Python Deep Learning Projects

Model data 

We will now define a function called model_data() that accepts a list of images and a list of labels. This function will apply the make_normalize() function on each image for the purpose of normalizing, and it will apply the make_encode() function on each label/segmentation array to obtain the encoded array. 

The return of this function is two lists, one containing the normalized images and the other containing the corresponding target arrays:

def model_data(images, labels):    """Function to perform normalize and encode operation on each image."""    # empty label and image list    array_lst = []    label_lst=[]        # apply normalize function on each image and encoding function on each label    for x,y in tqdm(zip(images, labels)): array_lst.append(np.rollaxis(normalized(x), ...