ValueError: Unexpected detail coefficient type: <class 'tuple'>. Detail coefficients must be a 3-tuple of arrays as returned by wavedec2. If you are using pywt.array_to_coeffs or pywt.unravel_coeffs, please specify output_format='wavedec2'

非常抱歉，看来我在修改代码时出现了新的错误。出现 `ValueError: Unexpected detail coefficient type: <class 'tuple'>...` 的错误是因为在量化子带时，遇到了一个非预期的类型，这可能是因为前面的DWT分解没有使用正确的输出格式。请在DWT分解时，将输出格式设置为 'wavedec2'，即： ```python coeffs = pywt.wavedec2(img, wavelet, level=level, mode='symmetric', axis=(-2,-1), output_format='wavedec2') ``` 这样就可以避免这个错误了。下面是修改后的完整代码： ```python import numpy as np import cv2 import pywt def dwt_compress(img, wavelet='haar', level=1): # 转换为浮点数类型 img = np.float32(img) # DWT分解 coeffs = pywt.wavedec2(img, wavelet, level=level, mode='symmetric', axis=(-2,-1), output_format='wavedec2') # 将每个子带进行量化 coeffs_list = list(coeffs) for i in range(1, len(coeffs_list)): coeffs_list[i] = (np.round(coeffs_list[i][0] / (2 ** (level - i + 1))) * (2 ** (level - i + 1)), coeffs_list[i][1], coeffs_list[i][2]) # DWT重构 img_compress = pywt.waverec2(coeffs_list, wavelet) # 转换为整数类型 img_compress = np.uint8(img_compress) return img_compress # 读取原始图像 img = cv2.imread('test.jpg', cv2.IMREAD_COLOR) # 对原始图像进行DWT压缩 img_compress = dwt_compress(img, wavelet='haar', level=1) # 计算PSNR psnr = cv2.PSNR(img, img_compress) print('PSNR:', psnr) ``` 希望这次能够解决您的问题，再次感谢您的指正！