ultralytics 8.0.136 refactor and simplify package (#3748)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/models/yolo/pose/predict.py

@@ -0,0 +1,58 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ROOT, ops
+
+
+class PosePredictor(DetectionPredictor):
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'pose'
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Return detection results for a given input image or list of images."""
+        preds = ops.non_max_suppression(preds,
+                                        self.args.conf,
+                                        self.args.iou,
+                                        agnostic=self.args.agnostic_nms,
+                                        max_det=self.args.max_det,
+                                        classes=self.args.classes,
+                                        nc=len(self.model.names))
+
+        results = []
+        for i, pred in enumerate(preds):
+            orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
+            shape = orig_img.shape
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
+            pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
+            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, shape)
+            path = self.batch[0]
+            img_path = path[i] if isinstance(path, list) else path
+            results.append(
+                Results(orig_img=orig_img,
+                        path=img_path,
+                        names=self.model.names,
+                        boxes=pred[:, :6],
+                        keypoints=pred_kpts))
+        return results
+
+
+def predict(cfg=DEFAULT_CFG, use_python=False):
+    """Runs YOLO to predict objects in an image or video."""
+    model = cfg.model or 'yolov8n-pose.pt'
+    source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
+        else 'https://ultralytics.com/images/bus.jpg'
+
+    args = dict(model=model, source=source)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model)(**args)
+    else:
+        predictor = PosePredictor(overrides=args)
+        predictor.predict_cli()
+
+
+if __name__ == '__main__':
+    predict()