add CRAFT training code

trainer/craft/.gitignore

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+__pycache__/
+model/__pycache__/
+wandb/*
+vis_result/*

trainer/craft/README.md

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+# CRAFT-train
+On the official CRAFT github, there are many people who want to train CRAFT models. 
+
+However, the training code is not published in the official CRAFT repository. 
+
+There are other reproduced codes, but there is a gap between their performance and performance reported in the original paper. (https://arxiv.org/pdf/1904.01941.pdf) 
+
+The trained model with this code recorded a level of performance similar to that of the original paper.
+
+```bash
+├── config
+│   ├── syn_train.yaml
+│   └── custom_data_train.yaml
+├── data
+│   ├── pseudo_label
+│   │   ├── make_charbox.py
+│   │   └── watershed.py
+│   ├── boxEnlarge.py
+│   ├── dataset.py
+│   ├── gaussian.py
+│   ├── imgaug.py
+│   └── imgproc.py
+├── loss
+│   └── mseloss.py
+├── metrics
+│   └── eval_det_iou.py
+├── model
+│   ├── craft.py
+│   └── vgg16_bn.py
+├── utils
+│   ├── craft_utils.py
+│   ├── inference_boxes.py
+│   └── utils.py
+├── trainSynth.py
+├── train.py
+├── train_distributed.py
+├── eval.py
+├── data_root_dir   (place dataset folder here)
+└── exp             (model and experiment result files will saved here)
+```
+
+### Installation
+
+Install using `pip`
+
+``` bash
+pip install -r requirements.txt
+```
+
+
+### Training
+1. Put your training, test data in the following format
+    ```
+    └── data_root_dir (you can change root dir in yaml file)
+        ├── ch4_training_images
+        │   ├── img_1.jpg
+        │   └── img_2.jpg
+        ├── ch4_training_localization_transcription_gt
+        │   ├── gt_img_1.txt
+        │   └── gt_img_2.txt
+        ├── ch4_test_images
+        │   ├── img_1.jpg
+        │   └── img_2.jpg
+        └── ch4_training_localization_transcription_gt
+            ├── gt_img_1.txt
+            └── gt_img_2.txt
+    ```
+   * localization_transcription_gt files format :
+   ```
+    377,117,463,117,465,130,378,130,Genaxis Theatre
+    493,115,519,115,519,131,493,131,[06]
+    374,155,409,155,409,170,374,170,###
+    ```
+2. Write configuration in yaml format (example config files are provided in `config` folder.)
+    * To speed up training time with multi-gpu, set num_worker > 0   
+3. Put the yaml file in the config folder
+4. Run training script like below (If you have multi-gpu, run train_distributed.py)
+5. Then, experiment results will be saved to ```./exp/[yaml]``` by default.
+
+* Step 1 : To train CRAFT with SynthText dataset from scratch
+    * Note : This step is not necessary if you use <a href="https://drive.google.com/file/d/1enVIsgNvBf3YiRsVkxodspOn55PIK-LJ/view?usp=sharing">this pretrain</a> as a checkpoint when start training step 2. You can download and put it in `exp/CRAFT_clr_amp_29500.pth` and change `ckpt_path` in the config file according to your local setup.
+    ```
+    CUDA_VISIBLE_DEVICES=0 python3 trainSynth.py --yaml=syn_train
+    ```
+
+* Step 2 : To train CRAFT with [SynthText + IC15] or custom dataset
+    ```
+    CUDA_VISIBLE_DEVICES=0 python3 train.py --yaml=custom_data_train               ## if you run on single GPU
+    CUDA_VISIBLE_DEVICES=0,1 python3 train_distributed.py --yaml=custom_data_train   ## if you run on multi GPU
+    ```
+
+### Arguments
+* ```--yaml``` : configuration file name
+
+### Evaluation
+* In the official repository issues, the author mentioned that the first row setting F1-score is around 0.75.
+* In the official paper, it is stated that the result F1-score of the second row setting is 0.87.
+    * If you adjust post-process parameter 'text_threshold' from 0.85 to 0.75, then F1-score reaches to 0.856.
+* It took 14h to train weak-supervision 25k iteration with 8 RTX 3090 Ti.
+    * Half of GPU assigned for training, and half of GPU assigned for supervision setting.
+
+| Training Dataset   | Evaluation Dataset   | Precision  | Recall  | F1-score  | pretrained model  |
+| ------------- |-----|:-----:|:-----:|:-----:|-----:|
+| SynthText      |  ICDAR2013 | 0.801 | 0.748 | 0.773| <a href="https://drive.google.com/file/d/1enVIsgNvBf3YiRsVkxodspOn55PIK-LJ/view?usp=sharing">download link</a>|
+| SynthText + ICDAR2015      | ICDAR2015  | 0.909 | 0.794 | 0.848| <a href="https://drive.google.com/file/d/1qUeZIDSFCOuGS9yo8o0fi-zYHLEW6lBP/view">download link</a>|

