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Duplicate from TencentARC/Caption-Anything
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from captioner import build_captioner, BaseCaptioner
from segmenter import build_segmenter
from text_refiner import build_text_refiner
import os
import argparse
import pdb
import time
from PIL import Image
class CaptionAnything():
def __init__(self, args):
self.args = args
self.captioner = build_captioner(args.captioner, args.device, args)
self.segmenter = build_segmenter(args.segmenter, args.device, args)
if not args.disable_gpt:
self.init_refiner()
def init_refiner(self):
if os.environ.get('OPENAI_API_KEY', None):
self.text_refiner = build_text_refiner(self.args.text_refiner, self.args.device, self.args)
def inference(self, image, prompt, controls, disable_gpt=False):
# segment with prompt
print("CA prompt: ", prompt, "CA controls",controls)
seg_mask = self.segmenter.inference(image, prompt)[0, ...]
mask_save_path = f'result/mask_{time.time()}.png'
if not os.path.exists(os.path.dirname(mask_save_path)):
os.makedirs(os.path.dirname(mask_save_path))
new_p = Image.fromarray(seg_mask.astype('int') * 255.)
if new_p.mode != 'RGB':
new_p = new_p.convert('RGB')
new_p.save(mask_save_path)
print('seg_mask path: ', mask_save_path)
print("seg_mask.shape: ", seg_mask.shape)
# captioning with mask
if self.args.enable_reduce_tokens:
caption, crop_save_path = self.captioner.inference_with_reduced_tokens(image, seg_mask, crop_mode=self.args.seg_crop_mode, filter=self.args.clip_filter, regular_box = self.args.regular_box)
else:
caption, crop_save_path = self.captioner.inference_seg(image, seg_mask, crop_mode=self.args.seg_crop_mode, filter=self.args.clip_filter, regular_box = self.args.regular_box)
# refining with TextRefiner
context_captions = []
if self.args.context_captions:
context_captions.append(self.captioner.inference(image))
if not disable_gpt and hasattr(self, "text_refiner"):
refined_caption = self.text_refiner.inference(query=caption, controls=controls, context=context_captions)
else:
refined_caption = {'raw_caption': caption}
out = {'generated_captions': refined_caption,
'crop_save_path': crop_save_path,
'mask_save_path': mask_save_path,
'context_captions': context_captions}
return out
def parse_augment():
parser = argparse.ArgumentParser()
parser.add_argument('--captioner', type=str, default="blip")
parser.add_argument('--segmenter', type=str, default="base")
parser.add_argument('--text_refiner', type=str, default="base")
parser.add_argument('--segmenter_checkpoint', type=str, default="segmenter/sam_vit_h_4b8939.pth")
parser.add_argument('--seg_crop_mode', type=str, default="w_bg", choices=['wo_bg', 'w_bg'], help="whether to add or remove background of the image when captioning")
parser.add_argument('--clip_filter', action="store_true", help="use clip to filter bad captions")
parser.add_argument('--context_captions', action="store_true", help="use surrounding captions to enhance current caption")
parser.add_argument('--regular_box', action="store_true", default = False, help="crop image with a regular box")
parser.add_argument('--device', type=str, default="cuda:0")
parser.add_argument('--port', type=int, default=6086, help="only useful when running gradio applications")
parser.add_argument('--debug', action="store_true")
parser.add_argument('--gradio_share', action="store_true")
parser.add_argument('--disable_gpt', action="store_true")
parser.add_argument('--enable_reduce_tokens', action="store_true", default=False)
parser.add_argument('--disable_reuse_features', action="store_true", default=False)
args = parser.parse_args()
if args.debug:
print(args)
return args
if __name__ == "__main__":
args = parse_augment()
# image_path = 'test_img/img3.jpg'
image_path = 'test_img/img13.jpg'
prompts = [
{
"prompt_type":["click"],
"input_point":[[500, 300], [1000, 500]],
"input_label":[1, 0],
"multimask_output":"True",
},
{
"prompt_type":["click"],
"input_point":[[900, 800]],
"input_label":[1],
"multimask_output":"True",
}
]
controls = {
"length": "30",
"sentiment": "positive",
# "imagination": "True",
"imagination": "False",
"language": "English",
}
model = CaptionAnything(args)
for prompt in prompts:
print('*'*30)
print('Image path: ', image_path)
image = Image.open(image_path)
print(image)
print('Visual controls (SAM prompt):\n', prompt)
print('Language controls:\n', controls)
out = model.inference(image_path, prompt, controls)