feature/first_batch_of_model_usability_upgrades (IDEA-Research#9)

* initial commit

* test updated requirements.txt

* move more code to inference utils

* PIL import fix

* add annotations utilities

* README.md updates

.gitignore

@@ -1,3 +1,7 @@
+# IDE
+.idea/
+.vscode/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -1,14 +1,21 @@
+# Grounding DINO 
+
+---
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/zero-shot-object-detection-with-grounding-dino.ipynb)
+
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-mscoco)](https://paperswithcode.com/sota/zero-shot-object-detection-on-mscoco?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/zero-shot-object-detection-on-odinw)](https://paperswithcode.com/sota/zero-shot-object-detection-on-odinw?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=grounding-dino-marrying-dino-with-grounded) \
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/grounding-dino-marrying-dino-with-grounded/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=grounding-dino-marrying-dino-with-grounded)
 
-# Grounding DINO 
+
 
 Official pytorch implementation of [Grounding DINO](https://arxiv.org/abs/2303.05499), a stronger open-set object detector. Code is available now!
 
 
 ## Highlight
+
 - **Open-Set Detection.** Detect **everything** with language!
 - **High Performancce.** COCO zero-shot **52.5 AP** (training without COCO data!). COCO fine-tune **63.0 AP**.
 - **Flexible.** Collaboration with Stable Diffusion for Image Editting.
@@ -23,21 +30,22 @@ Description
 <img src=".asset/hero_figure.png" alt="ODinW" width="100%">
 </details>
 
-## TODO List
+## TODO 
 
 - [x] Release inference code and demo.
 - [x] Release checkpoints.
 - [ ] Grounding DINO with Stable Diffusion and GLIGEN demos.
 
+## Install 
 
-## Usage
-### 1. Install 
 If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set.
+
 ```bash
 pip install -e .
 ```
 
-### 2. Run an inference demo 
+## Demo
+
 See the `demo/inference_on_a_image.py` for more details.
 ```bash
 CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
@@ -48,7 +56,8 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
   -t "cat ear."
 ```
 
-### Checkpoints
+## Checkpoints
+
 <!-- insert a table -->
 <table>
   <thead>
@@ -74,6 +83,7 @@ CUDA_VISIBLE_DEVICES=6 python demo/inference_on_a_image.py \
 </table>
 
 ## Results
+
 <details open>
 <summary><font size="4">
 COCO Object Detection Results
@@ -102,28 +112,26 @@ Marrying Grounding DINO with <a href="https://github.com/gligen/GLIGEN">GLIGEN</
 <img src=".asset/GD_GLIGEN.png" alt="GD_GLIGEN" width="100%">
 </details>
 
-
-
-
-
-
 ## Model
 
 Includes: a text backbone, an image backbone, a feature enhancer, a language-guided query selection, and a cross-modality decoder.
 
 ![arch](.asset/arch.png)
 
 
-# Links
+## Acknowledgement
+
 Our model is related to [DINO](https://github.com/IDEA-Research/DINO) and [GLIP](https://github.com/microsoft/GLIP). Thanks for their great work!
 
 We also thank great previous work including DETR, Deformable DETR, SMCA, Conditional DETR, Anchor DETR, Dynamic DETR, DAB-DETR, DN-DETR, etc. More related work are available at [Awesome Detection Transformer](https://github.com/IDEACVR/awesome-detection-transformer). A new toolbox [detrex](https://github.com/IDEA-Research/detrex) is available as well.
 
 Thanks [Stable Diffusion](https://github.com/Stability-AI/StableDiffusion) and [GLIGEN](https://github.com/gligen/GLIGEN) for their awesome models.
 
 
-# Bibtex
+## Citation
+
 If you find our work helpful for your research, please consider citing the following BibTeX entry.   
+
 ```bibtex
 @inproceedings{ShilongLiu2023GroundingDM,
   title={Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection},

groundingdino/util/inference.py

@@ -0,0 +1,97 @@
+from typing import Tuple, List
+
+import cv2
+import numpy as np
+import supervision as sv
+import torch
+from PIL import Image
+from torchvision.ops import box_convert
+
+import groundingdino.datasets.transforms as T
+from groundingdino.models import build_model
+from groundingdino.util.misc import clean_state_dict
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import get_phrases_from_posmap
+
+
+def preprocess_caption(caption: str) -> str:
+    result = caption.lower().strip()
+    if result.endswith("."):
+        return result
+    return result + "."
+
+
+def load_model(model_config_path: str, model_checkpoint_path: str):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = "cuda"
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    model.eval()
+    return model
+
+
+def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
+    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_source = Image.open(image_path).convert("RGB")
+    image = np.asarray(image_source)
+    image_transformed, _ = transform(image_source, None)
+    return image, image_transformed
+
+
+def predict(
+        model,
+        image: torch.Tensor,
+        caption: str,
+        box_threshold: float,
+        text_threshold: float
+) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
+    caption = preprocess_caption(caption=caption)
+
+    model = model.cuda()
+    image = image.cuda()
+
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+
+    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
+    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
+
+    mask = prediction_logits.max(dim=1)[0] > box_threshold
+    logits = prediction_logits[mask]  # logits.shape = (n, 256)
+    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
+
+    tokenizer = model.tokenizer
+    tokenized = tokenizer(caption)
+
+    phrases = [
+        get_phrases_from_posmap(logit > text_threshold, tokenized, caption).replace('.', '')
+        for logit
+        in logits
+    ]
+
+    return boxes, logits.max(dim=1)[0], phrases
+
+
+def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
+    h, w, _ = image_source.shape
+    boxes = boxes * torch.Tensor([w, h, w, h])
+    xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
+    detections = sv.Detections(xyxy=xyxy)
+
+    labels = [
+        f"{phrase} {logit:.2f}"
+        for phrase, logit
+        in zip(phrases, logits)
+    ]
+
+    box_annotator = sv.BoxAnnotator()
+    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
+    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+    return annotated_frame

requirements.txt

@@ -1 +1,9 @@
-transformers==4.5.1
+torch
+torchvision
+transformers
+addict
+yapf
+timm
+numpy
+opencv-python
+supervision==0.3.2