SAMTest.py

import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
import cv2
from segment_anything import sam_model_registry, SamPredictor

def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 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_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)   
    
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='green', facecolor=(0,0,0,0), lw=2))  

sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"

# sam_checkpoint = "sam_vit_b_01ec64.pth"
# model_type = "vit_b"
device = "cpu"

sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor1 = SamPredictor(sam)
img1_path = 'Real Wound.jpeg'
img1 = cv2.imread(img1_path)

predictor1.set_image(img1)
input_point = np.array([[40, 40], [136,130]])
input_label = np.array([0,1])
masks_left, scores_left, logits_left = predictor1.predict(
    point_coords=input_point,
    point_labels=input_label,
    multimask_output=False,
)


masks_left = np.transpose(masks_left, (1, 2, 0))
masked_left_img = img1*masks_left

plt.subplot(122)
plt.imshow(cv2.cvtColor(masked_left_img, cv2.COLOR_BGR2RGB))
plt.show()