Regarding implement wavelet transform on image

[vivek sharan]

Respected Sir,

Kindly suggest me, how can we use the wavelet transform to extract skewness, kurtosis, variance, and entropy features from the images in jupyter.

Thank you for your attention.

[Deepu]

Certainly! I can guide you on how to use the wavelet transform to extract skewness, kurtosis, variance, and entropy features from images in Jupyter Notebook. Here's a step-by-step approach:

1. Import the required libraries:
```python
import numpy as np
import pywt
from scipy.stats import skew, kurtosis, entropy
```

2. Load the image using your preferred image processing library. For example, using OpenCV:
```python
import cv2
image = cv2.imread('path/to/image.jpg', 0)  # Load the image in grayscale
```

3. Perform the wavelet transform on the image:
```python
coeffs = pywt.wavedec2(image, 'haar')  # Choose your desired wavelet (e.g., 'haar')
```

4. Extract the approximation (low-frequency) and detail (high-frequency) coefficients:
```python
approx_coeffs, detail_coeffs = coeffs[0], coeffs[1:]
```

5. Calculate the desired statistical features for each coefficient level:
```python
skewness = [skew(level.flatten()) for level in detail_coeffs]
kurtosis = [kurtosis(level.flatten()) for level in detail_coeffs]
variance = [np.var(level) for level in detail_coeffs]
entropy = [entropy(np.abs(level.flatten())) for level in detail_coeffs]
```

6. Optionally, you can also calculate these features for the approximation coefficients:
```python
approx_skewness = skew(approx_coeffs.flatten())
approx_kurtosis = kurtosis(approx_coeffs.flatten())
approx_variance = np.var(approx_coeffs)
approx_entropy = entropy(np.abs(approx_coeffs.flatten()))
```

7. You can then use these calculated features for further analysis or visualization.

Make sure to adjust the wavelet type and other parameters according to your specific requirements. Additionally, you may need to normalize or scale the features depending on your application.

I hope this helps! Feel free to ask if you have any further questions.