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| Author | SHA1 | Date |
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SlongLiu
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5a826116c8 | 2 years ago |
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SlongLiu
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08bd3c2e9b | 2 years ago |
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SlongLiu
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cc02608066 | 2 years ago |
15 changed files with 72 additions and 1863 deletions
@ -1,125 +0,0 @@ |
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import argparse |
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from functools import partial |
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import cv2 |
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import requests |
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import os |
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from io import BytesIO |
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from PIL import Image |
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import numpy as np |
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from pathlib import Path |
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import warnings |
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import torch |
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|
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# prepare the environment |
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os.system("python setup.py build develop --user") |
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os.system("pip install packaging==21.3") |
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os.system("pip install gradio") |
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warnings.filterwarnings("ignore") |
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import gradio as gr |
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from groundingdino.models import build_model |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import clean_state_dict |
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from groundingdino.util.inference import annotate, load_image, predict |
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import groundingdino.datasets.transforms as T |
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from huggingface_hub import hf_hub_download |
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# Use this command for evaluate the Grounding DINO model |
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config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py" |
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ckpt_repo_id = "ShilongLiu/GroundingDINO" |
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ckpt_filenmae = "groundingdino_swint_ogc.pth" |
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def load_model_hf(model_config_path, repo_id, filename, device='cpu'): |
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args = SLConfig.fromfile(model_config_path) |
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model = build_model(args) |
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args.device = device |
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cache_file = hf_hub_download(repo_id=repo_id, filename=filename) |
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checkpoint = torch.load(cache_file, map_location='cpu') |
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log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False) |
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print("Model loaded from {} \n => {}".format(cache_file, log)) |
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_ = model.eval() |
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return model |
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|
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def image_transform_grounding(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return init_image, image |
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|
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def image_transform_grounding_for_vis(init_image): |
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transform = T.Compose([ |
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T.RandomResize([800], max_size=1333), |
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]) |
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image, _ = transform(init_image, None) # 3, h, w |
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return image |
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model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae) |
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def run_grounding(input_image, grounding_caption, box_threshold, text_threshold): |
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init_image = input_image.convert("RGB") |
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original_size = init_image.size |
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_, image_tensor = image_transform_grounding(init_image) |
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image_pil: Image = image_transform_grounding_for_vis(init_image) |
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# run grounidng |
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boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cpu') |
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annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases) |
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image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)) |
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return image_with_box |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True) |
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parser.add_argument("--debug", action="store_true", help="using debug mode") |
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parser.add_argument("--share", action="store_true", help="share the app") |
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args = parser.parse_args() |
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block = gr.Blocks().queue() |
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with block: |
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gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)") |
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gr.Markdown("### Open-World Detection with Grounding DINO") |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(source='upload', type="pil") |
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grounding_caption = gr.Textbox(label="Detection Prompt") |
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run_button = gr.Button(label="Run") |
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with gr.Accordion("Advanced options", open=False): |
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box_threshold = gr.Slider( |
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label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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text_threshold = gr.Slider( |
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label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001 |
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) |
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with gr.Column(): |
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gallery = gr.outputs.Image( |
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type="pil", |
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# label="grounding results" |
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).style(full_width=True, full_height=True) |
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# gallery = gr.Gallery(label="Generated images", show_label=False).style( |
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# grid=[1], height="auto", container=True, full_width=True, full_height=True) |
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run_button.click(fn=run_grounding, inputs=[ |
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input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery]) |
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block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share) |
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,43 +0,0 @@ |
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batch_size = 1 |
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modelname = "groundingdino" |
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backbone = "swin_B_384_22k" |
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position_embedding = "sine" |
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pe_temperatureH = 20 |
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pe_temperatureW = 20 |
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return_interm_indices = [1, 2, 3] |
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backbone_freeze_keywords = None |
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enc_layers = 6 |
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dec_layers = 6 |
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pre_norm = False |
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dim_feedforward = 2048 |
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hidden_dim = 256 |
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dropout = 0.0 |
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nheads = 8 |
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num_queries = 900 |
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query_dim = 4 |
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num_patterns = 0 |
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num_feature_levels = 4 |
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enc_n_points = 4 |
