YOLOv5 数据增强data augmentation (七)

1.数据增强介绍

当我们训练集中的图片比较少时，容易造成网络的过拟合。为了避免这种情况一般我们要经过图像处理的方法，人为地去增加些图片数据，这样就会增加我们可用图片的数目，减少过拟合的可能性。

• 可以通过像素级的剪裁(Crop)、旋转(Rotation)、反转(Flip)、色调(Hue)、饱和度(Saturation)、曝光量(Exposure)、宽高比(Aspect)来做数据增强。
• 另外还可以在图片级数据增强，比如MixUp、CurMix、Mosaic、Blur
  

2.图片级像素增强

• Mixup: 如图在一张狗的图片中，叠加一只猫的图片，这样经过两幅图片的加权运算可以看到，可以看到这幅新的图片上既有狗又有猫。
• Cutout: 如图，将图片中某一块区域，填充为某种颜色，比如图中填充为黑色
• CutMix: 如图，将图片某一块区域剪裁掉，然后用另外一幅图像来填充剪裁区域
• Mosaic 数据增强：它是把四副图片拼成一幅大图，在YOLOv5中数据增强就是采用Mosaic方法，该方法由YOLOv5作者提出。如下图所示
  
  
  YOLOv5在训练过程中，将4副小图拼成一幅大图，4副小图在拼接时做了随机的处理，所以4副小图的大小形状是不一样的。
• 我们可以通过train.py --rect 去省略掉mosaic
• --rect,通过对整个数据集的宽高比进行排序，然后对相似的宽高比例图片组合在一起。
• 按宽高比排序的好处是，可以降低FLOPS运算，加速数据处理

3. 代码讲解

3.1 mosaic 代码

代码位置yolov5-3.1 > utils > datasets.py

def load_mosaic(self, index): 
    # loads images in a mosaic

    labels4 = []
    s = self.img_size
    #随机取mosaic中心点
    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
    #随机取其他三张图片的索引
    indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)]  # 3 additional image indices
    for i, index in enumerate(indices):
        # Load image
        # load_image 加载图片并根据设定的输入大小与图片原大小的比例ratio进行resize
        img, _, (h, w) = load_image(self, index)

        # 初始化大图 img4
        if i == 0:  # top left（左上角）
            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
            # 设置大图上的位置（左上角）
            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
              # 选取小图上的位置
            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
        elif i == 1:  # top right
            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
        elif i == 2:  # bottom left
            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
        elif i == 3:  # bottom right
            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)

        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
        #计算小图到大图上时所产生的偏移，用来计算mosaic增强后的标签的位置
        padw = x1a - x1b
        padh = y1a - y1b

        # Labels
        x = self.labels[index]  
        labels = x.copy()
        # 根据偏移量更新目标框位置
        if x.size > 0:  # Normalized xywh to pixel xyxy format
            labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
            labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
            labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
            labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
        labels4.append(labels)

    # Concat/clip labels
    if len(labels4):
        labels4 = np.concatenate(labels4, 0)
        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])  # use with random_perspective
        # img4, labels4 = replicate(img4, labels4)  # replicate

    # Augment
    # 进行mosaic的时候将四张图片整合到一起之后shape为[2*img_size,2*img_size]
    # 对mosaic 整合的图片进行随机旋转、平移、缩放、裁剪，并resize为输入大小img_size
    img4, labels4 = random_perspective(img4, labels4,
                                       degrees=self.hyp['degrees'],
                                       translate=self.hyp['translate'],
                                       scale=self.hyp['scale'],
                                       shear=self.hyp['shear'],
                                       perspective=self.hyp['perspective'],
                                       border=self.mosaic_border)  # border to remove

    return img4, labels4

3.2 load_img 代码

# load_image加载图片并根据设定的输入大小与图片原大小的比例ratio进行resize
def load_image(self, index):
     # loads 1 image from dataset, returns img, original hw, resized hw
    img = self.imgs[index]
    if img is None:  # not cached
        path = self.img_files[index]
        img = cv2.imread(path)  # BGR
        assert img is not None, 'Image Not Found ' + path
        h0, w0 = img.shape[:2]  # orig hw
        r = self.img_size / max(h0, w0)  # resize image to img_size
        if r != 1:  # always resize down, only resize up if training with augmentation
            interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
    else:
        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized

3.3 random_perspective

#随机透视变换
#计算方法为坐标向量和变换矩阵的乘积
def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
    # targets = [cls, xyxy]

    height = img.shape[0] + border[0] * 2  # shape(h,w,c)
    width = img.shape[1] + border[1] * 2

    # Center
    C = np.eye(3)
    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)

    # Perspective
    P = np.eye(3)
    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)

    # Rotation and Scale
    R = np.eye(3)
    a = random.uniform(-degrees, degrees)
    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
    s = random.uniform(1 - scale, 1 + scale)
    # s = 2 ** random.uniform(-scale, scale)
    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)

    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)

    # Translation
    T = np.eye(3)
    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
    # @表示矩阵乘法运算
    # Combined rotation matrix
    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
        if perspective:
            #透视变换函数，可保持直线不变形，但是平行线可能不再平行
            img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
        else:  # affine
            # 仿射变换函数，可实现旋转、平移、缩放；变换后的平行线依旧平行
            img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))

    # Visualize
    # import matplotlib.pyplot as plt
    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
    # ax[0].imshow(img[:, :, ::-1])  # base
    # ax[1].imshow(img2[:, :, ::-1])  # warped

    # Transform label coordinates
    n = len(targets)
    if n:
        # warp points
        xy = np.ones((n * 4, 3))
        xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
        xy = xy @ M.T  # transform
        if perspective:
            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
        else:  # affine
            xy = xy[:, :2].reshape(n, 8)

        # create new boxes
        x = xy[:, [0, 2, 4, 6]]
        y = xy[:, [1, 3, 5, 7]]
        xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

        # # apply angle-based reduction of bounding boxes
        # radians = a * math.pi / 180
        # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
        # x = (xy[:, 2] + xy[:, 0]) / 2
        # y = (xy[:, 3] + xy[:, 1]) / 2
        # w = (xy[:, 2] - xy[:, 0]) * reduction
        # h = (xy[:, 3] - xy[:, 1]) * reduction
        # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

        # clip boxes
        # 去除进行上面一系列操作后被裁剪过小的框；reject warped points outside of image
        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)

        # filter candidates
        i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
        targets = targets[i]
        targets[:, 1:5] = xy[i]

    return img, targets