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Added final report and final comments

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* Added final report to repo
* Added final comments to source code
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Sravan Balaji
2019-04-28 23:10:21 -04:00
parent edc19e1a84
commit 336d02a144
4 changed files with 89 additions and 82 deletions
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EECS_442_Computer_Vision_DIC_Project.pdf Normal file
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168
src/__main__.py
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@@ -1,3 +1,14 @@
#############################################################
# EECS 442: Computer Vision - W19 #
#############################################################
# Authors: Sravan Balaji & Kevin Monpara #
# Filename: __main__.py #
# Description: #
# Perform DIC and generate various plots for Young's #
# modulus, Poisson's ratio, matching method comparisons, #
# etc. #
#############################################################
import numpy as np
import cv2
from matplotlib import pyplot as plt
@@ -9,6 +20,7 @@ PIXELS_TO_MM = None
def read_images():
# Read images into numpy arrays
image_dir = '../Images/'
filenames = os.listdir(image_dir)
@@ -24,6 +36,7 @@ def read_images():
def find_displacement(images, ref_index, comp_index, match_method,
ref_template_size, search_window_size, grid_spacing):
# Compare subsets from reference image to compare_img to find displacement and strain
reference = images[ref_index]
compare_img = images[comp_index]
@@ -89,35 +102,11 @@ def find_displacement(images, ref_index, comp_index, match_method,
def plot_disp_and_strain(ref_img, img_data):
plt.figure()
# Subplot 1: Displacement
# ax = plt.subplot(3, 1, 1)
# ax.title.set_text("Displacements between image " + str(img_data.reference_index)
# + " and image " + str(img_data.comparison_index))
#
# plt.imshow(ref_img, # Show reference image
# cmap="gray", # Grayscale
# vmin=0, # Minimum pixel value
# vmax=255, # Maximum pixel value
# origin="lower") # Flip image so increasing row corresponds to increasing y
#
# # Displacement magnitude in mm
# disp_mag = np.sqrt(img_data.displacement[:, :, 0] ** 2 + img_data.displacement[:, :, 1] ** 2) * PIXELS_TO_MM
#
# plt.quiver(img_data.location[:, :, 0], # x coordinates of arrow locations
# img_data.location[:, :, 1], # y coordinates of arrow locations
# img_data.displacement[:, :, 0], # x components of arrow vectors
# img_data.displacement[:, :, 1], # y components of arrow vectors
# disp_mag, # arrow color (vector magnitude)
# cmap=plt.cm.jet, # color map (jet)
# units="dots", # units of arrow dimensions
# angles="xy") # arrows point from (x, y) to (x + u, y + v)
#
# plt.colorbar(ax=ax)
disp_matrix = np.zeros((ref_img.shape[0], ref_img.shape[1], 2))
strain_matrix = np.zeros((ref_img.shape[0], ref_img.shape[1], 2))
rows, cols, channels = img_data.location.shape
# Expand displacement and strain values into larger pixel windows for visualization
for r in range(rows):
for c in range(cols):
row_start = int(img_data.location[0, 0, 1] + r * img_data.grid_spacing)
@@ -157,10 +146,10 @@ def plot_disp_and_strain(ref_img, img_data):
+ " and image " + str(img_data.comparison_index))
ax.imshow(
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
origin="lower" # Flip image so increasing row corresponds to increasing y
)
@@ -176,10 +165,10 @@ def plot_disp_and_strain(ref_img, img_data):
+ " and image " + str(img_data.comparison_index))
ax.imshow(
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
origin="lower" # Flip image so increasing row corresponds to increasing y
)
@@ -195,10 +184,10 @@ def plot_disp_and_strain(ref_img, img_data):
+ " and image " + str(img_data.comparison_index))
ax.imshow(
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
ref_img, # Show reference image
cmap="gray", # Grayscale
vmin=0, # Minimum pixel value
vmax=255, # Maximum pixel value
origin="lower" # Flip image so increasing row corresponds to increasing y
)
@@ -270,6 +259,7 @@ def compare_matching_time():
methods = ["SQDIFF", "SQDIFF_NORMED", "CCORR", "CCORR_NORMED", "CCOEFF", "CCOEFF_NORMED"]
# Runtime in seconds from 3 trials
SQDIFF = [6.969048976898193, 6.824753761291504, 6.946980714797974]
SQDIFF_NORMED = [7.051121950149536, 7.040151357650757, 6.851653814315796]
CCORR = [7.0052430629730225, 6.867607116699219, 6.804780006408691]
@@ -292,6 +282,7 @@ def compare_matching_time():
