### Python Project Dependencies List

Source: https://github.com/dmmaze/comic-text-detector/blob/master/requirements.txt

This section specifies the Python packages and their minimum versions required to run the 'comic-text-detector' project. These dependencies are typically installed via pip from a requirements.txt file and include libraries for deep learning (PyTorch), ONNX model handling, image manipulation (Pillow, OpenCV), and general scientific computing (NumPy).

```Python
onnx>=1.9.0
onnx-simplifier>=0.3.6
opencv-python>=4.1.2
Pillow>=7.1.2
torch>=1.7.0
torchvision>=0.8.1
tqdm>=4.41.0
torchsummary

numpy
wandb
trdg
```

--------------------------------

### Prepare YOLOv5 Model for Text Block Detection Training

Source: https://github.com/dmmaze/comic-text-detector/blob/master/examples.ipynb

This Python snippet loads a trained YOLOv5 model from a PyTorch checkpoint, extracts its configuration (YAML) and state dictionary (weights), and then saves them into a new `.ckpt` file. This process prepares the model for subsequent training steps, specifically for the text block detector within the comic text detection pipeline.

```python
import torch
m = torch.load('yolov5sblk.pt')['model']
save_dict = {
    'cfg': m.yaml,
    'weights': m.state_dict()
}
torch.save(save_dict, 'yolov5sblk.ckpt')
```

--------------------------------

### Generate Synthetic Comic Text Data

Source: https://github.com/dmmaze/comic-text-detector/blob/master/examples.ipynb

This Python script demonstrates how to generate synthetic comic text data using `ComicTextSampler` and `render_comictext`. It configures detailed parameters for both Japanese and English text, including font directories, font statistics, font size, stroke width, color, text language, orientation, rotation, number of lines, length, and alignment. The script then renders a specified number of synthetic images based on these configurations.

```python
from text_rendering import ComicTextSampler, render_comictext, ALIGN_LEFT, ALIGN_CENTER
import copy

ja_sampler_dict = {
                'num_txtblk': 20,
                'font': {
                        'font_dir': 'data/examples/fonts',   # font file directory
                        'font_statics': 'data/font_statics_en.csv',     # Just a font list file, please create your own list and ignore the last two cols.
                        'num': 1200,     # first 500 of the fontlist will be used 

                        # params to mimic comic/manga text style
                        'size': {'value': [0.02, 0.03, 0.15],
                                'prob': [1, 0.4, 0.15]},
                        'stroke_width': {'value': [0, 0.1, 0.15],
                                        'prob': [1, 0.5, 0.2]},
                        'color': {'value': ['black', 'white', 'random'],
                                'prob': [1, 1, 0.4]},
                },
                'text': {
                        'lang': 'ja',   # render japanese, 'en' for english
                        'orientation': {'value': [1, 0],    # 1 is vertical text.
                                                'prob': [1, 0.3]},
                        'rotation': {'value': [0, 30, 60],
                                                'prob': [1, 0.3, 0.1]},
                        'num_lines': {'value': [0.15],
                                'prob': [1]}, 
                        'length': {'value': [0.3],
                                'prob': [1]},
                        'min_num_lines': 1,
                        'min_length': 3,
                        'alignment': {'value': [ALIGN_LEFT, ALIGN_CENTER],
                                'prob': [0.3, 1]}
                }
        }

jp_cts = ComicTextSampler((845, 1280), ja_sampler_dict, seed=0)
eng_dict = copy.deepcopy(ja_sampler_dict)
eng_dict['text']['lang'] = 'en'
eng_dict['text']['orientation'] = {'value': [1, 0],
                                'prob': [0, 1]}
eng_cts = ComicTextSampler((845, 1280), eng_dict, seed=0)

img_dir = r'data/examples'
save_dir = r'data/examples/annotations'
 
render_comictext([eng_cts, jp_cts], img_dir, save_dir=save_dir, save_prefix=None, render_num=10, label_dir=None, show=False)
```

--------------------------------

### Generate Annotations for Comic Images

Source: https://github.com/dmmaze/comic-text-detector/blob/master/examples.ipynb

This Python script utilizes a pre-trained model to generate various annotations for comic or manga images. It produces YOLO format bounding boxes for English and Japanese text blocks, a mask image, and ICDAR format bounding boxes for text lines. The process requires a specified model path and an input image directory.

```python
from inference import model2annotations

img_dir = r'data/examples'
model_path = r'data/comictextdetector.pt'
img_dir = r'data/examples'                              # can be dir list
save_dir = r'data/examples/annotations'
model2annotations(model_path, img_dir, save_dir, save_json=False)
```

--------------------------------

### Concatenate Models and Export to ONNX

Source: https://github.com/dmmaze/comic-text-detector/blob/master/examples.ipynb

This Python script concatenates multiple trained model weights (YOLOv5 text block detector, UNet segmentation head, and DBHead) into a single unified model. It then initializes this combined model and exports it to the ONNX format, which is suitable for deployment. The export process specifies the batch size and image dimensions for the ONNX graph.

```python
from utils.export import *
concate_models('data/yolov5sblk.ckpt', 'data/unet_best.ckpt', 'data/db_best.ckpt', 'data/textdetector.pt')

batch_size, imgsz = 1, 1024
cuda = torch.cuda.is_available()
device = 'cpu'
im = torch.zeros(batch_size, 3, imgsz, imgsz).to(device)
model_path = r'data/textdetector.pt'
model = TextDetBase(model_path, device=device).to(device)
export_onnx(model, im, model_path, 11)
```

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