yolov5中的Mosaic数据增强代码

以下是YOLOv5中实现Mosaic数据增强的代码：

def mosaic(self, img, boxes, labels):
    img_size = img.shape[:2]
    xc, yc = [int(random.uniform(img_size[i] * 0.4, img_size[i] * 0.6)) for i in range(2)]  # mosaic中心点坐标
    indices = [random.randint(0, len(self.img_files) - 1) for _ in range(3)]  # 随机选择三张图像进行拼接
    mosaic_img = np.zeros((2 * yc, 2 * xc, 3), dtype=np.uint8)
    mosaic_boxes, mosaic_labels = [], []
    for i, index in enumerate([0, 1, 2, 3]):
        img_path = self.img_files[indices[index]]
        img, _, _ = self.load_image(img_path)
        img_boxes = boxes[indices[index]].copy()
        img_labels = labels[indices[index]].copy()
        if i == 0:
            # 左上角
            x1a, y1a, x2a, y2a = self.rand_bbox(img_size, xc, yc)
            x1b, y1b, x2b, y2b = self.rand_bbox(img_size, xc, yc)
            mosaic_img[:yc, :xc, :] = img[y1a:y2a, x1a:x2a, :]
            mosaic_boxes.append(self.box_transform(img_boxes, (x1a, y1a, x2a, y2a)))
            mosaic_labels.append(img_labels)
        elif i == 1:
            # 右上角
            x1a, y1a, x2a, y2a = self.rand_bbox(img_size, xc, yc)
            x1b, y1b, x2b, y2b = self.rand_bbox(img_size, xc, yc)
            mosaic_img[:yc, xc:, :] = img[y1a:y2a, x1b:x2b, :]
            mosaic_boxes.append(self.box_transform(img_boxes, (x1b - xc, y1a, x2b - xc, y2a)))
            mosaic_labels.append(img_labels)
        elif i == 2:
            # 左下角
            x1a, y1a, x2a, y2a = self.rand_bbox(img_size, xc, yc)
            x1b, y1b, x2b, y2b = self.rand_bbox(img_size, xc, yc)
            mosaic_img[yc:, :xc, :] = img[y1b:y2b, x1a:x2a, :]
            mosaic_boxes.append(self.box_transform(img_boxes, (x1a, y1b - yc, x2a, y2b - yc)))
            mosaic_labels.append(img_labels)
        else:
            # 右下角
            x1a, y1a, x2a, y2a = self.rand_bbox(img_size, xc, yc)
            x1b, y1b, x2b, y2b = self.rand_bbox(img_size, xc, yc)
            mosaic_img[yc:, xc:, :] = img[y1b:y2b, x1b:x2b, :]
            mosaic_boxes.append(self.box_transform(img_boxes, (x1b - xc, y1b - yc, x2b - xc, y2b - yc)))
            mosaic_labels.append(img_labels)
    return mosaic_img, np.concatenate(mosaic_boxes, 0), np.concatenate(mosaic_labels, 0)

def rand_bbox(self, img_size, xc, yc):
    w, h = img_size
    size = min(w, h)
    x1 = random.randint(0, xc)
    y1 = random.randint(0, yc)
    x2 = random.randint(xc, size)
    y2 = random.randint(yc, size)
    return x1, y1, x2, y2

def box_transform(self, boxes, box):
    x1, y1, x2, y2 = box
    w, h = x2 - x1, y2 - y1
    boxes[:, [0, 2]] -= x1
    boxes[:, [1, 3]] -= y1
    boxes[:, [0, 2]] /= w
    boxes[:, [1, 3]] /= h
    boxes[:, [0, 2]] = np.clip(boxes[:, [0, 2]], 0, 1)
    boxes[:, [1, 3]] = np.clip(boxes[:, [1, 3]], 0, 1)
    boxes[:, 2:4] -= boxes[:, :2]
    boxes[:, :2] += (x1, y1)
    boxes[:, 2:4] += (x1, y1)
    return boxes