隔离分割对象

在执行 Segment Task 后，有时需要从推理结果中提取已隔离的对象。本指南提供了如何使用 Ultralytics Predict Mode 实现此目标的通用方法。

操作流程指南

1. 请参阅 Ultralytics Quickstart Installation section 以获取安装所需库的快速指南。

   ---

2. 加载模型并在源上运行 predict() 方法。

   from ultralytics import YOLO
   
   # Load a model
   model = YOLO("yolo26n-seg.pt")
   
   # Run inference
   results = model.predict()

'ultralytics/assets/bus.jpg'
'ultralytics/assets/zidane.jpg'

有关分割模型的更多信息，请访问 Segment Task 页面。要了解有关 predict() 方法的更多信息，请参阅文档的 Predict Mode 部分。

现在迭代结果和轮廓。对于想要将图像保存到文件的流程，可获取源图像 base-name 和检测 class-label 以供后续使用（可选）。

```{ .py .annotate }
from pathlib import Path

import numpy as np

# (2) Iterate detection results (helpful for multiple images)
for r in results:
    img = np.copy(r.orig_img)
    img_name = Path(r.path).stem  # source image base-name

    # Iterate each object contour (multiple detections)
    for ci, c in enumerate(r):
        # (1) Get detection class name
        label = c.names[c.boxes.cls.tolist().pop()]
```

1. To learn more about working with detection results, see [Boxes Section for Predict Mode](../modes/predict.md#boxes).
2. To learn more about `predict()` results see [Working with Results for Predict Mode](../modes/predict.md#working-with-results)

4. 首先从源图像生成二进制掩码，然后在掩码上绘制填充后的轮廓。这将允许对象从图像的其他部分中隔离出来。右侧展示了 bus.jpg 中检测到的其中一个 person 类对象的示例。

   { width="240", align="right" }

   import cv2
   
   # Create binary mask
   b_mask = np.zeros(img.shape[:2], np.uint8)
   
   # (1) Extract contour result
   contour = c.masks.xy.pop()
   # (2) Changing the type
   contour = contour.astype(np.int32)
   # (3) Reshaping
   contour = contour.reshape(-1, 1, 2)
   
   # Draw contour onto mask
   _ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)

   1. 有关 c.masks.xy 的更多信息，请参阅 Masks Section from Predict Mode。

   2. Here the values are cast into np.int32 for compatibility with drawContours() function from OpenCV.

   3. OpenCV 的 drawContours() 函数期望轮廓形状为 [N, 1, 2]，展开下方部分以获取更多详细信息。

   Expand to understand what is happening when defining the contour variable.

   - `c.masks.xy` :: Provides the coordinates of the mask contour points in the format `(x, y)`. For more details, refer to the [Masks Section from Predict Mode](../modes/predict.md#masks). - `.pop()` :: As `masks.xy` is a list containing a single element, this element is extracted using the `pop()` method. - `.astype(np.int32)` :: Using `masks.xy` will return with a data type of `float32`, but this won't be compatible with the OpenCV `drawContours()` function, so this will change the data type to `int32` for compatibility. - `.reshape(-1, 1, 2)` :: Reformats the data into the required shape of `[N, 1, 2]` where `N` is the number of contour points, with each point represented by a single entry `1`, and the entry is composed of `2` values. The `-1` denotes that the number of values along this dimension is flexible.

   Expand for an explanation of the drawContours() configuration.

   - Encapsulating the `contour` variable within square brackets, `[contour]`, was found to effectively generate the desired contour mask during testing. - The value `-1` specified for the `drawContours()` parameter instructs the function to draw all contours present in the image. - The `tuple` `(255, 255, 255)` represents the color white, which is the desired color for drawing the contour in this binary mask. - The addition of `cv2.FILLED` will color all pixels enclosed by the contour boundary the same, in this case, all enclosed pixels will be white. - See [OpenCV Documentation on `drawContours()`](https://docs.opencv.org/4.8.0/d6/d6e/group__imgproc__draw.html#ga746c0625f1781f1ffc9056259103edbc) for more information.

