YOLO的 result.py
#2025.1.3
"""
https://docs.ultralytics.com/zh/modes/predict/#inference-arguments
对yolo 目标检测、实例分割、关键点检测结果进行说明
https://docs.ultralytics.com/reference/engine/results/#ultralytics.engine.results.Masks.xy
对检测结果（mask) 进行说明
https://docs.ultralytics.com/zh/datasets/segment/#what-is-the-auto-annotation-feature-in-ultralytics-yolo
对实例分割数据集的概述，提供将coco标签格式转换为txt标签格式 ， 解释yolo实例分割数据集txt特点， 以及yolo自带的分割标注工具，可以对图片自动标注 （使用yolov11x.pt, sam_b.pt,
个人可以先使用yolov11x 对之前标注的 识别框蘑菇数据集进行训练，用来代替 yolov11x.pt 或者两个都可以试一试（）！！！！
https://docs.ultralytics.com/zh/guides/instance-segmentation-and-tracking/#how-can-i-implement-object-tracking-using-ultralytics-yolo11
YOLOv11实际应用，如 裁剪图像、距离计算、实例分割、停车场管理
"""
# C:\Users\Lenovo\miniconda3\envs\yolov8\Lib\site-packages\ultralytics\engine\results.py
"""
本文件主要是对上述yolo的 results.py文件进行学习， 利用yolo检测结果的输出信息
先把其文件代码复制到这里 （以免不小心对源文件进行误改动
"""
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Ultralytics Results, Boxes and Masks classes for handling inference results.
Usage: See https://docs.ultralytics.com/modes/predict/
"""
from copy import deepcopy
from functools import lru_cache
from pathlib import Path
import numpy as np
import torch
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER, SimpleClass, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.plotting import Annotator, colors, save_one_box
from ultralytics.utils.torch_utils import smart_inference_mode
class BaseTensor(SimpleClass):
"""
Base tensor class with additional methods for easy manipulation and device handling.
Attributes:
data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints.
orig_shape (Tuple[int, int]): Original shape of the image, typically in the format (height, width).
Methods:
cpu: Return a copy of the tensor stored in CPU memory.
numpy: Returns a copy of the tensor as a numpy array.
cuda: Moves the tensor to GPU memory, returning a new instance if necessary.
to: Return a copy of the tensor with the specified device and dtype.
Examples:
>>> import torch
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> orig_shape = (720, 1280)
>>> base_tensor = BaseTensor(data, orig_shape)
>>> cpu_tensor = base_tensor.cpu()
>>> numpy_array = base_tensor.numpy()
>>> gpu_tensor = base_tensor.cuda()
"""
def __init__(self, data, orig_shape) -> None:
"""
Initialize BaseTensor with prediction data and the original shape of the image.
Args:
data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints.
orig_shape (Tuple[int, int]): Original shape of the image in (height, width) format.
Examples:
>>> import torch
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> orig_shape = (720, 1280)
>>> base_tensor = BaseTensor(data, orig_shape)
"""
assert isinstance(data, (torch.Tensor, np.ndarray)), "data must be torch.Tensor or np.ndarray"
self.data = data
self.orig_shape = orig_shape
@property
def shape(self):
"""
Returns the shape of the underlying data tensor.
Returns:
(Tuple[int, ...]): The shape of the data tensor.
Examples:
>>> data = torch.rand(100, 4)
>>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
>>> print(base_tensor.shape)
(100, 4)
"""
return self.data.shape
def cpu(self):
"""
Returns a copy of the tensor stored in CPU memory.
Returns:
(BaseTensor): A new BaseTensor object with the data tensor moved to CPU memory.
Examples:
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda()
>>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
>>> cpu_tensor = base_tensor.cpu()
>>> isinstance(cpu_tensor, BaseTensor)
True
>>> cpu_tensor.data.device
device(type='cpu')
"""
return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)
def numpy(self):
"""
Returns a copy of the tensor as a numpy array.
Returns:
(np.ndarray): A numpy array containing the same data as the original tensor.
Examples:
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> orig_shape = (720, 1280)
>>> base_tensor = BaseTensor(data, orig_shape)
>>> numpy_array = base_tensor.numpy()
>>> print(type(numpy_array))
<class 'numpy.ndarray'>
"""
return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)
def cuda(self):
"""
Moves the tensor to GPU memory.
Returns:
(BaseTensor): A new BaseTensor instance with the data moved to GPU memory if it's not already a
numpy array, otherwise returns self.
Examples:
>>> import torch
>>> from ultralytics.engine.results import BaseTensor
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
>>> gpu_tensor = base_tensor.cuda()
>>> print(gpu_tensor.data.device)
cuda:0
"""
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.
Args:
*args (Any): Variable length argument list to be passed to torch.Tensor.to().
**kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to().
Returns:
(BaseTensor): A new BaseTensor instance with the data moved to the specified device and/or dtype.
Examples:
>>> base_tensor = BaseTensor(torch.randn(3, 4), orig_shape=(480, 640))
>>> cuda_tensor = base_tensor.to("cuda")
>>> float16_tensor = base_tensor.to(dtype=torch.float16)
"""
return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)
def __len__(self): # override len(results)
"""
Returns the length of the underlying data tensor.
Returns:
(int): The number of elements in the first dimension of the data tensor.
Examples:
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
>>> len(base_tensor)
2
"""
return len(self.data)
def __getitem__(self, idx):
"""
Returns a new BaseTensor instance containing the specified indexed elements of the data tensor.
Args:
idx (int | List[int] | torch.Tensor): Index or indices to select from the data tensor.
Returns:
(BaseTensor): A new BaseTensor instance containing the indexed data.
Examples:
>>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
>>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
>>> result = base_tensor[0] # Select the first row
>>> print(result.data)
tensor([1, 2, 3])
"""
return self.__class__(self.data[idx], self.orig_shape)
class Results(SimpleClass):
"""
A class for storing and manipulating inference results.
This class encapsulates the functionality for handling detection, segmentation, pose estimation,
and classification results from YOLO models.
Attributes:
orig_img (numpy.ndarray): Original image as a numpy array.
orig_shape (Tuple[int, int]): Original image shape in (height, width) format.
boxes (Boxes | None): Object containing detection bounding boxes.
masks (Masks | None): Object containing detection masks.
probs (Probs | None): Object containing class probabilities for classification tasks.
keypoints (Keypoints | None): Object containing detected keypoints for each object.
obb (OBB | None): Object containing oriented bounding boxes.
speed (Dict[str, float | None]): Dictionary of preprocess, inference, and postprocess speeds.
names (Dict[int, str]): Dictionary mapping class IDs to class names.
path (str): Path to the image file.
_keys (Tuple[str, ...]): Tuple of attribute names for internal use.
Methods:
update: Updates object attributes with new detection results.
cpu: Returns a copy of the Results object with all tensors on CPU memory.
numpy: Returns a copy of the Results object with all tensors as numpy arrays.
cuda: Returns a copy of the Results object with all tensors on GPU memory.
to: Returns a copy of the Results object with tensors on a specified device and dtype.
new: Returns a new Results object with the same image, path, and names.
plot: Plots detection results on an input image, returning an annotated image.
show: Shows annotated results on screen.
save: Saves annotated results to file.
verbose: Returns a log string for each task, detailing detections and classifications.
save_txt: Saves detection results to a text file.
save_crop: Saves cropped detection images.
tojson: Converts detection results to JSON format.
Examples:
>>> results = model("path/to/image.jpg")
>>> for result in results:
... print(result.boxes) # Print detection boxes
... result.show() # Display the annotated image
... result.save(filename="result.jpg") # Save annotated image
"""
def __init__(
self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None, speed=None
) -> None:
"""
Initialize the Results class for storing and manipulating inference results.
Args:
orig_img (numpy.ndarray): The original image as a numpy array.
path (str): The path to the image file.
names (Dict): A dictionary of class names.
boxes (torch.Tensor | None): A 2D tensor of bounding box coordinates for each detection.
masks (torch.Tensor | None): A 3D tensor of detection masks, where each mask is a binary image.
probs (torch.Tensor | None): A 1D tensor of probabilities of each class for classification task.
keypoints (torch.Tensor | None): A 2D tensor of keypoint coordinates for each detection.
obb (torch.Tensor | None): A 2D tensor of oriented bounding box coordinates for each detection.
speed (Dict | None): A dictionary containing preprocess, inference, and postprocess speeds (ms/image).
Examples:
>>> results = model("path/to/image.jpg")
>>> result = results[0] # Get the first result
>>> boxes = result.boxes # Get the boxes for the first result
>>> masks = result.masks # Get the masks for the first result
Notes:
For the default pose model, keypoint indices for human body pose estimation are:
0: Nose, 1: Left Eye, 2: Right Eye, 3: Left Ear, 4: Right Ear
5: Left Shoulder, 6: Right Shoulder, 7: Left Elbow, 8: Right Elbow
9: Left Wrist, 10: Right Wrist, 11: Left Hip, 12: Right Hip
13: Left Knee, 14: Right Knee, 15: Left Ankle, 16: Right Ankle
"""
self.orig_img = orig_img
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(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 = speed if speed is not None else {"preprocess": None, "inference": None, "postprocess": None}
self.names = names
self.path = path
self.save_dir = None
self._keys = "boxes", "masks", "probs", "keypoints", "obb"
def __getitem__(self, idx):
"""
Return a Results object for a specific index of inference results.
