75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import hashlib
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
import torch
|
|
from loguru import logger
|
|
|
|
from iopaint.helper import download_model
|
|
from iopaint.plugins.base_plugin import BasePlugin
|
|
from iopaint.plugins.segment_anything import SamPredictor, sam_model_registry
|
|
from iopaint.schema import RunPluginRequest
|
|
|
|
# 从小到大
|
|
SEGMENT_ANYTHING_MODELS = {
|
|
"vit_b": {
|
|
"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
|
|
"md5": "01ec64d29a2fca3f0661936605ae66f8",
|
|
},
|
|
"vit_l": {
|
|
"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
|
|
"md5": "0b3195507c641ddb6910d2bb5adee89c",
|
|
},
|
|
"vit_h": {
|
|
"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
|
|
"md5": "4b8939a88964f0f4ff5f5b2642c598a6",
|
|
},
|
|
"mobile_sam": {
|
|
"url": "https://github.com/Sanster/models/releases/download/MobileSAM/mobile_sam.pt",
|
|
"md5": "f3c0d8cda613564d499310dab6c812cd",
|
|
},
|
|
}
|
|
|
|
|
|
class InteractiveSeg(BasePlugin):
|
|
name = "InteractiveSeg"
|
|
support_gen_mask = True
|
|
|
|
def __init__(self, model_name, device):
|
|
super().__init__()
|
|
model_path = download_model(
|
|
SEGMENT_ANYTHING_MODELS[model_name]["url"],
|
|
SEGMENT_ANYTHING_MODELS[model_name]["md5"],
|
|
)
|
|
logger.info(f"SegmentAnything model path: {model_path}")
|
|
self.predictor = SamPredictor(
|
|
sam_model_registry[model_name](checkpoint=model_path).to(device)
|
|
)
|
|
self.prev_img_md5 = None
|
|
|
|
def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
|
|
img_md5 = hashlib.md5(req.image.encode("utf-8")).hexdigest()
|
|
return self.forward(rgb_np_img, req.clicks, img_md5)
|
|
|
|
@torch.inference_mode()
|
|
def forward(self, rgb_np_img, clicks: List[List], img_md5: str):
|
|
input_point = []
|
|
input_label = []
|
|
for click in clicks:
|
|
x = click[0]
|
|
y = click[1]
|
|
input_point.append([x, y])
|
|
input_label.append(click[2])
|
|
|
|
if img_md5 and img_md5 != self.prev_img_md5:
|
|
self.prev_img_md5 = img_md5
|
|
self.predictor.set_image(rgb_np_img)
|
|
|
|
masks, scores, _ = self.predictor.predict(
|
|
point_coords=np.array(input_point),
|
|
point_labels=np.array(input_label),
|
|
multimask_output=False,
|
|
)
|
|
mask = masks[0].astype(np.uint8) * 255
|
|
return mask
|