data_juicer.ops.mapper.image_segment_mapper 源代码

import numpy as np

from data_juicer.utils.constant import Fields, MetaKeys
from data_juicer.utils.lazy_loader import LazyLoader
from data_juicer.utils.mm_utils import load_data_with_context, load_image
from data_juicer.utils.model_utils import get_model, prepare_model

from ..base_op import OPERATORS, TAGGING_OPS, UNFORKABLE, Mapper
from ..op_fusion import LOADED_IMAGES

OP_NAME = 'image_segment_mapper'

torch = LazyLoader('torch')
ultralytics = LazyLoader('ultralytics')




@UNFORKABLE.register_module(OP_NAME)
@TAGGING_OPS.register_module(OP_NAME)
@OPERATORS.register_module(OP_NAME)
@LOADED_IMAGES.register_module(OP_NAME)
class ImageSegmentMapper(Mapper):
    """Perform segment-anything on images and return the bounding boxes."""

    _accelerator = 'cuda'



    def __init__(self,
                 imgsz=1024,
                 conf=0.05,
                 iou=0.5,
                 model_path='FastSAM-x.pt',
                 *args,
                 **kwargs):
        """
        Initialization method.

        :param imgsz: resolution for image resizing
        :param conf: confidence score threshold
        :param iou: IoU (Intersection over Union) score threshold
        :param model_path: the path to the FastSAM model. Model name should be
            one of ['FastSAM-x.pt', 'FastSAM-s.pt'].

        """
        kwargs.setdefault('mem_required', '800MB')
        super().__init__(*args, **kwargs)

        self.imgsz = imgsz
        self.conf = conf
        self.iou = iou

        self.model_key = prepare_model(model_type='fastsam',
                                       model_path=model_path)




    def process_single(self, sample, rank=None, context=False):
        # there is no image in this sample
        if self.image_key not in sample or not sample[self.image_key]:
            # N x M x 4 for N images, M boxes, 4 coords
            sample[Fields.meta][MetaKeys.bbox_tag] = np.empty((0, 0, 4),
                                                              dtype=np.float32)
            return sample

        if MetaKeys.bbox_tag in sample[Fields.meta]:
            return sample

        loaded_image_keys = sample[self.image_key]
        sample, images = load_data_with_context(sample, context,
                                                loaded_image_keys, load_image)

        model = get_model(self.model_key, rank=rank, use_cuda=self.use_cuda())
        sample[Fields.meta][MetaKeys.bbox_tag] = []

        for image in images:
            masks = model(image,
                          retina_masks=True,
                          imgsz=self.imgsz,
                          conf=self.conf,
                          iou=self.iou,
                          verbose=False)[0]
            sample[Fields.meta][MetaKeys.bbox_tag].append(
                masks.boxes.xywh.cpu().numpy())

        # match schema
        if len(sample[Fields.meta][MetaKeys.bbox_tag]) == 0:
            sample[Fields.meta][MetaKeys.bbox_tag] = np.empty((0, 0, 4),
                                                              dtype=np.float32)
        return sample