ultralytics 8.2.10 add Classify and OBB Tasks to Results.summary() (#11653)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>

tests/test_exports.py

@@ -18,6 +18,7 @@ from ultralytics.utils import (
     checks,
 )
 from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13
+
 from . import MODEL, SOURCE
 
 

tests/test_python.py

@@ -12,7 +12,7 @@ import yaml
 from PIL import Image
 
 from ultralytics import RTDETR, YOLO
-from ultralytics.cfg import TASK2DATA
+from ultralytics.cfg import MODELS, TASK2DATA
 from ultralytics.data.build import load_inference_source
 from ultralytics.utils import (
     ASSETS,
@@ -76,42 +76,27 @@ def test_predict_txt():
     _ = YOLO(MODEL)(source=txt_file, imgsz=32)
 
 
-def test_predict_img():
+@pytest.mark.parametrize("model_name", MODELS)
+def test_predict_img(model_name):
     """Test YOLO prediction on various types of image sources."""
-    model = YOLO(MODEL)
+    model = YOLO(WEIGHTS_DIR / model_name)
-    seg_model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
+    im = cv2.imread(str(SOURCE))  # uint8 numpy array
-    cls_model = YOLO(WEIGHTS_DIR / "yolov8n-cls.pt")
-    pose_model = YOLO(WEIGHTS_DIR / "yolov8n-pose.pt")
-    obb_model = YOLO(WEIGHTS_DIR / "yolov8n-obb.pt")
-    im = cv2.imread(str(SOURCE))
     assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1  # PIL
     assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1  # ndarray
+    assert len(model(torch.rand((2, 3, 32, 32)), imgsz=32)) == 2  # batch-size 2 Tensor, FP32 0.0-1.0 RGB order
     assert len(model(source=[im, im], save=True, save_txt=True, imgsz=32)) == 2  # batch
     assert len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2  # stream
-    assert len(model(torch.zeros(320, 640, 3).numpy(), imgsz=32)) == 1  # tensor to numpy
+    assert len(model(torch.zeros(320, 640, 3).numpy().astype(np.uint8), imgsz=32)) == 1  # tensor to numpy
     batch = [
         str(SOURCE),  # filename
         Path(SOURCE),  # Path
         "https://ultralytics.com/images/zidane.jpg" if ONLINE else SOURCE,  # URI
         cv2.imread(str(SOURCE)),  # OpenCV
         Image.open(SOURCE),  # PIL
-        np.zeros((320, 640, 3)),
+        np.zeros((320, 640, 3), dtype=np.uint8),  # numpy
-    ]  # numpy
+    ]
     assert len(model(batch, imgsz=32)) == len(batch)  # multiple sources in a batch
 
-    # Test tensor inference
-    im = torch.rand((4, 3, 32, 32))  # batch-size 4, FP32 0.0-1.0 RGB order
-    results = model(im, imgsz=32)
-    assert len(results) == im.shape[0]
-    results = seg_model(im, imgsz=32)
-    assert len(results) == im.shape[0]
-    results = cls_model(im, imgsz=32)
-    assert len(results) == im.shape[0]
-    results = pose_model(im, imgsz=32)
-    assert len(results) == im.shape[0]
-    results = obb_model(im, imgsz=32)
-    assert len(results) == im.shape[0]
-
 
 def test_predict_grey_and_4ch():
     """Test YOLO prediction on SOURCE converted to greyscale and 4-channel images."""
@@ -236,10 +221,10 @@ def test_predict_callback_and_setup():
         print(boxes)
 
 
-def test_results():
+@pytest.mark.parametrize("model", MODELS)
+def test_results(model):
     """Test various result formats for the YOLO model."""
-    for m in "yolov8n-pose.pt", "yolov8n-seg.pt", "yolov8n.pt", "yolov8n-cls.pt":
+    results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
-        results = YOLO(WEIGHTS_DIR / m)([SOURCE, SOURCE], imgsz=160)
     for r in results:
         r = r.cpu().numpy()
         r = r.to(device="cpu", dtype=torch.float32)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.9"
+__version__ = "8.2.10"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/cfg/__init__.py

@@ -53,6 +53,7 @@ TASK2METRIC = {
     "pose": "metrics/mAP50-95(P)",
     "obb": "metrics/mAP50-95(B)",
 }
+MODELS = {TASK2MODEL[task] for task in TASKS}
 
 ARGV = sys.argv or ["", ""]  # sometimes sys.argv = []
 CLI_HELP_MSG = f"""

ultralytics/engine/results.py

@@ -387,26 +387,32 @@ class Results(SimpleClass):
 
     def summary(self, normalize=False, decimals=5):
         """Convert the results to a summarized format."""
-        if self.probs is not None:
-            LOGGER.warning("Warning: Classify results do not support the `summary()` method yet.")
-            return
-
         # Create list of detection dictionaries
         results = []
-        data = self.boxes.data.cpu().tolist()
+        if self.probs is not None:
+            class_id = self.probs.top1
+            results.append(
+                {
+                    "name": self.names[class_id],
+                    "class": class_id,
+                    "confidence": round(self.probs.top1conf.item(), decimals),
+                }
+            )
+            return results
+
+        data = self.boxes or self.obb
+        is_obb = self.obb is not None
         h, w = self.orig_shape if normalize else (1, 1)
         for i, row in enumerate(data):  # xyxy, track_id if tracking, conf, class_id
-            box = {
+            class_id, conf = int(row.cls), round(row.conf.item(), decimals)
-                "x1": round(row[0] / w, decimals),
+            box = (row.xyxyxyxy if is_obb else row.xyxy).squeeze().reshape(-1, 2).tolist()
-                "y1": round(row[1] / h, decimals),
+            xy = {}
-                "x2": round(row[2] / w, decimals),
+            for i, b in enumerate(box):
-                "y2": round(row[3] / h, decimals),
+                xy[f"x{i + 1}"] = round(b[0] / w, decimals)
-            }
+                xy[f"y{i + 1}"] = round(b[1] / h, decimals)
-            conf = round(row[-2], decimals)
+            result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": xy}
-            class_id = int(row[-1])
+            if data.is_track:
-            result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": box}
+                result["track_id"] = int(row.id.item())  # track ID
-            if self.boxes.is_track:
-                result["track_id"] = int(row[-3])  # track ID
             if self.masks:
                 result["segments"] = {
                     "x": (self.masks.xy[i][:, 0] / w).round(decimals).tolist(),