# This file was extracted from the HV SDK Docusaurus examples.
# It is intended as a downloadable, runnable companion to the documentation.
# Set HSI_EXAMPLE_BASE_DIR and related env vars to use your own data.
# Source page: /hsi/hv_sdk/examples/classification#classify-one-scan-line

# region: setup
import os
from pathlib import Path

import joblib
import numpy as np
import qtec_hv_sdk as hs
from qtec_hv_sdk.preprocessing import make_reference
from qtec_hv_sdk.preprocessing import reflectance_calibration


BASE_DIR = Path(os.environ.get("HSI_EXAMPLE_BASE_DIR", "/path/to/HSI_data/nuts"))
if not BASE_DIR.exists():
    raise SystemExit(
        "Run: 'export HSI_EXAMPLE_BASE_DIR=/path/to/HSI_data/' to setup the "
        "folder containing the example datacubes."
    )
TRAIN_CUBE = os.environ.get("HSI_EXAMPLE_TRAIN_CUBE", "mix1.pam")
TEST_CUBE = os.environ.get("HSI_EXAMPLE_TEST_CUBE", "mix2.pam")
DARK_REF = os.environ.get("HSI_EXAMPLE_DARK_REF", "dark_ref.pam")
WHITE_REF = os.environ.get("HSI_EXAMPLE_WHITE_REF", "white_ref.pam")
CLASSIFIER_MODEL_PATH = Path(os.environ.get("HSI_EXAMPLE_CLASSIFIER_MODEL", "pixel_classifier.joblib"))


def make_references():
    dark = hs.open(str(BASE_DIR / DARK_REF))
    white = hs.open(str(BASE_DIR / WHITE_REF))
    return make_reference(dark), make_reference(white)


def open_reflectance_cube(cube_name=TRAIN_CUBE):
    dark_ref, white_ref = make_references()
    img = hs.open(str(BASE_DIR / cube_name))
    return reflectance_calibration(img, white_ref, dark_ref, clip=True)


def load_classifier_model():
    if not CLASSIFIER_MODEL_PATH.exists():
        raise SystemExit(
            f"Classifier model not found at {CLASSIFIER_MODEL_PATH}. "
            "Run 01_train_a_pixel_classifier_from_rois.py first, or set HSI_EXAMPLE_CLASSIFIER_MODEL."
        )
    return joblib.load(CLASSIFIER_MODEL_PATH)
# end region

# region: example
clf = load_classifier_model()

test_reflectance = open_reflectance_cube(TEST_CUBE)

line_index = 100
frame_arr = test_reflectance.array_plane(line_index, hs.lines)
pixels_frame = frame_arr.T

predicted = clf.predict(pixels_frame)
classes, counts = np.unique(predicted, return_counts=True)

print(dict(zip(classes, counts.astype(int))))
# end region