Args:
idx (int | slice): Index or slice to retrieve from the Results object.
Returns:
(Results): A new Results object containing the specified subset of inference results.
Examples:
>>> results = model("path/to/image.jpg") # Perform inference
>>> single_result = results[0] # Get the first result
>>> subset_results = results[1:4] # Get a slice of results
"""
return self._apply("__getitem__", idx)
def __len__(self):
"""
Return the number of detections in the Results object.
Returns:
(int): The number of detections, determined by the length of the first non-empty attribute
(boxes, masks, probs, keypoints, or obb).
Examples:
>>> results = Results(orig_img, path, names, boxes=torch.rand(5, 4))
>>> len(results)
5
"""
for k in self._keys:
v = getattr(self, k)
if v is not None:
return len(v)
def update(self, boxes=None, masks=None, probs=None, obb=None):
"""
Updates the Results object with new detection data.
This method allows updating the boxes, masks, probabilities, and oriented bounding boxes (OBB) of the
Results object. It ensures that boxes are clipped to the original image shape.
Args:
boxes (torch.Tensor | None): A tensor of shape (N, 6) containing bounding box coordinates and
confidence scores. The format is (x1, y1, x2, y2, conf, class).
masks (torch.Tensor | None): A tensor of shape (N, H, W) containing segmentation masks.
probs (torch.Tensor | None): A tensor of shape (num_classes,) containing class probabilities.
obb (torch.Tensor | None): A tensor of shape (N, 5) containing oriented bounding box coordinates.
Examples:
>>> results = model("image.jpg")
>>> new_boxes = torch.tensor([[100, 100, 200, 200, 0.9, 0]])
>>> results[0].update(boxes=new_boxes)
"""
if boxes is not None:
self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape)
if masks is not None:
self.masks = Masks(masks, self.orig_shape)
if probs is not None:
self.probs = probs
if obb is not None:
self.obb = OBB(obb, self.orig_shape)
def _apply(self, fn, *args, **kwargs):
"""
Applies a function to all non-empty attributes and returns a new Results object with modified attributes.
This method is internally called by methods like .to(), .cuda(), .cpu(), etc.
Args:
fn (str): The name of the function to apply.
*args (Any): Variable length argument list to pass to the function.
**kwargs (Any): Arbitrary keyword arguments to pass to the function.
Returns:
(Results): A new Results object with attributes modified by the applied function.
Examples:
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result_cuda = result.cuda()
... result_cpu = result.cpu()
"""
r = self.new()
for k in self._keys:
v = getattr(self, k)
if v is not None:
setattr(r, k, getattr(v, fn)(*args, **kwargs))
return r
def cpu(self):
"""
Returns a copy of the Results object with all its tensors moved to CPU memory.
This method creates a new Results object with all tensor attributes (boxes, masks, probs, keypoints, obb)
transferred to CPU memory. It's useful for moving data from GPU to CPU for further processing or saving.
Returns:
(Results): A new Results object with all tensor attributes on CPU memory.
Examples:
>>> results = model("path/to/image.jpg") # Perform inference
>>> cpu_result = results[0].cpu() # Move the first result to CPU
>>> print(cpu_result.boxes.device) # Output: cpu
"""
return self._apply("cpu")
def numpy(self):
"""
Converts all tensors in the Results object to numpy arrays.
Returns:
(Results): A new Results object with all tensors converted to numpy arrays.
Examples:
>>> results = model("path/to/image.jpg")
>>> numpy_result = results[0].numpy()
>>> type(numpy_result.boxes.data)
<class 'numpy.ndarray'>
Notes:
This method creates a new Results object, leaving the original unchanged. It's useful for
interoperability with numpy-based libraries or when CPU-based operations are required.
"""
return self._apply("numpy")
def cuda(self):
"""
Moves all tensors in the Results object to GPU memory.
Returns:
(Results): A new Results object with all tensors moved to CUDA device.
Examples:
>>> results = model("path/to/image.jpg")
>>> cuda_results = results[0].cuda() # Move first result to GPU
>>> for result in results:
... result_cuda = result.cuda() # Move each result to GPU
"""
return self._apply("cuda")
def to(self, *args, **kwargs):
"""
Moves all tensors in the Results object to the specified device and dtype.
Args:
*args (Any): Variable length argument list to be passed to torch.Tensor.to().
**kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to().
Returns:
(Results): A new Results object with all tensors moved to the specified device and dtype.
Examples:
>>> results = model("path/to/image.jpg")
>>> result_cuda = results[0].to("cuda") # Move first result to GPU
>>> result_cpu = results[0].to("cpu") # Move first result to CPU
>>> result_half = results[0].to(dtype=torch.float16) # Convert first result to half precision
"""
return self._apply("to", *args, **kwargs)
def new(self):
"""
Creates a new Results object with the same image, path, names, and speed attributes.
Returns:
(Results): A new Results object with copied attributes from the original instance.
Examples:
>>> results = model("path/to/image.jpg")
>>> new_result = results[0].new()
"""
return Results(orig_img=self.orig_img, path=self.path, names=self.names, speed=self.speed)
def plot(
self,
conf=True,
line_width=None,
font_size=None,
font="Arial.ttf",
pil=False,
img=None,
im_gpu=None,
kpt_radius=5,
kpt_line=True,
labels=True,
boxes=True,
masks=True,
probs=True,
show=False,
save=False,
filename=None,
color_mode="class",
):
"""
Plots detection results on an input RGB image.
Args:
conf (bool): Whether to plot detection confidence scores.
line_width (float | None): Line width of bounding boxes. If None, scaled to image size.
font_size (float | None): Font size for text. If None, scaled to image size.
font (str): Font to use for text.
pil (bool): Whether to return the image as a PIL Image.
img (np.ndarray | None): Image to plot on. If None, uses original image.
im_gpu (torch.Tensor | None): Normalized image on GPU for faster mask plotting.
kpt_radius (int): Radius of drawn keypoints.
kpt_line (bool): Whether to draw lines connecting keypoints.
labels (bool): Whether to plot labels of bounding boxes.
boxes (bool): Whether to plot bounding boxes.
masks (bool): Whether to plot masks.
probs (bool): Whether to plot classification probabilities.
show (bool): Whether to display the annotated image.
save (bool): Whether to save the annotated image.
filename (str | None): Filename to save image if save is True.
color_mode (bool): Specify the color mode, e.g., 'instance' or 'class'. Default to 'class'.
Returns:
(np.ndarray): Annotated image as a numpy array.
Examples:
>>> results = model("image.jpg")
>>> for result in results:
... im = result.plot()
... im.show()
"""
assert color_mode in {"instance", "class"}, f"Expected color_mode='instance' or 'class', not {color_mode}."
if img is None and isinstance(self.orig_img, torch.Tensor):
img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).cpu().numpy()
names = self.names
is_obb = self.obb is not None
pred_boxes, show_boxes = self.obb if is_obb else self.boxes, boxes
pred_masks, show_masks = self.masks, masks
pred_probs, show_probs = self.probs, probs
annotator = Annotator(
deepcopy(self.orig_img if img is None else img),
line_width,
font_size,
font,
pil or (pred_probs is not None and show_probs), # Classify tasks default to pil=True
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
)
idx = (
pred_boxes.id
if pred_boxes.id is not None and color_mode == "instance"
else pred_boxes.cls
if pred_boxes and color_mode == "class"
else reversed(range(len(pred_masks)))
)
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu)
# Plot Detect results
if pred_boxes is not None and show_boxes:
for i, d in enumerate(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
box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze()
annotator.box_label(
box,
label,
color=colors(
c
if color_mode == "class"
else id
if id is not None
else i
if color_mode == "instance"
else None,
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)
x = round(self.orig_shape[0] * 0.03)
annotator.text([x, x], text, txt_color=(255, 255, 255)) # TODO: allow setting colors
# Plot Pose results
if self.keypoints is not None:
for i, k in enumerate(reversed(self.keypoints.data)):
annotator.kpts(
k,
self.orig_shape,
radius=kpt_radius,
kpt_line=kpt_line,
kpt_color=colors(i, True) if color_mode == "instance" else None,
)
# Show results
if show:
annotator.show(self.path)
# Save results
if save:
annotator.save(filename)
return annotator.result()
def show(self, *args, **kwargs):
"""
Display the image with annotated inference results.
This method plots the detection results on the original image and displays it. It's a convenient way to
visualize the model's predictions directly.
Args:
*args (Any): Variable length argument list to be passed to the `plot()` method.
**kwargs (Any): Arbitrary keyword arguments to be passed to the `plot()` method.
Examples:
>>> results = model("path/to/image.jpg")
>>> results[0].show() # Display the first result
>>> for result in results:
... result.show() # Display all results
"""
self.plot(show=True, *args, **kwargs)
def save(self, filename=None, *args, **kwargs):
"""
Saves annotated inference results image to file.
