77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import hashlib
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import json
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from typing import List
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import cv2
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import numpy as np
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import torch
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from loguru import logger
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from iopaint.helper import download_model
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from iopaint.plugins.base_plugin import BasePlugin
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from iopaint.plugins.segment_anything import SamPredictor, sam_model_registry
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from iopaint.schema import RunPluginRequest
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# 从小到大
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SEGMENT_ANYTHING_MODELS = {
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"vit_b": {
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"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",
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"md5": "01ec64d29a2fca3f0661936605ae66f8",
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},
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"vit_l": {
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"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
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"md5": "0b3195507c641ddb6910d2bb5adee89c",
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},
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"vit_h": {
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"url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
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"md5": "4b8939a88964f0f4ff5f5b2642c598a6",
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},
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"mobile_sam": {
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"url": "https://github.com/Sanster/models/releases/download/MobileSAM/mobile_sam.pt",
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"md5": "f3c0d8cda613564d499310dab6c812cd",
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},
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}
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class InteractiveSeg(BasePlugin):
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name = "InteractiveSeg"
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support_gen_mask = True
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def __init__(self, model_name, device):
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super().__init__()
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model_path = download_model(
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SEGMENT_ANYTHING_MODELS[model_name]["url"],
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SEGMENT_ANYTHING_MODELS[model_name]["md5"],
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)
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logger.info(f"SegmentAnything model path: {model_path}")
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self.predictor = SamPredictor(
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sam_model_registry[model_name](checkpoint=model_path).to(device)
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)
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self.prev_img_md5 = None
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def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
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img_md5 = hashlib.md5(req.image.encode("utf-8")).hexdigest()
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return self.forward(rgb_np_img, req.clicks, img_md5)
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@torch.inference_mode()
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def forward(self, rgb_np_img, clicks: List[List], img_md5: str):
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input_point = []
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input_label = []
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for click in clicks:
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x = click[0]
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y = click[1]
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input_point.append([x, y])
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input_label.append(click[2])
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if img_md5 and img_md5 != self.prev_img_md5:
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self.prev_img_md5 = img_md5
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self.predictor.set_image(rgb_np_img)
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masks, scores, _ = self.predictor.predict(
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point_coords=np.array(input_point),
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point_labels=np.array(input_label),
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multimask_output=False,
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)
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mask = masks[0].astype(np.uint8) * 255
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return mask
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