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https://github.com/balajsra/EECS-442-WN19-DIC-Project.git
synced 2025-08-31 08:13:13 +00:00
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.
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@@ -3,6 +3,7 @@ import cv2
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from matplotlib import pyplot as plt
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import os
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import file_data
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from image_data import ImageData
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def main():
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@@ -11,7 +12,7 @@ def main():
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# Read in data from Section001_Data.txt
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specimen, load_disp_data = file_data.read_file("../Section001_Data.txt")
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# Keep track of Stress and Strains
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stresses = []
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strains = []
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@@ -111,21 +112,31 @@ def find_displacement(match_method):
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plt.figure(1)
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reference = images[8]
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compare_img = images[12]
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compare_img = images[9]
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plt.imshow(reference, cmap="gray", vmin=0, vmax=255)
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subset_size = 5
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subset_spacing = 20
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subset_spacing = 30
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search_size = 3
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x_range = range(650, 2080, subset_spacing)
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y_range = range(120, 500, subset_spacing)
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im_data = ImageData(len(y_range), len(x_range))
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for i in range(len(x_range)):
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for j in range(len(y_range)):
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x = x_range[i]
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y = y_range[j]
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for x in range(650, 2080, subset_spacing):
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for y in range(120, 500, subset_spacing):
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up_bound = (subset_size + 1) // 2
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low_bound = subset_size // 2
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subset = reference[y-low_bound:y+up_bound, x-low_bound:x+up_bound]
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search = compare_img[y-search_size:y+search_size+1, x-search_size:x+search_size+1]
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res = cv2.matchTemplate(image=compare_img, templ=subset, method=match_method)
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res = cv2.matchTemplate(image=search, templ=subset, method=match_method)
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minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(res)
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@@ -133,21 +144,27 @@ def find_displacement(match_method):
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dy = None
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if match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
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dx = minLoc[0] - x
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dy = minLoc[1] - y
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dx = minLoc[0] - search_size
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dy = minLoc[1] - search_size
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elif match_method in [cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
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cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
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dx = maxLoc[0] - x
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dy = maxLoc[1] - y
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dx = maxLoc[0] - search_size
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dy = maxLoc[1] - search_size
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plt.arrow(x=x, y=y, dx=dx, dy=dy, color="yellow", length_includes_head=True, shape="full")
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im_data.dx[j, i] = dx
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im_data.dy[j, i] = dy
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im_data.disp_mag[j, i] = np.sqrt((dx ** 2) + (dy ** 2))
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plt.quiver(x_range, y_range, im_data.dx, im_data.dy, im_data.disp_mag, cmap=plt.cm.jet)
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plt.show()
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if __name__ == '__main__':
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# main()
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for match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED,
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cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
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cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
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find_displacement(match_method)
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find_displacement(cv2.TM_SQDIFF_NORMED)
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# for match_method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED,
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# cv2.TM_CCORR, cv2.TM_CCORR_NORMED,
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# cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED]:
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# find_displacement(match_method)
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33
src/image_data.py
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33
src/image_data.py
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@@ -0,0 +1,33 @@
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#############################################################
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# EECS 442: Computer Vision - W19 #
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#############################################################
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# Authors: Sravan Balaji & Kevin Monpara #
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# Filename: image_data.py #
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# Description: #
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# #
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#############################################################
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import numpy as np
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class ImageData:
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# Displacement Data
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dx = None
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dy = None
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disp_mag = None
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# Strain Data
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eps_x = None
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eps_y = None
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eps_mag = None
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def __init__(self, num_rows, num_cols):
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matrix_shape = (num_rows, num_cols)
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self.dx = np.zeros(matrix_shape)
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self.dy = np.zeros(matrix_shape)
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self.disp_mag = np.zeros(matrix_shape)
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self.eps_x = np.zeros(matrix_shape)
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self.eps_y = np.zeros(matrix_shape)
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self.eps_mag = np.zeros(matrix_shape)
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