img = cam.getImage().scale(400, 400)
img2 = img.binarize()
def costImage(i):
# Make image black and white
img1 = img.binarize(int(i))
mat = img1.getNumpy()
countW = np.count_nonzero(mat)
countB = mat.size - countW
return ((countW - countB) / float(mat.size))**2
params = {} #bayesopt.initialize_params()
params['n_iterations'] = 15
params['n_init_samples'] = 5
valid_values = np.transpose(np.array(range(256), dtype=float, ndmin=2))
mvalue, x_out, error = bayesopt.optimize_discrete(costImage, valid_values,
params)
x_out = int(x_out)
print x_out
img1 = img.binarize(x_out)
img1 = img.sideBySide(img1).sideBySide(img2)
img1.drawText("Threshold: " + str(x_out))
img1.show()
foo = raw_input('Press any key')
start = clock() mvalue, x_out, error = bayesopt.optimize(testfunc, n, lb, ub, params) print "Result", x_out print "Seconds", clock() - start print "OO implementation" bo_test = BayesOptTest(n) bo_test.parameters = params bo_test.lower_bound = lb bo_test.upper_bound = ub start = clock() mvalue, x_out, error = bo_test.optimize() print "Result", x_out print "Seconds", clock() - start print "Callback discrete implementation" x_set = np.random.rand(100, n) start = clock() mvalue, x_out, error = bayesopt.optimize_discrete(testfunc, x_set, params) print "Result", x_out print "Seconds", clock() - start value = np.array([testfunc(i) for i in x_set]) print "Optimun", x_set[value.argmin()]
def optimize(self):
min_val, x_out, error = bo.optimize_discrete(self.evaluateSample,
self.x_set, self.params)
return min_val, x_out, error
mvalue, x_out, error = bayesopt.optimize(testfunc, n, lb, ub, params) print "Result", x_out print "Seconds", clock() - start print "OO implementation" bo_test = BayesOptTest(n) bo_test.parameters = params bo_test.lower_bound = lb bo_test.upper_bound = ub start = clock() mvalue, x_out, error = bo_test.optimize() print "Result", x_out print "Seconds", clock() - start print "Callback discrete implementation" x_set = np.random.rand(100,n) start = clock() mvalue, x_out, error = bayesopt.optimize_discrete(testfunc, x_set, params) print "Result", x_out print "Seconds", clock() - start value = np.array([testfunc(i) for i in x_set]) print "Optimun", x_set[value.argmin()]
#Load images
img = cam.getImage().scale(400,400)
img2 = img.binarize()
def costImage(i):
# Make image black and white
img1 = img.binarize(int(i))
mat = img1.getNumpy()
countW = np.count_nonzero(mat);
countB = mat.size-countW
return ((countW-countB)/float(mat.size))**2
params = {} #bayesopt.initialize_params()
params['n_iterations'] = 15
params['n_init_samples'] = 5
valid_values = np.transpose(np.array(range(256), dtype=float, ndmin=2))
mvalue, x_out, error = bayesopt.optimize_discrete(costImage,
valid_values, params)
x_out = int(x_out)
print(x_out)
img1 = img.binarize(x_out)
img1 = img.sideBySide(img1).sideBySide(img2)
img1.drawText("Threshold: "+str(x_out))
img1.show()
foo = raw_input('Press any key')
def optimize(self):
min_val, x_out, error = bo.optimize_discrete(self.evaluateSample,
self.x_set,
self.params)
return min_val, x_out, error