This method plots the detection results on the original image and saves the annotated image to a file. It
utilizes the `plot` method to generate the annotated image and then saves it to the specified filename.
Args:
filename (str | Path | None): The filename to save the annotated image. If None, a default filename
is generated based on the original image path.
*args (Any): Variable length argument list to be passed to the `plot` method.
**kwargs (Any): Arbitrary keyword arguments to be passed to the `plot` method.
Examples:
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save("annotated_image.jpg")
>>> # Or with custom plot arguments
>>> for result in results:
... result.save("annotated_image.jpg", conf=False, line_width=2)
"""
if not filename:
filename = f"results_{Path(self.path).name}"
self.plot(save=True, filename=filename, *args, **kwargs)
return filename
def verbose(self):
"""
Returns a log string for each task in the results, detailing detection and classification outcomes.
This method generates a human-readable string summarizing the detection and classification results. It includes
the number of detections for each class and the top probabilities for classification tasks.
Returns:
(str): A formatted string containing a summary of the results. For detection tasks, it includes the
number of detections per class. For classification tasks, it includes the top 5 class probabilities.
Examples:
>>> results = model("path/to/image.jpg")
>>> for result in results:
... print(result.verbose())
2 persons, 1 car, 3 traffic lights,
dog 0.92, cat 0.78, horse 0.64,
Notes:
- If there are no detections, the method returns "(no detections), " for detection tasks.
- For classification tasks, it returns the top 5 class probabilities and their corresponding class names.
- The returned string is comma-separated and ends with a comma and a space.
"""
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), "
if probs is not None:
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
log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "
return log_string
def save_txt(self, txt_file, save_conf=False):
"""
Save detection results to a text file.
Args:
txt_file (str | Path): Path to the output text file.
save_conf (bool): Whether to include confidence scores in the output.
Returns:
(str): Path to the saved text file.
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO("yolov8n.pt")
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save_txt("output.txt")
Notes:
- The file will contain one line per detection or classification with the following structure:
- For detections: `class confidence x_center y_center width height`
- For classifications: `confidence class_name`
- For masks and keypoints, the specific formats will vary accordingly.
- The function will create the output directory if it does not exist.
- If save_conf is False, the confidence scores will be excluded from the output.
- Existing contents of the file will not be overwritten; new results will be appended.
"""
is_obb = self.obb is not None
boxes = self.obb if is_obb else self.boxes
masks = self.masks
probs = self.probs
kpts = self.keypoints
texts = []
if probs is not None:
# Classify
[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):
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
line = (c, *(d.xyxyxyxyn.view(-1) if is_obb else d.xywhn.view(-1)))
if masks:
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 = 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)
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)
def save_crop(self, save_dir, file_name=Path("im.jpg")):
"""
Saves cropped detection images to specified directory.
This method saves cropped images of detected objects to a specified directory. Each crop is saved in a
subdirectory named after the object's class, with the filename based on the input file_name.
Args:
save_dir (str | Path): Directory path where cropped images will be saved.
file_name (str | Path): Base filename for the saved cropped images. Default is Path("im.jpg").
Notes:
- This method does not support Classify or Oriented Bounding Box (OBB) tasks.
- Crops are saved as 'save_dir/class_name/file_name.jpg'.
- The method will create necessary subdirectories if they don't exist.
- Original image is copied before cropping to avoid modifying the original.
Examples:
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save_crop(save_dir="path/to/crops", file_name="detection")
"""
if self.probs is not None:
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`.")
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,
)
def summary(self, normalize=False, decimals=5):
"""
Converts inference results to a summarized dictionary with optional normalization for box coordinates.
This method creates a list of detection dictionaries, each containing information about a single
detection or classification result. For classification tasks, it returns the top class and its
confidence. For detection tasks, it includes class information, bounding box coordinates, and
optionally mask segments and keypoints.
Args:
normalize (bool): Whether to normalize bounding box coordinates by image dimensions. Defaults to False.
decimals (int): Number of decimal places to round the output values to. Defaults to 5.
Returns:
(List[Dict]): A list of dictionaries, each containing summarized information for a single
detection or classification result. The structure of each dictionary varies based on the
task type (classification or detection) and available information (boxes, masks, keypoints).
Examples:
>>> results = model("image.jpg")
>>> summary = results[0].summary()
>>> print(summary)
"""
# Create list of detection dictionaries
results = []
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
is_obb = self.obb is not None
data = self.obb if is_obb else self.boxes
h, w = self.orig_shape if normalize else (1, 1)
for i, row in enumerate(data): # xyxy, track_id if tracking, conf, class_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 j, b in enumerate(box):
xy[f"x{j + 1}"] = round(b[0] / w, decimals)
xy[f"y{j + 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(),
"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).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 to_df(self, normalize=False, decimals=5):
"""
Converts detection results to a Pandas Dataframe.
This method converts the detection results into Pandas Dataframe format. It includes information
about detected objects such as bounding boxes, class names, confidence scores, and optionally
segmentation masks and keypoints.
Args:
normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
decimals (int): Number of decimal places to round the output values to. Defaults to 5.
Returns:
(DataFrame): A Pandas Dataframe containing all the information in results in an organized way.
Examples:
>>> results = model("path/to/image.jpg")
>>> df_result = results[0].to_df()
>>> print(df_result)
"""
import pandas as pd
return pd.DataFrame(self.summary(normalize=normalize, decimals=decimals))
def to_csv(self, normalize=False, decimals=5, *args, **kwargs):
"""
Converts detection results to a CSV format.
This method serializes the detection results into a CSV format. It includes information
about detected objects such as bounding boxes, class names, confidence scores, and optionally
segmentation masks and keypoints.
Args:
normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
decimals (int): Number of decimal places to round the output values to. Defaults to 5.
*args (Any): Variable length argument list to be passed to pandas.DataFrame.to_csv().
**kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_csv().
Returns:
(str): CSV containing all the information in results in an organized way.
Examples:
>>> results = model("path/to/image.jpg")
>>> csv_result = results[0].to_csv()
>>> print(csv_result)
"""
return self.to_df(normalize=normalize, decimals=decimals).to_csv(*args, **kwargs)
def to_xml(self, normalize=False, decimals=5, *args, **kwargs):
"""
Converts detection results to XML format.
This method serializes the detection results into an XML format. It includes information
about detected objects such as bounding boxes, class names, confidence scores, and optionally
segmentation masks and keypoints.
Args:
normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
decimals (int): Number of decimal places to round the output values to. Defaults to 5.
*args (Any): Variable length argument list to be passed to pandas.DataFrame.to_xml().
**kwargs (Any): Arbitrary keyword arguments to be passed to pandas.DataFrame.to_xml().
Returns:
(str): An XML string containing all the information in results in an organized way.
Examples:
>>> results = model("path/to/image.jpg")
>>> xml_result = results[0].to_xml()
>>> print(xml_result)
"""
check_requirements("lxml")
df = self.to_df(normalize=normalize, decimals=decimals)
return '<?xml version="1.0" encoding="utf-8"?>\n<root></root>' if df.empty else df.to_xml(*args, **kwargs)
def tojson(self, normalize=False, decimals=5):
"""Deprecated version of to_json()."""
LOGGER.warning("WARNING ⚠️ 'result.tojson()' is deprecated, replace with 'result.to_json()'.")
return self.to_json(normalize, decimals)
def to_json(self, normalize=False, decimals=5):
"""
Converts detection results to JSON format.
This method serializes the detection results into a JSON-compatible format. It includes information
about detected objects such as bounding boxes, class names, confidence scores, and optionally
segmentation masks and keypoints.
Args:
normalize (bool): Whether to normalize the bounding box coordinates by the image dimensions.
If True, coordinates will be returned as float values between 0 and 1. Defaults to False.
decimals (int): Number of decimal places to round the output values to. Defaults to 5.
Returns:
(str): A JSON string containing the serialized detection results.
Examples:
>>> results = model("path/to/image.jpg")
>>> json_result = results[0].to_json()
>>> print(json_result)
Notes:
- For classification tasks, the JSON will contain class probabilities instead of bounding boxes.
- For object detection tasks, the JSON will include bounding box coordinates, class names, and
confidence scores.
- If available, segmentation masks and keypoints will also be included in the JSON output.
- The method uses the `summary` method internally to generate the data structure before
converting it to JSON.
"""
import json
return json.dumps(self.summary(normalize=normalize, decimals=decimals), indent=2)
class Boxes(BaseTensor):
"""
A class for managing and manipulating detection boxes.
This class provides functionality for handling detection boxes, including their coordinates, confidence scores,
class labels, and optional tracking IDs. It supports various box formats and offers methods for easy manipulation
and conversion between different coordinate systems.
