import cv2
from PIL import Image
import numpy as np
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation


class SegmentationTool:

    def __init__(self,
                 segmentation_version='nvidia/segformer-b5-finetuned-ade-640-640'):

        self.segmentation_version = segmentation_version

        if segmentation_version == "openmmlab/upernet-convnext-tiny":
            self.feature_extractor = AutoImageProcessor.from_pretrained(self.segmentation_version)
            self.segmentation_model = UperNetForSemanticSegmentation.from_pretrained(self.segmentation_version)
        elif segmentation_version == "nvidia/segformer-b5-finetuned-ade-640-640":
            self.feature_extractor = SegformerFeatureExtractor.from_pretrained(self.segmentation_version)
            self.segmentation_model = SegformerForSemanticSegmentation.from_pretrained(self.segmentation_version)

    def _predict(self, image):
        inputs = self.feature_extractor(images=image, return_tensors="pt")
        outputs = self.segmentation_model(**inputs)
        prediction = \
            self.feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
        return prediction

    def _save_mask(self, prediction_array, mask_items=[]):
        mask = np.zeros_like(prediction_array, dtype=np.uint8)

        mask[np.isin(prediction_array, mask_items)] = 0
        mask[~np.isin(prediction_array, mask_items)] = 255

        buffer_size = 10

        # Dilate the binary image
        kernel = np.ones((buffer_size, buffer_size), np.uint8)
        dilated_image = cv2.dilate(mask, kernel, iterations=1)

        # Subtract the original binary image
        buffer_area = dilated_image - mask

        # Apply buffer area to the original image
        mask = cv2.bitwise_or(mask, buffer_area)

        #     # # Create a PIL Image object from the mask
        mask_image = Image.fromarray(mask, mode='L')
        # display(mask_image)

        # mask_image = mask_image.resize((512, 512))
        # mask_image.save(".tmp/mask_1.png", "PNG")
        # img = img.resize((512, 512))
        # img.save(".tmp/input_1.png", "PNG")
        return mask_image

    def _save_transparent_mask(self, img, prediction_array, mask_items=None):
        if mask_items is None:
            mask_items = []
        mask = np.array(img)
        mask[~np.isin(prediction_array, mask_items), :] = 255
        mask_image = Image.fromarray(mask).convert('RGBA')

        # Set the transparency of the pixels corresponding to object 1 to 0 (fully transparent)
        mask_data = mask_image.getdata()
        mask_data = [(r, g, b, 0) if r == 255 else (r, g, b, 255) for (r, g, b, a) in mask_data]
        mask_image.putdata(mask_data)

        return mask_image

    def get_mask(self, image_path=None, image=None, mask_items=None):
        if image_path:
            image = Image.open(image_path)
        else:
            if image is None:
                raise ValueError("no image provided")

        # display(image)
        # print(image)
        prediction = self._predict(image)

        label_ids = np.unique(prediction)

        # mask_items = [0, 3, 5, 8, 14]
        # mask_items = [8] # windowpane
        if mask_items is None:
            mask_items = []
        if 73 in label_ids or 50 in label_ids or 61 in label_ids:
            # mask_items = [0, 3, 5, 8, 14, 50, 61, 71, 73, 118, 124, 129]
            room = 'kitchen'
        elif 37 in label_ids or 65 in label_ids or (27 in label_ids and 47 in label_ids and 70 in label_ids):
            # mask_items = [0, 3, 5, 8, 14, 27, 65]
            room = 'bathroom'
        elif 7 in label_ids:
            room = 'bedroom'
        elif 23 in label_ids or 49 in label_ids:
            # mask_items = [0, 3, 5, 8, 14, 49]
            room = 'living room'
        elif 15 in label_ids and 19 in label_ids:
            room = 'dining room'
        else:
            room = 'room'
        label_ids_without_mask = [i for i in label_ids if i not in mask_items]

        items = [self.segmentation_model.config.id2label[i] for i in label_ids_without_mask]

        mask_image = self._save_mask(prediction, mask_items)
        transparent_mask_image = self._save_transparent_mask(image, prediction, mask_items)
        return mask_image, transparent_mask_image, image, items, room