api2.py

"""
api.py는 fastapi를 사용하고 있지만,
gradio를 사용한 웹 데모를 확인하고 싶다면 gradio 폴더 안에 있는 app-final.py를 사용하면 된다

api.py의 마스킹 부분을 제대로 수정한게,
api2.py임 

"""


import os
os.environ['CUDA_HOME'] = '/usr/local/cuda'
os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
from datetime import datetime

from pydantic import BaseModel
import spaces
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from huggingface_hub import snapshot_download
from PIL import Image
torch.jit.script = lambda f: f
from model.cloth_masker import AutoMasker, vis_mask
from model.pipeline import CatVTONPipeline
from utils import init_weight_dtype, resize_and_crop, resize_and_padding
from test import morph_close, morph_open, morph_open2, extend_mask_downward, extend_mask_downward2, image_equal
import cv2

from fastapi import FastAPI, File, Form, UploadFile, WebSocket, WebSocketDisconnect
from typing import List
from typing import Optional
import shutil

from fastapi.responses import JSONResponse
import uuid
import base64
from io import BytesIO

from fastapi.middleware.cors import CORSMiddleware
import asyncio

app = FastAPI()
origins = [
    "http://localhost",
    "http://localhost:8080",
    "http://localhost:3000",
    "http://127.0.0.1:8080",
    "http://127.0.0.1:3000",
]
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # You can set this to specific origins instead of '*' for security
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)
# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=origins,
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )
print ('starting app')
# api 연결하면서 추가한 코드 
def pil_to_base64(img: Image.Image) -> str:
    buffer = BytesIO()
    img.save(buffer, format="PNG")  # PNG 형식으로 저장
    return base64.b64encode(buffer.getvalue()).decode("utf-8")


# GPU에서 현재 할당된 메모리 확인 (GPU 0번 기준)
#allocated_memory = torch.cuda.memory_allocated(0) 
#print(f"GPU 0에서 할당된 메모리: {allocated_memory / (1024 ** 2)} MB")  # MB로 변환하여 출력


# 설정값을 환경 변수로 정의
BASE_MODEL_PATH = os.getenv("BASE_MODEL_PATH", "booksforcharlie/stable-diffusion-inpainting")
RESUME_PATH = os.getenv("RESUME_PATH", "zhengchong/CatVTON")
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "resource/demo/output")
WIDTH = int(os.getenv("WIDTH", 768))
HEIGHT = int(os.getenv("HEIGHT", 1024))

def image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h))

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid


repo_path = snapshot_download(repo_id=RESUME_PATH)
print ('repo_path')
# Pipeline
pipeline = CatVTONPipeline(
    base_ckpt=BASE_MODEL_PATH,
    attn_ckpt=repo_path,
    attn_ckpt_version="mix",
    weight_dtype=init_weight_dtype("no"),
    use_tf32=True,
    device='cuda'
)
# AutoMasker
mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
automasker = AutoMasker(
    densepose_ckpt=os.path.join(repo_path, "DensePose"),
    schp_ckpt=os.path.join(repo_path, "SCHP"),
    device='cuda', 
)


@spaces.GPU(duration=120)
# 매개변수로 fitting_type 추가해야 함. cloth_type 밑에.

async def submit_function(
    person_image,
    cloth_image,
    cloth_type,
    fitting_type,
    num_inference_steps,
    guidance_scale,
    seed,
    show_type
):
    #person_image, mask = person_image["background"], person_image["layers"][0] # person_image["layers"][0]이 유저가 그린 마스크 레이어임.
    #mask = Image.open(mask).convert("L")
    #if len(np.unique(np.array(mask))) == 1:
    #    mask = None # 사용자가 마스크를 그리지 않은 경우.
    #else:
    #    mask = np.array(mask)
    #    mask[mask > 0] = 255 # 배경이 검은색.
    #    mask = Image.fromarray(mask)
    mask = None
    tmp_folder = "resource/demo/output"
    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
    if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
        os.makedirs(os.path.join(tmp_folder, date_str[:8]))

    generator = None
    if seed != -1:
        generator = torch.Generator(device='cuda').manual_seed(seed)

    person_image = Image.open(person_image).convert("RGB")
    cloth_image = Image.open(cloth_image).convert("RGB")
    person_image = resize_and_crop(person_image, (768, 1024))
    cloth_image = resize_and_padding(cloth_image, (768, 1024))
    
