add .gitignore in the alldata directory

trainer/all_data/.gitignore

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+4digit*/**

trainer/all_data/folder.txt

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+place dataset folder here

trainer/all_data/generate_digits_random_fs_bg_fg.py

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+
+import cv2
+import numpy as np
+import os
+import random
+from PIL import Image, ImageDraw, ImageFont
+
+X_RAND_VALUE = 2
+Y_RAND_VALUE = 1
+ROTATE_ANGLE = 3
+
+
+BG_COLORS = [
+    (33, 40, 45), (36, 51, 62), (35, 37, 154),
+    (0, 38, 202), (239, 255, 255), (241, 255, 255)
+]
+
+DIGIT_COLORS = [(34, 199, 253), (25, 214, 253)]
+
+def generate_4digit_image():
+    bg_color = random.choice(BG_COLORS)
+    font_size = random.randint(24, 30)
+    font = ImageFont.truetype("arial.ttf", font_size)
+    
+    # 扩大画布尺寸(50x160)提供足够缓冲空间
+    canvas = np.zeros((50, 160, 3), dtype=np.uint8)
+    canvas[:,:] = bg_color
+    pil_img = Image.fromarray(canvas)
+    draw = ImageDraw.Draw(pil_img)
+    
+    digits = []
+    for i in range(4):
+        digit = str(random.randint(0, 9))
+        digits.append(digit)
+        x_offset = random.randint(-X_RAND_VALUE, X_RAND_VALUE)
+        y_offset = random.randint(-Y_RAND_VALUE, Y_RAND_VALUE)
+        digit_color = random.choice(DIGIT_COLORS)
+        # 调整数字绘制位置到画布中心区域
+        draw.text((20+i*32+x_offset, 12+y_offset), digit,
+                 font=font, fill=digit_color)
+    
+    angle = random.uniform(-ROTATE_ANGLE, ROTATE_ANGLE)
+    rotated = pil_img.rotate(angle, expand=True, fillcolor=bg_color)
+    # 安全裁剪区域(从扩大后的画布中心裁剪)
+    rotated = rotated.crop((20, 10, 148, 42))
+    
+    return np.array(rotated), ''.join(digits)
+
+def generate_train_dataset(num_samples=1000):
+    os.makedirs('4digit_train', exist_ok=True)
+    with open('4digit_train/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            # print(f"type of label : {type(label)}")
+            label = str(label).zfill(4)
+            img_path = f'4digit_train/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")
+
+def generate_valid_dataset(num_samples=200):
+    os.makedirs('4digit_valid', exist_ok=True)
+    with open('4digit_valid/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            label = str(label).zfill(4)
+
+            img_path = f'4digit_valid/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")            
+
+def generate_eval_dataset(num_samples=200):
+    os.makedirs('4digit_eval', exist_ok=True)
+    with open('4digit_eval/labels.csv', 'w') as f:
+        f.write(f"filename,words\n")
+        for i in range(num_samples):
+            img, label = generate_4digit_image()
+            label = str(label).zfill(4)
+            img_path = f'4digit_eval/{i:04d}.jpg'
+            cv2.imwrite(img_path, img)
+            f.write(f"{i:04d}.jpg,{label}\n")  
+if __name__ == "__main__":
+    generate_train_dataset()
+    generate_eval_dataset()
+    generate_valid_dataset()

trainer/all_data/split_dataset.py

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+
+import os
+import shutil
+import csv
+
+def split_dataset(labels_path, img_source_dir, train_dir='train', valid_dir='valid'):
+    # 创建目标文件夹
+    os.makedirs(train_dir, exist_ok=True)
+    os.makedirs(valid_dir, exist_ok=True)
+    
+    # 初始化CSV写入器
+    train_csv = open(os.path.join(train_dir, 'labels.csv'), 'w', newline='')
+    valid_csv = open(os.path.join(valid_dir, 'labels.csv'), 'w', newline='')
+    train_writer = csv.writer(train_csv)
+    valid_writer = csv.writer(valid_csv)
+    
+    with open(labels_path, 'r') as f:
+        lines = f.readlines()
+    
+    for i, line in enumerate(lines):
+        parts = line.strip().split(',')
+        img_name = parts[0].strip()
+
+
+        label = parts[1] if len(parts) > 1 else ''
+        src_path = os.path.join(img_source_dir, img_name)
+        print(f"处理图片: {img_name}, 标签: {label}")
+        if i < 700:  # 训练集
+            dst_path = os.path.join(train_dir, img_name)
+            train_writer.writerow([img_name, label])
+        else:        # 验证集
+            dst_path = os.path.join(valid_dir, img_name)
+            valid_writer.writerow([img_name, label])
+        
+        if os.path.exists(src_path):
+            shutil.copy2(src_path, dst_path)
+        else:
+            print(f"警告：源图片不存在 {src_path}")
+    
+    train_csv.close()
+    valid_csv.close()
+
+# 使用示例
+split_dataset(
+    labels_path='en_sample/labels.csv',
+    img_source_dir='en_sample'
+)