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dec_n_points = 4 |
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two_stage_type = "standard" |
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two_stage_bbox_embed_share = False |
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two_stage_class_embed_share = False |
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transformer_activation = "relu" |
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dec_pred_bbox_embed_share = True |
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dn_box_noise_scale = 1.0 |
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dn_label_noise_ratio = 0.5 |
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dn_label_coef = 1.0 |
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dn_bbox_coef = 1.0 |
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embed_init_tgt = True |
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dn_labelbook_size = 2000 |
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max_text_len = 256 |
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text_encoder_type = "bert-base-uncased" |
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use_text_enhancer = True |
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use_fusion_layer = True |
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use_checkpoint = True |
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use_transformer_ckpt = True |
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use_text_cross_attention = True |
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text_dropout = 0.0 |
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fusion_dropout = 0.0 |
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fusion_droppath = 0.1 |
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sub_sentence_present = True |
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@ -1,242 +0,0 @@ |
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from typing import Tuple, List |
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import cv2 |
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import numpy as np |
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import supervision as sv |
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import torch |
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from PIL import Image |
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from torchvision.ops import box_convert |
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import groundingdino.datasets.transforms as T |
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from groundingdino.models import build_model |
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from groundingdino.util.misc import clean_state_dict |
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from groundingdino.util.slconfig import SLConfig |
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from groundingdino.util.utils import get_phrases_from_posmap |
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# ---------------------------------------------------------------------------------------------------------------------- |
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# OLD API |
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# ---------------------------------------------------------------------------------------------------------------------- |
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def preprocess_caption(caption: str) -> str: |
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result = caption.lower().strip() |
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if result.endswith("."): |
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return result |
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return result + "." |
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def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"): |
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args = SLConfig.fromfile(model_config_path) |
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args.device = device |
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model = build_model(args) |
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checkpoint = torch.load(model_checkpoint_path, map_location="cpu") |
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model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False) |
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model.eval() |
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return model |
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def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]: |
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transform = T.Compose( |
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[ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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] |
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) |
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image_source = Image.open(image_path).convert("RGB") |
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image = np.asarray(image_source) |
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image_transformed, _ = transform(image_source, None) |
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return image, image_transformed |
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def predict( |
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model, |
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image: torch.Tensor, |
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caption: str, |
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box_threshold: float, |
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text_threshold: float, |
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device: str = "cuda" |
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) -> Tuple[torch.Tensor, torch.Tensor, List[str]]: |
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caption = preprocess_caption(caption=caption) |
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model = model.to(device) |
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image = image.to(device) |
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with torch.no_grad(): |
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outputs = model(image[None], captions=[caption]) |
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prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0] # prediction_logits.shape = (nq, 256) |
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prediction_boxes = outputs["pred_boxes"].cpu()[0] # prediction_boxes.shape = (nq, 4) |
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mask = prediction_logits.max(dim=1)[0] > box_threshold |
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logits = prediction_logits[mask] # logits.shape = (n, 256) |
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boxes = prediction_boxes[mask] # boxes.shape = (n, 4) |
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tokenizer = model.tokenizer |
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tokenized = tokenizer(caption) |
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phrases = [ |
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get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '') |
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for logit |
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in logits |
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] |
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return boxes, logits.max(dim=1)[0], phrases |
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def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray: |
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h, w, _ = image_source.shape |
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boxes = boxes * torch.Tensor([w, h, w, h]) |
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xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() |
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detections = sv.Detections(xyxy=xyxy) |
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labels = [ |
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f"{phrase} {logit:.2f}" |
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for phrase, logit |
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in zip(phrases, logits) |
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] |
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box_annotator = sv.BoxAnnotator() |
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annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR) |
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annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels) |
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return annotated_frame |
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# ---------------------------------------------------------------------------------------------------------------------- |
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# NEW API |
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# ---------------------------------------------------------------------------------------------------------------------- |
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class Model: |
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def __init__( |
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self, |
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model_config_path: str, |
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model_checkpoint_path: str, |
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device: str = "cuda" |
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): |
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self.model = load_model( |
