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| from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection | |
| import torch | |
| from transformers import SamModel, SamProcessor | |
| import spaces | |
| import numpy as np | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| sam_model = SamModel.from_pretrained("facebook/sam-vit-base").to("cuda") | |
| sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-base") | |
| model_id = "IDEA-Research/grounding-dino-base" | |
| dino_processor = AutoProcessor.from_pretrained(model_id) | |
| dino_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) | |
| def infer_dino(img, text_queries, score_threshold): | |
| queries="" | |
| for query in text_queries: | |
| queries += f"{query}. " | |
| width, height = img.shape[:2] | |
| target_sizes=[(width, height)] | |
| inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = dino_model(**inputs) | |
| outputs.logits = outputs.logits.cpu() | |
| outputs.pred_boxes = outputs.pred_boxes.cpu() | |
| results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids, | |
| box_threshold=score_threshold, | |
| target_sizes=target_sizes) | |
| return results | |
| def query_image(img, text_queries, dino_threshold): | |
| text_queries = text_queries | |
| text_queries = text_queries.split(",") | |
| dino_output = infer_dino(img, text_queries, dino_threshold) | |
| result_labels=[] | |
| for pred in dino_output: | |
| boxes = pred["boxes"].cpu() | |
| scores = pred["scores"].cpu() | |
| labels = pred["labels"] | |
| box = [torch.round(pred["boxes"][0], decimals=2), torch.round(pred["boxes"][1], decimals=2), | |
| torch.round(pred["boxes"][2], decimals=2), torch.round(pred["boxes"][3], decimals=2)] | |
| for box, score, label in zip(boxes, scores, labels): | |
| if label != "": | |
| inputs = sam_processor( | |
| img, | |
| input_boxes=[[[box]]], | |
| return_tensors="pt" | |
| ).to("cuda") | |
| with torch.no_grad(): | |
| outputs = sam_model(**inputs) | |
| mask = sam_processor.image_processor.post_process_masks( | |
| outputs.pred_masks.cpu(), | |
| inputs["original_sizes"].cpu(), | |
| inputs["reshaped_input_sizes"].cpu() | |
| )[0][0][0].numpy() | |
| mask = mask[np.newaxis, ...] | |
| result_labels.append((mask, label)) | |
| return img, result_labels | |
| import gradio as gr | |
| description = "This Space combines [GroundingDINO](https://huggingface.co/IDEA-Research/grounding-dino-base), a bleeding-edge zero-shot object detection model with [SAM](https://huggingface.co/facebook/sam-vit-base), the state-of-the-art mask generation model. SAM normally doesn't accept text input. Combining SAM with OWLv2 makes SAM text promptable. Try the example or input an image and comma separated candidate labels to segment." | |
| demo = gr.Interface( | |
| query_image, | |
| inputs=[gr.Image(label="Image Input"), gr.Textbox(label = "Candidate Labels"), gr.Slider(0, 1, value=0.05, label="Confidence Threshold for GroundingDINO")], | |
| outputs="annotatedimage", | |
| title="GroundingDINO 🤝 SAM for Zero-shot Segmentation", | |
| description=description, | |
| examples=[ | |
| ["./cats.png", "cat, fishnet", 0.16],["./bee.jpg", "bee, flower", 0.16] | |
| ], | |
| ) | |
| demo.launch(debug=True) |