import requests import numpy as np import matplotlib.pyplot as plt from PIL import Image from transformers import TFSamModel, SamProcessor import tensorflow as tf model = TFSamModel.from_pretrained("flaviagiammarino/medsam-vit-base") processor = SamProcessor.from_pretrained("flaviagiammarino/medsam-vit-base") img_url = "https://raw.githubusercontent.com/bowang-lab/MedSAM/main/assets/img_demo.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="tf") outputs = model(**inputs, multimask_output=False) probs = processor.image_processor.post_process_masks([tf.sigmoid(outputs.pred_masks).numpy()[0],], inputs["original_sizes"].numpy(), inputs["reshaped_input_sizes"].numpy(), 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()