from abc import ABCMeta, abstractmethod
import os
import h5py
import numpy as np
from tqdm import trange
from torch.multiprocessing import Pool, set_start_method

set_start_method("spawn", force=True)

import sys

ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
sys.path.insert(0, ROOT_DIR)
from components import load_component


class BaseDumper(metaclass=ABCMeta):
    def __init__(self, config):
        self.config = config
        self.img_seq = []
        self.dump_seq = []  # feature dump seq

    @abstractmethod
    def get_seqs(self):
        raise NotImplementedError

    @abstractmethod
    def format_dump_folder(self):
        raise NotImplementedError

    @abstractmethod
    def format_dump_data(self):
        raise NotImplementedError

    def initialize(self):
        self.extractor = load_component(
            "extractor", self.config["extractor"]["name"], self.config["extractor"]
        )
        self.get_seqs()
        self.format_dump_folder()

    def extract(self, index):
        img_path, dump_path = self.img_seq[index], self.dump_seq[index]
        if not self.config["extractor"]["overwrite"] and os.path.exists(dump_path):
            return
        kp, desc = self.extractor.run(img_path)
        self.write_feature(kp, desc, dump_path)

    def dump_feature(self):
        print("Extrating features...")
        self.num_img = len(self.dump_seq)
        pool = Pool(self.config["extractor"]["num_process"])
        iteration_num = self.num_img // self.config["extractor"]["num_process"]
        if self.num_img % self.config["extractor"]["num_process"] != 0:
            iteration_num += 1
        for index in trange(iteration_num):
            indicies_list = range(
                index * self.config["extractor"]["num_process"],
                min(
                    (index + 1) * self.config["extractor"]["num_process"], self.num_img
                ),
            )
            pool.map(self.extract, indicies_list)
        pool.close()
        pool.join()

    def write_feature(self, pts, desc, filename):
        with h5py.File(filename, "w") as ifp:
            ifp.create_dataset("keypoints", pts.shape, dtype=np.float32)
            ifp.create_dataset("descriptors", desc.shape, dtype=np.float32)
            ifp["keypoints"][:] = pts
            ifp["descriptors"][:] = desc

    def form_standard_dataset(self):
        dataset_path = os.path.join(
            self.config["dataset_dump_dir"],
            self.config["data_name"]
            + "_"
            + self.config["extractor"]["name"]
            + "_"
            + str(self.config["extractor"]["num_kpt"])
            + ".hdf5",
        )

        pair_data_type = ["K1", "K2", "R", "T", "e", "f"]
        num_pairs = len(self.data["K1"])
        with h5py.File(dataset_path, "w") as f:
            print("collecting pair info...")
            for type in pair_data_type:
                dg = f.create_group(type)
                for idx in range(num_pairs):
                    data_item = np.asarray(self.data[type][idx])
                    dg.create_dataset(
                        str(idx), data_item.shape, data_item.dtype, data=data_item
                    )

            for type in ["img_path1", "img_path2"]:
                dg = f.create_group(type)
                for idx in range(num_pairs):
                    dg.create_dataset(
                        str(idx),
                        [1],
                        h5py.string_dtype(encoding="ascii"),
                        data=self.data[type][idx].encode("ascii"),
                    )

            # dump desc
            print("collecting desc and kpt...")
            desc1_g, desc2_g, kpt1_g, kpt2_g = (
                f.create_group("desc1"),
                f.create_group("desc2"),
                f.create_group("kpt1"),
                f.create_group("kpt2"),
            )
            for idx in trange(num_pairs):
                desc_file1, desc_file2 = h5py.File(
                    self.data["fea_path1"][idx], "r"
                ), h5py.File(self.data["fea_path2"][idx], "r")
                desc1, desc2, kpt1, kpt2 = (
                    desc_file1["descriptors"][()],
                    desc_file2["descriptors"][()],
                    desc_file1["keypoints"][()],
                    desc_file2["keypoints"][()],
                )
                desc1_g.create_dataset(str(idx), desc1.shape, desc1.dtype, data=desc1)
                desc2_g.create_dataset(str(idx), desc2.shape, desc2.dtype, data=desc2)
                kpt1_g.create_dataset(str(idx), kpt1.shape, kpt1.dtype, data=kpt1)
                kpt2_g.create_dataset(str(idx), kpt2.shape, kpt2.dtype, data=kpt2)