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Fix `save_txt` in track mode and add Keypoints and Probs (#2921)

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Laughing 2 years ago committed by GitHub
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commit f4b34fc30b
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2 changed files with 158 additions and 60 deletions
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62
tests/test_python.py
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@ -207,42 +207,34 @@ def test_predict_callback_and_setup():
print(boxes)
def test_result():
model = YOLO('yolov8n-pose.pt')
res = model([SOURCE, SOURCE])
res[0].plot(conf=True, boxes=False)
res[0].plot(pil=True)
res[0] = res[0].cpu().numpy()
print(res[0].path, res[0].keypoints)
model = YOLO('yolov8n-seg.pt')
res = model([SOURCE, SOURCE])
res[0].plot(conf=True, boxes=False, masks=True)
res[0].plot(pil=True)
res[0] = res[0].cpu().numpy()
print(res[0].path, res[0].masks.data)
model = YOLO('yolov8n.pt')
res = model(SOURCE)
res[0].plot(pil=True)
res[0].plot()
res[0] = res[0].cpu().numpy()
print(res[0].path)
model = YOLO('yolov8n-cls.pt')
res = model(SOURCE)
res[0].plot(probs=False)
res[0].plot(pil=True)
res[0].plot()
res[0] = res[0].cpu().numpy()
print(res[0].path)
def _test_results_api(res):
# General apis except plot
res = res.cpu().numpy()
# res = res.cuda()
res = res.to(device='cpu', dtype=torch.float32)
res.save_txt('label.txt', save_conf=False)
res.save_txt('label.txt', save_conf=True)
res.save_crop('crops/')
res.tojson(normalize=False)
res.tojson(normalize=True)
res.plot(pil=True)
res.plot(conf=True, boxes=False)
res.plot()
print(res.path)
for k in res.keys:
print(getattr(res, k).data)
def test_results():
for m in ['yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt']:
model = YOLO(m)
res = model([SOURCE, SOURCE])
_test_results_api(res[0])
def test_track():
im = cv2.imread(str(SOURCE))
model = YOLO(MODEL)
seg_model = YOLO('yolov8n-seg.pt')
pose_model = YOLO('yolov8n-pose.pt')
model.track(source=im)
seg_model.track(source=im)
pose_model.track(source=im)
for m in ['yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt']:
model = YOLO(m)
res = model.track(source=im)
_test_results_api(res[0])

156
ultralytics/yolo/engine/results.py
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@ -23,7 +23,13 @@ class BaseTensor(SimpleClass):
"""
def __init__(self, data, orig_shape) -> None:
"""Initialize BaseTensor with data and original shape."""
"""Initialize BaseTensor with data and original shape.
Args:
data (torch.Tensor | np.ndarray): Predictions, such as bboxes, masks and keypoints.
orig_shape (tuple): Original shape of image.
"""
assert isinstance(data, (torch.Tensor, np.ndarray))
self.data = data
self.orig_shape = orig_shape
@ -34,19 +40,19 @@ class BaseTensor(SimpleClass):
def cpu(self):
"""Return a copy of the tensor on CPU memory."""
return self.__class__(self.data.cpu(), self.orig_shape)
return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)
def numpy(self):
"""Return a copy of the tensor as a numpy array."""
return self.__class__(self.data.numpy(), self.orig_shape)
return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)
def cuda(self):
"""Return a copy of the tensor on GPU memory."""
return self.__class__(self.data.cuda(), self.orig_shape)
return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape)
def to(self, *args, **kwargs):
"""Return a copy of the tensor with the specified device and dtype."""
return self.__class__(self.data.to(*args, **kwargs), self.orig_shape)
return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)
def __len__(self): # override len(results)
"""Return the length of the data tensor."""