   ---

5. 接下来有两种方法可以继续处理图像，每种方法都有后续选项。

   对象隔离选项

# Create 3-channel mask
mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)

# Isolate object with binary mask
isolated = cv2.bitwise_and(mask3ch, img)

# (1) Bounding box coordinates
x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32)
# Crop image to object region
iso_crop = isolated[y1:y2, x1:x2]

6. 接下来的操作完全由你（开发者）决定。 这里展示了一个可能的后续步骤（将图像保存到文件以备后续使用）的基本示例。
   • 注意： 此步骤是可选的，如果你的特定用例不需要，可以跳过。

# Save isolated object to file
_ = cv2.imwrite(f"{img_name}_{label}-{ci}.png", iso_crop)

完整示例代码

这里将前一部分的所有步骤合并为一个代码块。为了重复使用，定义一个函数来执行 for 循环中包含的部分或全部命令是最理想的，但这留给读者作为练习。

from pathlib import Path

import cv2
import numpy as np

from ultralytics import YOLO

m = YOLO("yolo26n-seg.pt")  # (4)!
res = m.predict(source="path/to/image.jpg")  # (3)!

# Iterate detection results (5)
for r in res:
    img = np.copy(r.orig_img)
    img_name = Path(r.path).stem

    # Iterate each object contour (6)
    for ci, c in enumerate(r):
        label = c.names[c.boxes.cls.tolist().pop()]

        b_mask = np.zeros(img.shape[:2], np.uint8)

        # Create contour mask (1)
        contour = c.masks.xy.pop().astype(np.int32).reshape(-1, 1, 2)
        _ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)

        # Choose one:

        # OPTION-1: Isolate object with black background
        mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
        isolated = cv2.bitwise_and(mask3ch, img)

        # OPTION-2: Isolate object with transparent background (when saved as PNG)
        isolated = np.dstack([img, b_mask])

        # OPTIONAL: detection crop (from either OPT1 or OPT2)
        x1, y1, x2, y2 = c.boxes.xyxy.cpu().numpy().squeeze().astype(np.int32)
        iso_crop = isolated[y1:y2, x1:x2]

        # Add your custom post-processing here (2)

1. 填充 contour 的行在这里被合并为一行，而在上面被拆分成了多行。
2. {==这里放什么由你决定！==}
3. 更多信息请参阅 Predict Mode。
4. 更多信息请参阅 Segment Task。
5. 了解更多关于 Working with Results
6. 了解更多关于 Segmentation Mask Results

常见问题 (FAQ)

如何使用 Ultralytics YOLO26 进行分割任务的对象隔离？

要使用 Ultralytics YOLO26 隔离对象，请遵循以下步骤：

1. 加载模型并运行推理：

   from ultralytics import YOLO
   
   model = YOLO("yolo26n-seg.pt")
   results = model.predict(source="path/to/your/image.jpg")

2. 生成二进制掩码并绘制轮廓：

   import cv2
   import numpy as np
   
   img = np.copy(results[0].orig_img)
   b_mask = np.zeros(img.shape[:2], np.uint8)
   contour = results[0].masks.xy[0].astype(np.int32).reshape(-1, 1, 2)
   cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)

3. 使用二进制掩码隔离对象：

   mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
   isolated = cv2.bitwise_and(mask3ch, img)

有关更多信息，请参考 Predict Mode 和 Segment Task 指南。

分割后有哪些保存隔离对象的选项？

Ultralytics YOLO26 提供了两种主要的隔离对象保存选项：

1. 带黑色背景：

   mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
   isolated = cv2.bitwise_and(mask3ch, img)

2. 带透明背景：

   isolated = np.dstack([img, b_mask])

更多详情，请访问 Predict Mode 部分。

如何使用 Ultralytics YOLO26 将隔离的对象裁剪到其边界框？

将隔离对象裁剪到其边界框：

1. 获取边界框坐标：

   x1, y1, x2, y2 = results[0].boxes.xyxy[0].cpu().numpy().astype(np.int32)

2. 裁剪隔离后的图像：

   iso_crop = isolated[y1:y2, x1:x2]

在 Predict Mode 文档中了解更多关于边界框结果的信息。

为什么要在分割任务中使用 Ultralytics YOLO26 进行对象隔离？

Ultralytics YOLO26 提供：

• 高速实时对象检测和分割。
• 准确的边界框和掩码生成，用于精确的对象隔离。
• 全面的文档和易于使用的 API，以实现高效开发。

在 Segment Task documentation 中探索使用 YOLO 的优势。

我可以使用 Ultralytics YOLO26 保存包含背景的隔离对象吗？

Yes, this is a built-in feature in Ultralytics YOLO26. Use the save_crop argument in the predict() method. For example:

results = model.predict(source="path/to/your/image.jpg", save_crop=True)

Read more about the save_crop argument in the Predict Mode Inference Arguments section.