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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os | |
| import warnings | |
| from typing import Union | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from ..util import HWC3, resize_image | |
| from .automatic_mask_generator import SamAutomaticMaskGenerator | |
| from .build_sam import sam_model_registry | |
| class SamDetector: | |
| def __init__(self, mask_generator: SamAutomaticMaskGenerator): | |
| self.mask_generator = mask_generator | |
| def from_pretrained(cls, pretrained_model_or_path, model_type="vit_h", filename="sam_vit_h_4b8939.pth", subfolder=None, cache_dir=None): | |
| """ | |
| Possible model_type : vit_h, vit_l, vit_b, vit_t | |
| download weights from https://github.com/facebookresearch/segment-anything | |
| """ | |
| if os.path.isdir(pretrained_model_or_path): | |
| model_path = os.path.join(pretrained_model_or_path, filename) | |
| else: | |
| model_path = hf_hub_download(pretrained_model_or_path, filename, subfolder=subfolder, cache_dir=cache_dir) | |
| sam = sam_model_registry[model_type](checkpoint=model_path) | |
| if torch.cuda.is_available(): | |
| sam.to("cuda") | |
| mask_generator = SamAutomaticMaskGenerator(sam) | |
| return cls(mask_generator) | |
| def show_anns(self, anns): | |
| if len(anns) == 0: | |
| return | |
| sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True) | |
| h, w = anns[0]['segmentation'].shape | |
| final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB") | |
| for ann in sorted_anns: | |
| m = ann['segmentation'] | |
| img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8) | |
| for i in range(3): | |
| img[:,:,i] = np.random.randint(255, dtype=np.uint8) | |
| final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m*255))) | |
| return np.array(final_img, dtype=np.uint8) | |
| def __call__(self, input_image: Union[np.ndarray, Image.Image]=None, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs) -> Image.Image: | |
| if "image" in kwargs: | |
| warnings.warn("image is deprecated, please use `input_image=...` instead.", DeprecationWarning) | |
| input_image = kwargs.pop("image") | |
| if input_image is None: | |
| raise ValueError("input_image must be defined.") | |
| if not isinstance(input_image, np.ndarray): | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| # Generate Masks | |
| masks = self.mask_generator.generate(input_image) | |
| # Create map | |
| map = self.show_anns(masks) | |
| detected_map = map | |
| detected_map = HWC3(detected_map) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |