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https://github.com/balajsra/EECS-442-WN19-DIC-Project.git
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Template Matching Changes and Small Style Fixes
* Added reference and comparison index variables to ImageData * Re-worked how template and search window is calculated and specified * Small code style changes with comments * Separated plotting from calculation of displacement and strain into separate functions * Made new function to compare matching methods * Expanded x and y ranges of grid points * Saved matching method comparison plots to file, deleted old incorrect plots
This commit is contained in:
Sravan Balaji
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186
src/__main__.py
186
src/__main__.py
@@ -20,24 +20,18 @@ def read_images():
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return images
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def find_displacement(match_method):
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images = read_images()
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def find_displacement(images, ref_index, comp_index, match_method):
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reference = images[ref_index]
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compare_img = images[comp_index]
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specimen, load_disp_data = file_data.read_file("../Section001_Data.txt")
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ref_template_size = (5, 5)
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search_window_size = (5, 3)
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grid_spacing = 10
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reference = images[8]
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compare_img = images[9]
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x_range = range(200, 2200, grid_spacing)
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y_range = range(100, 500, grid_spacing)
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subset_size = 5
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subset_spacing = 10
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search_size = 5
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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 = image_data.ImageData(len(y_range), len(x_range))
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disp_mag = np.zeros((len(y_range), len(x_range)))
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im_data = image_data.ImageData(len(y_range), len(x_range), ref_index, comp_index)
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for i in range(len(y_range)):
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for j in range(len(x_range)):
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@@ -46,90 +40,156 @@ def find_displacement(match_method):
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im_data.location[i, j, :] = np.array([x, y])
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up_bound = (subset_size + 1) // 2
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low_bound = subset_size // 2
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template_x = [x - (ref_template_size[0] // 2), x + ((ref_template_size[0] + 1) // 2)]
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template_y = [y - (ref_template_size[1] // 2), y + ((ref_template_size[1] + 1) // 2)]
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search_x = [template_x[0] - search_window_size[0], template_x[1] + search_window_size[0]]
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search_y = [template_y[0] - search_window_size[1], template_y[1] + search_window_size[1]]
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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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ref_template = reference[template_y[0]:template_y[1], template_x[0]:template_x[1]]
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search_window = compare_img[search_y[0]:search_y[1], search_x[0]:search_x[1]]
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res = cv2.matchTemplate(image=search, templ=subset, method=match_method)
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res = cv2.matchTemplate(
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image=search_window, # Search window in compare image
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templ=ref_template, # Template from reference image
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method=match_method # Matching Method
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)
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minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(src=res)
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(src=res)
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dx = None
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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] + (subset_size // 2)) - search_size
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dy = (minLoc[1] + (subset_size // 2)) - search_size
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dx = min_loc[0] - search_window_size[0]
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dy = min_loc[1] - search_window_size[1]
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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] + (subset_size // 2)) - search_size
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dy = (maxLoc[1] + (subset_size // 2)) - search_size
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dx = max_loc[0] - search_window_size[0]
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dy = max_loc[1] - search_window_size[1]
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im_data.displacement[i, j, :] = np.array([dx, dy])
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disp_mag[i, j] = np.sqrt((dx ** 2) + (dy ** 2))
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# Strain in x direction
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im_data.strain[:, :, 0] = np.gradient(
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im_data.displacement[:, :, 0], # x displacements
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im_data.location[:, 0, 1], # y (row) positions
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im_data.location[0, :, 0])[1] # x (col) positions
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im_data.location[0, :, 0] # x (col) positions
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)[1] # Gradient in cols direction
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# Strain in y direction
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im_data.strain[:, :, 1] = np.gradient(
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im_data.displacement[:, :, 1], # y displacements
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im_data.location[:, 0, 1], # y (row) positions
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im_data.location[0, :, 0])[0] # x (col) positions
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im_data.location[0, :, 0] # x (col) positions
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)[0] # Gradient in rows direction
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##########################
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# Figure 1: Displacement #
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##########################
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plt.figure(1)
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plt.subplot(2, 1, 1)
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return reference, im_data
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plt.imshow(reference, # Show reference image
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cmap="gray", # Grayscale
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vmin=0, # Minimum pixel value
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vmax=255, # Maximum pixel value
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def plot_disp_and_strain(ref_img, img_data):
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plt.figure()
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# Subplot 1: Displacement
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ax = plt.subplot(2, 1, 1)
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ax.title.set_text("Displacements between image " + str(img_data.reference_index)
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+ " and image " + str(img_data.comparison_index))
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plt.imshow(ref_img, # Show reference image
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cmap="gray", # Grayscale
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vmin=0, # Minimum pixel value
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vmax=255, # Maximum pixel value
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origin="lower") # Flip image so increasing row corresponds to increasing y
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plt.quiver(im_data.location[:, :, 0], # x coordinates of arrow locations
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im_data.location[:, :, 1], # y coordinates of arrow locations
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im_data.displacement[:, :, 0], # x components of arrow vectors
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im_data.displacement[:, :, 1], # y components of arrow vectors
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disp_mag, # arrow color (vector magnitude)
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cmap=plt.cm.jet, # color map (jet)
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units="dots") # units of arrow dimensions
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disp_mag = np.sqrt(img_data.displacement[:, :, 0] ** 2 + img_data.displacement[:, :, 1] ** 2)
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####################
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# Figure 1: Strain #
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####################
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plt.subplot(2, 1, 2)
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plt.quiver(img_data.location[:, :, 0], # x coordinates of arrow locations
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img_data.location[:, :, 1], # y coordinates of arrow locations
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img_data.displacement[:, :, 0], # x components of arrow vectors
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img_data.displacement[:, :, 1], # y components of arrow vectors
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disp_mag, # arrow color (vector magnitude)
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cmap=plt.cm.jet, # color map (jet)
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units="dots", # units of arrow dimensions
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angles="xy") # arrows point from (x, y) to (x + u, y + v)
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plt.imshow(reference, # Show reference image
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cmap="gray", # Grayscale
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vmin=0, # Minimum pixel value
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vmax=255, # Maximum pixel value
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# Subplot 2: Strain
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ax = plt.subplot(2, 1, 2)
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ax.title.set_text("Strain between image " + str(img_data.reference_index)
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+ " and image " + str(img_data.comparison_index))
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plt.imshow(ref_img, # Show reference image
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cmap="gray", # Grayscale
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vmin=0, # Minimum pixel value
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vmax=255, # Maximum pixel value
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origin="lower") # Flip image so increasing row corresponds to increasing y
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strain_mag = np.sqrt(im_data.strain[:, :, 0] ** 2 + im_data.strain[:, :, 1] ** 2)
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strain_mag = np.sqrt(img_data.strain[:, :, 0] ** 2 + img_data.strain[:, :, 1] ** 2)
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plt.quiver(im_data.location[0, :, 0],
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im_data.location[:, 0, 1],
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im_data.strain[:, :, 0],
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im_data.strain[:, :, 1],
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plt.quiver(img_data.location[0, :, 0],
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img_data.location[:, 0, 1],
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img_data.strain[:, :, 0],
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img_data.strain[:, :, 1],
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strain_mag,
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cmap=plt.cm.jet,
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units="dots")
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units="dots",
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angles="xy")
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plt.show()
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def compare_matching_methods(images, ref_idx, comp_idx):
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plt.figure()
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plt.suptitle("Displacements between image " + str(ref_idx) + " and image " + str(comp_idx))
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i = 1
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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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reference_img, img_data = find_displacement(images, ref_idx, comp_idx, match_method)
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ax = plt.subplot(3, 2, i)
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title = None
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if match_method == cv2.TM_SQDIFF:
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title = "SQDIFF"
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elif match_method == cv2.TM_SQDIFF_NORMED:
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title = "SQDIFF_NORMED"
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elif match_method == cv2.TM_CCORR:
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title = "CCORR"
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elif match_method == cv2.TM_CCORR_NORMED:
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title = "CCORR_NORMED"
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elif match_method == cv2.TM_CCOEFF:
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title = "CCOEFF"
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elif match_method == cv2.TM_CCOEFF_NORMED:
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title = "CCOEFF_NORMED"
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ax.title.set_text(title)
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plt.imshow(reference_img, cmap="gray", vmin=0, vmax=255, origin="lower")
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disp_mag = np.sqrt(img_data.displacement[:, :, 0] ** 2 + img_data.displacement[:, :, 1] ** 2)
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plt.quiver(img_data.location[:, :, 0], img_data.location[:, :, 1], img_data.displacement[:, :, 0],
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img_data.displacement[:, :, 1], disp_mag, cmap=plt.cm.jet, units="dots", angles="xy")
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i += 1
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plt.show()
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if __name__ == '__main__':
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find_displacement(cv2.TM_SQDIFF_NORMED)
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images = read_images()
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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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specimen, load_disp_data = file_data.read_file("../Section001_Data.txt")
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compare_matching_methods(images, 560, 561)
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# Matching Methods
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# cv2.TM_SQDIFF
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# cv2.TM_SQDIFF_NORMED
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# cv2.TM_CCORR
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# cv2.TM_CCORR_NORMED
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# cv2.TM_CCOEFF
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# cv2.TM_CCOEFF_NORMED
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reference_img, img_data = find_displacement(images, 560, 561, cv2.TM_CCORR_NORMED)
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plot_disp_and_strain(reference_img, img_data)
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@@ -11,11 +11,16 @@ import numpy as np
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class ImageData:
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reference_index = None
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comparison_index = None
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location = None
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displacement = None
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strain = None
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def __init__(self, num_rows, num_cols):
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def __init__(self, num_rows, num_cols, ref_index, comp_index):
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self.reference_index = ref_index
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self.comparison_index = comp_index
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matrix_shape = (num_rows, num_cols, 2)
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self.location = np.zeros(matrix_shape)
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