210 lines
7.0 KiB
Python
210 lines
7.0 KiB
Python
import argparse
|
|
import glob
|
|
import os
|
|
from PIL import Image
|
|
import sys
|
|
from torchvision import transforms
|
|
from torchvision.transforms.functional import InterpolationMode
|
|
import torch
|
|
import aiohttp
|
|
import asyncio
|
|
import subprocess
|
|
import numpy as np
|
|
import io
|
|
import aiofiles
|
|
|
|
SIZE = 384
|
|
BLIP_MODEL_URL = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
|
|
|
|
def get_parser(**parser_kwargs):
|
|
parser = argparse.ArgumentParser(**parser_kwargs)
|
|
parser.add_argument(
|
|
"--img_dir",
|
|
type=str,
|
|
nargs="?",
|
|
const=True,
|
|
default="input",
|
|
help="directory with images to be captioned",
|
|
),
|
|
parser.add_argument(
|
|
"--out_dir",
|
|
type=str,
|
|
nargs="?",
|
|
const=True,
|
|
default="output",
|
|
help="directory to put captioned images",
|
|
),
|
|
parser.add_argument(
|
|
"--format",
|
|
type=str,
|
|
nargs="?",
|
|
const=True,
|
|
default="filename",
|
|
help="'filename', 'mrwho', 'txt', or 'caption'",
|
|
),
|
|
parser.add_argument(
|
|
"--nucleus",
|
|
type=bool,
|
|
nargs="?",
|
|
const=True,
|
|
default=False,
|
|
help="use nucleus sampling instead of beam",
|
|
),
|
|
parser.add_argument(
|
|
"--q_factor",
|
|
type=float,
|
|
nargs="?",
|
|
const=True,
|
|
default=1.0,
|
|
help="adjusts the likelihood of a word being repeated",
|
|
),
|
|
parser.add_argument(
|
|
"--min_length",
|
|
type=int,
|
|
nargs="?",
|
|
const=True,
|
|
default=22,
|
|
help="adjusts the likelihood of a word being repeated",
|
|
),
|
|
|
|
return parser
|
|
|
|
def load_image(raw_image, device):
|
|
transform = transforms.Compose([
|
|
#transforms.CenterCrop(SIZE),
|
|
transforms.Resize((SIZE, SIZE), interpolation=InterpolationMode.BICUBIC),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
|
|
])
|
|
image = transform(raw_image).unsqueeze(0).to(device)
|
|
return image
|
|
|
|
def get_out_file_name(out_dir, base_name, ext):
|
|
return os.path.join(out_dir, f"{base_name}{ext}")
|
|
|
|
async def main(opt):
|
|
print("starting")
|
|
import models.blip
|
|
|
|
sample = False
|
|
if opt.nucleus:
|
|
sample = True
|
|
|
|
input_dir = opt.img_dir
|
|
print("input_dir: ", input_dir)
|
|
|
|
config_path = "scripts/BLIP/configs/med_config.json"
|
|
|
|
cache_folder = ".cache"
|
|
model_cache_path = ".cache/model_base_caption_capfilt_large.pth"
|
|
|
|
if not os.path.exists(cache_folder):
|
|
os.makedirs(cache_folder)
|
|
|
|
if not os.path.exists(opt.out_dir):
|
|
os.makedirs(opt.out_dir)
|
|
|
|
if not os.path.exists(model_cache_path):
|
|
print(f"Downloading model to {model_cache_path}... please wait")
|
|
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.get(BLIP_MODEL_URL) as res:
|
|
with open(model_cache_path, 'wb') as f:
|
|
async for chunk in res.content.iter_chunked(1024):
|
|
f.write(chunk)
|
|
print(f"Model cached to: {model_cache_path}")
|
|
else:
|
|
print(f"Model already cached to: {model_cache_path}")
|
|
|
|
blip_decoder = models.blip.blip_decoder(pretrained=model_cache_path, image_size=SIZE, vit='base', med_config=config_path)
