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Bug/#111 #120

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Apr 17, 2015
Merged

Bug/#111 #120

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baee242
Added support for Centered Moving Average function using 'np.convolve…
cgddrd Apr 17, 2015
1f64a41
Minor code cleanup.
cgddrd Apr 17, 2015
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67,397 changes: 0 additions & 67,397 deletions src/template_matching_scaling/notebooks/cache_flat.pkl
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67,397 changes: 0 additions & 67,397 deletions src/template_matching_scaling/notebooks/cache_wiltshire_inside_10cm.pkl
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56,174 changes: 0 additions & 56,174 deletions src/template_matching_scaling/notebooks/cache_wiltshire_inside_15cm.pkl
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33,728 changes: 0 additions & 33,728 deletions src/template_matching_scaling/notebooks/cache_wiltshire_inside_15cm_obstacle.pkl
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112,289 changes: 0 additions & 112,289 deletions src/template_matching_scaling/notebooks/cache_wiltshire_outside.pkl
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11,428 changes: 17 additions & 11,411 deletions src/template_matching_scaling/notebooks/template_match_scaled.ipynb
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1,033 changes: 26 additions & 1,007 deletions ...emplate_matching_scaling/notebooks/terrain_motion_displacement_flat_10cm_non_scaled.ipynb
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70 changes: 21 additions & 49 deletions src/template_matching_scaling/template_matching_scaling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@
import math
import matplotlib.pyplot as plt
import argparse
import numpy as np

from tse.tse_fileio import TSEFileIO
from tse.tse_datautils import TSEDataUtils
Expand All @@ -18,8 +17,6 @@
from collections import OrderedDict
from pprint import pprint

import json

__author__ = 'connorgoddard'


Expand Down Expand Up @@ -49,7 +46,7 @@ def load_calibration_data(self, file_path):
raw_data = TSEFileIO.read_file(file_path, split_delimiter=",", start_position=1)
return dict(TSEDataUtils.string_2d_list_to_int_2d_list(raw_data))

def search_image(self, patch_height, match_method, use_scaling=False, force_cont_search=False, plot_results=False):
def search_image(self, patch_height, match_method, use_scaling=False, exhaustive_search=False, plot_results=False):

run_results = []

Expand All @@ -72,11 +69,14 @@ def search_image(self, patch_height, match_method, use_scaling=False, force_cont

if use_scaling is True:

run_results.append(TSEResult(i, self.scan_search_window_scaling(template_patch, template_patch_origin_point, match_method, force_cont_search)))
run_results.append(TSEResult(i, self.scan_search_window_scaling(template_patch,
template_patch_origin_point,
match_method, exhaustive_search)))

else:

run_results.append(TSEResult(i, self.scan_search_window(template_patch, template_patch_origin_point, match_method, force_cont_search)))
run_results.append(TSEResult(i, self.scan_search_window(template_patch, template_patch_origin_point,
match_method, exhaustive_search)))

if plot_results:
# self._plot_axis.set_xlabel('Row Number (px)')
Expand All @@ -86,7 +86,7 @@ def search_image(self, patch_height, match_method, use_scaling=False, force_cont

return run_results

def scan_search_window(self, template_patch, template_patch_origin, match_method, force_cont_search=False):
def scan_search_window(self, template_patch, template_patch_origin, match_method, exhaustive_search=False):

image_height, image_width = self._hsv_img2.shape[:2]

Expand Down Expand Up @@ -135,13 +135,13 @@ def scan_search_window(self, template_patch, template_patch_origin, match_method
else:
stop = True

if (force_cont_search is False) and (stop is True):
if (exhaustive_search is False) and (stop is True):
break

# We need to return the 'Y' with the best score (i.e. the displacement)
return best_position

def scan_search_window_scaling(self, template_patch, template_patch_origin, match_method, force_cont_search=False):
def scan_search_window_scaling(self, template_patch, template_patch_origin, match_method, exhaustive_search=False):

image_height, image_width = self._hsv_img2.shape[:2]

Expand Down Expand Up @@ -219,7 +219,7 @@ def scan_search_window_scaling(self, template_patch, template_patch_origin, matc
else:
stop = True

if (force_cont_search is False) and (stop is True):
if (exhaustive_search is False) and (stop is True):
break

# We need to return the 'Y' with the best score (i.e. the displacement)
Expand All @@ -236,7 +236,7 @@ def plot_results(self, results, match_method):
x.append(val.row)
y.append(val.displacement)

y_moving_average = TSEDataUtils.calc_moving_average_array(y, 10)
y_moving_average = TSEDataUtils.calc_moving_average(y, 10)

self.plot(x, y, "{0}.".format(plot_format_color), 100, match_method.match_name)
self.plot(x[len(x) - len(y_moving_average):], y_moving_average, "{0}-".format(plot_format_color), 100, "MVAV_{0}".format(match_method.match_name))
Expand All @@ -253,7 +253,8 @@ def plot(self, data_x, data_y, plot_format, max_boundary_offset, plot_name):
self._plot_axis.legend(loc='upper left', shadow=True)


def start_tests(image_pairs, patch_sizes, match_methods, config_file, use_scaling=False, force_cont_search=False, plot_results=False):
def start_tests(image_pairs, patch_sizes, match_methods, config_file, use_scaling=False, exhaustive_search=False,
plot_results=False):

