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61 lines
2.5 KiB
61 lines
2.5 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import cv2 |
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import torch |
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from PIL import Image |
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from ultralytics.engine.predictor import BasePredictor |
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from ultralytics.engine.results import Results |
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from ultralytics.utils import DEFAULT_CFG, ops |
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class ClassificationPredictor(BasePredictor): |
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""" |
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A class extending the BasePredictor class for prediction based on a classification model. |
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Notes: |
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- Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. |
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Example: |
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```python |
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from ultralytics.utils import ASSETS |
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from ultralytics.models.yolo.classify import ClassificationPredictor |
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args = dict(model='yolov8n-cls.pt', source=ASSETS) |
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predictor = ClassificationPredictor(overrides=args) |
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predictor.predict_cli() |
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``` |
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""" |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
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"""Initializes ClassificationPredictor setting the task to 'classify'.""" |
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super().__init__(cfg, overrides, _callbacks) |
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self.args.task = "classify" |
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self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor" |
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def preprocess(self, img): |
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"""Converts input image to model-compatible data type.""" |
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if not isinstance(img, torch.Tensor): |
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is_legacy_transform = any( |
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self._legacy_transform_name in str(transform) for transform in self.transforms.transforms |
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) |
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if is_legacy_transform: # to handle legacy transforms |
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img = torch.stack([self.transforms(im) for im in img], dim=0) |
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else: |
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img = torch.stack( |
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[self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0 |
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) |
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img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) |
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return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 |
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def postprocess(self, preds, img, orig_imgs): |
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"""Post-processes predictions to return Results objects.""" |
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if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list |
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) |
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results = [] |
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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] |
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img_path = self.batch[0][i] |
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results.append(Results(orig_img, path=img_path, names=self.model.names, probs=pred)) |
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return results
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