|
|
blip_decoder.eval()
|
|
|
|
print("loading model to cuda")
|
|
|
|
blip_decoder = blip_decoder.to(torch.device("cuda"))
|
|
|
|
ext = ('.jpg', '.jpeg', '.png', '.webp', '.tif', '.tga', '.tiff', '.bmp', '.gif')
|
|
|
|
i = 0
|
|
|
|
for idx, img_file_name in enumerate(glob.iglob(os.path.join(opt.img_dir, "*.*"))):
|
|
if img_file_name.endswith(ext):
|
|
caption = None
|
|
file_ext = os.path.splitext(img_file_name)[1]
|
|
if (file_ext in ext):
|
|
async with aiofiles.open(img_file_name, "rb") as input_file:
|
|
print("working image: ", img_file_name)
|
|
|
|
image_bin = await input_file.read()
|
|
image = Image.open(io.BytesIO(image_bin))
|
|
|
|
if not image.mode == "RGB":
|
|
image = image.convert("RGB")
|
|
|
|
image = load_image(image, device=torch.device("cuda"))
|
|
|
|
if opt.nucleus:
|
|
captions = blip_decoder.generate(image, sample=True, top_p=opt.q_factor)
|
|
else:
|
|
captions = blip_decoder.generate(image, sample=sample, num_beams=16, min_length=opt.min_length, \
|
|
max_length=48, repetition_penalty=opt.q_factor)
|
|
|
|
caption = captions[0]
|
|
|
|
if opt.format in ["mrwho","joepenna"]:
|
|
prefix = f"{i:05}@"
|
|
i += 1
|
|
caption = prefix+caption
|
|
elif opt.format == "filename":
|
|
postfix = f"_{i}"
|
|
i += 1
|
|
caption = caption+postfix
|
|
|
|
if opt.format in ["txt","text","caption"]:
|
|
out_base_name = os.path.splitext(os.path.basename(img_file_name))[0]
|
|
|
|
if opt.format in ["txt","text"]:
|
|
out_file = get_out_file_name(opt.out_dir, out_base_name, ".txt")
|
|
|
|
if opt.format in ["caption"]:
|
|
out_file = get_out_file_name(opt.out_dir, out_base_name, ".caption")
|
|
|
|
if opt.format in ["txt","text","caption"]:
|
|
print("writing caption to: ", out_file)
|
|
async with aiofiles.open(out_file, "w") as out_file:
|
|
await out_file.write(caption)
|
|
|
|
if opt.format in ["filename", "mrwho", "joepenna"]:
|
|
caption = caption.replace("/", "").replace("\\", "") # must clean slashes using filename
|
|
out_file = get_out_file_name(opt.out_dir, caption, file_ext)
|
|
async with aiofiles.open(out_file, "wb") as out_file:
|
|
await out_file.write(image_bin)
|
|
elif opt.format == "json":
|
|
raise NotImplementedError
|
|
elif opt.format == "parquet":
|
|
raise NotImplementedError
|
|
|
|
def isWindows():
|
|
return sys.platform.startswith("win")
|
|
|
|
if __name__ == "__main__":
|
|
parser = get_parser()
|
|
opt = parser.parse_args()
|
|
|
|
if opt.format not in ["filename", "mrwho", "joepenna", "txt", "text", "caption"]:
|
|
raise ValueError("format must be 'filename', 'mrwho', 'txt', or 'caption'")
|
|
|
|
if (isWindows()):
|
|
print("Windows detected, using asyncio.WindowsSelectorEventLoopPolicy")
|
|
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
|
|
else:
|
|
print("Unix detected, using default asyncio event loop policy")
|
|
|
|
if not os.path.exists("scripts/BLIP"):
|
|
print("BLIP not found, cloning BLIP repo")
|
|
subprocess.run(["git", "clone", "https://github.com/salesforce/BLIP", "scripts/BLIP"])
|
|
blip_path = "scripts/BLIP"
|
|
sys.path.append(blip_path)
|
|
|
|
asyncio.run(main(opt))
|
|
|