TEST(save_point_cloud, serializes_a_point_cloud_by_writing_each_point_on_a_separate_line) {
options opts = { 3, &incremental_generator };
point_cloud test_cloud;
generate_point_cloud(&test_cloud, opts);
FILE *out_stream = tmpfile();
save_point_cloud(&test_cloud, out_stream);
rewind(out_stream);
char line[256];
// Header, we can discard it for this test
fgets(line, 256, out_stream);
fgets(line, 256, out_stream);
ASSERT_STREQ("0\t0\n", line);
fgets(line, 256, out_stream);
ASSERT_STREQ("1\t1\n", line);
fgets(line, 256, out_stream);
ASSERT_STREQ("2\t2\n", line);
fclose(out_stream);
}
TEST(save_point_cloud, serializes_a_point_cloud_by_writing_a_header_containing_the_cloud_size) {
options opts = { 34, &incremental_generator };
point_cloud test_cloud;
generate_point_cloud(&test_cloud, opts);
FILE *out_stream = tmpfile();
save_point_cloud(&test_cloud, out_stream);
rewind(out_stream);
char header[256];
fgets(header, 256, out_stream);
ASSERT_STREQ("# size=34\n", header);
fclose(out_stream);
}
int main(int argc, char **argv) {
if (argc < 5) {
std::cout << "Usage: "
<< argv[0]
<< " path_to_scans/ output.ply icp_iters subsample_probability" << std::endl;
return 1;
}
// Command line parameters
string pc_filepath = argv[1];
string pc_file_out_ply = argv[2];
int icp_num_iters = std::atoi(argv[3]);
double probability = std::atof(argv[4]);
if (pc_filepath.c_str()[pc_filepath.length() - 1] != '/') {
pc_filepath += "/";
}
std::ifstream file(pc_filepath + "config.txt");
string line;
getline(file, line);
std::istringstream in(line);
int num_images;
in >> num_images;
Mat pc_a = load_kinect_frame(pc_filepath + "image_0.png",
pc_filepath + "depth_0.txt");
Mat allSamples = selectRandomPoints(pc_a, probability);
// Used for accumulating the rigid transformation matrix
Mat transformation = Mat::eye(4, 4, CV_32F);
Mat rotation, translation;
clock_t time;
for (int image_num = 1; image_num < num_images; ++image_num) {
std::cout << "REGISTERING IMAGE " << image_num << std::endl;
time = clock();
// Load the point cloud to be registered
string str_num = std::to_string(image_num);
Mat pc_b = load_kinect_frame(pc_filepath + "image_" + str_num + ".png",
pc_filepath + "depth_" + str_num + ".txt");
Mat pc_b_sample = selectRandomPoints(pc_b, probability);
// Apply the previous transformations to b so that it is positioned near
// the last accumulated points
extractRigidTransform(transformation, rotation, translation);
pc_b_sample = applyTransformation(pc_b_sample, rotation, translation);
pc_b = applyTransformation(pc_b, rotation, translation);
// Perform the specified number of icp iterations
for (int i = 0; i < icp_num_iters; ++i) {
// Find the nearest neighbor pairs in the two point clouds
Mat a, b;
nearest_neighbors(allSamples, pc_b_sample, a, b);
// Find the optimal rotation and translation matrix to transform b onto a
Mat r, t;
rigid_transform_3D(b, a, r, t);
// Apply the rigid transformation to the b point cloud
pc_b_sample = applyTransformation(pc_b_sample, r, t);
pc_b = applyTransformation(pc_b, r, t);
transformation *= getRigidTransform(r, t);
}
// Combine the two point clouds and save to disk
Mat combined, combinedSample;
vconcat(pc_a, pc_b, combined);
vconcat(allSamples, pc_b_sample, combinedSample);
pc_a = combined;
allSamples = combinedSample;
save_point_cloud(combined, pc_file_out_ply);
std::cout << "complete " << ((float)(clock() - time)) / CLOCKS_PER_SEC << std::endl;
}
return 0;
}