| import requests | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from transformers import SamModel, SamProcessor | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = SamModel.from_pretrained("flaviagiammarino/medsam-vit-base").to(device) | |
| processor = SamProcessor.from_pretrained("flaviagiammarino/medsam-vit-base") | |
| img_url = "https://huggingface.co/flaviagiammarino/medsam-vit-base/resolve/main/scripts/input.png" | |
| raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") | |
| input_boxes = [95., 255., 190., 350.] | |
| inputs = processor(raw_image, input_boxes=[[input_boxes]], return_tensors="pt").to(device) | |
| outputs = model(**inputs, multimask_output=False) | |
| probs = processor.image_processor.post_process_masks(outputs.pred_masks.sigmoid().cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu(), binarize=False) | |
| def show_mask(mask, ax, random_color): | |
| if random_color: | |
| color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) | |
| else: | |
| color = np.array([251/255, 252/255, 30/255, 0.6]) | |
| h, w = mask.shape[-2:] | |
| mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) | |
| ax.imshow(mask_image) | |
| def show_box(box, ax): | |
| x0, y0 = box[0], box[1] | |
| w, h = box[2] - box[0], box[3] - box[1] | |
| ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor="blue", facecolor=(0, 0, 0, 0), lw=2)) | |
| fig, ax = plt.subplots(1, 2, figsize=(10, 5)) | |
| ax[0].imshow(np.array(raw_image)) | |
| show_box(input_boxes, ax[0]) | |
| ax[0].set_title("Input Image and Bounding Box") | |
| ax[0].axis("off") | |
| ax[1].imshow(np.array(raw_image)) | |
| show_mask(mask=probs[0] > 0.5, ax=ax[1], random_color=False) | |
| show_box(input_boxes, ax[1]) | |
| ax[1].set_title("MedSAM Segmentation") | |
| ax[1].axis("off") | |
| plt.show() |