Attributes:
data (torch.Tensor | numpy.ndarray): The raw tensor containing detection boxes and associated data.
orig_shape (Tuple[int, int]): The original image dimensions (height, width).
is_track (bool): Indicates whether tracking IDs are included in the box data.
xyxy (torch.Tensor | numpy.ndarray): Boxes in [x1, y1, x2, y2] format.
conf (torch.Tensor | numpy.ndarray): Confidence scores for each box.
cls (torch.Tensor | numpy.ndarray): Class labels for each box.
id (torch.Tensor | numpy.ndarray): Tracking IDs for each box (if available).
xywh (torch.Tensor | numpy.ndarray): Boxes in [x, y, width, height] format.
xyxyn (torch.Tensor | numpy.ndarray): Normalized [x1, y1, x2, y2] boxes relative to orig_shape.
xywhn (torch.Tensor | numpy.ndarray): Normalized [x, y, width, height] boxes relative to orig_shape.
Methods:
cpu(): Returns a copy of the object with all tensors on CPU memory.
numpy(): Returns a copy of the object with all tensors as numpy arrays.
cuda(): Returns a copy of the object with all tensors on GPU memory.
to(*args, **kwargs): Returns a copy of the object with tensors on specified device and dtype.
Examples:
>>> import torch
>>> boxes_data = torch.tensor([[100, 50, 150, 100, 0.9, 0], [200, 150, 300, 250, 0.8, 1]])
>>> orig_shape = (480, 640) # height, width
>>> boxes = Boxes(boxes_data, orig_shape)
>>> print(boxes.xyxy)
>>> print(boxes.conf)
>>> print(boxes.cls)
>>> print(boxes.xywhn)
"""
def __init__(self, boxes, orig_shape) -> None:
"""
Initialize the Boxes class with detection box data and the original image shape.
This class manages detection boxes, providing easy access and manipulation of box coordinates,
confidence scores, class identifiers, and optional tracking IDs. It supports multiple formats
for box coordinates, including both absolute and normalized forms.
Args:
boxes (torch.Tensor | np.ndarray): A tensor or numpy array with detection boxes of shape
(num_boxes, 6) or (num_boxes, 7). Columns should contain
[x1, y1, x2, y2, confidence, class, (optional) track_id].
orig_shape (Tuple[int, int]): The original image shape as (height, width). Used for normalization.
Attributes:
data (torch.Tensor): The raw tensor containing detection boxes and their associated data.
orig_shape (Tuple[int, int]): The original image size, used for normalization.
is_track (bool): Indicates whether tracking IDs are included in the box data.
Examples:
>>> import torch
>>> boxes = torch.tensor([[100, 50, 150, 100, 0.9, 0]])
>>> orig_shape = (480, 640)
>>> detection_boxes = Boxes(boxes, orig_shape)
>>> print(detection_boxes.xyxy)
tensor([[100., 50., 150., 100.]])
"""
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
super().__init__(boxes, orig_shape)
self.is_track = n == 7
self.orig_shape = orig_shape
@property
def xyxy(self):
"""
Returns bounding boxes in [x1, y1, x2, y2] format.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (n, 4) containing bounding box
coordinates in [x1, y1, x2, y2] format, where n is the number of boxes.
Examples:
>>> results = model("image.jpg")
>>> boxes = results[0].boxes
>>> xyxy = boxes.xyxy
>>> print(xyxy)
"""
return self.data[:, :4]
@property
def conf(self):
"""
Returns the confidence scores for each detection box.
Returns:
(torch.Tensor | numpy.ndarray): A 1D tensor or array containing confidence scores for each detection,
with shape (N,) where N is the number of detections.
Examples:
>>> boxes = Boxes(torch.tensor([[10, 20, 30, 40, 0.9, 0]]), orig_shape=(100, 100))
>>> conf_scores = boxes.conf
>>> print(conf_scores)
tensor([0.9000])
"""
return self.data[:, -2]
@property
def cls(self):
"""
Returns the class ID tensor representing category predictions for each bounding box.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class IDs for each detection box.
The shape is (N,), where N is the number of boxes.
Examples:
>>> results = model("image.jpg")
>>> boxes = results[0].boxes
>>> class_ids = boxes.cls
>>> print(class_ids) # tensor([0., 2., 1.])
"""
return self.data[:, -1]
@property
def id(self):
"""
Returns the tracking IDs for each detection box if available.
Returns:
(torch.Tensor | None): A tensor containing tracking IDs for each box if tracking is enabled,
otherwise None. Shape is (N,) where N is the number of boxes.
Examples:
>>> results = model.track("path/to/video.mp4")
>>> for result in results:
... boxes = result.boxes
... if boxes.is_track:
... track_ids = boxes.id
... print(f"Tracking IDs: {track_ids}")
... else:
... print("Tracking is not enabled for these boxes.")
Notes:
- This property is only available when tracking is enabled (i.e., when `is_track` is True).
- The tracking IDs are typically used to associate detections across multiple frames in video analysis.
"""
return self.data[:, -3] if self.is_track else None
@property
@lru_cache(maxsize=2) # maxsize 1 should suffice
def xywh(self):
"""
Convert bounding boxes from [x1, y1, x2, y2] format to [x, y, width, height] format.
Returns:
(torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height] format, where x_center, y_center are the coordinates of
the center point of the bounding box, width, height are the dimensions of the bounding box and the
shape of the returned tensor is (N, 4), where N is the number of boxes.
Examples:
>>> boxes = Boxes(torch.tensor([[100, 50, 150, 100], [200, 150, 300, 250]]), orig_shape=(480, 640))
>>> xywh = boxes.xywh
>>> print(xywh)
tensor([[100.0000, 50.0000, 50.0000, 50.0000],
[200.0000, 150.0000, 100.0000, 100.0000]])
"""
return ops.xyxy2xywh(self.xyxy)
@property
@lru_cache(maxsize=2)
def xyxyn(self):
"""
Returns normalized bounding box coordinates relative to the original image size.
This property calculates and returns the bounding box coordinates in [x1, y1, x2, y2] format,
normalized to the range [0, 1] based on the original image dimensions.
Returns:
(torch.Tensor | numpy.ndarray): Normalized bounding box coordinates with shape (N, 4), where N is
the number of boxes. Each row contains [x1, y1, x2, y2] values normalized to [0, 1].
Examples:
>>> boxes = Boxes(torch.tensor([[100, 50, 300, 400, 0.9, 0]]), orig_shape=(480, 640))
>>> normalized = boxes.xyxyn
>>> print(normalized)
tensor([[0.1562, 0.1042, 0.4688, 0.8333]])
"""
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):
"""
Returns normalized bounding boxes in [x, y, width, height] format.
This property calculates and returns the normalized bounding box coordinates in the format
[x_center, y_center, width, height], where all values are relative to the original image dimensions.
Returns:
(torch.Tensor | numpy.ndarray): Normalized bounding boxes with shape (N, 4), where N is the
number of boxes. Each row contains [x_center, y_center, width, height] values normalized
to [0, 1] based on the original image dimensions.
Examples:
>>> boxes = Boxes(torch.tensor([[100, 50, 150, 100, 0.9, 0]]), orig_shape=(480, 640))
>>> normalized = boxes.xywhn
>>> print(normalized)
tensor([[0.1953, 0.1562, 0.0781, 0.1042]])
"""
xywh = ops.xyxy2xywh(self.xyxy)
xywh[..., [0, 2]] /= self.orig_shape[1]
xywh[..., [1, 3]] /= self.orig_shape[0]
return xywh
class Masks(BaseTensor):
"""
A class for storing and manipulating detection masks.
This class extends BaseTensor and provides functionality for handling segmentation masks,
including methods for converting between pixel and normalized coordinates.
Attributes:
data (torch.Tensor | numpy.ndarray): The raw tensor or array containing mask data.
orig_shape (tuple): Original image shape in (height, width) format.
xy (List[numpy.ndarray]): A list of segments in pixel coordinates.
xyn (List[numpy.ndarray]): A list of normalized segments.
Methods:
cpu(): Returns a copy of the Masks object with the mask tensor on CPU memory.
numpy(): Returns a copy of the Masks object with the mask tensor as a numpy array.
cuda(): Returns a copy of the Masks object with the mask tensor on GPU memory.
to(*args, **kwargs): Returns a copy of the Masks object with the mask tensor on specified device and dtype.
Examples:
>>> masks_data = torch.rand(1, 160, 160)
>>> orig_shape = (720, 1280)
>>> masks = Masks(masks_data, orig_shape)
>>> pixel_coords = masks.xy
>>> normalized_coords = masks.xyn
"""
def __init__(self, masks, orig_shape) -> None:
"""
Initialize the Masks class with detection mask data and the original image shape.
Args:
masks (torch.Tensor | np.ndarray): Detection masks with shape (num_masks, height, width).
orig_shape (tuple): The original image shape as (height, width). Used for normalization.
Examples:
>>> import torch
>>> from ultralytics.engine.results import Masks
>>> masks = torch.rand(10, 160, 160) # 10 masks of 160x160 resolution
>>> orig_shape = (720, 1280) # Original image shape
>>> mask_obj = Masks(masks, orig_shape)
"""
if masks.ndim == 2:
masks = masks[None, :]
super().__init__(masks, orig_shape)
@property
@lru_cache(maxsize=1)
def xyn(self):
"""
Returns normalized xy-coordinates of the segmentation masks.
