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

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
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Glenn Jocher 2024-05-06 12:26:01 +02:00 • committed by GitHub
parent f2f1afd269
commit 9d48190e6d
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5 changed files with 46 additions and 53 deletions
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1
tests/test_exports.py
View file

@ -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

39
tests/test_python.py
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@ -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)
seg_model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
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))
model = YOLO(WEIGHTS_DIR / model_name)
im = cv2.imread(str(SOURCE)) # uint8 numpy array
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)),
] # numpy
np.zeros((320, 640, 3), dtype=np.uint8), # 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 / m)([SOURCE, SOURCE], imgsz=160)
results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
for r in results:
r = r.cpu().numpy()
r = r.to(device="cpu", dtype=torch.float32)

2
ultralytics/__init__.py
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@ -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

1
ultralytics/cfg/__init__.py
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@ -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"""

38
ultralytics/engine/results.py
View file

@ -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 = {
"x1": round(row[0] / w, decimals),
"y1": round(row[1] / h, decimals),
"x2": round(row[2] / w, decimals),
"y2": round(row[3] / h, decimals),
}
conf = round(row[-2], decimals)
class_id = int(row[-1])
result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": box}
if self.boxes.is_track:
result["track_id"] = int(row[-3]) # track ID
class_id, conf = int(row.cls), round(row.conf.item(), decimals)
box = (row.xyxyxyxy if is_obb else row.xyxy).squeeze().reshape(-1, 2).tolist()
xy = {}
for i, b in enumerate(box):
xy[f"x{i + 1}"] = round(b[0] / w, decimals)
xy[f"y{i + 1}"] = round(b[1] / h, decimals)
result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": xy}
if data.is_track:
result["track_id"] = int(row.id.item()) # track ID
if self.masks:
result["segments"] = {
"x": (self.masks.xy[i][:, 0] / w).round(decimals).tolist(),