def test_chamfer(self):
import point_cloud_utils as pcu
import numpy as np
# a and b are arrays where each row contains a point
# Note that the point sets can have different sizes (e.g [100, 3], [111, 3])
a = np.random.rand(100, 3)
b = np.random.rand(100, 3)
chamfer_dist = pcu.chamfer(a, b)
def test_batched_chamfer(self):
import point_cloud_utils as pcu
import numpy as np
# a and b are each contain 10 batches each of which contain 100 points of dimension 3
# i.e. a[i, :, :] is the i^th point set which contains 100 points
# Note that the point sets can have different sizes (e.g [10, 100, 3], [10, 111, 3])
a = np.random.rand(10, 100, 3)
b = np.random.rand(10, 100, 3)
chamfer_dist = pcu.chamfer(a, b)
def full_evaluation(self, opt_solution_path, checkpoint_path, method):
dataset = pd.read_csv(opt_solution_path, header=None, index_col=None)
X = dataset.drop(dataset.columns[-1], axis=1).values
Y = dataset[dataset.columns[-1]].values
model = load(checkpoint_path)
Y_ = model.predict(X)
checker_flag = 0
other_best_idx = []
true_flag = int(np.sum(Y))
all_sol_count = X.shape[0]
cheb_all = np.zeros(all_sol_count)
man_all = np.zeros(all_sol_count)
euc_all = np.zeros(all_sol_count)
chamfer = np.zeros(all_sol_count)
angular = np.zeros(all_sol_count)
idx_array = []
res = np.sum(Y_[np.where(Y_ == Y)])
if int(res) == 0:
return False
else:
julia = Triangulation(K=self.K, R1=self.R1, R2=self.R2, T1=self.T1, T2=self.T2)
julia.load_imgs(self.img1_path, self.img2_path)
julia.findRootSIFTFeatures(n_components=self.n_components)
for idx, item in enumerate(Y_):
if Y[idx] == Y_[idx] and item == 1.0:
checker_flag+=1
idx_array.append(idx)
else:
other_best_idx.append(idx)
julia.matchingRootSIFTFeatures_advanced(X[idx])
julia.findRTmatrices()
julia.point_cloud(plot=self.draw_plot, title="Our method #{}".format(idx))
pred = julia.pts3D
stop = 1
metrics_1 = Hausdorff(u=pred, v=self.target)
metrics_2 = Hausdorff(u=self.target, v=pred)
chamfer_dist = pcu.chamfer(pred, self.target)
ang_sim = AngularSimilarity(pred, self.target)
ang_dist = ang_sim.compute_distance()
if method == 'avg':
dist_cheb_avg = np.add(metrics_1.distance(d_type="cheb", criteria="avg"),
metrics_2.distance(d_type="cheb", criteria="avg"))/2
dist_man_avg = np.add(metrics_1.distance(d_type="man", criteria="avg"),
metrics_2.distance(d_type="man", criteria="avg"))/2
dist_euc_avg = np.add(metrics_1.distance(d_type="euc", criteria="avg"),
metrics_2.distance(d_type="euc", criteria="avg"))/2
elif method == 'min':
dist_cheb_avg = min(metrics_1.distance(d_type="cheb", criteria="avg"),
metrics_2.distance(d_type="cheb", criteria="avg"))
dist_man_avg = min(metrics_1.distance(d_type="man", criteria="avg"),
metrics_2.distance(d_type="man", criteria="avg"))
dist_euc_avg = min(metrics_1.distance(d_type="euc", criteria="avg"),
metrics_2.distance(d_type="euc", criteria="avg"))
elif method == "max":
dist_cheb_avg = max(metrics_1.distance(d_type="cheb", criteria="avg"),
metrics_2.distance(d_type="cheb", criteria="avg"))
dist_man_avg = max(metrics_1.distance(d_type="man", criteria="avg"),
metrics_2.distance(d_type="man", criteria="avg"))
dist_euc_avg = max(metrics_1.distance(d_type="euc", criteria="avg"),
metrics_2.distance(d_type="euc", criteria="avg"))
else:
return False
cheb_all[idx] = dist_cheb_avg
man_all[idx] = dist_man_avg
euc_all[idx] = dist_euc_avg
chamfer[idx] = chamfer_dist
angular[idx] = ang_dist
min_chamfer_other = np.max(chamfer[other_best_idx])
min_angular_other = np.max(angular[other_best_idx])
min_cheb_other = np.max(cheb_all[other_best_idx])
min_man_other = np.max(man_all[other_best_idx])
min_euc_other = np.max(euc_all[other_best_idx])
min_angular = np.max(angular[idx_array])
min_chamfer = np.max(chamfer[idx_array])
min_cheb = np.max(cheb_all[idx_array])
min_man = np.max(man_all[idx_array])
min_euc = np.max(euc_all[idx_array])
print("\nPredicted : {} out of actual: {}".format(checker_flag, true_flag))
# print("Other: Cheb: {:3f} Man: {:3f} Euc: {:3f}".format(min_cheb_other, min_man_other, min_euc_other))
# print("Our: Cheb: {:3f} Man: {:3f} Euc: {:3f}".format(min_cheb, min_man, min_euc))
# print()
print("Other: Chamfer: {:3f}".format(min_angular_other))
print("Our: Chamfer: {:3f}".format(min_angular))
print()
if min_angular < min_angular_other:
return True
else:
return False