import cv2 as cv                  # 导入OpenCV库
import numpy as np                # 导入numpy库
import matplotlib.pyplot as plt   # 导入matplotlib的pyplot库
import os                         # 导入os库

# 确保文件路径正确
image_path = 'hanzi1.jpg'  # 去掉了错误的引号

# 读取图片，0表示灰度图像
img = cv.imread(image_path, 0)
if img is None:
    print(f"Error: 无法加载图像 {image_path}")
else:
    plt.subplot(1, 7, 1)
    plt.imshow(img, cmap='gray')
    plt.axis('off')
#全局阈值处理
blur = cv.GaussianBlur(img, (5, 5), 0) # 阈值处理的效果受噪声影响较大，常需要先进行滤波等预处理
_, th = cv.threshold(blur, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
th1_inv = 255 -th
plt.subplot(1,7,2)
plt.imshow(th1_inv,cmap='gray')
plt.axis('off')

#腐蚀
kernel1 = np.ones((3,3), np.uint8)
eroded_img = cv.erode(th1_inv, kernel1, iterations=4)
plt.subplot(1,7,3)
plt.imshow(eroded_img,cmap='gray')
plt.axis('off')
#膨胀
kernel2 = cv.getStructuringElement(cv.MORPH_CROSS, (4, 4)).astype(np.uint8)
dilated_img = cv.dilate(eroded_img, kernel2,iterations=8)
plt.subplot(1,7,4)
plt.imshow(dilated_img,cmap='gray')
plt.axis('off')
#闭运算
kernel3 = np.ones((5, 5), np.uint8)
closed_img = cv.morphologyEx(dilated_img, cv.MORPH_CLOSE, kernel3,iterations=25)
plt.subplot(1,7,5)
plt.imshow(closed_img,cmap='gray')
plt.axis('off')
#边缘检测
img_blur = cv.GaussianBlur(closed_img,(3,3), 0) #高斯滤波
canny = cv.Canny(img_blur, 100, 200) #Canny边缘检测
contours, hierarchy = cv.findContours(canny, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) # 查找轮廓
cv.drawContours(canny, contours, -1, (255), 2) # 画边界框
plt.subplot(1,7,6)
plt.imshow(canny,cmap='gray')
plt.axis('off')
#轮廓检测
img_copy = th.copy()# 复制图像以绘制边界框
valid_contours_indices = []# 创建一个列表来存储通过周长筛选的轮廓索引
for i, c in enumerate(contours):
    perimeter = cv.arcLength(c, True)
    print("轮廓周长：", perimeter)
    x, y, w, h = cv.boundingRect(c)
    # 根据周长阈值筛选并绘制边界框
    if perimeter > 350 and perimeter < 600:
        cv.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
        valid_contours_indices.append(i)# 将符合条件的轮廓索引添加到列表中
        chars_folder = 'chars'
        if not os.path.exists(chars_folder):
          os.makedirs(chars_folder)
    for i in valid_contours_indices:
    # 获取当前轮廓的边界框坐标
        x, y, w, h = cv.boundingRect(contours[i])
        # 切割图像
        roi = th[y:y+h+15, x:x+w+15]
        # 创建文件保存路径
        filename = os.path.join(chars_folder, f'{i}.jpg')
        # 保存图像
        cv.imwrite(filename, roi)
plt.subplot(1,7,7)
plt.imshow(img_copy,cmap='gray')
plt.axis('off')
plt.show()