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