[LEADERG AI ZOO] Jupyter-Image-Segmentation-UNet-Keras

Language:

繁體中文

 

 

 

 

[Introduction]

 

 

Use UNet to segment the image.

 

 

 

[Instructions]

 

 

1. 1_annotation_labelme_json.ipynb

Open the labeling tool LabelMe.

 

 

Setting parameters:

• image_folder: image folder

• annotation_path: output annotation file path

 

 

After the setting is completed, it can be executed.

 

 

2. 2_labelme_json_to_dataset.ipynb

Convert LabelMe label files into training datasets.

 

 

Setting parameters:

• label_path: label the folder of the file

• output_path: the path of the output file

• mask_path: the path of the mask

 

 

After the setting is completed, it can be executed.

 

 

3. 3_delete_log.ipynb

Delete the log file.

 

 

4. 4_train.ipynb

Perform training.

 

 

Setting parameters:

• model_file: the path of the output model file

• epoch_steps: the number of steps in each epoch

• num_epochs: the number of epochs

• log_dir: the path of the log folder

• train_folder: the path of the training folder

 

 

After the setting is completed, it can be executed.

 

 

5. 5_kill_tensorboard.ipynb

Delete the old tensorboard program.

 

 

6. 6_tensorboard.ipynb

Execute tensorboard.

 

 

7. 7_inference.ipynb

Perform inferences.

 

 

Setting parameters:

• model_file: input model file path

• image_file: Inferred image file path

• predict_file: the path of the inference result file

 

 

After the setting is completed, it can be executed.

 

 

8. 8_inference_folder.ipynb

Perform inferences on the folder.

 

 

Setting parameters:

• model_file: input model file path

• image_file: the path of the inferred image folder

• predict_file: the path of the inference result file

 

 

After the setting is completed, it can be executed.

After execution, you can see the results of UNet's inference.

 

 

Jupyter-Image-Segmentation-UNet-Keras-inference.png

 

 

9. 9_inference_api.ipynb

Use API to perform inference.

 

 

10. 10_inference_api_browser.ipynb

Open the browser to use the API.

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