    #예외처리
    #man
    compare_image_mlvl0 = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
    compare_image_mlvl0 = resize_and_crop(compare_image_mlvl0, (768, 1024))

    compare_image_mlvl1 = Image.open("./resource/demo/example/person/men/m_lvl1.png").convert("RGB")
    compare_image_mlvl1 = resize_and_crop(compare_image_mlvl1, (768, 1024))

    compare_image_mlvl2 = Image.open("./resource/demo/example/person/men/m_lvl2.png").convert("RGB")
    compare_image_mlvl2 = resize_and_crop(compare_image_mlvl2, (768, 1024))

    compare_image_mlvl3 = Image.open("./resource/demo/example/person/men/m_lvl3.png").convert("RGB")
    compare_image_mlvl3 = resize_and_crop(compare_image_mlvl3, (768, 1024))

    #womam
    compare_image_wlvl0 = Image.open("./resource/demo/example/person/women/w_lvl0.png").convert("RGB")
    compare_image_wlvl0 = resize_and_crop(compare_image_wlvl0, (768, 1024))
    
    compare_image_wlvl1 = Image.open("./resource/demo/example/person/women/w_lvl1.png").convert("RGB")
    compare_image_wlvl1 = resize_and_crop(compare_image_wlvl1, (768, 1024))
    
    compare_image_wlvl2 = Image.open("./resource/demo/example/person/women/w_lvl2.png").convert("RGB")
    compare_image_wlvl2 = resize_and_crop(compare_image_wlvl2, (768, 1024))
    
    compare_image_wlvl3 = Image.open("./resource/demo/example/person/women/w_lvl3.png").convert("RGB")
    compare_image_wlvl3 = resize_and_crop(compare_image_wlvl3, (768, 1024))

# Process mask
    if mask is not None:
        mask = resize_and_crop(mask, (WIDTH, HEIGHT))
    else:
        if image_equal(person_image, compare_image_mlvl3):
            person_image2 = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl3_lower_sam_v2.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)
            
            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            

        elif image_equal(person_image, compare_image_wlvl3):
            person_image2 = Image.open("./resource/demo/example/person/women/w_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']
            # 이후 처리
            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl3_lower_sam_v2.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            

        elif image_equal(person_image, compare_image_mlvl2):
            person_image2 = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl2_lower_sam_v2.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            


        elif image_equal(person_image, compare_image_wlvl2):
            person_image2 = Image.open("./resource/demo/example/person/women/w_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']
            # 이후 처리
            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl2_lower_sam_v2.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            



        elif image_equal(person_image, compare_image_mlvl1):
            person_image2 = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            

        elif image_equal(person_image, compare_image_wlvl1):
            person_image2 = Image.open("./resource/demo/example/person/women/w_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']
            # 이후 처리
            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            

        elif image_equal(person_image, compare_image_mlvl0):
            person_image2 = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
                         

        elif image_equal(person_image, compare_image_wlvl0):
            person_image2 = Image.open("./resource/demo/example/person/women/w_lvl0.png").convert("RGB")
            person_image2 = resize_and_crop(person_image2, (WIDTH, HEIGHT))
            mask = automasker(
                person_image2,
                cloth_type
            )['mask']
            # 이후 처리
            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))

            mask_np = np.array(mask) 
            sam_mask_upper_np = np.array(sam_mask_upper)
            sam_mask_lower_np = np.array(sam_mask_lower)

            if cloth_type == "upper":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)
                result_np = np.where(sam_mask_lower_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_upper_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            elif cloth_type == "lower":
                kernel = np.ones((10, 10), np.uint8)
                sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)
                result_np = np.where(sam_mask_upper_np== 255, 0, mask_np)
                result_np = np.where(sam_mask_lower_np== 255, 255, result_np)
                mask = Image.fromarray(result_np)
            else:
                mask = Image.fromarray(mask_np)
            

        else:
            mask = automasker(
                person_image,
                cloth_type
            )['mask']

    # mask.save("./app_mask_created.png")

        # 가끔 bmi지수 높은 아바타의 경우, upper mask를 정확히 생성해내지 못하는 경우가 있어 수동으로 한 번 더 처리해줌.
        
        # 튀어나온 부분 밀어버리기 (는 사용자가 그린 mask에 대해서는 시행되면 안되므로, else문 안에 넣어두기)
        #if cloth_type == "upper":
        #    height = (np.array(mask)).shape[0]
        #    y_threshold = int(height * 0.7) # 이미지 높이의 50퍼센트 이하. 50퍼센트가 딱 적당함.

            # 밑부분 제거
        #    mask = remove_bottom_part(np.array(mask), y_threshold)
        # 위 방법으로 해결 불가임. 튀어나온 부분
        # input 된 target 이미지마다, 생성되는 mask 영역의 크기가 다르기 때문. mask 파일 자체의 크기는 같을 지언정.
        



