_path, frame_idx)
    if grabbed is None:
        print(f"  ! cannot read {video_path}")
        return None
    frame, frame_idx, n_frames = grabbed
    # Big-step size for ] / [ : 5% of total length, ~3 min in a 1h video.
    big_step = max(1, int(round(0.05 * n_frames))) if n_frames > 0 else 250

    fig, ax = plt.subplots(figsize=(14, 8))
    try:
        fig.canvas.manager.set_window_title("pick targets")
    except Exception:
        pass
    # Use a crosshair cursor over the axes so it's obvious where the click
    # will land. matplotlib's toolbar resets the cursor to POINTER (arrow) on
    # every mouse-move when no tool is active, so we intercept set_cursor:
    # whenever it asks for POINTER, we substitute SELECT_REGION (crosshair).
    # Tool modes (zoom/pan) keep their native cursors.
    if _Cursors is not None:
        _orig_set_cursor = fig.canvas.set_cursor

        def _set_cursor_with_crosshair(cursor):
            if cursor == _Cursors.POINTER:
                cursor = _Cursors.SELECT_REGION
            return _orig_set_cursor(cursor)

        fig.canvas.set_cursor = _set_cursor_with_crosshair
        try:
            fig.canvas.set_cursor(_Cursors.SELECT_REGION)
        except Exception:
            pass
    else:
        # Last-ditch: just set the Tk widget's cursor once and hope the
        # toolbar doesn't immediately overwrite it.
        try:
            fig.canvas.get_tk_widget().config(cursor="tcross")
        except Exception:
            pass
    img_artist = ax.imshow(frame)
    ax.set_axis_off()
    fig.tight_layout()

    state = {
        "points": list(initial_points) if initial_points else [],
        "action": None,          # 'save' | 'skip' | 'quit' | 'unusable'
        "frame": frame,
        "frame_idx": frame_idx,
        "drawn": [],             # artists drawn on top of the image
    }

    def update_title():
        nb = len(state["points"])
        nxt = (
            f"click {CLICK_LABELS[nb]}"
            if nb < 3
            else "ENTER=save | r=reset d=skip u=unusable q=quit | . , [ ] # = step frame"
        )
        ax.set_title(
            f'{status_prefix}  frame {state["frame_idx"]}  |  {nxt}',
            fontsize=10,
        )

    def redraw_points():
        for a in state["drawn"]:
            try:
                a.remove()
            except Exception:
                pass
        state["drawn"].clear()
        for i, (x, y) in enumerate(state["points"]):
            color = CLICK_COLORS[i]
            label = CLICK_LABELS[i]
            (cross,) = ax.plot(x, y, marker="+", color=color, markersize=22, mew=2)
            (ring,) = ax.plot(
                x, y, marker="o", color=color, markersize=22,
                fillstyle="none", mew=2,
            )
            txt = ax.text(
                x + 14, y - 14, label,
                color=color, fontsize=10, weight="bold",
            )
            state["drawn"].extend([cross, ring, txt])
        if len(state["points"]) >= 2:
            (line1,) = ax.plot(
                [state["points"][0][0], state["points"][1][0]],
                [state["points"][0][1], state["points"][1][1]],
                color="white", linewidth=0.7, alpha=0.6,
            )
            state["drawn"].append(line1)
        if len(state["points"]) == 3:
            (line2,) = ax.plot(
                [state["points"][1][0], state["points"][2][0]],
                [state["points"][1][1], state["points"][2][1]],
                color="white", linewidth=0.7, alpha=0.6,
            )
            state["drawn"].append(line2)
            # ROI overlay — draw the 6 computed rectangles on top of the frame
            try:
                polys = compute_roi_polygons(state["points"])
            except Exception as e:
                polys = []
                print(f"  (ROI preview failed: {e})")
            for j, poly in enumerate(polys):
                # Close the polygon by repeating the first point
                xs = list(poly[:, 0]) + [poly[0, 0]]
                ys = list(poly[:, 1]) + [poly[0, 1]]
                (line,) = ax.plot(
                    xs, ys, color="yellow", linewidth=1.5, alpha=0.9,
                )
                state["drawn"].append(line)
                cx = float(np.mean(poly[:, 0]))
                cy = float(np.mean(poly[:, 1]))
                lbl = ax.text(
                    cx, cy, str(j + 1),
                    color="yellow", fontsize=14, weight="bold",
                    ha="center", va="center",
                )
                state["drawn"].append(lbl)
        update_title()
        fig.canvas.draw_idle()

    def reload_frame(new_idx: int):
        grabbed = grab_frame(video_path, new_idx)
        if grabbed is None:
            return
        new_frame, new_idx, _ = grabbed
        state["frame"] = new_frame
        state["frame_idx"] = new_idx
        img_artist.set_data(new_frame)
        # Keep clicked targets + ROI overlay in place across frame-stepping —
        # press 'r' to clear them explicitly.
        redraw_points()

    def on_click(event):
        if event.inaxes is not ax:
            return
        if event.button != 1:  # left click only
            return
        if event.xdata is None or event.ydata is None:
            return
        # Skip clicks fired while the toolbar's pan/zoom is active.
        toolbar = getattr(fig.canvas, "toolbar", None)
        if toolbar is not None and getattr(toolbar, "mode", ""):
            return
        x, y = float(event.xdata), float(event.ydata)
        if len(state["points"]) < 3:
            state["points"].append((x, y))
        else:
            # 3 points already there — replace the nearest one. Lets the user
            # nudge pre-loaded targets in --redo mode, or correct a bad click.
            dists = [(x - px) ** 2 + (y - py) ** 2 for px, py in state["points"]]
            i_nearest = min(range(3), key=dists.__getitem__)
            state["points"][i_nearest] = (x, y)
        redraw_points()