This property calculates and caches the normalized xy-coordinates of the segmentation masks. The coordinates
are normalized relative to the original image shape.
Returns:
(List[numpy.ndarray]): A list of numpy arrays, where each array contains the normalized xy-coordinates
of a single segmentation mask. Each array has shape (N, 2), where N is the number of points in the
mask contour.
Examples:
>>> results = model("image.jpg")
>>> masks = results[0].masks
>>> normalized_coords = masks.xyn
>>> print(normalized_coords[0]) # Normalized coordinates of the first mask
"""
return [
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.data)
]
@property
@lru_cache(maxsize=1)
def xy(self):
"""
Returns the [x, y] pixel coordinates for each segment in the mask tensor.
This property calculates and returns a list of pixel coordinates for each segmentation mask in the
Masks object. The coordinates are scaled to match the original image dimensions.
Returns:
(List[numpy.ndarray]): A list of numpy arrays, where each array contains the [x, y] pixel
coordinates for a single segmentation mask. Each array has shape (N, 2), where N is the
number of points in the segment.
Examples:
>>> results = model("image.jpg")
>>> masks = results[0].masks
>>> xy_coords = masks.xy
>>> print(len(xy_coords)) # Number of masks
>>> print(xy_coords[0].shape) # Shape of first mask's coordinates
"""
return [
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
for x in ops.masks2segments(self.data)
]
class Keypoints(BaseTensor):
"""
A class for storing and manipulating detection keypoints.
This class encapsulates functionality for handling keypoint data, including coordinate manipulation,
normalization, and confidence values.
Attributes:
data (torch.Tensor): The raw tensor containing keypoint data.
orig_shape (Tuple[int, int]): The original image dimensions (height, width).
has_visible (bool): Indicates whether visibility information is available for keypoints.
xy (torch.Tensor): Keypoint coordinates in [x, y] format.
xyn (torch.Tensor): Normalized keypoint coordinates in [x, y] format, relative to orig_shape.
conf (torch.Tensor): Confidence values for each keypoint, if available.
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(*args, **kwargs): Returns a copy of the keypoints tensor with specified device and dtype.
Examples:
>>> import torch
>>> from ultralytics.engine.results import Keypoints
>>> keypoints_data = torch.rand(1, 17, 3) # 1 detection, 17 keypoints, (x, y, conf)
>>> orig_shape = (480, 640) # Original image shape (height, width)
>>> keypoints = Keypoints(keypoints_data, orig_shape)
>>> print(keypoints.xy.shape) # Access xy coordinates
>>> print(keypoints.conf) # Access confidence values
>>> keypoints_cpu = keypoints.cpu() # Move keypoints to CPU
"""
@smart_inference_mode() # avoid keypoints < conf in-place error
def __init__(self, keypoints, orig_shape) -> None:
"""
Initializes the Keypoints object with detection keypoints and original image dimensions.
This method processes the input keypoints tensor, handling both 2D and 3D formats. For 3D tensors
(x, y, confidence), it masks out low-confidence keypoints by setting their coordinates to zero.
Args:
keypoints (torch.Tensor): A tensor containing keypoint data. Shape can be either:
- (num_objects, num_keypoints, 2) for x, y coordinates only
- (num_objects, num_keypoints, 3) for x, y coordinates and confidence scores
orig_shape (Tuple[int, int]): The original image dimensions (height, width).
Examples:
>>> kpts = torch.rand(1, 17, 3) # 1 object, 17 keypoints (COCO format), x,y,conf
>>> orig_shape = (720, 1280) # Original image height, width
>>> keypoints = Keypoints(kpts, orig_shape)
"""
if keypoints.ndim == 2:
keypoints = keypoints[None, :]
if keypoints.shape[2] == 3: # x, y, conf
mask = keypoints[..., 2] < 0.5 # points with conf < 0.5 (not visible)
keypoints[..., :2][mask] = 0
super().__init__(keypoints, orig_shape)
self.has_visible = self.data.shape[-1] == 3
@property
@lru_cache(maxsize=1)
def xy(self):
"""
Returns x, y coordinates of keypoints.
Returns:
(torch.Tensor): A tensor containing the x, y coordinates of keypoints with shape (N, K, 2), where N is
the number of detections and K is the number of keypoints per detection.
Examples:
>>> results = model("image.jpg")
>>> keypoints = results[0].keypoints
>>> xy = keypoints.xy
>>> print(xy.shape) # (N, K, 2)
>>> print(xy[0]) # x, y coordinates of keypoints for first detection
Notes:
- The returned coordinates are in pixel units relative to the original image dimensions.
- If keypoints were initialized with confidence values, only keypoints with confidence >= 0.5 are returned.
- This property uses LRU caching to improve performance on repeated access.
"""
return self.data[..., :2]
@property
@lru_cache(maxsize=1)
def xyn(self):
"""
Returns normalized coordinates (x, y) of keypoints relative to the original image size.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or array of shape (N, K, 2) containing normalized keypoint
coordinates, where N is the number of instances, K is the number of keypoints, and the last
dimension contains [x, y] values in the range [0, 1].
Examples:
>>> keypoints = Keypoints(torch.rand(1, 17, 2), orig_shape=(480, 640))
>>> normalized_kpts = keypoints.xyn
>>> print(normalized_kpts.shape)
torch.Size([1, 17, 2])
"""
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):
"""
Returns confidence values for each keypoint.
Returns:
(torch.Tensor | None): A tensor containing confidence scores for each keypoint if available,
otherwise None. Shape is (num_detections, num_keypoints) for batched data or (num_keypoints,)
for single detection.
Examples:
>>> keypoints = Keypoints(torch.rand(1, 17, 3), orig_shape=(640, 640)) # 1 detection, 17 keypoints
>>> conf = keypoints.conf
>>> print(conf.shape) # torch.Size([1, 17])
"""
return self.data[..., 2] if self.has_visible else None
class Probs(BaseTensor):
"""
A class for storing and manipulating classification probabilities.
This class extends BaseTensor and provides methods for accessing and manipulating
classification probabilities, including top-1 and top-5 predictions.
Attributes:
data (torch.Tensor | numpy.ndarray): The raw tensor or array containing classification probabilities.
orig_shape (tuple | None): The original image shape as (height, width). Not used in this class.
top1 (int): Index of the class with the highest probability.
top5 (List[int]): Indices of the top 5 classes by probability.
top1conf (torch.Tensor | numpy.ndarray): Confidence score of the top 1 class.
top5conf (torch.Tensor | numpy.ndarray): Confidence scores of the top 5 classes.
Methods:
cpu(): Returns a copy of the probabilities tensor on CPU memory.
numpy(): Returns a copy of the probabilities tensor as a numpy array.
cuda(): Returns a copy of the probabilities tensor on GPU memory.
to(*args, **kwargs): Returns a copy of the probabilities tensor with specified device and dtype.
Examples:
>>> probs = torch.tensor([0.1, 0.3, 0.6])
>>> p = Probs(probs)
>>> print(p.top1)
2
>>> print(p.top5)
[2, 1, 0]
>>> print(p.top1conf)
tensor(0.6000)
>>> print(p.top5conf)
tensor([0.6000, 0.3000, 0.1000])
"""
def __init__(self, probs, orig_shape=None) -> None:
"""
Initialize the Probs class with classification probabilities.
This class stores and manages classification probabilities, providing easy access to top predictions and their
confidences.
Args:
probs (torch.Tensor | np.ndarray): A 1D tensor or array of classification probabilities.
orig_shape (tuple | None): The original image shape as (height, width). Not used in this class but kept for
consistency with other result classes.
Attributes:
data (torch.Tensor | np.ndarray): The raw tensor or array containing classification probabilities.
top1 (int): Index of the top 1 class.
top5 (List[int]): Indices of the top 5 classes.
top1conf (torch.Tensor | np.ndarray): Confidence of the top 1 class.
top5conf (torch.Tensor | np.ndarray): Confidences of the top 5 classes.
Examples:
>>> import torch
>>> probs = torch.tensor([0.1, 0.3, 0.2, 0.4])
>>> p = Probs(probs)
>>> print(p.top1)
3
>>> print(p.top1conf)
tensor(0.4000)
>>> print(p.top5)
[3, 1, 2, 0]
"""
super().__init__(probs, orig_shape)
@property
@lru_cache(maxsize=1)
def top1(self):
"""
Returns the index of the class with the highest probability.
Returns:
(int): Index of the class with the highest probability.
Examples:
>>> probs = Probs(torch.tensor([0.1, 0.3, 0.6]))
>>> probs.top1
2
"""
return int(self.data.argmax())
@property
@lru_cache(maxsize=1)
def top5(self):
"""
Returns the indices of the top 5 class probabilities.
Returns:
(List[int]): A list containing the indices of the top 5 class probabilities, sorted in descending order.
Examples:
>>> probs = Probs(torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]))
>>> print(probs.top5)
[4, 3, 2, 1, 0]
"""
return (-self.data).argsort(0)[:5].tolist() # this way works with both torch and numpy.
