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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 predict.py \
--config configs/quick_start/bisenet_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.
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
...
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
)
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
)
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
| |--...
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 predict.py \
--config configs/quick_start/bisenet_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
--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.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 predict.py \
--config configs/quick_start/bisenet_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
.
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