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c31821c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | from __future__ import print_function
import os
import numpy as np
from PIL import Image
from typing import Union
from modules import devices
from annotator.util import load_model
from annotator.annotator_path import models_path
from controlnet_aux import SamDetector
from controlnet_aux.segment_anything import sam_model_registry, SamAutomaticMaskGenerator
class SamDetector_Aux(SamDetector):
model_dir = os.path.join(models_path, "mobile_sam")
def __init__(self, mask_generator: SamAutomaticMaskGenerator, sam):
super().__init__(mask_generator)
self.device = devices.device
self.model = sam.to(self.device).eval()
@classmethod
def from_pretrained(cls):
"""
Possible model_type : vit_h, vit_l, vit_b, vit_t
download weights from https://huggingface.co/dhkim2810/MobileSAM
"""
remote_url = os.environ.get(
"CONTROLNET_MOBILE_SAM_MODEL_URL",
"https://huggingface.co/dhkim2810/MobileSAM/resolve/main/mobile_sam.pt",
)
model_path = load_model(
"mobile_sam.pt", remote_url=remote_url, model_dir=cls.model_dir
)
sam = sam_model_registry["vit_t"](checkpoint=model_path)
cls.model = sam.to(devices.device).eval()
mask_generator = SamAutomaticMaskGenerator(cls.model)
return cls(mask_generator, sam)
def __call__(self, input_image: Union[np.ndarray, Image.Image]=None, detect_resolution=512, image_resolution=512, output_type="cv2", **kwargs) -> np.ndarray:
self.model.to(self.device)
image = super().__call__(input_image=input_image, detect_resolution=detect_resolution, image_resolution=image_resolution, output_type=output_type, **kwargs)
return np.array(image).astype(np.uint8) |