@property
@lru_cache(maxsize=1)
def top1conf(self):
"""
Returns the confidence score of the highest probability class.
This property retrieves the confidence score (probability) of the class with the highest predicted probability
from the classification results.
Returns:
(torch.Tensor | numpy.ndarray): A tensor containing the confidence score of the top 1 class.
Examples:
>>> results = model("image.jpg") # classify an image
>>> probs = results[0].probs # get classification probabilities
>>> top1_confidence = probs.top1conf # get confidence of top 1 class
>>> print(f"Top 1 class confidence: {top1_confidence.item():.4f}")
"""
return self.data[self.top1]
@property
@lru_cache(maxsize=1)
def top5conf(self):
"""
Returns confidence scores for the top 5 classification predictions.
This property retrieves the confidence scores corresponding to the top 5 class probabilities
predicted by the model. It provides a quick way to access the most likely class predictions
along with their associated confidence levels.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or array containing the confidence scores for the
top 5 predicted classes, sorted in descending order of probability.
Examples:
>>> results = model("image.jpg")
>>> probs = results[0].probs
>>> top5_conf = probs.top5conf
>>> print(top5_conf) # Prints confidence scores for top 5 classes
"""
return self.data[self.top5]
class OBB(BaseTensor):
"""
A class for storing and manipulating Oriented Bounding Boxes (OBB).
This class provides functionality to handle oriented bounding boxes, including conversion between
different formats, normalization, and access to various properties of the boxes.
Attributes:
data (torch.Tensor): The raw OBB tensor containing box coordinates and associated data.
orig_shape (tuple): Original image size as (height, width).
is_track (bool): Indicates whether tracking IDs are included in the box data.
xywhr (torch.Tensor | numpy.ndarray): Boxes in [x_center, y_center, width, height, rotation] format.
conf (torch.Tensor | numpy.ndarray): Confidence scores for each box.
cls (torch.Tensor | numpy.ndarray): Class labels for each box.
id (torch.Tensor | numpy.ndarray): Tracking IDs for each box, if available.
xyxyxyxy (torch.Tensor | numpy.ndarray): Boxes in 8-point [x1, y1, x2, y2, x3, y3, x4, y4] format.
xyxyxyxyn (torch.Tensor | numpy.ndarray): Normalized 8-point coordinates relative to orig_shape.
xyxy (torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in [x1, y1, x2, y2] format.
Methods:
cpu(): Returns a copy of the OBB object with all tensors on CPU memory.
numpy(): Returns a copy of the OBB object with all tensors as numpy arrays.
cuda(): Returns a copy of the OBB object with all tensors on GPU memory.
to(*args, **kwargs): Returns a copy of the OBB object with tensors on specified device and dtype.
Examples:
>>> boxes = torch.tensor([[100, 50, 150, 100, 30, 0.9, 0]]) # xywhr, conf, cls
>>> obb = OBB(boxes, orig_shape=(480, 640))
>>> print(obb.xyxyxyxy)
>>> print(obb.conf)
>>> print(obb.cls)
"""
def __init__(self, boxes, orig_shape) -> None:
"""
Initialize an OBB (Oriented Bounding Box) instance with oriented bounding box data and original image shape.
This class stores and manipulates Oriented Bounding Boxes (OBB) for object detection tasks. It provides
various properties and methods to access and transform the OBB data.
Args:
boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes,
with shape (num_boxes, 7) or (num_boxes, 8). The last two columns contain confidence and class values.
If present, the third last column contains track IDs, and the fifth column contains rotation.
orig_shape (Tuple[int, int]): Original image size, in the format (height, width).
Attributes:
data (torch.Tensor | numpy.ndarray): The raw OBB tensor.
orig_shape (Tuple[int, int]): The original image shape.
is_track (bool): Whether the boxes include tracking IDs.
Raises:
AssertionError: If the number of values per box is not 7 or 8.
Examples:
>>> import torch
>>> boxes = torch.rand(3, 7) # 3 boxes with 7 values each
>>> orig_shape = (640, 480)
>>> obb = OBB(boxes, orig_shape)
>>> print(obb.xywhr) # Access the boxes in xywhr format
"""
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
super().__init__(boxes, orig_shape)
self.is_track = n == 8
self.orig_shape = orig_shape
@property
def xywhr(self):
"""
Returns boxes in [x_center, y_center, width, height, rotation] format.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or numpy array containing the oriented bounding boxes with format
[x_center, y_center, width, height, rotation]. The shape is (N, 5) where N is the number of boxes.
Examples:
>>> results = model("image.jpg")
>>> obb = results[0].obb
>>> xywhr = obb.xywhr
>>> print(xywhr.shape)
torch.Size([3, 5])
"""
return self.data[:, :5]
@property
def conf(self):
"""
Returns the confidence scores for Oriented Bounding Boxes (OBBs).
This property retrieves the confidence values associated with each OBB detection. The confidence score
represents the model's certainty in the detection.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or numpy array of shape (N,) containing confidence scores
for N detections, where each score is in the range [0, 1].
Examples:
>>> results = model("image.jpg")
>>> obb_result = results[0].obb
>>> confidence_scores = obb_result.conf
>>> print(confidence_scores)
"""
return self.data[:, -2]
@property
def cls(self):
"""
Returns the class values of the oriented bounding boxes.
Returns:
(torch.Tensor | numpy.ndarray): A tensor or numpy array containing the class values for each oriented
bounding box. The shape is (N,), where N is the number of boxes.
Examples:
>>> results = model("image.jpg")
>>> result = results[0]
>>> obb = result.obb
>>> class_values = obb.cls
>>> print(class_values)
"""
return self.data[:, -1]
@property
def id(self):
"""
Returns the tracking IDs of the oriented bounding boxes (if available).
Returns:
(torch.Tensor | numpy.ndarray | None): A tensor or numpy array containing the tracking IDs for each
oriented bounding box. Returns None if tracking IDs are not available.
Examples:
>>> results = model("image.jpg", tracker=True) # Run inference with tracking
>>> for result in results:
... if result.obb is not None:
... track_ids = result.obb.id
... if track_ids is not None:
... print(f"Tracking IDs: {track_ids}")
"""
return self.data[:, -3] if self.is_track else None
@property
@lru_cache(maxsize=2)
def xyxyxyxy(self):
"""
Converts OBB format to 8-point (xyxyxyxy) coordinate format for rotated bounding boxes.
Returns:
(torch.Tensor | numpy.ndarray): Rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2), where N is
the number of boxes. Each box is represented by 4 points (x, y), starting from the top-left corner and
moving clockwise.
Examples:
>>> obb = OBB(torch.tensor([[100, 100, 50, 30, 0.5, 0.9, 0]]), orig_shape=(640, 640))
>>> xyxyxyxy = obb.xyxyxyxy
>>> print(xyxyxyxy.shape)
torch.Size([1, 4, 2])
"""
return ops.xywhr2xyxyxyxy(self.xywhr)
@property
@lru_cache(maxsize=2)
def xyxyxyxyn(self):
"""
Converts rotated bounding boxes to normalized xyxyxyxy format.
Returns:
(torch.Tensor | numpy.ndarray): Normalized rotated bounding boxes in xyxyxyxy format with shape (N, 4, 2),
where N is the number of boxes. Each box is represented by 4 points (x, y), normalized relative to
the original image dimensions.
Examples:
>>> obb = OBB(torch.rand(10, 7), orig_shape=(640, 480)) # 10 random OBBs
>>> normalized_boxes = obb.xyxyxyxyn
>>> print(normalized_boxes.shape)
torch.Size([10, 4, 2])
"""
xyxyxyxyn = self.xyxyxyxy.clone() if isinstance(self.xyxyxyxy, torch.Tensor) else np.copy(self.xyxyxyxy)
xyxyxyxyn[..., 0] /= self.orig_shape[1]
xyxyxyxyn[..., 1] /= self.orig_shape[0]
return xyxyxyxyn
@property
@lru_cache(maxsize=2)
def xyxy(self):
"""
Converts oriented bounding boxes (OBB) to axis-aligned bounding boxes in xyxy format.
This property calculates the minimal enclosing rectangle for each oriented bounding box and returns it in
xyxy format (x1, y1, x2, y2). This is useful for operations that require axis-aligned bounding boxes, such
as IoU calculation with non-rotated boxes.
Returns:
(torch.Tensor | numpy.ndarray): Axis-aligned bounding boxes in xyxy format with shape (N, 4), where N
is the number of boxes. Each row contains [x1, y1, x2, y2] coordinates.
Examples:
>>> import torch
>>> from ultralytics import YOLO
>>> model = YOLO("yolov8n-obb.pt")
>>> results = model("path/to/image.jpg")
>>> for result in results:
... obb = result.obb
... if obb is not None:
... xyxy_boxes = obb.xyxy
... print(xyxy_boxes.shape) # (N, 4)
Notes:
- This method approximates the OBB by its minimal enclosing rectangle.
- The returned format is compatible with standard object detection metrics and visualization tools.
- The property uses caching to improve performance for repeated access.
