yolov8的数据增强怎么写
时间: 2023-10-13 22:20:49 浏览: 46
YoloV8的数据增强可以使用常见的图像增强方法,如随机裁剪、旋转、缩放、翻转等。以下是一些常用的数据增强方法的代码实现:
1. 随机裁剪
```
def random_crop(image, boxes):
"""
随机裁剪图像和对应的框
"""
height, width = image.shape[:2]
# 随机选择裁剪位置
x = np.random.randint(width)
y = np.random.randint(height)
# 随机选择裁剪尺寸
w = np.random.randint(width - x)
h = np.random.randint(height - y)
# 裁剪图像和框
image = image[y:y+h, x:x+w]
boxes[:, 0] = np.maximum((boxes[:, 0] - x) / w, 0)
boxes[:, 1] = np.maximum((boxes[:, 1] - y) / h, 0)
boxes[:, 2] = np.minimum((boxes[:, 2] - x) / w, 1)
boxes[:, 3] = np.minimum((boxes[:, 3] - y) / h, 1)
return image, boxes
```
2. 随机旋转
```
def random_rotate(image, boxes):
"""
随机旋转图像和对应的框
"""
height, width = image.shape[:2]
# 随机选择旋转角度
angle = np.random.randint(-10, 10)
# 计算旋转矩阵
rotation_matrix = cv2.getRotationMatrix2D((width/2, height/2), angle, 1)
# 旋转图像
image = cv2.warpAffine(image, rotation_matrix, (width, height))
# 旋转框
x1 = boxes[:, 0] * width
y1 = boxes[:, 1] * height
x2 = boxes[:, 2] * width
y2 = boxes[:, 3] * height
corners = np.stack([x1, y1, x2, y2], axis=1)
corners = np.hstack([corners, np.ones((corners.shape[0], 1))])
corners = np.dot(corners, rotation_matrix.T)
corners[:, :2] = np.maximum(corners[:, :2], 0)
corners[:, :2] = np.minimum(corners[:, :2], np.array([width, height]))
corners[:, 2:4] = np.maximum(corners[:, 2:4], 0)
corners[:, 2:4] = np.minimum(corners[:, 2:4], np.array([width, height]))
boxes[:, 0] = corners[:, 0] / width
boxes[:, 1] = corners[:, 1] / height
boxes[:, 2] = corners[:, 2] / width
boxes[:, 3] = corners[:, 3] / height
return image, boxes
```
3. 随机缩放
```
def random_scale(image, boxes):
"""
随机缩放图像和对应的框
"""
height, width = image.shape[:2]
# 随机选择缩放比例
scale = np.random.uniform(0.7, 1.3)
# 缩放图像
image = cv2.resize(image, (int(width*scale), int(height*scale)))
# 缩放框
boxes[:, 0] *= scale
boxes[:, 1] *= scale
boxes[:, 2] *= scale
boxes[:, 3] *= scale
return image, boxes
```
4. 随机翻转
```
def random_flip(image, boxes):
"""
随机翻转图像和对应的框
"""
# 随机选择水平翻转或垂直翻转
if np.random.randint(2) == 0:
image = cv2.flip(image, 1)
boxes[:, 0], boxes[:, 2] = 1 - boxes[:, 2], 1 - boxes[:, 0]
else:
image = cv2.flip(image, 0)
boxes[:, 1], boxes[:, 3] = 1 - boxes[:, 3], 1 - boxes[:, 1]
return image, boxes
```
将上述数据增强方法组合起来,可以得到一个完整的数据增强函数,代码如下:
```
def data_augmentation(image, boxes):
# 随机裁剪
image, boxes = random_crop(image, boxes)
# 随机旋转
image, boxes = random_rotate(image, boxes)
# 随机缩放
image, boxes = random_scale(image, boxes)
# 随机翻转
image, boxes = random_flip(image, boxes)
return image, boxes
```