def calibration_radius_error(dist, images, must_latex=True):
global detector, connect4
titles = ["\\texttt{minRadius}", "\\texttt{maxRadius}", "\\texttt{minDist}", "Grid circles", "Noise circles",
"Total", "Score"]
results = []
counter = 0
factor = 3.0 * dist
for img in images:
table = []
best_score = -1000
# how many pixels for a circle radius on a 320x240px image when standing one meter away
one_meter_value = 6
best_value = []
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
gray = cv2.medianBlur(gray, 3)
while one_meter_value < 8:
dist_value = int(round(one_meter_value / dist))
upper_bound = (dist_value + 1)
while upper_bound < (factor * one_meter_value) / dist:
lower_bound = (dist_value - 1)
while lower_bound > (one_meter_value / factor) / dist:
min_radius = int(lower_bound)
max_radius = int(upper_bound)
max_error = connect4.computeMaxPixelError(min_radius)
min_dist = round(lower_bound * 1.125, 2)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, min_dist, param1=48, param2=10.5,
minRadius=min_radius, maxRadius=max_radius)
if circles is None:
score = 0
nb_of_grid_circles = 0
circles = [[]]
else:
# circles = np.uint16(np.around(circles))
try:
detector.runDetection(circles[0], pixel_error_margin=max_error)
nb_of_grid_circles = len(detector.relativeCoordinates)
score = round(get_f_score(nb_of_grid_circles, len(circles[0]) - nb_of_grid_circles), 4)
except c4.CircleGridNotFoundException:
score = 0
nb_of_grid_circles = 0
if score > best_score:
best_score = score
best_value = [(min_radius, max_radius)]
elif abs(score - best_score) < 0.00001:
best_value.append((min_radius, max_radius))
line = [lower_bound, upper_bound, min_dist, nb_of_grid_circles,
len(circles[0]) - nb_of_grid_circles, len(circles[0]), score]
table.append(line)
lower_bound -= 1
upper_bound += 1
one_meter_value += 1
print "radius : image " + str(counter) + " finished"
results.append(best_value)
if must_latex:
latex_generator.generate_longtable(titles, "../../latex/generated_radius_" +
str(dist) + "_" + str(counter), table)
counter += 1
return results
def calibration_param1(dist, images, must_latex=True):
global detector, connect4
titles = ["\\texttt{param1}", "Grid circles", "Noise circles",
"Total", "Score"]
results = []
counter = 0
min_radius = connect4.estimateMinRadius(dist)
max_radius = connect4.estimateMaxRadius(dist)
max_error = connect4.computeMaxPixelError(min_radius)
min_dist = int(min_radius * 1.195)
param2 = 10.5
for img in images:
table = []
best_value = []
best_score = 0
# how many pixels for a circle radius on a 320x240px image when standing one meter away
param1 = 30
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (3, 3), 0)
gray = cv2.medianBlur(gray, 3)
while param1 < 200:
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, min_dist, param1=param1, param2=param2,
minRadius=min_radius, maxRadius=max_radius)
if circles is None:
score = 0
nb_of_grid_circles = 0
circles = [[]]
else:
try:
detector.runDetection(circles[0], pixel_error_margin=max_error)
nb_of_grid_circles = len(detector.relativeCoordinates)
score = round(get_f_score(nb_of_grid_circles, len(circles[0]) - nb_of_grid_circles), 4)
except c4.CircleGridNotFoundException:
score = 0
nb_of_grid_circles = 0
if score > best_score:
best_score = score
best_value = [param1]
elif abs(score - best_score) < 0.00001:
best_value.append(param1)
line = [param1, nb_of_grid_circles, len(circles[0]) - nb_of_grid_circles, len(circles[0]), score]
table.append(line)
param1 += 1
results.append(best_value)
print "param1 : image " + str(counter) + " finished"
if must_latex:
latex_generator.generate_longtable(titles, "../../latex/generated_param1_" +
str(dist) + "_" + str(counter), table)
counter += 1
return results
def evaluate(best_values, param, dist):
scores = {}
titles = ["\\texttt{param" + param + "}", "Occurrences"]
table = []
for iteration in best_values:
for value in iteration:
if value in scores:
scores[value] += 1
else:
scores[value] = 1
for value in scores:
line = [value, scores[value]]
table.append(line)
latex_generator.generate_longtable(titles, "../../latex/value/" + str(param) + "_" + str(dist), table)
return best_values