"""
x = self.xyxyxyxy[..., 0]
y = self.xyxyxyxy[..., 1]
return (
torch.stack([x.amin(1), y.amin(1), x.amax(1), y.amax(1)], -1)
if isinstance(x, torch.Tensor)
else np.stack([x.min(1), y.min(1), x.max(1), y.max(1)], -1)
)
使用predict函数保存模型预测的txt信息
from ultralytics import YOLO
import cv2
from PIL import Image
model_path = r'E:\pycharm\pythonProject_instance_segmentation\ultralytics-8.3.57_me_use\instance_segment_demo\weights\best.pt'
# image_path = r'E:\pycharm\pythonProject_instance_segmentation\ultralytics-8.3.57_me_use\instance_segment_demo\images_labels_depth\iamges\train\380_1680297422-6515028_rgb_png_jpg.rf.420df37b6dfdb75bb77c0b2149c52e8f.jpg'
# image_path = r'E:\pycharm\pythonProject_instance_segmentation\datasets\me\color_images\1128.jpg'
# image_path = r"E:\pycharm\pythonProject_instance_segmentation\yolo_results_study\images\colors\57.jpg"
image_path = r"E:\pycharm\pyProject_d435i\my_demo\demo_photo\images\colors\133.jpg"
img = cv2.imread(image_path)
# 加载 YOLO 模型
model = YOLO(model_path)
# 执行推理
results = model.predict(img )
print('len(results)=',len(results)) #len(results)= 1
# print('results',results) #输出内容与 results[0] 相同
print('len(results[0])=',len(results[0])) #len(results[0])= 9 对应九个掩膜
# print('results[0]',results[0])
"""
ultralytics.engine.results.Results object with attributes:
boxes: ultralytics.engine.results.Boxes object
keypoints: None
masks: ultralytics.engine.results.Masks object
names: {0: 'mushroom'}
obb: None
orig_img: array([[[ 82, 105, 113],
[ 78, 100, 106],
[ 89, 104, 107],
...,
[175, 171, 147],
[180, 176, 152],
[177, 173, 149]],
[[ 82, 105, 113],
[ 85, 104, 111],
[ 89, 104, 107],
...,
[178, 174, 150],
[180, 176, 152],
[176, 172, 148]],
[[ 81, 102, 110],
[ 86, 105, 112],
[ 90, 105, 108],
...,
[183, 177, 154],
[183, 177, 154],
[181, 175, 152]],
...,
[[ 46, 51, 52],
[ 47, 52, 53],
[ 45, 51, 50],
...,
[ 98, 95, 80],
[ 94, 91, 76],
[ 96, 93, 78]],
[[ 46, 50, 51],
[ 47, 51, 52],
[ 46, 51, 50],
...,
[103, 100, 86],
[ 96, 93, 79],
[ 96, 93, 79]],
[[ 46, 50, 51],
[ 48, 52, 53],
[ 47, 52, 51],
...,
[105, 102, 88],
[ 99, 96, 82],
[ 98, 95, 81]]], dtype=uint8)
orig_shape: (720, 1280)
path: 'image0.jpg'
probs: None
save_dir: None
speed: {'preprocess': 4.985570907592773, 'inference': 38.98024559020996, 'postprocess': 6.981372833251953}
"""
print('len(results[0][0])=',len(results[0][0])) #len(results[0][0])= 1
# print('results[0][0]',results[0][0]) #输出内容与 results[0] 相同
# results[0][0].save_txt("me_133_demo03.txt") #只保存了第一个 掩膜的 txt信息
# results[0][0].show() #显示预测结果的图片png 并保存在Temp文件夹, 并且只显示 第一个掩膜对应的预测结果， 其余8个实例还是与原图一样
#可行，保存每个预测信息（9个掩膜）
# for result in results:
# result.save_txt("me_133.txt")
# for result in results[0]: #results[1]报错，IndexError: list index out of range
# result.save_txt("me_133_demo02.txt") #保存了每个掩膜的txt信息
# results[0].save_txt("me_133_demo.txt") #保存了所有实例的txt 掩膜信息
#下述代码可以保存 txt，内容包括9个掩膜信息， 将txt文件保存在 E:\pycharm\pythonProject_instance_segmentation\yolo_results_study_demo02\runs\detect\exp7\labels
model.predict(source=image_path,
imgsz=640, #加快推理时间
project=r'E:\pycharm\pythonProject_instance_segmentation\yolo_results_study_demo02\runs\detect', #保存预测结果的项目目录
name='exp', #保存预测结果的子目录
# save=True, #保存预测结果的图片
# show=True, #显示预测结果，但是没有一直显示
# conf=0.2,
# iou=0.7,
# agnostic_nms=True,
# visualize=True, # visualize model features maps
# line_width=2, # line width of the bounding boxes
show_conf=False, # do not show prediction confidence
show_labels=False, # do not show prediction labels
save_txt=True, # save results as .txt file
# save_crop=True, # save cropped images with results
)
# results.save_txt("me_133_demo.txt") #报错 AttributeError: 'list' object has no attribute 'save_txt'
保存的txt文件
0 0.490625 0.0611111 0.490625 0.0694444 0.475 0.0972222 0.475 0.1 0.473437 0.102778 0.473437 0.105556 0.470313 0.111111 0.465625 0.111111 0.465625 0.183333 0.470313 0.183333 0.473437 0.188889 0.473437 0.191667 0.476562 0.197222 0.476562 0.2 0.48125 0.208333 0.482812 0.208333 0.485938 0.213889 0.4875 0.213889 0.489062 0.216667 0.490625 0.216667 0.492188 0.219444 0.496875 0.219444 0.498437 0.222222 0.509375 0.222222 0.510938 0.225 0.523438 0.225 0.525 0.222222 0.53125 0.222222 0.532812 0.219444 0.5375 0.219444 0.542188 0.211111 0.54375 0.211111 0.554688 0.191667 0.554688 0.188889 0.55625 0.186111 0.55625 0.183333 0.559375 0.177778 0.564062 0.177778 0.564062 0.113889 0.559375 0.113889 0.55625 0.108333 0.55625 0.102778 0.5375 0.0694444 0.5375 0.0611111
0 0.278125 0.716667 0.278125 0.725 0.275 0.730556 0.273438 0.730556 0.271875 0.733333 0.270312 0.733333 0.264062 0.744444 0.2625 0.744444 0.254687 0.758333 0.254687 0.761111 0.248437 0.772222 0.248437 0.775 0.245312 0.780556 0.240625 0.780556 0.240625 0.872222 0.246875 0.872222 0.248437 0.875 0.248437 0.877778 0.25625 0.891667 0.25625 0.894444 0.257812 0.897222 0.259375 0.897222 0.264062 0.905556 0.265625 0.905556 0.267188 0.908333 0.270312 0.908333 0.271875 0.911111 0.273438 0.911111 0.276563 0.916667 0.276563 0.925 0.314063 0.925 0.314063 0.916667 0.317187 0.911111 0.31875 0.911111 0.320312 0.908333 0.323438 0.908333 0.325 0.905556 0.326562 0.905556 0.329688 0.9 0.33125 0.9 0.340625 0.883333 0.342187 0.883333 0.342187 0.880556 0.346875 0.872222 0.346875 0.866667 0.348437 0.863889 0.348437 0.861111 0.35 0.858333 0.35 0.855556 0.353125 0.85 0.353125 0.844444 0.354688 0.841667 0.354688 0.833333 0.35625 0.830556 0.35625 0.819444 0.357812 0.816667 0.357812 0.808333 0.359375 0.805556 0.364062 0.805556 0.364062 0.797222 0.359375 0.797222 0.357812 0.794444 0.357812 0.788889 0.35625 0.786111 0.35625 0.777778 0.354688 0.775 0.354688 0.769444 0.353125 0.766667 0.353125 0.763889 0.348437 0.755556 0.348437 0.752778 0.339063 0.736111 0.3375 0.736111 0.334375 0.730556 0.332812 0.730556 0.329688 0.725 0.329688 0.716667
0 0.334375 0.127778 0.334375 0.136111 0.33125 0.141667 0.328125 0.141667 0.326562 0.144444 0.325 0.144444 0.315625 0.161111 0.309375 0.161111 0.309375 0.266667 0.315625 0.266667 0.317187 0.269444 0.317187 0.272222 0.31875 0.275 0.31875 0.277778 0.325 0.288889 0.326562 0.288889 0.33125 0.297222 0.332812 0.297222 0.334375 0.3 0.335938 0.3 0.339063 0.305556 0.339063 0.313889 0.365625 0.313889 0.365625 0.305556 0.367188 0.302778 0.36875 0.302778 0.370313 0.3 0.375 0.3 0.376563 0.297222 0.38125 0.297222 0.382812 0.294444 0.384375 0.294444 0.389062 0.286111 0.390625 0.286111 0.396875 0.275 0.398438 0.275 0.398438 0.272222 0.403125 0.263889 0.403125 0.261111 0.404687 0.258333 0.404687 0.255556 0.40625 0.252778 0.40625 0.25 0.409375 0.244444 0.414062 0.244444 0.414062 0.186111 0.409375 0.186111 0.40625 0.180556 0.40625 0.177778 0.404687 0.175 0.404687 0.172222 0.389062 0.144444 0.385938 0.144444 0.384375 0.141667 0.382812 0.141667 0.38125 0.138889 0.379687 0.138889 0.378125 0.136111 0.378125 0.127778
