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| import argparse | |
| from functools import partial | |
| import cv2 | |
| import requests | |
| import os | |
| from io import BytesIO | |
| from PIL import Image | |
| import numpy as np | |
| from pathlib import Path | |
| import gradio as gr | |
| import warnings | |
| import torch | |
| import cv2 | |
| import numpy as np | |
| from groundingdino.models import build_model | |
| from groundingdino.util.slconfig import SLConfig | |
| from groundingdino.util.utils import clean_state_dict | |
| from groundingdino.util.inference import annotate, load_image, predict | |
| import groundingdino.datasets.transforms as T | |
| from huggingface_hub import hf_hub_download | |
| # Use this command for evaluate the GLIP-T model | |
| config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py" | |
| ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
| ckpt_filenmae = "groundingdino_swint_ogc.pth" | |
| def load_model_hf(model_config_path, repo_id, filename, device='cpu'): | |
| args = SLConfig.fromfile(model_config_path) | |
| model = build_model(args) | |
| args.device = device | |
| cache_file = hf_hub_download(repo_id=repo_id, filename=filename) | |
| checkpoint = torch.load(cache_file, map_location='cpu') | |
| log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) | |
| print("Model loaded from {} \n => {}".format(cache_file, log)) | |
| _ = model.eval() | |
| return model | |
| def image_transform_grounding(init_image): | |
| transform = T.Compose([ | |
| T.RandomResize([800], max_size=1333), | |
| T.ToTensor(), | |
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| image, _ = transform(init_image, None) # 3, h, w | |
| return init_image, image | |
| def image_transform_grounding_for_vis(init_image): | |
| transform = T.Compose([ | |
| T.RandomResize([800], max_size=1333), | |
| ]) | |
| image, _ = transform(init_image, None) # 3, h, w | |
| return image | |
| model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) | |
| def run_grounding(input_image, describe): | |
| pil_img = Image.fromarray(input_image) | |
| init_image = pil_img.convert("RGB") | |
| grounding_caption = describe | |
| box_threshold = 0.25 | |
| text_threshold = 0.25 | |
| _, image_tensor = image_transform_grounding(init_image) | |
| image_pil: Image = image_transform_grounding_for_vis(init_image) | |
| boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, | |
| device='cpu') | |
| annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases) | |
| image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)) | |
| return image_with_box |