trainer/craft/config/custom_data_train.yaml

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+wandb_opt: False
+
+results_dir: "./exp/"
+vis_test_dir: "./vis_result/"
+
+data_root_dir: "./data_root_dir/"
+score_gt_dir: None # "/data/ICDAR2015_official_supervision"
+mode: "weak_supervision"
+
+
+train:
+  backbone : vgg
+  use_synthtext: False # If you want to combine SynthText in train time as CRAFT did, you can turn on this option
+  synth_data_dir: "/data/SynthText/"
+  synth_ratio: 5
+  real_dataset: custom
+  ckpt_path: "./pretrained_model/CRAFT_clr_amp_29500.pth"
+  eval_interval: 1000
+  batch_size: 5
+  st_iter: 0
+  end_iter: 25000
+  lr: 0.0001
+  lr_decay: 7500
+  gamma: 0.2
+  weight_decay: 0.00001
+  num_workers: 0 # On single gpu, train.py execution only works when num worker = 0 / On multi-gpu, you can set num_worker > 0 to speed up
+  amp: True
+  loss: 2
+  neg_rto: 0.3
+  n_min_neg: 5000
+  data:
+    vis_opt: False
+    pseudo_vis_opt: False
+    output_size: 768
+    do_not_care_label: ['###', '']
+    mean: [0.485, 0.456, 0.406]
+    variance: [0.229, 0.224, 0.225]
+    enlarge_region : [0.5, 0.5] # x axis, y axis
+    enlarge_affinity: [0.5, 0.5]
+    gauss_init_size: 200
+    gauss_sigma: 40
+    watershed:
+      version: "skimage"
+      sure_fg_th: 0.75
+      sure_bg_th: 0.05
+    syn_sample: -1
+    custom_sample: -1
+    syn_aug:
+      random_scale:
+        range: [1.0, 1.5, 2.0]
+        option: False
+      random_rotate:
+        max_angle: 20
+        option: False
+      random_crop:
+        version: "random_resize_crop_synth"
+        option: True
+      random_horizontal_flip:
+        option: False
+      random_colorjitter:
+        brightness: 0.2
+        contrast: 0.2
+        saturation: 0.2
+        hue: 0.2
+        option: True
+    custom_aug:
+      random_scale:
+        range: [ 1.0, 1.5, 2.0 ]
+        option: False
+      random_rotate:
+        max_angle: 20
+        option: True
+      random_crop:
+        version: "random_resize_crop"
+        scale: [0.03, 0.4]
+        ratio: [0.75, 1.33]
+        rnd_threshold: 1.0
+        option: True
+      random_horizontal_flip:
+        option: True
+      random_colorjitter:
+        brightness: 0.2
+        contrast: 0.2
+        saturation: 0.2
+        hue: 0.2
+        option: True
+
+test:
+  trained_model : null
+  custom_data:
+    test_set_size: 500
+    test_data_dir: "./data_root_dir/"
+    text_threshold: 0.75
+    low_text: 0.5
+    link_threshold: 0.2
+    canvas_size: 2240
+    mag_ratio: 1.75
+    poly: False
+    cuda: True
+    vis_opt: False

trainer/craft/config/load_config.py

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+import os
+import yaml
+from functools import reduce
+
+CONFIG_PATH = os.path.dirname(__file__)
+
+def load_yaml(config_name):
+
+    with open(os.path.join(CONFIG_PATH, config_name)+ '.yaml') as file:
+        config = yaml.safe_load(file)
+
+    return config
+
+class DotDict(dict):
+    def __getattr__(self, k):
+        try:
+            v = self[k]
+        except:
+            return super().__getattr__(k)
+        if isinstance(v, dict):
+            return DotDict(v)
+        return v
+
+    def __getitem__(self, k):
+        if isinstance(k, str) and '.' in k:
+            k = k.split('.')
+        if isinstance(k, (list, tuple)):
+            return reduce(lambda d, kk: d[kk], k, self)
+        return super().__getitem__(k)
+
+    def get(self, k, default=None):
+        if isinstance(k, str) and '.' in k:
+            try:
+                return self[k]
+            except KeyError:
+                return default
+        return super().get(k, default=default)