    # 추가로 Fitting Type에 따라 마스크 처리 (else문 내부)
    if fitting_type == "standard":

        # mlvl3에 대한 upper lower 각각.
        if image_equal(person_image, compare_image_mlvl3) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl3) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl3_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # mlvl2에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl2) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl2) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl2_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # mlvl1에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl1) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl1) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
        
        # mlvl0에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl0) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        elif image_equal(person_image, compare_image_mlvl0) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        
        # wlvl3에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl3) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl3) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl3_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # wlvl2에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl2) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl2) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl2_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # wlvl1에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl1) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl1) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
        
        # wlvl0에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl0) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        elif image_equal(person_image, compare_image_wlvl0) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=100)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # 그 외 디폴트
        # flag2
        else:
            if cloth_type == "upper":
                opened_mask = morph_open(mask)
                extended_mask = extend_mask_downward2(np.array(mask), pixels=50)
                mask = extended_mask
            else:
                opened_mask = morph_open(mask)
                extended_mask = extend_mask_downward(np.array(mask), pixels=70)
                final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
                final_mask = morph_open(final_mask)
                mask = final_mask

    elif fitting_type == "loose" :
        # mlvl3에 대한 upper lower 각각.
        if image_equal(person_image, compare_image_mlvl3) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl3) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl3_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # mlvl2에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl2) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl2) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl2_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # mlvl1에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl1) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_mlvl1) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
        
        # mlvl0에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_mlvl0) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/m_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        elif image_equal(person_image, compare_image_mlvl0) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/m_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        
        # wlvl3에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl3) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl3_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl3) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl3_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # wlvl2에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl2) and cloth_type == "upper":
            # flag
            opened_mask = morph_open(mask)
            
            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl2_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1110)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            # final_mask = morph_close(morph_open(final_mask)) 원래 주석 아닌데 주석처리함. 1110
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl2) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl2_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # wlvl1에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl1) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl1_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
            
        elif image_equal(person_image, compare_image_wlvl1) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl1_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask
        
        # wlvl0에 대한 upper lower 각각.
        elif image_equal(person_image, compare_image_wlvl0) and cloth_type == "upper":
            opened_mask = morph_open(mask)

            sam_mask_upper = Image.open("./resource/demo/example/person/sam/w_lvl0_upper_sam.png").convert("L")
            sam_mask_upper = resize_and_crop(sam_mask_upper, (WIDTH, HEIGHT))
            sam_mask_upper_np = np.array(sam_mask_upper)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_upper_np = cv2.dilate(sam_mask_upper_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_upper_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        elif image_equal(person_image, compare_image_wlvl0) and cloth_type == "lower":
            opened_mask = morph_open(mask)

            sam_mask_lower = Image.open("./resource/demo/example/person/sam/w_lvl0_lower_sam.png").convert("L")
            sam_mask_lower = resize_and_crop(sam_mask_lower, (WIDTH, HEIGHT))
            sam_mask_lower_np = np.array(sam_mask_lower)

            # 추가한 부분 (1113)
            kernel = np.ones((40, 40), np.uint8)
            sam_mask_lower_np = cv2.dilate(sam_mask_lower_np, kernel, iterations=1)

            extended_mask = extend_mask_downward(sam_mask_lower_np, pixels=200)

            #최종 마스크 처리 (test.py 설명 참고)
            final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
            final_mask = morph_close(morph_open(final_mask))
            mask = final_mask

        # 그 외 디폴트
        else:
            if cloth_type == "upper":
                opened_mask = morph_open(mask)
                extended_mask = extend_mask_downward2(np.array(mask), pixels=70)
                mask = extended_mask
            else:
                opened_mask = morph_open(mask)
                extended_mask = extend_mask_downward(np.array(mask), pixels=100)
                final_mask = Image.fromarray(np.array(opened_mask) | np.array(extended_mask))
                #morph_open 감싸던 morph_close 없앰.
                final_mask = morph_open(final_mask)
                mask = final_mask
        

    # 블러처리        
    mask = mask_processor.blur(mask, blur_factor=9)

    # Inference
    # try:
    # result_image = pipeline(
    #     image=person_image,
    #     condition_image=cloth_image,
    #     mask=mask,
    #     num_inference_steps=num_inference_steps,
    #     guidance_scale=guidance_scale,
    #     generator=generator
    # )[0]
    # print(pipeline.progress)
    # except Exception as e:
    #     raise gr.Error(
    #         "An error occurred. Please try again later: {}".format(e)
    #     )

    #inside of submit_function
    pipeline.progress = 0.02
    pipeline_future = asyncio.to_thread(
        pipeline,
        image=person_image,
        condition_image=cloth_image,
        mask=mask,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator
    )
    
