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import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
sys.path.insert(0, 'iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/')
from centernet.config import add_centernet_config
from ..grit_src.grit.config import add_grit_config
from ..grit_src.grit.predictor import VisualizationDemo
# constants
WINDOW_NAME = "GRiT"
def dense_pred_to_caption(predictions):
boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None
object_description = predictions["instances"].pred_object_descriptions.data
new_caption = ""
for i in range(len(object_description)):
new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; "
return new_caption
def dense_pred_to_caption_only_name(predictions):
# boxes = predictions["instances"].pred_boxes if predictions["instances"].has("pred_boxes") else None
object_description = predictions["instances"].pred_object_descriptions.data
new_caption = ",".join(object_description)
del predictions
# for i in range(len(object_description)):
# new_caption += (object_description[i] + ": " + str([int(a) for a in boxes[i].tensor.cpu().detach().numpy()[0]])) + "; "
return new_caption
def setup_cfg(args):
cfg = get_cfg()
# if args["cpu"]:
# cfg.MODEL.DEVICE="cpu"
add_centernet_config(cfg)
add_grit_config(cfg)
cfg.merge_from_file(args["config_file"])
cfg.merge_from_list(args["opts"])
# Set score_threshold for builtin models
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args["confidence_threshold"]
cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args["confidence_threshold"]
if args["test_task"]:
cfg.MODEL.TEST_TASK = args["test_task"]
cfg.MODEL.BEAM_SIZE = 1
cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
cfg.USE_ACT_CHECKPOINT = False
cfg.freeze()
return cfg
def get_parser(device):
arg_dict = {'config_file': "iGPT/models/grit_src/configs/GRiT_B_DenseCap_ObjectDet.yaml", 'device': device, 'confidence_threshold': 0.5, 'test_task': 'DenseCap', 'opts': ["MODEL.WEIGHTS", "model_zoo/grit_b_densecap_objectdet.pth"]}
return arg_dict
def image_caption_api(image_src, device):
args2 = get_parser(device)
cfg = setup_cfg(args2)
demo = VisualizationDemo(cfg)
if image_src:
img = read_image(image_src, format="BGR")
predictions, visualized_output = demo.run_on_image(img)
new_caption = dense_pred_to_caption(predictions)
return new_caption
def init_demo(device):
args2 = get_parser(device)
cfg = setup_cfg(args2)
demo = VisualizationDemo(cfg)
return demo
if __name__=="__main__":
import os
os.environ['CUDA_VISIBLE_DEVICES']='7'
print(image_caption_api("images/dancing_example_4.mp4_20230417_135359.263.jpg",'cuda')) |