grayscale_clahe.py

from skimage import exposure

def grayscale_exposure_equalize(batch_x_y):
    """Processes a batch with images by grayscaling, normalization and
    histogram equalization.
    
    Args:
        batch_x_y: a single batch of data containing a numpy array of images
            and a list of corresponding labels.
    
    Returns:
        Numpy array of processed images and a list of labels (unchanged).
    """
    x_sub, y_sub = batch_x_y[0], batch_x_y[1]
    x_processed_sub = numpy.zeros(x_sub.shape[:-1])
    for x in range(len(x_sub)):
        # Grayscale
        img_gray = numpy.dot(x_sub[x][...,:3], [0.299, 0.587, 0.114])
        # Normalization
        img_gray_norm = img_gray / (img_gray.max() + 1)
        # CLAHE. num_bins will be initialized in ipyparallel client
        img_gray_norm = exposure.equalize_adapthist(img_gray_norm, nbins=num_bins)
        
        x_processed_sub[x,...] = img_gray_norm
    return (x_processed_sub, y_sub)