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misc_functions.py
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misc_functions.py
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"""
Created on Thu Oct 21 11:09:09 2017
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import os
import copy
import numpy as np
from PIL import Image, ImageFilter
import matplotlib.cm as mpl_color_map
import torch
from torch.autograd import Variable
from torchvision import models
def convert_to_grayscale(im_as_arr):
"""
Converts 3d image to grayscale
Args:
im_as_arr (numpy arr): RGB image with shape (D,W,H)
returns:
grayscale_im (numpy_arr): Grayscale image with shape (1,W,D)
"""
grayscale_im = np.sum(np.abs(im_as_arr), axis=0)
im_max = np.percentile(grayscale_im, 99)
im_min = np.min(grayscale_im)
grayscale_im = (np.clip((grayscale_im - im_min) / (im_max - im_min), 0, 1))
grayscale_im = np.expand_dims(grayscale_im, axis=0)
return grayscale_im
def save_gradient_images(gradient, file_path):
# Normalize
gradient = gradient - gradient.min()
gradient /= gradient.max()
# Save image
save_image(gradient, file_path)
def save_class_activation_images(org_img, activation_map, file_path):
"""
Saves cam activation map and activation map on the original image
Args:
org_img (PIL img): Original image
activation_map (numpy arr): Activation map (grayscale) 0-255
file_name (str): File name of the exported image
"""
# Grayscale activation map
heatmap, heatmap_on_image = apply_colormap_on_image(org_img, activation_map, 'jet')
# Save colored heatmap
path_to_file = file_path + '_Cam_Heatmap.png'
save_image(heatmap, path_to_file)
# Save heatmap on iamge
path_to_file = file_path + '_Cam_On_Image.png'
save_image(heatmap_on_image, path_to_file)
# SAve grayscale heatmap
path_to_file = file_path + '_Cam_Grayscale.png'
save_image(activation_map, path_to_file)
def apply_colormap_on_image(org_im, activation, colormap_name):
"""
Apply heatmap on image
Args:
org_img (PIL img): Original image
activation_map (numpy arr): Activation map (grayscale) 0-255
colormap_name (str): Name of the colormap
"""
# Get colormap
color_map = mpl_color_map.get_cmap(colormap_name)
no_trans_heatmap = color_map(activation)
# Change alpha channel in colormap to make sure original image is displayed
heatmap = copy.copy(no_trans_heatmap)
heatmap[:, :, 3] = 0.4
heatmap = Image.fromarray((heatmap*255).astype(np.uint8))
no_trans_heatmap = Image.fromarray((no_trans_heatmap*255).astype(np.uint8))
# Apply heatmap on iamge
heatmap_on_image = Image.new("RGBA", org_im.size)
heatmap_on_image = Image.alpha_composite(heatmap_on_image, org_im.convert('RGBA'))
heatmap_on_image = Image.alpha_composite(heatmap_on_image, heatmap)
return no_trans_heatmap, heatmap_on_image
def format_np_output(np_arr):
"""
This is a (kind of) bandaid fix to streamline saving procedure.
It converts all the outputs to the same format which is 3xWxH
with using sucecssive if clauses.
Args:
im_as_arr (Numpy array): Matrix of shape 1xWxH or WxH or 3xWxH
"""
# Phase/Case 1: The np arr only has 2 dimensions
# Result: Add a dimension at the beginning
if len(np_arr.shape) == 2:
np_arr = np.expand_dims(np_arr, axis=0)
# Phase/Case 2: Np arr has only 1 channel (assuming first dim is channel)
# Result: Repeat first channel and convert 1xWxH to 3xWxH
if np_arr.shape[0] == 1:
np_arr = np.repeat(np_arr, 3, axis=0)
# Phase/Case 3: Np arr is of shape 3xWxH
# Result: Convert it to WxHx3 in order to make it saveable by PIL
if np_arr.shape[0] == 3:
np_arr = np_arr.transpose(1, 2, 0)
# Phase/Case 4: NP arr is normalized between 0-1
# Result: Multiply with 255 and change type to make it saveable by PIL
if np.max(np_arr) <= 1:
np_arr = (np_arr*255).astype(np.uint8)
return np_arr
def save_image(im, path):
"""
Saves a numpy matrix or PIL image as an image
Args:
im_as_arr (Numpy array): Matrix of shape DxWxH
path (str): Path to the image
"""
if isinstance(im, (np.ndarray, np.generic)):
im = format_np_output(im)
im = Image.fromarray(im)
im.save(path)