0 0.489062 0.705556 0.489062 0.716667 0.485938 0.722222 0.484375 0.722222 0.475 0.738889 0.475 0.741667 0.473437 0.744444 0.473437 0.747222 0.470313 0.752778 0.465625 0.752778 0.465625 0.822222 0.470313 0.822222 0.473437 0.827778 0.473437 0.833333 0.475 0.836111 0.475 0.844444 0.476562 0.847222 0.476562 0.85 0.479688 0.855556 0.479688 0.858333 0.48125 0.861111 0.48125 0.863889 0.482812 0.866667 0.484375 0.866667 0.485938 0.869444 0.485938 0.872222 0.489062 0.877778 0.490625 0.877778 0.496875 0.888889 0.498437 0.888889 0.501562 0.894444 0.503125 0.894444 0.504687 0.897222 0.510938 0.897222 0.5125 0.9 0.515625 0.9 0.517187 0.902778 0.51875 0.902778 0.520312 0.905556 0.520312 0.913889 0.534375 0.913889 0.534375 0.905556 0.535937 0.902778 0.5375 0.902778 0.539062 0.9 0.542188 0.9 0.54375 0.897222 0.548437 0.897222 0.55 0.894444 0.553125 0.894444 0.55625 0.888889 0.557813 0.888889 0.570312 0.866667 0.570312 0.863889 0.573438 0.858333 0.573438 0.852778 0.575 0.85 0.575 0.841667 0.578125 0.836111 0.582812 0.836111 0.582812 0.791667 0.578125 0.791667 0.575 0.786111 0.575 0.783333 0.573438 0.780556 0.573438 0.775 0.571875 0.772222 0.571875 0.769444 0.570312 0.766667 0.570312 0.763889 0.567187 0.758333 0.567187 0.752778 0.564062 0.747222 0.564062 0.744444 0.551562 0.722222 0.55 0.722222 0.548437 0.719444 0.546875 0.719444 0.545313 0.716667 0.545313 0.705556
0 0.68125 0.372222 0.68125 0.380556 0.670313 0.4 0.665625 0.4 0.665625 0.511111 0.671875 0.511111 0.675 0.516667 0.675 0.519444 0.676562 0.519444 0.682813 0.530556 0.684375 0.530556 0.689062 0.538889 0.690625 0.538889 0.692187 0.541667 0.7 0.541667 0.701563 0.544444 0.710938 0.544444 0.7125 0.547222 0.714063 0.547222 0.715625 0.55 0.715625 0.558333 0.73125 0.558333 0.73125 0.55 0.732813 0.547222 0.734375 0.547222 0.735937 0.544444 0.739062 0.544444 0.740625 0.541667 0.74375 0.541667 0.75 0.530556 0.751562 0.530556 0.759375 0.516667 0.759375 0.513889 0.760938 0.511111 0.760938 0.508333 0.7625 0.505556 0.7625 0.502778 0.765625 0.497222 0.765625 0.491667 0.767187 0.488889 0.767187 0.486111 0.76875 0.483333 0.76875 0.466667 0.770312 0.463889 0.770312 0.455556 0.76875 0.452778 0.76875 0.438889 0.767187 0.436111 0.767187 0.430556 0.764063 0.425 0.764063 0.422222 0.760938 0.416667 0.760938 0.413889 0.754687 0.402778 0.754687 0.4 0.74375 0.380556 0.74375 0.372222
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0 0.664062 0.0611111 0.664062 0.0722222 0.6625 0.075 0.660937 0.075 0.657812 0.0805556 0.65625 0.0805556 0.65625 0.0833333 0.65 0.0944444 0.65 0.0972222 0.648438 0.1 0.648438 0.102778 0.645312 0.108333 0.640625 0.108333 0.640625 0.205556 0.645312 0.205556 0.657812 0.227778 0.659375 0.227778 0.665625 0.238889 0.667188 0.238889 0.66875 0.241667 0.673437 0.241667 0.675 0.244444 0.679688 0.244444 0.68125 0.247222 0.682813 0.247222 0.684375 0.25 0.684375 0.258333 0.689062 0.258333 0.689062 0.25 0.690625 0.247222 0.692187 0.247222 0.69375 0.244444 0.696875 0.244444 0.698438 0.241667 0.701563 0.241667 0.703125 0.238889 0.704687 0.238889 0.707812 0.233333 0.709375 0.233333 0.71875 0.216667 0.71875 0.213889 0.720312 0.211111 0.721875 0.211111 0.721875 0.208333 0.723437 0.205556 0.723437 0.202778 0.728125 0.194444 0.728125 0.191667 0.729688 0.188889 0.729688 0.177778 0.73125 0.175 0.73125 0.130556 0.729688 0.127778 0.729688 0.119444 0.728125 0.116667 0.728125 0.113889 0.726562 0.111111 0.726562 0.108333 0.725 0.105556 0.725 0.102778 0.721875 0.0972222 0.721875 0.0944444 0.717188 0.0861111 0.717188 0.0833333 0.714063 0.0777778 0.7125 0.0777778 0.709375 0.0722222 0.709375 0.0611111
0 0.29375 0.361111 0.29375 0.369444 0.290625 0.375 0.289062 0.375 0.282813 0.386111 0.28125 0.386111 0.278125 0.391667 0.278125 0.394444 0.276563 0.397222 0.275 0.397222 0.275 0.4 0.270312 0.408333 0.265625 0.408333 0.265625 0.513889 0.271875 0.513889 0.275 0.519444 0.275 0.522222 0.282813 0.536111 0.282813 0.538889 0.284375 0.538889 0.290625 0.55 0.292188 0.55 0.29375 0.552778 0.296875 0.552778 0.298438 0.555556 0.301562 0.555556 0.303125 0.558333 0.304688 0.558333 0.30625 0.561111 0.30625 0.569444 0.340625 0.569444 0.340625 0.561111 0.34375 0.555556 0.346875 0.555556 0.348437 0.552778 0.35 0.552778 0.353125 0.547222 0.354688 0.547222 0.357812 0.541667 0.359375 0.541667 0.371875 0.519444 0.371875 0.516667 0.375 0.511111 0.375 0.508333 0.378125 0.502778 0.378125 0.5 0.379687 0.497222 0.379687 0.486111 0.38125 0.483333 0.38125 0.452778 0.379687 0.45 0.379687 0.441667 0.378125 0.438889 0.378125 0.433333 0.375 0.427778 0.375 0.425 0.373437 0.422222 0.373437 0.419444 0.371875 0.416667 0.371875 0.413889 0.367188 0.405556 0.367188 0.402778 0.359375 0.388889 0.357812 0.388889 0.353125 0.380556 0.351562 0.380556 0.35 0.377778 0.348437 0.377778 0.346875 0.375 0.345313 0.375 0.342187 0.369444 0.342187 0.361111
0 0.775 0.627778 0.775 0.636111 0.771875 0.641667 0.770312 0.641667 0.76875 0.644444 0.767187 0.644444 0.760938 0.655556 0.759375 0.655556 0.748438 0.675 0.748438 0.677778 0.746875 0.680556 0.740625 0.680556 0.740625 0.766667 0.745313 0.766667 0.748438 0.772222 0.748438 0.775 0.75 0.777778 0.75 0.783333 0.7625 0.805556 0.764063 0.805556 0.765625 0.808333 0.767187 0.808333 0.76875 0.811111 0.770312 0.811111 0.773438 0.816667 0.775 0.816667 0.776563 0.819444 0.779688 0.819444 0.78125 0.822222 0.785937 0.822222 0.789062 0.827778 0.789062 0.836111 0.809375 0.836111 0.809375 0.827778 0.8125 0.822222 0.815625 0.822222 0.817187 0.819444 0.821875 0.819444 0.823438 0.816667 0.826563 0.816667 0.828125 0.813889 0.829687 0.813889 0.839063 0.797222 0.840625 0.797222 0.84375 0.791667 0.84375 0.788889 0.848437 0.780556 0.848437 0.777778 0.853125 0.769444 0.853125 0.766667 0.854688 0.763889 0.854688 0.755556 0.85625 0.752778 0.85625 0.744444 0.857813 0.741667 0.857813 0.738889 0.859375 0.736111 0.864062 0.736111 0.864062 0.713889 0.859375 0.713889 0.85625 0.708333 0.85625 0.7 0.854688 0.697222 0.854688 0.691667 0.853125 0.688889 0.853125 0.683333 0.85 0.677778 0.85 0.675 0.848437 0.672222 0.848437 0.669444 0.84375 0.661111 0.84375 0.658333 0.835938 0.644444 0.834375 0.644444 0.832812 0.641667 0.83125 0.641667 0.828125 0.636111 0.828125 0.627778
参考资料：
1.yolov8预测函数prdict返回结果分析_yolov8 model.predict-CSDN博客
