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