test.py

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

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_masker2 import AutoMasker, vis_mask
from model.pipeline import CatVTONPipeline
from utils import init_weight_dtype, resize_and_crop, resize_and_padding

# 패키지 추가
import cv2

def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--base_model_path",
        type=str,
        default="booksforcharlie/stable-diffusion-inpainting",
        # default="runwayml/stable-diffusion-inpainting",
        help=(
            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
        ),
    )
    parser.add_argument(
        "--resume_path",
        type=str,
        default="zhengchong/CatVTON",
        help=(
            "The Path to the checkpoint of trained tryon model."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="resource/demo/output",
        help="The output directory where the model predictions will be written.",
    )

    parser.add_argument(
        "--width",
        type=int,
        default=768,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--height",
        type=int,
        default=1024,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--repaint", 
        action="store_true", 
        help="Whether to repaint the result image with the original background."
    )
    parser.add_argument(
        "--allow_tf32",
        action="store_true",
        default=True,
        help=(
            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
        ),
    )
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
        ),
    )
    
    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    return args

args = parse_args()
repo_path = snapshot_download(repo_id=args.resume_path)

# 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', 
)

person_image = Image.open("./resource/demo/example/person/men/m_lvl0.png").convert("RGB")
mask = automasker(
            person_image,
            'short dress'
        )['mask'] # 여기서 리턴되는 mask는 PIL 이미지임.(cloth_masker.py 참조) # 참고로 ['densepose']로 densepose도 확인가능. 


### 여기서 mask modify에 사용된 코드를 app.py에 체크해놓은 부분에 추가하면 된다!


# mask = 연산결과를 거친 마스크

# mask = mask_processor.blur(mask, blur_factor=9)
masked_person = vis_mask(person_image, mask) # app.py에서도 blur 처리 한 다음에 vis_mask 메서드 호출함.

mask.save("./test_mask.png")  # 마스크를 PNG 파일로 저장
masked_person.save("./test_masked_person.png")  # 마스크와 target img가 합쳐진 사진을 PNG 파일로 저장