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Prediction

In addition to analyzing the IOU, ACC and Kappa, we can also check the segmentation effect of some specific samples, and inspire further optimization ideas from Bad Case.

The predict.py script is specially used to visualize prediction cases. The command format is as follows:

python tools/predict.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output/iter_1000/model.pdparams \
       --image_path dataset/optic_disc_seg/JPEGImages/H0003.jpg \
       --save_dir output/result

Where image_path can be the path of a picture, a file list containing image paths, or a directory. At this time, that image or all images in file list or directory will be predicted and the visualization results will be saved.

Similarly, you can use --aug_pred to turn on multi-scale flip prediction, and --is_slide to turn on sliding window prediction.

1.Prepare Dataset

• When performing prediction, only the original image is needed. You should prepare the contents of test.txt as follows:

  images/image1.jpg
  images/image2.jpg
  ...
  

• When calling predict.py for visualization, annotated images can be included in the file list. When predicting, the model will automatically ignore the annotated images given in the file list. Therefore, you can also directly use the training and validating datasets to do predictions. In other words, if the content of your train.txt is as follows：

  images/image1.jpg labels/label1.png
  images/image2.jpg labels/label2.png
  ...
  

2.API

Parameter Analysis of Forecast API

paddleseg.core.predict(
                    model,
                    model_path,
                    transforms,
                    image_list,
                    image_dir=None,
                    save_dir='output',
                    aug_pred=False,
                    scales=1.0,
                    flip_horizontal=True,
                    flip_vertical=False,
                    is_slide=False,
                    stride=None,
                    crop_size=None,
                    custom_color=None
)

• Parameters

Parameter	Type	Effection	Is Required	Default
model	nn.Layer	Segmentation model	Yes	-
model_path	str	The path of parameters in best model	Yes	-
transforms	transform.Compose	Preprocess the input image	Yes	-
image_list	list	List of image paths to be predicted	Yes	-
image_dir	str	The directory of the image path to be predicted	No	None
save_dir	str	Output directory	No	'output'
aug_pred	bool	Whether to use multi-scale and flip augmentation for prediction	No	False
scales	list/float	Set the zoom factor, take effect when aug_pred is True	No	1.0
flip_horizontal	bool	Whether to use horizontal flip, take effect when aug_pred is True	No	True
flip_vertical	bool	Whether to use vertical flip, take effect when aug_pred is True	No	False
is_slide	bool	Whether to evaluate through a sliding window	No	False
stride	tuple/list	Set the width and height of the sliding window, effective when is_slide is True	No	None
crop_size	tuple/list	Set the width and height of the crop of the sliding window, which takes effect when is_slide is True	No	None
custom_color	list	Set custom segmentation prediction colors,len(custom_color) = 3 * (pixel classes)	No	Default color map

Import the API interface and start predicting.

from paddleseg.core import predict
predict(
        model,
        model_path='output/best_model/model.pdparams',# Model path
        transforms=transforms, # Transform.Compose， Preprocess the input image
        image_list=image_list, # List of image paths to be predicted。
        image_dir=image_dir, # The directory where the picture to be predicted is located
        save_dir='output/results' # Output path
    )

3.Instruction of File Structure

If you don't specify the output location, added_prediction and pseudo_color_prediction will be generated under the default folder output/results, which store the results of the pseudo map and blended prediction respectively.

output/result
    |
    |--added_prediction
    |  |--image1.jpg
    |  |--image2.jpg
    |  |--...
    |
    |--pseudo_color_prediction
    |  |--image1.jpg
    |  |--image2.jpg
    |  |--...

4.Custom Color Map

After prediction, what we get is the predicted segmentation result depend on default color map. Take the medical video disc segmentation dataset as an example:

​ pseudo map / blended prediction

In the segmentation result, the foreground is marked in red, and the background is marked in black. If you want to use other colors, you can refer to the following command:

python tools/predict.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output/iter_1000/model.pdparams \
       --image_path data/optic_disc_seg/JPEGImages/H0003.jpg \
       --save_dir output/result \
       --custom_color 0 0 0 255 255 255

The segmentation results are as follows:

​ pseudo map / blended prediction

• Parameters
• You can see that we added --custom_color 0 0 0 255 255 255 at the end. What does this mean? In the RGB image, the final color of each pixel is determined by the components of the three RGB channels, so every three digits after the command line parameter represent the color of a pixel, and the color of each class in label.txt is releated to 3 digits sequentially.
• If you use a custom color map, the number of input color values should be equal to 3 * pixel classes (depending on the dataset you are using). For example, if your data set has 3 types of pixels, you can consider run:

python tools/predict.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output/iter_1000/model.pdparams \
       --image_path data/optic_disc_seg/JPEGImages/H0003.jpg \
       --save_dir output/result \
       --custom_color 0 0 0 100 100 100 200 200 200

We recommend that you refer to the RGB color value comparison table to set --custom_color.