import os
import copy
import khandy
import numpy as np
from .base import OnnxModel
from collections import OrderedDict
from .base import check_image_dtype_and_shape


class InsectIdentifier(OnnxModel):
    def __init__(self):
        current_dir = os.path.dirname(os.path.abspath(__file__))
        model_path = os.path.join(
            current_dir,
            "__pycache__/MuGeminorum/insecta/quarrying_insect_identifier.onnx",
        )
        label_map_path = os.path.join(
            current_dir,
            "__pycache__/MuGeminorum/insecta/quarrying_insectid_label_map.txt",
        )
        super(InsectIdentifier, self).__init__(model_path)
        self.label_name_dict = self._get_label_name_dict(label_map_path)
        self.names = [
            self.label_name_dict[i]["chinese_name"]
            for i in range(len(self.label_name_dict))
        ]
        self.num_classes = len(self.label_name_dict)

    @staticmethod
    def _get_label_name_dict(filename):
        records = khandy.load_list(filename)
        label_name_dict = {}
        for record in records:
            label, chinese_name, latin_name = record.split(",")
            label_name_dict[int(label)] = OrderedDict(
                [("chinese_name", chinese_name), ("latin_name", latin_name)]
            )

        return label_name_dict

    @staticmethod
    def _preprocess(image):
        check_image_dtype_and_shape(image)
        # image size normalization
        image = khandy.letterbox_image(image, 224, 224)
        # image channel normalization
        image = khandy.normalize_image_channel(image, swap_rb=True)
        # image dtype normalization
        # image dtype and value range normalization
        mean, stddev = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
        image = khandy.normalize_image_value(image, mean, stddev, "auto")
        # to tensor
        image = np.transpose(image, (2, 0, 1))
        image = np.expand_dims(image, axis=0)
        return image

    def predict(self, image):
        inputs = self._preprocess(image)
        logits = self.forward(inputs)
        probs = khandy.softmax(logits)
        return probs

    def identify(self, image, topk=5):
        assert isinstance(topk, int)
        if topk <= 0 or topk > self.num_classes:
            topk = self.num_classes

        probs = self.predict(image)
        topk_probs, topk_indices = khandy.top_k(probs, topk)
        results = []
        for ind, prob in zip(topk_indices[0], topk_probs[0]):
            one_result = copy.deepcopy(self.label_name_dict[ind])
            one_result["probability"] = prob
            results.append(one_result)

        return results