GitLab

- Remove half() calls for DocLayoutYOLO and YOLOv8 models
- This change prevents potential errors when running models on CPU

- Update config version to1.2.0
- Refactor model initialization in model_init.py- Update dependencies in requirements.txt files
- Remove unused imports and models
- Add conditional imports for table models

- Add support for Apple M1 chips (mps device)
- Refactor image processing for better performance and compatibility
- Update model loading and inference for various devices
- Adjust batch processing and memory management

- Add UnimerMBartConfig and UnimerSwinConfig classes
- Implement UnimerSwinImageProcessor for image preprocessing- Create necessary __init__.py files for module structure

- Remove unnecessary __getitem__ method
- Simplify image cropping in detect_math_formula_region
- Improve code readability and efficiency

- Set NPUDTCompile to false for better performance on NPU
- Adjust batch ratio

- Remove unused @ImportPIL decorator from load_images_from_pdf function
- Update image shape handling in YOLOv11.py for better compatibility

These changes improve code readability and performance without altering the original functionality.

- Replace PIL with cv2 for image processing
- Fix issues with image cropping and resizing
- Add boundary checks and error handling
- Optimize code for better performance and readability

- Remove PIL usage across multiple files
- Convert image processing functions to use NumPy arrays
- Update crop_img function to work with NumPy arrays
- Modify image loading and resizing to use NumPy and OpenCV
- Clean up unused imports and comments related to PIL

- Increase batch ratio to 8 for GPU memory >=16GB
- Improve inference performance on systems with higher GPU memory

- Simplify batch ratio logic for GPU memory >= 16GB
- Remove unnecessary conditions for 20GB and 40GB memory

- Simplify batch ratio logic for GPU memory >= 16GB
- Remove unnecessary conditions for 20GB and 40GB memory

- Sort detected images by area before processing to enhance MFR accuracy
- Implement stable sorting to maintain original order of images with equal

- Move batch model initialization outside the loop
- Collect page dimensions before analyzing- Update page info dictionary structure
- Add null dimensions for non-analyzed pages

- Update the logic for determining `end_page_id` to handle negative values
- This change ensures proper behavior when `end_page_id` is set to -1 or other negative values

- Add license ID information to the log for better traceability
- Improve logging format to include both license ID and expiration date

- Add ImportError handling to silence known import-related exceptions
- Improve generic exception handling to log error messages- Maintain existing specific exception handlers for license-related issues

- Add license verification logic for Ascend plugin
- Handle different license-related exceptions with appropriate error messages
- Log success message with license expiration date if verification passes
- Fall back to CPU model if license verification fails or plugin is not available

- Move environment variable settings for NPU, MPS, and other configurations to the global scope in doc_analyze_by_custom_model.py
- Remove redundant environment variable settings in pdf_extract_kit.py
- This change ensures consistent configuration across the application and avoids potential conflicts or duplicate settings

- Remove redundant imports for StructTableModel and TableMasterPaddleModel
- Reorder imports to group related modules together
- Update import structure for better readability and maintainability

- Remove unused utility functions
- Update import statements for better readability
- Add conditional imports for Ascend plugin
- Refactor table model initialization to support NPU

- Increase batch size from 8 to 256 for language detection inference
- Add timing measurement for language detection process

- Update batch ratio calculation logic to better utilize available GPU memory
- Improve logging for all GPU memory sizes

- Update batch ratio calculation for GPU memory range
- Increase upper limit for batch ratio 16 from 24 to 32 GB

- Update conditions for batch ratio assignment:
  -8 <= gpu_memory < 10: batch_ratio = 2 - 10 <= gpu_memory <= 12: batch_ratio =4
- This fix ensures proper batch ratio selection for GPU memory sizes

- Improve batch ratio calculation based on GPU memory
- Enhance performance for devices with 8GB or more VRAM

- Reduce batch_ratio by 1 for better performance and stability
- This change ensures more consistent memory usage when processing documents