def plot_frame_data(frame_data):
# Use data from LabVIEW file to plot displacement, load, and stress curves
plt.figure()
x = []
@@ -324,6 +315,7 @@ def plot_frame_data(frame_data):
def stress_strain_curve(specimen, frame_data, strain):
# Plot stress strain curve using LabVIEW file and DIC to compare results
plt.figure()
x = []
@@ -350,6 +342,7 @@ def stress_strain_curve(specimen, frame_data, strain):
def strain_measurement(images, match_method, ref_template_size, search_window_size, pt1, pt2):
# Pick 2 points on image, track their displacements, compute the strain of the line connecting them
orig_len_x = np.abs(pt2[0] - pt1[0])
orig_len_y = np.abs(pt2[1] - pt1[1])
@@ -407,6 +400,7 @@ def strain_measurement(images, match_method, ref_template_size, search_window_si
def poisson_ratio(strain_x, strain_y):
# Plot Transverse vs. Axial strain
plt.figure()
plt.title("Transverse vs. Axial Strain")
plt.xlabel("Axial Strain")
@@ -417,48 +411,56 @@ def poisson_ratio(strain_x, strain_y):
if __name__ == '__main__':
# Read in images from Images folder
images = read_images()
# Read in numerical data from LabVIEW output file
specimen, frame_data = file_data.read_file("../Section001_Data.txt")
# Width = 16.61 mm = 404 pixels
PIXELS_TO_MM = specimen.w / 404
# plot_frame_data(frame_data)
# Plot data from LabVIEW file
plot_frame_data(frame_data)
# strain_x, strain_y = strain_measurement(
# images=images,
# match_method=cv2.TM_CCORR_NORMED,
# ref_template_size=(9, 9),
# search_window_size=(5, 3),
# pt1=[725, 200],
# pt2=[1975, 400]
# )
#
# stress_strain_curve(specimen, frame_data, strain_x)
#
# poisson_ratio(strain_x, strain_y)
#
# compare_matching_time()
#
# compare_matching_quality(
# images=images,
# ref_index=8,
# comp_index=9,
# ref_template_size=(5, 5),
# search_window_size=(10, 5),
# grid_spacing=5
# )
# compare_matching_quality(
# images=images,
# ref_index=560,
# comp_index=561,
# ref_template_size=(5, 5),
# search_window_size=(10, 5),
# grid_spacing=5
# )
# Get axial and transverse strain measurements using 2 points
strain_x, strain_y = strain_measurement(
images=images,
match_method=cv2.TM_CCORR_NORMED,
ref_template_size=(9, 9),
search_window_size=(5, 3),
pt1=[725, 200],
pt2=[1975, 400]
)
# Matching Methods
# Plot Stress-Strain curve from LabVIEW data as well as
stress_strain_curve(specimen, frame_data, strain_x)
# Plot Transverse vs Axial strain to get Poisson's Ratio
poisson_ratio(strain_x, strain_y)
# Plot runtime of different matching methods
compare_matching_time()
# Plot displacement using different matching methods
compare_matching_quality(
images=images,
ref_index=8,
comp_index=9,
ref_template_size=(5, 5),
search_window_size=(10, 5),
grid_spacing=5
)
compare_matching_quality(
images=images,
ref_index=560,
comp_index=561,
ref_template_size=(5, 5),
search_window_size=(10, 5),
grid_spacing=5
)
# Matching Methods available in OpenCV
# cv2.TM_SQDIFF
# cv2.TM_SQDIFF_NORMED
# cv2.TM_CCORR
@@ -466,19 +468,21 @@ if __name__ == '__main__':
# cv2.TM_CCOEFF
# cv2.TM_CCOEFF_NORMED
# for i in range(8, 680, 25):
# reference_img, img_data = find_displacement(
# images=images,
# ref_index=i,
# comp_index=i+5,
# match_method=cv2.TM_CCORR_NORMED,
# ref_template_size=(5, 5),
# search_window_size=(10, 5),
# grid_spacing=5
# )
#
# plot_disp_and_strain(reference_img, img_data)
# Generate displacement and strain plots at regular intervals in tensile test
for i in range(8, 680, 25):
reference_img, img_data = find_displacement(
images=images,
ref_index=i,
comp_index=i+5,
match_method=cv2.TM_CCORR_NORMED,
ref_template_size=(5, 5),
search_window_size=(10, 5),
grid_spacing=5
)
plot_disp_and_strain(reference_img, img_data)
# Generate displacement and strain plot for image right before fracture
reference_img, img_data = find_displacement(
images=images,
ref_index=657,

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src/file_data.py
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@@ -38,6 +38,7 @@ def read_file(filepath):
data_start = -1
in_frame_data = False
# Read lines of file into specimen variables and frame data
for line in file:
if "Width" in line:
index = line.find("\t")

2
src/image_data.py
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@@ -4,6 +4,8 @@
# Authors: Sravan Balaji & Kevin Monpara #
# Filename: image_data.py #
# Description: #
# Class definition for ImageData. Holds information about #
# displacement and strain between two images #
# #
#############################################################