SAM_PredictObjectsFromBoxes_example.py

import numpy as np
import time
import matplotlib.pyplot as plt
import cv2
import sys
import io
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
import torch

# params
model_size = "medium" # small, medium, large
device = "cuda:0" # cuda:0, cpu
image_name = "cartagena.png"

# load image
image = cv2.imread('image_examples/' + image_name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)


# plotting methods
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))  

# get middle point of image
input_boxes = torch.tensor([
    [15, 400, 269, 525],
    [300, 40, 435, 395],
], device=device)

sys.path.append("..")

# download model checkpoint (https://github.com/facebookresearch/segment-anything#model-checkpoints)
if(model_size == "small"):
    sam_checkpoint = "model_checkpoint/sam_vit_b_01ec64.pth"
    model_type = "vit_b"

if(model_size == "medium"):
    sam_checkpoint = "model_checkpoint/sam_vit_l_0b3195.pth"
    model_type = "vit_l"

if(model_size == "large"):
    sam_checkpoint = "model_checkpoint/sam_vit_h_4b8939.pth"
    model_type = "vit_h"

# load model and send to device
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)

#load predictor
predictor = SamPredictor(sam)

predictor.set_image(image)

# get time
start = time.time()


# predict mask
transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes, image.shape[:2])
masks, _, _ = predictor.predict_torch(
    point_coords=None,
    point_labels=None,
    boxes=transformed_boxes,
    multimask_output=False,
)

plt.figure(figsize=(10, 10))
plt.imshow(image)
for mask in masks:
    show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
for box in input_boxes:
    show_box(box.cpu().numpy(), plt.gca())
plt.axis('off')
plt.show()  


# get time
end = time.time()
print('Elapsed time = ' + str((end - start)*1000) + ' ms')