    def on_key(event):
        k = event.key or ""
        if k in ("escape", "q"):
            state["action"] = "quit"
            plt.close(fig)
        elif k == "r":
            state["points"].clear()
            redraw_points()
        elif k == "d":
            state["action"] = "skip"
            plt.close(fig)
        elif k == "u":
            state["action"] = "unusable"
            plt.close(fig)
        elif k == "enter":
            if len(state["points"]) == 3:
                state["action"] = "save"
                plt.close(fig)
        elif k == ".":
            reload_frame(state["frame_idx"] + 25)
        elif k == ",":
            reload_frame(state["frame_idx"] - 25)
        elif k == "]":
            reload_frame(state["frame_idx"] + big_step)
        elif k == "[":
            reload_frame(state["frame_idx"] - big_step)
        elif k == "#":
            if n_frames > 0:
                reload_frame(n_frames // 2)

    fig.canvas.mpl_connect("button_press_event", on_click)
    fig.canvas.mpl_connect("key_press_event", on_key)
    update_title()
    plt.show()  # blocks until the figure is closed

    if state["action"] == "save":
        return {
            "action": "save",
            "frame_idx": state["frame_idx"],
            "points": state["points"],
        }
    if state["action"] == "unusable":
        return {"action": "unusable", "frame_idx": state["frame_idx"]}
    if state["action"] in ("skip", "quit"):
        return {"action": state["action"]}
    # Window closed via the WM "X" button — treat as quit so the loop stops
    return {"action": "quit"}


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--redo", action="store_true",
        help="re-pick videos that already have JSON sidecars",
    )
    parser.add_argument(
        "--frame", type=int, default=125,
        help="default frame index to display (default 125 ≈ 5 s @ 25 fps)",
    )
    parser.add_argument(
        "--limit", type=int, default=None,
        help="only process the first N videos",
    )
    args = parser.parse_args()

    if not INVENTORY_CSV.exists():
        sys.exit(
            f"Inventory not found at {INVENTORY_CSV}. "
            "Run build_video_inventory.py first."
        )

    inv = pd.read_csv(INVENTORY_CSV)
    todo = inv[inv["in_xlsx"] & ~inv["already_tracked"]].copy()
    todo = todo.sort_values(
        ["session_date", "machine_name", "session_time"]
    ).reset_index(drop=True)

    TARGETS_DIR.mkdir(parents=True, exist_ok=True)

    def sidecar_for(mp4_path: str) -> Path:
        return TARGETS_DIR / (Path(mp4_path).stem + ".json")

    if not args.redo:
        todo = todo[
            ~todo["mp4_path"].apply(lambda p: sidecar_for(p).exists())
        ].reset_index(drop=True)

    if args.limit:
        todo = todo.head(args.limit)

    n = len(todo)
    if n == 0:
        print("Nothing to pick. All eligible videos already have target JSONs.")
        return

    print(
        f"Picking targets for {n} videos. "
        "Window keys: ENTER=save  r=reset  d=skip  u=unusable  q=quit  "
        ".,[]=step frame  |  pan/zoom via toolbar"
    )
    saved = skipped = unusable = 0
    for i, row in todo.iterrows():
        mp4 = Path(row["mp4_path"])
        prefix = f"[{i + 1}/{n}] {row['machine_name']} {row['session_datetime']}"
        print(f"\n{prefix}")

        # If --redo and a JSON sidecar exists, pre-load its points (only for
        # regular saves — unusable sidecars are left as-is and shown empty).
        initial_points = None
        existing = sidecar_for(row["mp4_path"])
        if args.redo and existing.exists():
            try:
                prev = json.loads(existing.read_text())
                if not prev.get("unusable") and prev.get("reference_points"):
                    initial_points = [tuple(p) for p in prev["reference_points"]]
                    print(f"  pre-loaded {len(initial_points)} previous point(s)")
            except Exception as e:
                print(f"  ! could not read previous sidecar: {e}")

        result = pick_one(mp4, args.frame, prefix, initial_points=initial_points)
        if result is None or result.get("action") == "quit":
            print("  quitting picker.")
            break
        if result["action"] == "skip":
            skipped += 1
            print("  skipped (no JSON written, will be re-asked next run).")
            continue
        if result["action"] == "unusable":
            try:
                reason = input("  reason for marking unusable (Enter to skip): ").strip()
            except EOFError:
                reason = ""
            payload = {
                "video_path": str(mp4),
                "unusable": True,
                "reason": reason,
                "marked_at": dt.datetime.now().isoformat(timespec="seconds"),
            }
            out_path = sidecar_for(row["mp4_path"])
            out_path.write_text(json.dumps(payload, indent=2))
            unusable += 1
            print(f"  marked unusable → {out_path.name}")
            continue
        if result["action"] == "save":
            payload = {
                "video_path": str(mp4),
                "frame_index": int(result["frame_idx"]),
                "reference_points": [list(map(int, p)) for p in result["points"]],
                "order": ["top", "corner", "left"],
                "picked_at": dt.datetime.now().isoformat(timespec="seconds"),
            }
            out_path = sidecar_for(row["mp4_path"])
            out_path.write_text(json.dumps(payload, indent=2))
            saved += 1
            print(f"  saved → {out_path.name}")

    remaining = n - saved - skipped - unusable
    print(
        f"\nDone. saved={saved}  unusable={unusable}  "
        f"skipped(this run)={skipped}  remaining={remaining}"
    )


if __name__ == "__main__":
    main()