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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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88
ultralytics/engine/results.py
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@ -98,15 +98,15 @@ class Results(SimpleClass):
self.probs = Probs(probs) if probs is not None else None
self.keypoints = Keypoints(keypoints, self.orig_shape) if keypoints is not None else None
self.obb = OBB(obb, self.orig_shape) if obb is not None else None
self.speed = {'preprocess': None, 'inference': None, 'postprocess': None} # milliseconds per image
self.speed = {"preprocess": None, "inference": None, "postprocess": None} # milliseconds per image
self.names = names
self.path = path
self.save_dir = None
self._keys = 'boxes', 'masks', 'probs', 'keypoints', 'obb'
self._keys = "boxes", "masks", "probs", "keypoints", "obb"
def __getitem__(self, idx):
"""Return a Results object for the specified index."""
return self._apply('__getitem__', idx)
return self._apply("__getitem__", idx)
def __len__(self):
"""Return the number of detections in the Results object."""
@ -146,19 +146,19 @@ class Results(SimpleClass):
def cpu(self):
"""Return a copy of the Results object with all tensors on CPU memory."""
return self._apply('cpu')
return self._apply("cpu")
def numpy(self):
"""Return a copy of the Results object with all tensors as numpy arrays."""
return self._apply('numpy')
return self._apply("numpy")
def cuda(self):
"""Return a copy of the Results object with all tensors on GPU memory."""
return self._apply('cuda')
return self._apply("cuda")
def to(self, *args, **kwargs):
"""Return a copy of the Results object with tensors on the specified device and dtype."""
return self._apply('to', *args, **kwargs)
return self._apply("to", *args, **kwargs)
def new(self):
"""Return a new Results object with the same image, path, and names."""
@ -169,7 +169,7 @@ class Results(SimpleClass):
conf=True,
line_width=None,
font_size=None,
font='Arial.ttf',
font="Arial.ttf",
pil=False,
img=None,
im_gpu=None,
@ -229,14 +229,20 @@ class Results(SimpleClass):
font_size,
font,
pil or (pred_probs is not None and show_probs), # Classify tasks default to pil=True
example=names)
example=names,
)
# Plot Segment results
if pred_masks and show_masks:
if im_gpu is None:
img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
im_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
2, 0, 1).flip(0).contiguous() / 255
im_gpu = (
torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device)
.permute(2, 0, 1)
.flip(0)
.contiguous()
/ 255
)
idx = pred_boxes.cls if pred_boxes else range(len(pred_masks))
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu)
@ -244,14 +250,14 @@ class Results(SimpleClass):
if pred_boxes is not None and show_boxes:
for d in reversed(pred_boxes):
c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
name = ('' if id is None else f'id:{id} ') + names[c]
label = (f'{name} {conf:.2f}' if conf else name) if labels else None
name = ("" if id is None else f"id:{id} ") + names[c]
label = (f"{name} {conf:.2f}" if conf else name) if labels else None
box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze()
annotator.box_label(box, label, color=colors(c, True), rotated=is_obb)
# Plot Classify results
if pred_probs is not None and show_probs:
text = ',\n'.join(f'{names[j] if names else j} {pred_probs.data[j]:.2f}' for j in pred_probs.top5)
text = ",\n".join(f"{names[j] if names else j} {pred_probs.data[j]:.2f}" for j in pred_probs.top5)
x = round(self.orig_shape[0] * 0.03)
annotator.text([x, x], text, txt_color=(255, 255, 255)) # TODO: allow setting colors
@ -264,11 +270,11 @@ class Results(SimpleClass):
def verbose(self):
"""Return log string for each task."""
