import sys
from pathlib import Path

import numpy as np
import pytest

# Ensure project root is on the import path when tests run directly.
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from backend.py.app.inference.classical import detect_classical
from backend.py.app.inference.dl import detect_dl


def _synthetic_edge_image(size: int = 64) -> np.ndarray:
    img = np.zeros((size, size, 3), dtype=np.uint8)
    img[size // 4 : 3 * size // 4, size // 4, :] = 255
    img[size // 4 : 3 * size // 4, 3 * size // 4, :] = 255
    img[size // 4, size // 4 : 3 * size // 4, :] = 255
    img[3 * size // 4, size // 4 : 3 * size // 4, :] = 255
    return img


def test_detect_classical_edges_highlights_pixels():
    img = _synthetic_edge_image()
    overlay, meta = detect_classical(
        img,
        detector="Edges (Canny)",
        canny_low=50,
        canny_high=150,
        harris_k=0.04,
        harris_block=2,
        harris_ksize=3,
        hough_thresh=50,
        hough_min_len=10,
        hough_max_gap=5,
        ellipse_min_area=100,
        max_ellipses=1,
        line_detector="hough",
    )

    assert overlay.shape == img.shape
    assert overlay.dtype == np.uint8
    assert meta["num_edge_pixels"] > 0
    # Expect some pixels painted green in RGB space.
    assert np.any(np.all(overlay == np.array([0, 255, 0], dtype=np.uint8), axis=-1))


def test_detect_dl_dexined_morphological_thinning_default():
    img = _synthetic_edge_image()
    overlay, meta = detect_dl(img, detector="Edges (Canny)", model_choice="dexined.onnx", params={})

    assert overlay.shape == img.shape
    assert overlay.dtype == np.uint8
    assert meta.get("error") is None
    assert meta["adapter"] == "dexined"
    assert 0.0 <= meta["edge_prob_mean"] <= 1.0
    assert meta["fuse_min"] <= meta["edge_prob_mean"] <= meta["fuse_max"]
    assert 0.0 <= meta["edge_map_mean"] <= 1.0
    assert 0.0 <= meta["edge_map_std"] <= 1.0
    assert meta["model_path"].endswith("dexined.onnx")
    assert meta["dexined_use_marching_squares"] is False
    assert meta["dexined_threshold_mode"] == "adaptive"
    assert 0.0 <= meta["dexined_threshold"] <= 1.0
    assert meta.get("skeleton_pixels", 0) > 0
    assert meta.get("subpixel_points", 0) >= 0
    assert np.any(np.all(overlay == np.array([0, 255, 0], dtype=np.uint8), axis=-1))


def test_detect_dl_dexined_marching_squares_with_fixed_threshold():
    img = _synthetic_edge_image()
    params = {
        "dexined_use_marching_squares": True,
        "dexined_threshold_mode": "fixed",
        "dexined_threshold_value": 0.05,
    }
    overlay, meta = detect_dl(img, detector="Edges (Canny)", model_choice="dexined.onnx", params=params)

    assert overlay.shape == img.shape
    assert overlay.dtype == np.uint8
    assert meta.get("error") is None
    assert meta["dexined_use_marching_squares"] is True
    assert meta["dexined_threshold_mode"] == "fixed"
    assert meta["dexined_threshold"] == pytest.approx(0.05, abs=1e-4)
    assert meta.get("marching_squares_segments", 0) >= 0
    if meta.get("marching_squares_segments", 0):
        assert "marching_squares_samples" in meta
        greenish = (overlay[..., 1] > overlay[..., 0] + 10) & (overlay[..., 1] > overlay[..., 2] + 10)
        assert np.count_nonzero(greenish) > 0