Add online layout model prediction

[can-gaa-hou]

Thank you for your contribution to the MindOCR repo.
Before submitting this PR, please make sure:

• You have read the Contributing Guidelines on pull requests
• Your code builds clean without any errors or warnings
• You are using approved terminology
• You have added unit tests

Motivation

Currently YOLOv8 model only supports offline inference with mindspore lite. This PR is adding online YOLOv8 model inference for layout analysis task. Add predict_layout.py for everyone to use.

Test Plan

Running the following command and check the output files under ./inferrence_results

python tools/infer/text/predict_layout.py  --image_dir ./configs/layout/yolov8/images/example.jpg --layout_algorithm YOLOv8 --visualize_output True

The visualize output should looks like this

and there is also a txt file as shown like the following

{"image_id": 0, "category_id": 1, "bbox": [308.649, 559.189, 240.211, 81.412], "score": 0.98431}
{"image_id": 0, "category_id": 1, "bbox": [50.435, 673.018, 240.232, 70.262], "score": 0.98414}
{"image_id": 0, "category_id": 3, "bbox": [322.805, 348.831, 225.949, 203.302], "score": 0.98019}
{"image_id": 0, "category_id": 1, "bbox": [308.658, 638.657, 240.31, 70.583], "score": 0.97986}
{"image_id": 0, "category_id": 1, "bbox": [50.616, 604.736, 240.044, 70.086], "score": 0.9797}
{"image_id": 0, "category_id": 1, "bbox": [50.409, 423.237, 240.132, 183.652], "score": 0.97805}
{"image_id": 0, "category_id": 1, "bbox": [308.66, 293.918, 240.181, 47.497], "score": 0.97471}
{"image_id": 0, "category_id": 1, "bbox": [308.64, 707.13, 240.271, 36.028], "score": 0.97427}
{"image_id": 0, "category_id": 1, "bbox": [308.697, 230.568, 240.062, 43.545], "score": 0.96921}
{"image_id": 0, "category_id": 4, "bbox": [51.787, 100.444, 240.267, 273.653], "score": 0.96839}
{"image_id": 0, "category_id": 5, "bbox": [308.637, 74.439, 237.878, 149.174], "score": 0.96707}
{"image_id": 0, "category_id": 1, "bbox": [50.615, 70.667, 240.068, 22.0], "score": 0.94156}
{"image_id": 0, "category_id": 2, "bbox": [50.549, 403.5, 67.392, 12.85], "score": 0.92577}
{"image_id": 0, "category_id": 1, "bbox": [51.384, 374.84, 171.939, 10.736], "score": 0.76692}