from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
import os
from tqdm import tqdm

model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
torch.set_float32_matmul_precision(['high', 'highest'][0])
model.to('cuda')
model.eval()

# Data settings
image_size = (1024, 1024)
transform_image = transforms.Compose([
    transforms.Resize(image_size),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

data = []
root_dir = r'C:\Users\admin\Downloads\raw'
for dirpath, dirnames, filenames in os.walk(root_dir):
    for filename in filenames:
        file_path = os.path.join(dirpath, filename)
        data.append(file_path)
for file_path in tqdm(data):
    image = Image.open(file_path).convert('RGB')  
    input_images = transform_image(image).unsqueeze(0).to('cuda')

    # Prediction
    with torch.no_grad():
        preds = model(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image.size)
    image.putalpha(mask)

    save_name, save_extension = os.path.splitext(file_path)
    save_path = f'{save_name}.png'
    save_path = save_path.replace('raw', 'output')
    save_folder = os.path.dirname(save_path)
    os.makedirs(save_folder, exist_ok=True)
    image.save(save_path)

from madmom.features.downbeats import RNNBarProcessor

proc = CRFBeatDetectionProcessor(fps=100)
act = RNNBeatProcessor()(input_audio_path)
data = proc(act)
print(data.tolist())