mirror of
https://github.com/karl0ss/comfy_fm24_newgens.git
synced 2025-10-24 12:13:58 +01:00
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
try:
|
|
from rembg import remove
|
|
REMBG_AVAILABLE = True
|
|
except ImportError as e:
|
|
print(f"Warning: rembg not available: {e}")
|
|
REMBG_AVAILABLE = False
|
|
|
|
try:
|
|
import onnxruntime as ort
|
|
ONNX_AVAILABLE = True
|
|
except ImportError:
|
|
ONNX_AVAILABLE = False
|
|
|
|
from PIL import Image
|
|
from tqdm import tqdm
|
|
import os
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
|
|
def process_images_in_batch(batch, directory, use_gpu):
|
|
"""
|
|
Process a batch of images: remove their backgrounds and save the results.
|
|
|
|
Args:
|
|
batch (list): List of filenames to process (without path or extension).
|
|
directory (str): Base directory to locate input files.
|
|
use_gpu (bool): Whether to enable GPU support.
|
|
|
|
Returns:
|
|
int: Number of images successfully processed in this batch.
|
|
"""
|
|
if not REMBG_AVAILABLE:
|
|
return 0
|
|
|
|
success_count = 0
|
|
for filename in batch:
|
|
input_path = os.path.join(directory, f"{filename}.png")
|
|
output_path = os.path.join(directory, f"{filename}.png")
|
|
|
|
try:
|
|
with Image.open(input_path) as img:
|
|
# Initialize ONNX session options with GPU support if required
|
|
if ONNX_AVAILABLE:
|
|
session_options = ort.SessionOptions()
|
|
providers = ["CUDAExecutionProvider"] if use_gpu else ["CPUExecutionProvider"]
|
|
ort.set_default_logger_severity(3) # Suppress non-critical logging
|
|
|
|
# Initialize the rembg remove function with appropriate providers
|
|
output = remove(img, session_options=session_options, providers=providers)
|
|
else:
|
|
# Fallback to CPU-only processing
|
|
output = remove(img)
|
|
|
|
output.save(output_path)
|
|
|
|
success_count += 1
|
|
except Exception as e:
|
|
print(f"Error processing {input_path}: {str(e)}")
|
|
|
|
return success_count
|
|
|
|
def remove_bg_from_file_list(directory, filenames, max_workers=2, batch_size=2, use_gpu=False):
|
|
"""
|
|
Process a list of specified filenames: remove their backgrounds and save the results.
|
|
|
|
Args:
|
|
directory (str): Path to the directory containing images.
|
|
filenames (list): List of filenames (without path or extension) to process.
|
|
max_workers (int): Maximum number of threads to use for parallel processing.
|
|
batch_size (int): Number of images to process per batch.
|
|
use_gpu (bool): Whether to enable GPU support.
|
|
|
|
Returns:
|
|
int: The number of images successfully processed.
|
|
"""
|
|
if not REMBG_AVAILABLE:
|
|
print("Background removal not available (rembg not installed). Skipping background removal.")
|
|
return 0
|
|
|
|
processed_count = 0
|
|
|
|
# Divide filenames into batches
|
|
batches = [filenames[i:i + batch_size] for i in range(0, len(filenames), batch_size)]
|
|
|
|
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
|
with tqdm(total=len(filenames), desc="Removing Backgrounds", unit="image") as pbar:
|
|
futures = {
|
|
executor.submit(process_images_in_batch, batch, directory, use_gpu): batch
|
|
for batch in batches
|
|
}
|
|
|
|
for future in futures:
|
|
processed_count += future.result()
|
|
pbar.update(len(futures[future]))
|
|
|
|
return processed_count |