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Resize means rescale the input image to a certain size according to a certain rule. PaddleSeg support the following three ways to resize:
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Unpadding Resize the input image to a certain fixed size and send it to the network for training. Do the same when predict.
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Step-Scaling Resize the input image based on a certain ratio, which randomly changes within a certain range with a certain step. The input image remains the same during prediction.
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Range-Scaling Resize the input image based on the long edge, which means scale the long edge to a certain length, and the short edge changes based on the original image ratio. The image's long edge will be rescaled to another assigned length during prediction.
Range-Scaling's diagram is as follows:
| Resize method | config parameter | meaning | Note |
|---|---|---|---|
| Unpadding | AUG.FIX_RESIZE_SIZE | the fixed size of Resize | |
| Step-Scaling | AUG.MIN_SCALE_FACTOR | the minimum ratio of Resize | |
| AUG.MAX_SCALE_FACTOR | the maximum ratio of Resize | ||
| AUG.SCALE_STEP_SIZE | the step size of Resize | ||
| Range-Scaling | AUG.MIN_RESIZE_VALUE | the minimum value of the long edge of image | |
| AUG.MAX_RESIZE_VALUE | the maximum value of the long edge of image | ||
| AUG.INF_RESIZE_VALUE | the assigned fixed length when rescale the long edge | must range between [AUG.MIN_RESIZE_VALUE, AUG.MAX_RESIZE_VALUE] . |
PS：All configuration parameters in this article can be set in your yaml file in the configs directory
PaddleSeg support following two ways to flip:
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Vertival flip(Mirror) Flip the image left and right with 50% probability.
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Horizontal Flip Flip the image upside down with a certain probability.
The above two kinds of switches operate independently and can be used in combination. Therefore, there are 4 possible situations for image flipping as follows:
| Image flip method | Config parameter | Meaning | Note |
|---|---|---|---|
| Mirror | AUG.MIRROR | vertival flip switch | open when set with True |
| Flip | AUG.FLIP | horizontal flip switch | open when set with True |
| AUG.FLIP_RATIO | Control the probability of flip upside down | invalid whtn AUG.FLIP is False |
Rich Crop is a set of data enhancement strategies open by PaddleSeg based on actual business experience. It is a data enhancement strategy for segmentation scenarios with less labeled data and complex test data. The process is shown in the figure below:
Rich Crop refers to a variety of transformations on the image to ensure the rich diversity of the data during the training process. It includes the following 4 transformations:
-Blur Use Gaussian Blur to smooth the image.
-Rotation
Image rotation, the rotation angle is randomly selected within a certain range. And the extra area generated by the rotation is filled with the value of DATASET.PADDING_VALUE.
-Aspect Image aspect ratio adjustment, resize after cropping a certain area from the image according to a certain size and aspect ratio.
-Color jitter The image color is jittered, and the three color attributes of brightness, saturation and contrast are adjusted.
| Rich crop method | Config parameter | Meaning | Note |
|---|---|---|---|
| Rich crop | AUG.RICH_CROP.ENABLE | Rich crop overall switch | open with True, close all transformation with False |
| Blur | AUG.RICH_CROP.BLUR | Image blur switch | open with True, close with False |
| AUG.RICH_CROP.BLUR_RATIO | the probability of blur | invalid when AUG.RICH_CROP.BLUR is False | |
| Rotation | AUG.RICH_CROP.MAX_ROTATION | maximum rotation angle of clockwise rotation | range between 0~90°，Actual rotation angle is randomly set within [-AUG.RICH_CROP.MAX_ROTATION, AUG.RICH_CROP.MAX_ROTATION] |
| Aspect | AUG.RICH_CROP.MIN_AREA_RATIO | The minimum area ratio of the cropped image to the original image | The value is 0~1, the smaller the value, the larger the range of change, if it is 0, no adjustment will be made |
| AUG.RICH_CROP.ASPECT_RATIO | The range of cropped image aspect ratio | The value is non-negative, the smaller the value, the larger the range of change, if it is 0, no adjustment is made | |
| Color jitter | AUG.RICH_CROP.BRIGHTNESS_JITTER_RATIO | Brightness adjustment factor | The value is between 0~1, the larger the value, the larger the range of change, if it is 0, no adjustment is made |
| AUG.RICH_CROP.SATURATION_JITTER_RATIO | Saturation adjustment factor | The value is 0~1, the larger the value, the larger the range of change, if it is 0, no adjustment is made | |
| AUG.RICH_CROP.CONTRAST_JITTER_RATIO | Contrast adjustment factor | The value is 0~1, the larger the value, the larger the range of change, if it is 0, no adjustment is made |
Randomly crop pictures and label images. This step crop the input images of the network to a fixed size.
Random crop process is divided into 3 situations:
- When the input image size is equal to CROP_SIZE, return to the original image.
- When the input image size is larger than CROP_SIZE, crop directly.
- When the input image size is smaller than CROP_SIZE, use the
DATASET.PADDING_VALUEvalue and theDATASET.IGNORE_INDEXvalue to fill the image and label image, and then crop them.