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scripts/pt_example.py

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+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")
+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))
+
+plt.imshow(np.array(raw_image))
+ax = plt.gca()
+for mask in masks:
+    show_mask(mask, ax=ax, random_color=False)
+show_box(input_boxes, ax)
+plt.axis("off")
+plt.tight_layout()
+plt.show()