import logging
from skimage.morphology import skeletonize
import cv2
import numpy as np

from app.service.utils.new_oss_client import oss_get_image

logger = logging.getLogger()


class LoadBodyImage:
    name = "LoadBodyImage"

    def __init__(self, minio_client):
        self.minio_client = minio_client

    @classmethod
    def get_name(cls):
        return cls.name

    def __call__(self, result):
        result["name"] = "mannequin"
        result['body_image'] = oss_get_image(oss_client=self.minio_client, bucket=result['body_path'].split("/", 1)[0], object_name=result['body_path'].split("/", 1)[1], data_type="PIL")
        return result


class LoadImage:
    name = "LoadImage"

    def __init__(self, minio_client):
        self.minio_client = minio_client

    @classmethod
    def get_name(cls):
        return cls.name

    def __call__(self, result):
        if result.get("merge_image_path"):
            result['merge_image'], _ = self.read_image(result['merge_image_path'])
        result['image'], result['pre_mask'] = self.read_image(result['path'])
        result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY))
        result['keypoint'] = self.get_keypoint(result['name'])
        result['img_shape'] = result['image'].shape
        result['ori_shape'] = result['image'].shape
        return result

    @staticmethod
    def get_lines(img):
        binary = cv2.adaptiveThreshold(img, 255,
                                       cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                       cv2.THRESH_BINARY_INV,
                                       25, 10)
        binary_bool = binary > 0
        skeleton = skeletonize(binary_bool, method='zhang')
        mask = skeleton
        result = np.ones_like(img) * 255
        result[mask] = img[mask]
        return result

    def read_image(self, image_path):
        image_mask = None
        image = oss_get_image(oss_client=self.minio_client, bucket=image_path.split("/", 1)[0], object_name=image_path.split("/", 1)[1], data_type="cv2")
        if len(image.shape) == 2:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
        if image.shape[2] == 4:  # 如果是四通道 mask
            image_mask = image[:, :, 3]
            image = image[:, :, :3]

        if image.shape[:2] <= (50, 50):
            # 计算新尺寸
            new_size = (image.shape[1] * 2, image.shape[0] * 2)
            # 调整大小
            image = cv2.resize(image, new_size, interpolation=cv2.INTER_LINEAR)
        return image, image_mask

    @staticmethod
    def get_keypoint(name):
        if name in ['blouse', 'outwear', 'dress', 'tops', 'blouse_merge', 'outwear_merge', 'dress_merge', 'tops_merge']:
            keypoint = 'shoulder'
        elif name in ['trousers', 'skirt', 'bottoms', 'trousers_merge', 'skirt_merge', 'bottoms_merge']:
            keypoint = 'waistband'
        elif name in ['bag', 'bag_merge']:
            keypoint = 'hand_point'
        elif name in ['shoes', 'shoes_merge']:
            keypoint = 'toe'
        elif name in ['hairstyle', 'hairstyle_merge']:
            keypoint = 'head_point'
        elif name in ['earring', 'earring_merge']:
            keypoint = 'ear_point'
        elif name in ['others', 'others_merge']:
            keypoint = "others"
        else:
            raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, "
                           f"bag, shoes, hairstyle, earring.")
        return keypoint