# Create a dictionary to store the results of all image pairs -> patch sizes -> match methods for a given pair of images.
image_dict = {}
Expand All @@ -271,7 +272,8 @@ def start_tests(image_pairs, patch_sizes, match_methods, config_file, use_scalin
match_dict = {}

for match_method in match_methods:
match_dict[match_method.match_name] = match.search_image(patch_size, match_method, use_scaling, force_cont_search, plot_results)
match_dict[match_method.match_name] = match.search_image(patch_size, match_method, use_scaling,
exhaustive_search, plot_results)

patch_dict[patch_size] = match_dict

Expand Down Expand Up @@ -317,7 +319,8 @@ def start_tests(image_pairs, patch_sizes, match_methods, config_file, use_scalin
for match_method in match_methods:

# Store the results for a given match method.
match_dict[match_method.match_name] = match.search_image(patch_size, match_method, use_scaling, force_cont_search, plot_results)
match_dict[match_method.match_name] = match.search_image(patch_size, match_method, use_scaling,
exhaustive_search, plot_results)

if column_count == (column_max - 1):
row_count += 1
Expand Down Expand Up @@ -364,7 +367,7 @@ def main():
parser.add_argument('-m', '--methods', nargs='+', dest="match_methods", type=str, required=True)
parser.add_argument('-s', '--scaling', dest='scaling', action='store_true')
parser.add_argument('-d', '--drawplot', dest='plot_results', action='store_true')
parser.add_argument('-f', '--forcecontsearch', dest='force_cont_search', action='store_true')
parser.add_argument('-es', '--exhaustivesearch', dest='exhaustive_search', action='store_true')

args = vars(parser.parse_args())

Expand All @@ -389,43 +392,12 @@ def main():
parser.error("Error: \"{0}\" is not a valid matching method option.\nSupported Methods: \'DistanceEuclidean\', \'DistanceCorr\', \'HistCorrel\', \'HistChiSqr\' ".format(method))

# Start the tests using settings passed in as command-line arguments.
results_dict = start_tests(args['image_pairs'], list(OrderedDict.fromkeys(args['patch_sizes'])), match_methods, args['calib_file'], use_scaling=args['scaling'], force_cont_search=args['force_cont_search'], plot_results=args['plot_results'])

pprint(results_dict)

results_pair1_100 = results_dict['IMG1.JPG_IMG2.JPG'][100]

raw_results_pair1_100 = []
image_rows = []

for key in results_pair1_100:
raw_results_pair1_100.append([o.displacement for o in results_pair1_100[key]])
image_rows = [o.row for o in results_pair1_100[key]]

print raw_results_pair1_100

averaged_results_pair1_100 = TSEDataUtils.calc_element_wise_average(raw_results_pair1_100)

print averaged_results_pair1_100

print len(raw_results_pair1_100[0])
print len(averaged_results_pair1_100)

# filtered_results_pair1_100 = TSEDataUtils.filter_outliers_ab_dist_median(averaged_results_pair1_100)
#
# image_rows = np.array(image_rows)[TSEDataUtils.filter_outliers_ab_dist_median_indices(averaged_results_pair1_100)]
#
# plt.plot(image_rows, np.array(filtered_results_pair1_100), "b.")
#
# y_moving_average = TSEDataUtils.calc_moving_average_array(np.array(filtered_results_pair1_100), 20)
#
# plt.plot(image_rows[len(image_rows) - len(y_moving_average):], y_moving_average, "g-")
#
# plt.show()
results_dict = start_tests(args['image_pairs'], list(OrderedDict.fromkeys(args['patch_sizes'])), match_methods,
args['calib_file'], use_scaling=args['scaling'],
exhaustive_search=args['exhaustive_search'], plot_results=args['plot_results'])

if args['plot_results']:
plt.show()


if __name__ == '__main__':
main()
4 changes: 2 additions & 2 deletions src/template_matching_scaling/tests/unittests/test_tse_datautils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,8 +55,8 @@ def test_calc_moving_average_array(self):
expected_result = np.array([float((2+1) / 2.0), float((3+2)/2.0), float((4+3)/2.0), float((5+4)/2.0), float((6+5)/2.0)])

# Using both Numpy test asserts and nosetest asserts.
np.testing.assert_equal(TSEDataUtils.calc_moving_average_array(test_data, points_to_average), expected_result)
assert_true(np.array_equal(TSEDataUtils.calc_moving_average_array(test_data, points_to_average), expected_result))
np.testing.assert_equal(TSEDataUtils.calc_moving_average(test_data, points_to_average), expected_result)
assert_true(np.array_equal(TSEDataUtils.calc_moving_average(test_data, points_to_average), expected_result))

def test_convert_array_to_numpy_array(self):
test_data = [1, 2, 3, 4, 5, 6]
Expand Down
14 changes: 12 additions & 2 deletions src/template_matching_scaling/tse/tse_datautils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,11 +33,21 @@ def convert_list_to_int(list_to_convert):
return map(int, list_to_convert)

@staticmethod
def calc_moving_average_array(values, window, mode='valid'):
def calc_centered_moving_average(values, window):

# Using 'same' mode for the 'numpy.convolve' function will centre the convolution window at each point of the array overlap.
# WARNING: Boundary effects will be observed at the ends as a result of the arrays not fully overlapping.
return TSEDataUtils.calc_moving_average(values, window, 'same')


@staticmethod
def calc_moving_average(values, window, mode='valid'):

weights = np.repeat(1.0, window)/window

# Including 'valid' MODE will REQUIRE there to be enough data points.
# Including 'valid' MODE will REQUIRE there to be enough data points before beginning convolution.
# 'valid' mode only convolve where points overlap exactly. This is equivalent to a Simple Moving Average.
# See: http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html
return np.convolve(values, weights, mode)

@staticmethod
Expand Down