@ -90,8 +96,8 @@ class Results(SimpleClass):
self.orig_shape = orig_img.shape[:2]
self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None # native size boxes
self.masks = Masks(masks, self.orig_shape) if masks is not None else None # native size or imgsz masks
self.probs = probs if probs is not None else None
self.keypoints = keypoints if keypoints is not None else None
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.speed = {'preprocess': None, 'inference': None, 'postprocess': None} # milliseconds per image
self.names = names
self.path = path
@ -229,13 +235,11 @@ class Results(SimpleClass):
annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if pred_probs is not None and show_probs:
n5 = min(len(names), 5)
top5i = pred_probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
text = f"{', '.join(f'{names[j] if names else j} {pred_probs[j]:.2f}' for j in top5i)}, "
text = f"{', '.join(f'{names[j] if names else j} {pred_probs.data[j]:.2f}' for j in pred_probs.top5)}, "
annotator.text((32, 32), text, txt_color=(255, 255, 255)) # TODO: allow setting colors
if keypoints is not None:
for k in reversed(keypoints):
for k in reversed(keypoints.data):
annotator.kpts(k, self.orig_shape, kpt_line=kpt_line)
return annotator.result()
@ -250,9 +254,7 @@ class Results(SimpleClass):
if len(self) == 0:
return log_string if probs is not None else f'{log_string}(no detections), '
if probs is not None:
n5 = min(len(self.names), 5)
top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
log_string += f"{', '.join(f'{self.names[j]} {probs[j]:.2f}' for j in top5i)}, "
log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, "
if boxes:
for c in boxes.cls.unique():
n = (boxes.cls == c).sum() # detections per class
@ -274,9 +276,7 @@ class Results(SimpleClass):
texts = []
if probs is not None:
# Classify
n5 = min(len(self.names), 5)
top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
[texts.append(f'{probs[j]:.2f} {self.names[j]}') for j in top5i]
[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):
@ -286,7 +286,7 @@ class Results(SimpleClass):
seg = masks[j].xyn[0].copy().reshape(-1) # reversed mask.xyn, (n,2) to (n*2)
line = (c, *seg)
if kpts is not None:
kpt = (kpts[j][:, :2].cpu() / d.orig_shape[[1, 0]]).reshape(-1).tolist()
kpt = kpts[j].xyn.reshape(-1).tolist()
line += (*kpt, )
line += (conf, ) * save_conf + (() if id is None else (id, ))
texts.append(('%g ' * len(line)).rstrip() % line)
@ -322,6 +322,10 @@ class Results(SimpleClass):
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.')
return
import json
# Create list of detection dictionaries
@ -338,7 +342,7 @@ class Results(SimpleClass):
x, y = self.masks.xy[i][:, 0], self.masks.xy[i][:, 1] # numpy array
result['segments'] = {'x': (x / w).tolist(), 'y': (y / h).tolist()}
if self.keypoints is not None:
x, y, visible = self.keypoints[i].cpu().unbind(dim=1) # torch Tensor
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()}
results.append(result)
@ -386,8 +390,7 @@ class Boxes(BaseTensor):
assert n in (6, 7), f'expected `n` in [6, 7], but got {n}' # xyxy, (track_id), conf, cls
super().__init__(boxes, orig_shape)
self.is_track = n == 7
self.orig_shape = torch.as_tensor(orig_shape, device=boxes.device) if isinstance(boxes, torch.Tensor) \
else np.asarray(orig_shape)
self.orig_shape = orig_shape
@property
def xyxy(self):
@ -419,13 +422,19 @@ class Boxes(BaseTensor):
@lru_cache(maxsize=2)
def xyxyn(self):
"""Return the boxes in xyxy format normalized by original image size."""
return self.xyxy / self.orig_shape[[1, 0, 1, 0]]
xyxy = self.xyxy.clone() if isinstance(self.xyxy, torch.Tensor) else np.copy(self.xyxy)
xyxy[..., [0, 2]] /= self.orig_shape[1]
xyxy[..., [1, 3]] /= self.orig_shape[0]
return xyxy
@property
@lru_cache(maxsize=2)
def xywhn(self):
"""Return the boxes in xywh format normalized by original image size."""
return self.xywh / self.orig_shape[[1, 0, 1, 0]]
xywh = ops.xyxy2xywh(self.xyxy)
xywh[..., [0, 2]] /= self.orig_shape[1]
xywh[..., [1, 3]] /= self.orig_shape[0]
return xywh
@property
def boxes(self):
@ -439,11 +448,11 @@ class Masks(BaseTensor):
A class for storing and manipulating detection masks.
