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medsam-vit-base

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medsam-vit-base / README.md
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metadata
license: apache-2.0
pipeline_tag: image-segmentation
tags:
  - medical
widget:
  - src: >-
      https://raw.githubusercontent.com/bowang-lab/MedSAM/main/assets/img_demo.png
    example_title: Abdomen CT Scan

Model Card for MedSAM

MedSAM is a fine-tuned version of SAM for the medical domain.

Usage 

import requests
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from transformers import SamModel, SamProcessor

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://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="pt").to(device)
outputs = model(**inputs, multimask_output=False)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())

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(masks[0], ax=ax[1], random_color=False)
show_box(input_boxes, ax[1])
ax[1].set_title("MedSAM Segmentation")
ax[1].axis("off")
plt.show()

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