mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 13:34:23 +08:00
73dbb41920
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: andresinsitu <andres.rodriguez@ingenieriainsitu.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
# 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
|