Spaces:
Build error
Build error
| 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')) |