trainer/craft/config/syn_train.yaml

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+wandb_opt: False
+
+results_dir: "./exp/"
+vis_test_dir: "./vis_result/"
+data_dir:
+  synthtext: "/data/SynthText/"
+  synthtext_gt: NULL
+
+train:
+  backbone : vgg
+  dataset: ["synthtext"]
+  ckpt_path: null
+  eval_interval: 1000
+  batch_size: 5
+  st_iter: 0
+  end_iter: 50000
+  lr: 0.0001
+  lr_decay: 15000
+  gamma: 0.2
+  weight_decay: 0.00001
+  num_workers: 4
+  amp: True
+  loss: 3
+  neg_rto: 1
+  n_min_neg: 1000
+  data:
+    vis_opt: False
+    output_size: 768
+    mean: [0.485, 0.456, 0.406]
+    variance: [0.229, 0.224, 0.225]
+    enlarge_region : [0.5, 0.5] # x axis, y axis
+    enlarge_affinity: [0.5, 0.5]
+    gauss_init_size: 200
+    gauss_sigma: 40
+    syn_sample : -1
+    syn_aug:
+      random_scale:
+        range: [1.0, 1.5, 2.0]
+        option: False
+      random_rotate:
+        max_angle: 20
+        option: False
+      random_crop:
+        version: "random_resize_crop_synth"
+        rnd_threshold : 1.0
+        option: True
+      random_horizontal_flip:
+        option: False
+      random_colorjitter:
+        brightness: 0.2
+        contrast: 0.2
+        saturation: 0.2
+        hue: 0.2
+        option: True
+
+test:
+  trained_model: null
+  icdar2013:
+    test_set_size: 233
+    cuda: True
+    vis_opt: True
+    test_data_dir : "/data/ICDAR2013/"
+    text_threshold: 0.85
+    low_text: 0.5
+    link_threshold: 0.2
+    canvas_size: 960
+    mag_ratio: 1.5
+    poly: False

trainer/craft/data/boxEnlarge.py

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+import math
+import numpy as np
+
+
+def pointAngle(Apoint, Bpoint):
+    angle = (Bpoint[1] - Apoint[1]) / ((Bpoint[0] - Apoint[0]) + 10e-8)
+    return angle
+
+def pointDistance(Apoint, Bpoint):
+    return math.sqrt((Bpoint[1] - Apoint[1])**2 + (Bpoint[0] - Apoint[0])**2)
+
+def lineBiasAndK(Apoint, Bpoint):
+
+    K = pointAngle(Apoint, Bpoint)
+    B = Apoint[1] - K*Apoint[0]
+    return K, B
+
+def getX(K, B, Ypoint):
+    return int((Ypoint-B)/K)
+
+def sidePoint(Apoint, Bpoint, h, w, placehold, enlarge_size):
+
+    K, B = lineBiasAndK(Apoint, Bpoint)
+    angle = abs(math.atan(pointAngle(Apoint, Bpoint)))
+    distance = pointDistance(Apoint, Bpoint)
+
+    x_enlarge_size, y_enlarge_size = enlarge_size
+
+    XaxisIncreaseDistance = abs(math.cos(angle) * x_enlarge_size * distance)
+    YaxisIncreaseDistance = abs(math.sin(angle) * y_enlarge_size * distance)
+
+    if placehold == 'leftTop':
+        x1 = max(0, Apoint[0] - XaxisIncreaseDistance)
+        y1 = max(0, Apoint[1] - YaxisIncreaseDistance)
+    elif placehold == 'rightTop':
+        x1 = min(w, Bpoint[0] + XaxisIncreaseDistance)
+        y1 = max(0, Bpoint[1] - YaxisIncreaseDistance)
+    elif placehold == 'rightBottom':
+        x1 = min(w, Bpoint[0] + XaxisIncreaseDistance)
+        y1 = min(h, Bpoint[1] + YaxisIncreaseDistance)
+    elif placehold == 'leftBottom':
+        x1 = max(0, Apoint[0] - XaxisIncreaseDistance)
+        y1 = min(h, Apoint[1] + YaxisIncreaseDistance)
+    return int(x1), int(y1)
+
+def enlargebox(box, h, w, enlarge_size, horizontal_text_bool):
+
+    if not horizontal_text_bool:
+        enlarge_size = (enlarge_size[1], enlarge_size[0])
+
+    box = np.roll(box, -np.argmin(box.sum(axis=1)), axis=0)
+
+    Apoint, Bpoint, Cpoint, Dpoint = box
+    K1, B1 = lineBiasAndK(box[0], box[2])
+    K2, B2 = lineBiasAndK(box[3], box[1])
+    X = (B2 - B1)/(K1 - K2)
+    Y = K1 * X + B1
+    center = [X, Y]
+
+    x1, y1 = sidePoint(Apoint, center, h, w, 'leftTop', enlarge_size)
+    x2, y2 = sidePoint(center, Bpoint, h, w, 'rightTop', enlarge_size)
+    x3, y3 = sidePoint(center, Cpoint, h, w, 'rightBottom', enlarge_size)
+    x4, y4 = sidePoint(Dpoint, center, h, w, 'leftBottom', enlarge_size)
+    newcharbox = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])
+    return newcharbox