【YOLOv8 代码解读】数据增强代码梳理

1. LetterBox增强

当输入图片的尺寸和模型实际接收的尺寸可能不一致时，通常需要使用LetterBox增强技术。具体步骤是先将图片按比例缩放，将较长的边缩放到设定的尺寸以后，再将较短的边进行填充，最终短边的长度为stride的倍数即可。这种方法可以保留原始图像的纵横比，同时还可以使图像更加适合目标检测算法的输入。
在YOLOv8代码中，ultralytics/data/augment.py中class LetterBox类别实现了该功能。

import cv2
import numpy as np

class LetterBox:
    """Resize image and padding for detection, instance segmentation, pose."""

    def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, center=True, stride=32):
        """Initialize LetterBox object with specific parameters."""
        self.new_shape = new_shape
        self.auto = auto
        self.scaleFill = scaleFill
        self.scaleup = scaleup
        self.stride = stride
        self.center = center  # Put the image in the middle or top-left

    def __call__(self, labels=None, image=None):
        """Return updated labels and image with added border."""
        if labels is None:
            labels = {}
        img = labels.get("img") if image is None else image
        shape = img.shape[:2]  # current shape [height, width]
        new_shape = labels.pop("rect_shape", self.new_shape)
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)

        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not self.scaleup:  # only scale down, do not scale up (for better val mAP)
            r = min(r, 1.0)

        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        if self.auto:  # minimum rectangle
            dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride)  # wh padding
        elif self.scaleFill:  # stretch
            dw, dh = 0.0, 0.0
            new_unpad = (new_shape[1], new_shape[0])
            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

        if self.center:
            dw /= 2  # divide padding into 2 sides
            dh /= 2

        if shape[::-1] != new_unpad:  # resize
            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
        top, bottom = int(round(dh - 0.1)) if self.center else 0, int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)) if self.center else 0, int(round(dw + 0.1))
        img = cv2.copyMakeBorder(
            img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)
        )  # add border
        if labels.get("ratio_pad"):
            labels["ratio_pad"] = (labels["ratio_pad"], (left, top))  # for evaluation

        if len(labels):
            labels = self._update_labels(labels, ratio, dw, dh)
            labels["img"] = img
            labels["resized_shape"] = new_shape
            return labels
        else:
            return img


new_shape=(640, 640)
aug = LetterBox(new_shape,center=True)

labels=None
img = cv2.imread("./2.png",-1)

lettered_img = aug(labels,img)

cv2.imshow('v8 letter_box',lettered_img)
cv2.waitKey(0)