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ultralytics 8.1.34 Inference API robust imgsz checks (#9274)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-03-25 02:00:38 +01:00 committed by GitHub
parent dcb953bc41
commit 5be2ffbd13
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3 changed files with 24 additions and 12 deletions
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2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = "8.1.33"
__version__ = "8.1.34"
from ultralytics.data.explorer.explorer import Explorer
from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

32
ultralytics/engine/results.py
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@ -385,10 +385,10 @@ class Results(SimpleClass):
BGR=True,
)
def summary(self, normalize=False):
def summary(self, normalize=False, decimals=5):
"""Convert the results to a summarized format."""
if self.probs is not None:
LOGGER.warning("Warning: Classify task do not support `summary` and `tojson` yet.")
LOGGER.warning("Warning: Classify results do not support the `summary()` method yet.")
return
# Create list of detection dictionaries
@ -396,28 +396,38 @@ class Results(SimpleClass):
data = self.boxes.data.cpu().tolist()
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": row[0] / w, "y1": row[1] / h, "x2": row[2] / w, "y2": row[3] / h}
conf = row[-2]
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])
name = self.names[class_id]
result = {"name": name, "class": class_id, "confidence": conf, "box": box}
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
if self.masks:
x, y = self.masks.xy[i][:, 0], self.masks.xy[i][:, 1] # numpy array
result["segments"] = {"x": (x / w).tolist(), "y": (y / h).tolist()}
result["segments"] = {
"x": (self.masks.xy[i][:, 0] / w).round(decimals).tolist(),
"y": (self.masks.xy[i][:, 1] / h).round(decimals).tolist(),
}
if self.keypoints is not None:
x, y, visible = self.keypoints[i].data[0].cpu().unbind(dim=1) # torch Tensor
result["keypoints"] = {"x": (x / w).tolist(), "y": (y / h).tolist(), "visible": visible.tolist()}
result["keypoints"] = {
"x": (x / w).numpy().round(decimals).tolist(), # decimals named argument required
"y": (y / h).numpy().round(decimals).tolist(),
"visible": visible.numpy().round(decimals).tolist(),
}
results.append(result)
return results
def tojson(self, normalize=False):
def tojson(self, normalize=False, decimals=5):
"""Convert the results to JSON format."""
import json
return json.dumps(self.summary(normalize=normalize), indent=2)
return json.dumps(self.summary(normalize=normalize, decimals=decimals), indent=2)
class Boxes(BaseTensor):

2
ultralytics/utils/checks.py
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@ -142,6 +142,8 @@ def check_imgsz(imgsz, stride=32, min_dim=1, max_dim=2, floor=0):
imgsz = [imgsz]
elif isinstance(imgsz, (list, tuple)):
imgsz = list(imgsz)
elif isinstance(imgsz, str): # i.e. '640' or '[640,640]'
imgsz = [int(imgsz)] if imgsz.isnumeric() else eval(imgsz)
else:
raise TypeError(
f"'imgsz={imgsz}' is of invalid type {type(imgsz).__name__}. "