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()