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visualization.py
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visualization.py
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import image_processing
import matplotlib as mpl
mpl.use('TkAgg') # hack around bug in matplotlib. see https://stackoverflow.com/questions/21784641/installation-issue-with-matplotlib-python
from matplotlib import pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import scipy as sp
import typing
def show_image(
location: typing.Tuple[int, int, int],
title: str,
img: np.ndarray,
width: int = 15,
height: int = 3,
open_new_window: bool = True,
vmin: float =-5000.0,
vmax: float = 5000.0,
cmap: str = 'gray',
fontsize: int = 10,
postprocessing: bool = False) -> None:
"""
Displays an image in a multi-image display window
:param location: (r,c,n) tuple where r is the # of display rows, c is the # of display cols, and n is the position for img
:param title: string with title
:param img: ndarray with image
:param width: int for width
:param open_new_window: boolean true if you have not already created a new plt figure
:param vmin: float min value to display in single layer image
:param vmax: float max value to display in single layer image
:param cmap: colormap for display of single layer images
"""
if postprocessing:
img = image_processing.postprocess_segmentation_images(img)
if open_new_window:
plt.figure(figsize=(height, width))
plt.subplot(*location)
plt.title(title, fontsize=fontsize)
plt.axis('off')
if len(img.shape) == 3:
plt.imshow(img)
else:
plt.imshow(img, cmap=cmap, vmin=vmin, vmax=vmax)
if open_new_window:
plt.show()
plt.close()
def visualize_algorithm_state(
image: np.ndarray,
responsibilities: np.ndarray,
components: int,
i: int,
iterations: int,
means_list: typing.List[float],
stdevs_list: typing.List[float],
log_likelihoods_list: typing.List[float],
init_means_list: typing.List[float],
init_variances_list: typing.List[float],
init_stdevs_list: typing.List[float],
init_weights_list: typing.List[float],
init_log_likelihoods: typing.List[float]) -> None:
"""
:param image: ndarray with grayscale image
:param responsibilities: NxMxK matrix of responsibility values as defined in equation 9.23
:param i: current iteration index
:param iterations: total number of iterations
:param means_list: list of mean values, one for each component
:param stdevs_list: list of stdev values, one for each component
"""
segmentation_output = image_processing.compute_segmentation(responsibilities, means_list)
'''
Visualizations 1 and 2 are subplots of the same figure to show the evolution of the state of the algorithm.
'''
plt.figure(0)
# Visualization 1: segmentation image
show_image((2, iterations, 1 + i),
"Segmentation #" + str(i),
segmentation_output,
width=60,
height=30,
fontsize=14,
vmin=np.min(segmentation_output),
vmax=np.max(segmentation_output),
open_new_window=False,
postprocessing=False)
# Visualization 2: Show Gaussian curves of each model.
curve_points_input = np.linspace(0, 1, 1000)
for k in range(components):
plt.subplot(2, iterations, 1 + iterations + i)
plt.title("Gaussian Model Curves", fontsize=10)
plt.xlabel("Pixel Values")
if i == 0:
plt.ylabel("Responsibility")
plt.ylim([0, 10])
if means_list[k] < 0.99: # use mean value to color the line; if the mean is too high the line will be invisible (white)
plt.plot(curve_points_input,
sp.stats.norm.pdf(curve_points_input, means_list[k], stdevs_list[k]),
color=(means_list[k], means_list[k], means_list[k]))
else: # therefore if the line will be invisible, make it a dotted line instead
plt.scatter(curve_points_input[1:1000:40],
sp.stats.norm.pdf(curve_points_input[1:1000:40], means_list[k], stdevs_list[k]),
color=(0, 0, 0), marker='.')
'''
Visualizations 3 and 4 created only on the final Expectation Maximization iteration to summarize the results.
'''
if i == iterations - 1:
# Visualization 3: On final iteration, show un-segmented and final segmentation results on the final iteration
plt.figure(1)
ax = plt.subplot(3, iterations, 1 + iterations + i)
plt.subplot(3, 2, 1)
plt.title("Original Image", fontsize=10)
plt.imshow(image, cmap='gray', vmin=np.min(image), vmax=np.max(segmentation_output))
plt.subplot(3, 2, 2)
plt.title("Final Segmented Image After " + str(iterations) + " Iterations", fontsize=10)
postprocessed_segmentation_output = image_processing.postprocess_segmentation_images(segmentation_output)
plt.imshow(postprocessed_segmentation_output, cmap='gray', vmin=np.min(segmentation_output), vmax=np.max(segmentation_output))
plt.subplot(3, 2, 3)
plt.title("Pixel Value Histogram", fontsize=10)
plt.ylabel("# Occurrences")
ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
plt.hist(image.flatten(), bins=128)
plt.subplot(3, 2, 4)
plt.title("Pixel Value Histogram", fontsize=10)
ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
plt.hist(image.flatten(), bins=128)
plt.subplot(3, 2, 5)
plt.title("Initial Gaussian Mixture Curves", fontsize=10)
plt.xlabel("Pixel Values")
plt.ylabel("Responsibility")
for k in range(components):
if init_means_list[k] < 0.99: # use mean value to color the line; if the mean is too high the line will be invisible (white)
plt.plot(curve_points_input,
sp.stats.norm.pdf(curve_points_input, init_means_list[k], init_stdevs_list[k]),
color=(init_means_list[k], init_means_list[k], init_means_list[k]))
else: # therefore if the line will be invisible, make it a dotted line instead
plt.scatter(curve_points_input[1:1000:40],
sp.stats.norm.pdf(curve_points_input[1:1000:40], init_means_list[k], init_stdevs_list[k]),
color=(0, 0, 0), marker='.')
plt.subplot(3, 2, 6)
plt.title("Final Gaussian Mixture Curves After " + str(iterations) + " Iterations", fontsize=10)
plt.xlabel("Pixel Values")
for k in range(components):
if means_list[k] < 0.99: # use mean value to color the line; if the mean is too high the line will be invisible (white)
plt.plot(curve_points_input,
sp.stats.norm.pdf(curve_points_input, means_list[k], stdevs_list[k]),
color=(means_list[k], means_list[k], means_list[k]))
else: # therefore if the line will be invisible, make it a dotted line instead
plt.scatter(curve_points_input[1:1000:40],
sp.stats.norm.pdf(curve_points_input[1:1000:40], means_list[k], stdevs_list[k]),
color=(0, 0, 0), marker='.')
plt.tight_layout(pad=0.8, w_pad=0.8, h_pad=0.8)
# Visualization 4: On final iteration, show plot of total log likelihood
plt.figure(2)
plt.title("Total Log Likelihood")
plt.xlabel("Iteration")
plt.ylabel("Total Log Likelihood")
x = np.linspace(1, iterations, iterations)
plt.plot(x, log_likelihoods_list[:], 'ro')
plt.xticks(x, x)