log_string = ''
log_string = ""
probs = self.probs
boxes = self.boxes
if len(self) == 0:
return log_string if probs is not None else f'{log_string}(no detections), '
return log_string if probs is not None else f"{log_string}(no detections), "
if probs is not None:
log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, "
if boxes:
@ -293,7 +299,7 @@ class Results(SimpleClass):
texts = []
if probs is not None:
# Classify
[texts.append(f'{probs.data[j]:.2f} {self.names[j]}') for j in probs.top5]
[texts.append(f"{probs.data[j]:.2f} {self.names[j]}") for j in probs.top5]
elif boxes:
# Detect/segment/pose
for j, d in enumerate(boxes):
@ -304,16 +310,16 @@ class Results(SimpleClass):
line = (c, *seg)
if kpts is not None:
kpt = torch.cat((kpts[j].xyn, kpts[j].conf[..., None]), 2) if kpts[j].has_visible else kpts[j].xyn
line += (*kpt.reshape(-1).tolist(), )
line += (conf, ) * save_conf + (() if id is None else (id, ))
texts.append(('%g ' * len(line)).rstrip() % line)
line += (*kpt.reshape(-1).tolist(),)
line += (conf,) * save_conf + (() if id is None else (id,))
texts.append(("%g " * len(line)).rstrip() % line)
if texts:
Path(txt_file).parent.mkdir(parents=True, exist_ok=True) # make directory
with open(txt_file, 'a') as f:
f.writelines(text + '\n' for text in texts)
with open(txt_file, "a") as f:
f.writelines(text + "\n" for text in texts)
def save_crop(self, save_dir, file_name=Path('im.jpg')):
def save_crop(self, save_dir, file_name=Path("im.jpg")):
"""
Save cropped predictions to `save_dir/cls/file_name.jpg`.
@ -322,21 +328,23 @@ class Results(SimpleClass):
file_name (str | pathlib.Path): File name.
"""
if self.probs is not None:
LOGGER.warning('WARNING ⚠️ Classify task do not support `save_crop`.')
LOGGER.warning("WARNING ⚠️ Classify task do not support `save_crop`.")
return
if self.obb is not None:
LOGGER.warning('WARNING ⚠️ OBB task do not support `save_crop`.')
LOGGER.warning("WARNING ⚠️ OBB task do not support `save_crop`.")
return
for d in self.boxes:
save_one_box(d.xyxy,
self.orig_img.copy(),
file=Path(save_dir) / self.names[int(d.cls)] / f'{Path(file_name)}.jpg',
BGR=True)
save_one_box(
d.xyxy,
self.orig_img.copy(),
file=Path(save_dir) / self.names[int(d.cls)] / f"{Path(file_name)}.jpg",
BGR=True,
)
def tojson(self, normalize=False):
"""Convert the object to JSON format."""
if self.probs is not None:
LOGGER.warning('Warning: Classify task do not support `tojson` yet.')
LOGGER.warning("Warning: Classify task do not support `tojson` yet.")
return
import json
@ -346,19 +354,19 @@ 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}
box = {"x1": row[0] / w, "y1": row[1] / h, "x2": row[2] / w, "y2": row[3] / h}
conf = row[-2]
class_id = int(row[-1])
name = self.names[class_id]
result = {'name': name, 'class': class_id, 'confidence': conf, 'box': box}
result = {"name": name, "class": class_id, "confidence": conf, "box": box}
if self.boxes.is_track:
result['track_id'] = int(row[-3]) # track ID
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": (x / w).tolist(), "y": (y / h).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).tolist(), "y": (y / h).tolist(), "visible": visible.tolist()}
results.append(result)
# Convert detections to JSON
@ -397,7 +405,7 @@ class Boxes(BaseTensor):
if boxes.ndim == 1:
boxes = boxes[None, :]
n = boxes.shape[-1]
assert n in (6, 7), f'expected 6 or 7 values but got {n}' # xyxy, track_id, conf, cls
assert n in (6, 7), f"expected 6 or 7 values but got {n}" # xyxy, track_id, conf, cls
super().__init__(boxes, orig_shape)
self.is_track = n == 7
self.orig_shape = orig_shape
@ -474,7 +482,8 @@ class Masks(BaseTensor):
"""Return normalized segments."""
return [
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.data)]
for x in ops.masks2segments(self.data)
]
@property
@lru_cache(maxsize=1)
@ -482,7 +491,8 @@ class Masks(BaseTensor):
"""Return segments in pixel coordinates."""
return [
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.data)]
for x in ops.masks2segments(self.data)
]
class Keypoints(BaseTensor):
@ -610,7 +620,7 @@ class OBB(BaseTensor):
if boxes.ndim == 1:
boxes = boxes[None, :]
n = boxes.shape[-1]
assert n in (7, 8), f'expected 7 or 8 values but got {n}' # xywh, rotation, track_id, conf, cls
assert n in (7, 8), f"expected 7 or 8 values but got {n}" # xywh, rotation, track_id, conf, cls
super().__init__(boxes, orig_shape)
self.is_track = n == 8
self.orig_shape = orig_shape