Args:
masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
masks (torch.Tensor | np.ndarray): A tensor containing the detection masks, with shape (num_masks, height, width).
orig_shape (tuple): Original image size, in the format (height, width).
Attributes:
masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
masks (torch.Tensor | np.ndarray): A tensor containing the detection masks, with shape (num_masks, height, width).
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
@ -496,3 +505,100 @@ class Masks(BaseTensor):
def pandas(self):
"""Convert the object to a pandas DataFrame (not yet implemented)."""
LOGGER.warning("WARNING ⚠️ 'Masks.pandas' method is not yet implemented.")
class Keypoints(BaseTensor):
"""
A class for storing and manipulating detection keypoints.
Args:
keypoints (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_dets, num_kpts, 2/3).
orig_shape (tuple): Original image size, in the format (height, width).
Attributes:
keypoints (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_dets, num_kpts, 2/3).
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
xy (list): A list of keypoints (pixels) which includes x, y keypoints of each detection.
xyn (list): A list of keypoints (normalized) which includes x, y keypoints of each detection.
Methods:
cpu(): Returns a copy of the keypoints tensor on CPU memory.
numpy(): Returns a copy of the keypoints tensor as a numpy array.
cuda(): Returns a copy of the keypoints tensor on GPU memory.
to(): Returns a copy of the keypoints tensor with the specified device and dtype.
"""
def __init__(self, keypoints, orig_shape) -> None:
if keypoints.ndim == 2:
keypoints = keypoints[None, :]
super().__init__(keypoints, orig_shape)
self.has_visible = self.data.shape[-1] == 3
@property
@lru_cache(maxsize=1)
def xy(self):
return self.data[..., :2]
@property
@lru_cache(maxsize=1)
def xyn(self):
xy = self.xy.clone() if isinstance(self.xy, torch.Tensor) else np.copy(self.xy)
xy[..., 0] /= self.orig_shape[1]
xy[..., 1] /= self.orig_shape[0]
return xy
@property
@lru_cache(maxsize=1)
def conf(self):
return self.data[..., 3] if self.has_visible else None
class Probs(BaseTensor):
"""
A class for storing and manipulating classify predictions.
Args:
probs (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_class, ).
Attributes:
probs (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_class).
Properties:
top5 (list[int]): Top 1 indice.
top1 (int): Top 5 indices.
Methods:
cpu(): Returns a copy of the probs tensor on CPU memory.
numpy(): Returns a copy of the probs tensor as a numpy array.
cuda(): Returns a copy of the probs tensor on GPU memory.
to(): Returns a copy of the probs tensor with the specified device and dtype.
"""
def __init__(self, probs, orig_shape=None) -> None:
super().__init__(probs, orig_shape)
@property
@lru_cache(maxsize=1)
def top5(self):
"""Return the indices of top 5."""
return (-self.data).argsort(0)[:5].tolist() # this way works with both torch and numpy.
@property
@lru_cache(maxsize=1)
def top1(self):
"""Return the indices of top 1."""
return int(self.data.argmax())
@property
@lru_cache(maxsize=1)
def top5conf(self):
"""Return the confidences of top 5."""
return self.data[self.top5]
@property
@lru_cache(maxsize=1)
def top1conf(self):
"""Return the confidences of top 1."""
return self.data[self.top1]

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