Restrict Search Size

* Added variables to restrict search size for matching pattern.
* Rotated images back to normal that were rotated for some reason
* Created an ImageData class to hold displacements and strains in each image.
This commit is contained in:
Sravan Balaji
2019-04-24 19:04:39 -04:00
parent 3557a5d804
commit cbb8e954e8
4 changed files with 65 additions and 15 deletions

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@@ -3,6 +3,7 @@ import cv2
from matplotlib import pyplot as plt
import os
import file_data
from image_data import ImageData
def main():
@@ -11,7 +12,7 @@ def main():
# Read in data from Section001_Data.txt
specimen, load_disp_data = file_data.read_file("../Section001_Data.txt")
# Keep track of Stress and Strains
stresses = []
strains = []
@@ -111,21 +112,31 @@ def find_displacement(match_method):
plt.figure(1)
reference = images[8]
compare_img = images[12]
compare_img = images[9]
plt.imshow(reference, cmap="gray", vmin=0, vmax=255)
subset_size = 5
subset_spacing = 20
subset_spacing = 30
search_size = 3
x_range = range(650, 2080, subset_spacing)
y_range = range(120, 500, subset_spacing)
im_data = ImageData(len(y_range), len(x_range))
for i in range(len(x_range)):
for j in range(len(y_range)):
x = x_range[i]
y = y_range[j]
for x in range(650, 2080, subset_spacing):
for y in range(120, 500, subset_spacing):
up_bound = (subset_size + 1) // 2
low_bound = subset_size // 2
subset = reference[y-low_bound:y+up_bound, x-low_bound:x+up_bound]
search = compare_img[y-search_size:y+search_size+1, x-search_size:x+search_size+1]
res = cv2.matchTemplate(image=compare_img, templ=subset, method=match_method)
res = cv2.matchTemplate(image=search, templ=subset, method=match_method)
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(res)
@@ -133,21 +144,27 @@ def find_displacement(match_method):
dy = None
if match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
dx = minLoc[0] - x
dy = minLoc[1] - y
dx = minLoc[0] - search_size
dy = minLoc[1] - search_size
elif match_method in [cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
dx = maxLoc[0] - x
dy = maxLoc[1] - y
dx = maxLoc[0] - search_size
dy = maxLoc[1] - search_size
plt.arrow(x=x, y=y, dx=dx, dy=dy, color="yellow", length_includes_head=True, shape="full")
im_data.dx[j, i] = dx
im_data.dy[j, i] = dy
im_data.disp_mag[j, i] = np.sqrt((dx ** 2) + (dy ** 2))
plt.quiver(x_range, y_range, im_data.dx, im_data.dy, im_data.disp_mag, cmap=plt.cm.jet)
plt.show()
if __name__ == '__main__':
# main()
for match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED,
cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
find_displacement(match_method)
find_displacement(cv2.TM_SQDIFF_NORMED)
# for match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED,
# cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
# cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
# find_displacement(match_method)

33
src/image_data.py Normal file
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@@ -0,0 +1,33 @@
#############################################################
# EECS 442: Computer Vision - W19 #
#############################################################
# Authors: Sravan Balaji & Kevin Monpara #
# Filename: image_data.py #
# Description: #
# #
#############################################################
import numpy as np
class ImageData:
# Displacement Data
dx = None
dy = None
disp_mag = None
# Strain Data
eps_x = None
eps_y = None
eps_mag = None
def __init__(self, num_rows, num_cols):
matrix_shape = (num_rows, num_cols)
self.dx = np.zeros(matrix_shape)
self.dy = np.zeros(matrix_shape)
self.disp_mag = np.zeros(matrix_shape)
self.eps_x = np.zeros(matrix_shape)
self.eps_y = np.zeros(matrix_shape)
self.eps_mag = np.zeros(matrix_shape)