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model_config_path=model_config_path, |
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model_checkpoint_path=model_checkpoint_path, |
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device=device |
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).to(device) |
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self.device = device |
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def predict_with_caption( |
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self, |
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image: np.ndarray, |
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caption: str, |
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box_threshold: float = 0.35, |
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text_threshold: float = 0.25 |
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) -> Tuple[sv.Detections, List[str]]: |
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""" |
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import cv2 |
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image = cv2.imread(IMAGE_PATH) |
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model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
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detections, labels = model.predict_with_caption( |
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image=image, |
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caption=caption, |
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box_threshold=BOX_THRESHOLD, |
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text_threshold=TEXT_THRESHOLD |
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) |
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import supervision as sv |
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box_annotator = sv.BoxAnnotator() |
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annotated_image = box_annotator.annotate(scene=image, detections=detections, labels=labels) |
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""" |
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processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
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boxes, logits, phrases = predict( |
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model=self.model, |
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image=processed_image, |
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caption=caption, |
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box_threshold=box_threshold, |
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text_threshold=text_threshold) |
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source_h, source_w, _ = image.shape |
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detections = Model.post_process_result( |
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source_h=source_h, |
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source_w=source_w, |
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boxes=boxes, |
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logits=logits) |
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return detections, phrases |
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|
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def predict_with_classes( |
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self, |
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image: np.ndarray, |
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classes: List[str], |
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box_threshold: float, |
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text_threshold: float |
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) -> sv.Detections: |
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""" |
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import cv2 |
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image = cv2.imread(IMAGE_PATH) |
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model = Model(model_config_path=CONFIG_PATH, model_checkpoint_path=WEIGHTS_PATH) |
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detections = model.predict_with_classes( |
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image=image, |
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classes=CLASSES, |
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box_threshold=BOX_THRESHOLD, |
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text_threshold=TEXT_THRESHOLD |
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) |
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import supervision as sv |
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box_annotator = sv.BoxAnnotator() |
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annotated_image = box_annotator.annotate(scene=image, detections=detections) |
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""" |
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caption = ", ".join(classes) |
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processed_image = Model.preprocess_image(image_bgr=image).to(self.device) |
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boxes, logits, phrases = predict( |
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model=self.model, |
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image=processed_image, |
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caption=caption, |
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box_threshold=box_threshold, |
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text_threshold=text_threshold) |
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source_h, source_w, _ = image.shape |
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detections = Model.post_process_result( |
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source_h=source_h, |
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source_w=source_w, |
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boxes=boxes, |
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logits=logits) |
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class_id = Model.phrases2classes(phrases=phrases, classes=classes) |
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detections.class_id = class_id |
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return detections |
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|
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@staticmethod |
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def preprocess_image(image_bgr: np.ndarray) -> torch.Tensor: |
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transform = T.Compose( |
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[ |
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T.RandomResize([800], max_size=1333), |
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T.ToTensor(), |
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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] |
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) |
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image_pillow = Image.fromarray(cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)) |
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image_transformed, _ = transform(image_pillow, None) |
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return image_transformed |
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@staticmethod |
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def post_process_result( |
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source_h: int, |
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source_w: int, |
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boxes: torch.Tensor, |
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logits: torch.Tensor |
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) -> sv.Detections: |
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boxes = boxes * torch.Tensor([source_w, source_h, source_w, source_h]) |
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xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy() |
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confidence = logits.numpy() |
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return sv.Detections(xyxy=xyxy, confidence=confidence) |
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@staticmethod |
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def phrases2classes(phrases: List[str], classes: List[str]) -> np.ndarray: |
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class_ids = [] |
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for phrase in phrases: |
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try: |
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class_ids.append(classes.index(phrase)) |
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except ValueError: |
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class_ids.append(None) |
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return np.array(class_ids) |
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@ -1,10 +1 @@ |
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torch |
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torchvision |
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transformers |
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addict |
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yapf |
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timm |
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numpy |
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opencv-python |
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supervision==0.4.0 |
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pycocotools |
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transformers==4.5.1 |
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