test2.py

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

sys.path.append("..")

# Defining sam model
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
device = "cuda"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)

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 return_mask_img(mask):
    color = np.concatenate([np.random.random(3)*255, np.array([0.6])], axis=0)
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)

    return 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))


def do_sam(image, output_folder, x,y):
    image = cv2.imread(image)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    predictor.set_image(image)

    input_point = np.array([[x, y]])
    input_label = np.array([1])
    """
    plt.figure(figsize=(10,10))
    plt.imshow(image)
    show_points(input_point, input_label, plt.gca())
    plt.axis('on')
    plt.show()
    """

    masks, scores, logits = predictor.predict(
        point_coords=input_point,
        point_labels=input_label,
        multimask_output=True,
    )

    for i, (mask, score) in enumerate(zip(masks, scores)):
        """
        plt.figure(figsize=(10,10))
        plt.imshow(image)
        show_mask(mask, plt.gca())
        show_points(input_point, input_label, plt.gca())
        plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
        plt.axis('off')
        plt.show()
        """
        dest_path = os.path.join(output_folder, f'mask{i}.jpg')

        mask_image = return_mask_img(mask)
        cv2.imwrite(dest_path, mask_image)
        print(f'success: {dest_path}')