    # Immediately print progress after starting pipeline()
    #print(pipeline.progress)
    await asyncio.sleep(1)
    async def print_progress():
        print('starting printing progress: ', pipeline_future)
        while pipeline.progress < 0.9:
            print(f"Progress: {pipeline.progress}")
            await asyncio.sleep(1)
        # Optionally, print the final progress after completion
        print(f"Final Progress: {pipeline.progress}")

    progress_task = asyncio.create_task(print_progress())
    #await print_progress()
    #progress_task
    result = await pipeline_future

    # Wait for the result_image after the print statement
    result_image = result[0]
    await progress_task

    # Post-process
    masked_person = vis_mask(person_image, mask)
    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
    save_result_image.save(result_save_path)
    if show_type == "result only":
        return {"result_image": result_image, "masked_person": masked_person}
    else:
        width, height = person_image.size
        if show_type == "input & result":
            condition_width = width // 2
            conditions = image_grid([person_image, cloth_image], 2, 1)
        else:
            condition_width = width // 3
            conditions = image_grid([person_image, masked_person , cloth_image], 3, 1)
        conditions = conditions.resize((condition_width, height), Image.NEAREST)
        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
        new_result_image.paste(conditions, (0, 0))
        new_result_image.paste(result_image, (condition_width + 5, 0))
    return new_result_image



#add websocket related function inside process_image or submit_function so that it sends the progress every 1 second 

# FastAPI 함수 정의
@app.post("/process-image")
async def process_image(
    cloth_type: str = Form(...),
    fitting_type: str = Form(...),
    person_image: UploadFile = File(...),
    cloth_image: UploadFile = File(...)
):
    try:
        # 고유한 파일 이름 생성
        person_filename = f"received_{uuid.uuid4().hex}_{person_image.filename}"
        cloth_filename = f"received_{uuid.uuid4().hex}_{cloth_image.filename}"

        print ('person_filename: ', person_filename)
        print ('cloth_filename: ', cloth_filename)

        # 이미지 저장 디렉토리 생성
        os.makedirs("uploads", exist_ok=True)

        # 업로드된 이미지 저장
        person_path = os.path.join("uploads", person_filename)
        cloth_path = os.path.join("uploads", cloth_filename)

        with open(person_path, "wb") as buffer:
            shutil.copyfileobj(person_image.file, buffer)

        with open(cloth_path, "wb") as buffer:
            shutil.copyfileobj(cloth_image.file, buffer)


        pipeline.progress = 0.02

        # 이미지 처리 함수 호출
        # num inference : 50보다 25가 나은듯
        result = await submit_function(
            person_image=person_path,
            cloth_image=cloth_path,
            cloth_type=cloth_type,
            fitting_type=fitting_type,
            num_inference_steps=25,  
            guidance_scale=2.5,       
            seed=42,                  
            show_type='result only'
        )

        print ('processing done')
        # 반환된 이미지 추출
        result_image = result['result_image']
        masked_person = result['masked_person']
        result_image.save('results/result.png')
        # 이미지를 Base64로 인코딩
        result_image_b64 = pil_to_base64(result_image)
        masked_person_b64 = pil_to_base64(masked_person)

        # 임시 파일 삭제 (필요 시)
        os.remove(person_path)
        os.remove(cloth_path)

        return {
            "message": "이미지가 처리되었습니다",
            "result_image": result_image_b64,
            "masked_person": masked_person_b64
        }
    except Exception as e:
        return JSONResponse(status_code=500, content={"message": f"오류 발생: {str(e)}"})
#blocking language
#syncronous language
@app.websocket("/ws/")
async def websocket_endpoint(websocket: WebSocket):
    print('awaiting websocket connection')
    await websocket.accept()
    print ('websocket accepted')
    try:
        while True:
            if pipeline.progress >= 0.94:
                await asyncio.sleep(0.1);
            if pipeline.progress >= 0.94:
                break
            if pipeline.progress < 0.02:
                pipeline.progress = 0.02
            await websocket.send_text(f"{pipeline.progress}")
            await asyncio.sleep(0.5)
        await websocket.send_text("Processing complete")
    except WebSocketDisconnect:
        print(f"Client disconnected")
    finally:
        await websocket.close()


@app.post("/send-to-ssh")
async def send_to_ssh(
    cloth_type: str = Form(...),
    fitting_type: str = Form(...),
    person_image: UploadFile = File(...),  # 이미지 파일 업로드로 처리
    cloth_image: UploadFile = File(...)
):
    # 받은 데이터를 처리하거나 저장하는 로직
    return {"message": "데이터가 성공적으로 처리되었습니다."}

@app.get('/test')
async def test():
    return JSONResponse(status_code=200, content={"message": "hello"})


def person_example_fn(image_path):
    return image_path