functions (1)

Example Python program functions (1).py Python version 3.x or newer. To check the Python version use:

python --version

Modules

• import os
• import numpy as np
• import pandas as pd
• from tqdm import tqdm
• import keras.backend as K
• import cv2
• import matplotlib.pyplot as plt
• from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
• from matplotlib.figure import Figure

Methods

• def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
• def display_activations(activation_maps, cmap='inferno'):
• def norm_pixels(img):
• def get_figure(img, img_size=(250,70), dpi=70., cmap='inferno'):
• def crop(img, xmin=700,xmax=4300,vmin=2500,vmax=16000):

Code

Python example

# Default Python's libraries
import os


import numpy as np
import pandas as pd
from tqdm import tqdm
import keras.backend as K

# Graphic libraries
import cv2
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure


def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
    '''Get model activation map at any layer.

    model: Keras ConvNet model
    model_inputs: Input image to be evaluated: Shape (1, 70, 250)
    print_shape_only: Flag to only print activation map shape
    layer_name: name or list of layer names to plot.
    Returns: Desired activation maps
    '''
    activations = []
    inp = model.input

    model_multi_inputs_cond = True
    if not isinstance(inp, list):
        # only one input! let's wrap it in a list.
        inp = [inp]
        model_multi_inputs_cond = False

    # Modifications to accept a list of layer names
    outputs = []
    if layer_name == None:
        layer_name = [model.layers[i].name for i in range(len(model.layers))]

    if layer_name != None and type(layer_name) is str:
        layer_name =[layer_name]

    for layer in model.layers:
        for name in layer_name:
            if layer.name == name:
                outputs.append(layer.output)

    funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions

    if model_multi_inputs_cond:
        list_inputs = []
        list_inputs.extend(model_inputs)
        list_inputs.append(0.)
    else:
        list_inputs = [model_inputs, 0.]

    # Learning phase. 0 = Test mode (no dropout or batch normalization)
    # layer_outputs = [func([model_inputs, 0.])[0] for func in funcs]
    layer_outputs = [func(list_inputs)[0] for func in funcs]

    for layer_activations in layer_outputs:
        activations.append(layer_activations)

    return activations


def display_activations(activation_maps, cmap='inferno'):
    '''Display activations obtained from get_activations function.

    You can parse any matplotlib built in cmap.
    '''
    batch_size = activation_maps[0].shape[0]
    assert batch_size == 1, 'One image at a time to visualize.'
    for i, activation_map in enumerate(activation_maps):
      #  print('Displaying activation map {}'.format(i))
        shape = activation_map.shape
        if len(shape) == 4:
            activations = np.hstack(np.transpose(activation_map[0], (2, 0, 1)))
        elif len(shape) == 2:
            # try to make it square as much as possible. we can skip some activations.
            activations = activation_map[0]
            num_activations = len(activations)
            if num_activations > 1024:  # too hard to display it on the screen.
                square_param = int(np.floor(np.sqrt(num_activations)))
                activations = activations[0: square_param * square_param]
                activations = np.reshape(activations, (square_param, square_param))
            else:
                activations = np.expand_dims(activations, axis=0)
        else:
            raise Exception('len(shape) = 3 has not been implemented.')

        plt.imshow(activations, interpolation='None', cmap=cmap)

    return None        


def norm_pixels(img):
    '''Normalise pixel values from the activation maps in range 0 -255'''
    pix = ((img) - img.min()) / ((img.max() - img.min()))
    return pix * 255


def get_figure(img, img_size=(250,70), dpi=70., cmap='inferno'):
    '''Get numpy array figure from matplotlib canvas. 

    '''
    h_size, v_size = img_size
    xinch = h_size / dpi
    yinch = v_size  / dpi 

    fig = plt.figure(figsize=(xinch,yinch))
    width, height = fig.get_size_inches() * fig.get_dpi() 

    canvas = FigureCanvas(fig)
    ax = fig.gca()
    ax.imshow(img,cmap=cmap)
    ax.axis('off')
    plt.close()

    canvas.draw()       # draw the canvas, cache the renderer
    image = np.fromstring(canvas.tostring_rgb(), dtype='uint8')
    img = image.reshape(int(height), int(width), 3)

    return img


def crop(img, xmin=700,xmax=4300,vmin=2500,vmax=16000):
    '''Remove matplotlib canvas from image.

    Returns: cropped image'''
    return img[xmin:xmax,vmin:vmax]

Useful Links

• Articles: https://python-commandments.org/
• Python shell: https://bsdnerds.org/learn-python/
• Tutorial: https://pythonprogramminglanguage.com/