import os
from pathlib import Path
import sys
import torch
from PIL import Image, ImageOps
import numpy as np
from utils_ootd import get_mask_location
from cloths_db import cloths_map, modeL_db
PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
sys.path.insert(0, str(PROJECT_ROOT))

from preprocess.openpose.run_openpose import OpenPose
from preprocess.humanparsing.run_parsing import Parsing
from ootd.inference_ootd_hd import OOTDiffusionHD
from ootd.inference_ootd_dc import OOTDiffusionDC
from preprocess.openpose.annotator.openpose.util import draw_bodypose



openpose_model = OpenPose(0)



parsing_model_dc = Parsing(0)
ootd_model_dc = OOTDiffusionDC(0)

category_dict = ['upperbody', 'lowerbody', 'dress']
category_dict_utils = ['upper_body', 'lower_body', 'dresses']


example_path = os.path.join(os.path.dirname(__file__), 'examples')
garment_path = os.path.join(os.path.dirname(__file__), 'examples','garment')




openpose_model.preprocessor.body_estimation.model.to('cuda')


ootd_model_dc.pipe.to('cuda')
ootd_model_dc.image_encoder.to('cuda')
ootd_model_dc.text_encoder.to('cuda')

def process_dc(vton_img, garm_img, category):
    model_type = 'dc'
    if category == 'Upper-body':
        category = 0
    elif category == 'Lower-body':
        category = 1
    else:
        category =2

    with torch.no_grad():
        # openpose_model_dc.preprocessor.body_estimation.model.to('cuda')
        # ootd_model_dc.pipe.to('cuda')
        # ootd_model_dc.image_encoder.to('cuda')
        # ootd_model_dc.text_encoder.to('cuda')
        
        garm_img = Image.open(garm_img).resize((768, 1024))
        vton_img = Image.open(vton_img).resize((768, 1024))
        keypoints  = openpose_model(vton_img.resize((384, 512)))

        print(len(keypoints["pose_keypoints_2d"]))
        print(keypoints["pose_keypoints_2d"])

        # person_image = np.asarray(vton_img)


        # print(len(person_image))
        

        # person_image = np.asarray(Image.open(vton_img).resize((768, 1024))) 

        # output = draw_bodypose(canvas=person_image,candidate=candidate, subset=subset )
        # output_image = Image.fromarray(output)
        # output_image.save('keypose.png')



        left_point = keypoints["pose_keypoints_2d"][2]
        right_point = keypoints["pose_keypoints_2d"][5]

        neck_point = keypoints["pose_keypoints_2d"][1]
        hip_point = keypoints["pose_keypoints_2d"][8]



        print(f'left shoulder - {left_point}')
        print(f'right shoulder - {right_point}')
 
        # #find disctance using Euclidian distance
        shoulder_width_pixels = round(np.sqrt( np.power((right_point[0]-left_point[0]),2) + np.power((right_point[1]-left_point[1]),2)),2)

        height_pixels  = round(np.sqrt( np.power((neck_point[0]-hip_point[0]),2) + np.power((neck_point[1]-hip_point[1]),2)),2) *2


        # # Assuming an average human height 
        average_height_cm = 172.72 *1.5

        # Conversion factor from pixels to cm
        conversion_factor = average_height_cm / height_pixels

        # Convert shoulder width to real-world units
        shoulder_width_cm = shoulder_width_pixels * conversion_factor

        print(f'Shoulder width (in pixels): {shoulder_width_pixels}')
        print(f'Estimated height (in pixels): {height_pixels}')
        print(f'Conversion factor (pixels to cm): {conversion_factor}')
        print(f'Shoulder width (in cm): {shoulder_width_cm}')
        print(f'Shoulder width (in INCH): {round(shoulder_width_cm/2.54,1)}')

        model_parse, face_mask = parsing_model_dc(vton_img.resize((384, 512)))

        # model_parse_image = convert_to_image(model_parse)
        # face_mask_image = convert_to_image(face_mask)

        # Save the images
        # model_parse_image.save('model_parse_image.png')
        # face_mask_image.save('face_mask_image.png')

        


        mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints)


        # up_mask, up_mask_gray = get_mask_location(model_type, category_dict_utils[0], model_parse, keypoints)
        # lo_mask, lo_mask_gray = get_mask_location(model_type, category_dict_utils[1], model_parse, keypoints)


        # mask = Image.composite(up_mask,lo_mask,up_mask)
        # mask_gray = Image.composite(up_mask_gray, lo_mask_gray,up_mask)

        mask = mask.resize((768, 1024), Image.NEAREST)
        mask_gray = mask_gray.resize((768, 1024), Image.NEAREST)
        # Save the resized masks
        mask.save("mask_resized.png")
        mask_gray.save("mask_gray_resized.png")
        
        masked_vton_img = Image.composite(mask_gray, vton_img, mask)
        masked_vton_img.save("masked_vton_img.png")

        print(f'category is {category}')

        # images = ootd_model_dc(
        #     model_type=model_type,
        #     category=category_dict[category],
        #     image_garm=garm_img,
        #     image_vton=masked_vton_img,
        #     mask=mask,
        #     image_ori=vton_img,
        #     num_samples=1,
        #     num_steps=10,
        #     image_scale=  2.0,
        #     seed=-1,
        # )
    # return None
    return None


if __name__ == '__main__':
    model_dc = os.path.join(example_path, 'model/model_8.png')
    garment_dc = os.path.join(example_path, 'garment/048554_1.jpg')


    print(process_dc(model_dc,garment_dc,0))