def add_views_to_depth_pruner(data: UndistortedDataSet, neighbors, dp):
for shot in neighbors:
if not data.clean_depthmap_exists(shot.id):
continue
depth, plane, score = data.load_clean_depthmap(shot.id)
height, width = depth.shape
color_image = data.load_undistorted_image(shot.id)
labels = load_segmentation_labels(data, shot)
height, width = depth.shape
image = scale_down_image(color_image, width, height)
labels = scale_image(labels, image.shape[1], image.shape[0],
cv2.INTER_NEAREST)
K = shot.camera.get_K_in_pixel_coordinates(width, height)
R = shot.pose.get_rotation_matrix()
t = shot.pose.translation
dp.add_view(K, R, t, depth, plane, image, labels)
def add_views_to_depth_estimator(data: UndistortedDataSet, neighbors, de):
"""Add neighboring views to the DepthmapEstimator."""
num_neighbors = data.config["depthmap_num_matching_views"]
for shot in neighbors[:num_neighbors + 1]:
assert shot.camera.projection_type == "perspective"
color_image = data.load_undistorted_image(shot.id)
mask = load_combined_mask(data, shot)
gray_image = cv2.cvtColor(color_image, cv2.COLOR_RGB2GRAY)
original_height, original_width = gray_image.shape
width = min(original_width, int(data.config["depthmap_resolution"]))
height = width * original_height // original_width
image = scale_down_image(gray_image, width, height)
mask = scale_down_image(mask, width, height, cv2.INTER_NEAREST)
K = shot.camera.get_K_in_pixel_coordinates(width, height)
R = shot.pose.get_rotation_matrix()
t = shot.pose.translation
de.add_view(K, R, t, image, mask)
def export(
reconstruction,
index,
image_graph,
tracks_manager,
base_output_path,
data: DataSet,
undistorted,
udata: UndistortedDataSet,
with_points,
export_only,
):
logger.info("Reconstruction %d" % index)
output_path = os.path.join(base_output_path, "recon%d" % index)
io.mkdir_p(output_path)
io.mkdir_p(os.path.join(output_path, "visualize"))
io.mkdir_p(os.path.join(output_path, "txt"))
io.mkdir_p(os.path.join(output_path, "models"))
shot_index = {image: i for i, image in enumerate(reconstruction.shots)}
fvis = open(os.path.join(output_path, "vis.dat"), "w")
fvis.write("VISDATA\n")
fvis.write("%d\n" % len(shot_index))
for image, i in shot_index.items():
shot = reconstruction.shots[image]
base = "%08d" % i
logger.info("Image: %s %s" % (image, base))
# vis.dat for this image
if image_graph:
adj_indices = []
for adj_image in image_graph[image]:
weight = image_graph[image][adj_image]["weight"]
if weight > 0 and adj_image in shot_index:
adj_indices.append(shot_index[adj_image])
num_covisible = len(adj_indices)
fvis.write("%d " % i)
fvis.write("%d " % num_covisible)
for ai in adj_indices:
fvis.write("%d " % ai)
fvis.write("\n")
# radially undistort the original image
camera = shot.camera
if undistorted:
undistorted_image = udata.load_undistorted_image(image)
else:
original_image = data.load_image(image)[:, :, ::-1]
original_h, original_w = original_image.shape[:2]
K = camera.get_K_in_pixel_coordinates(original_w, original_h)
distortion = np.array([camera.k1, camera.k2, 0, 0])
undistorted_image = cv2.undistort(original_image, K, distortion)
# resize and save the undistorted to visualize/%08d.jpg
resized_image = features.resized_image(
undistorted_image, data.config["feature_process_size"])
new_image_path = os.path.join(output_path, "visualize", base + ".jpg")
cv2.imwrite(new_image_path, resized_image)
# write camera projection matrix to txt/%08d.txt
resized_h, resized_w = resized_image.shape[:2]
resized_K = camera.get_K_in_pixel_coordinates(resized_w, resized_h)
P = resized_K.dot(shot.pose.get_world_to_cam()[:3])
new_txt = os.path.join(output_path, "txt", base + ".txt")
with open(new_txt, "wb") as f:
np.savetxt(f, P, str("%f"), header="CONTOUR")
fvis.close()
# txt
with open(os.path.join(output_path, "pmvs_txt"), "w") as f:
f.write("level 1\n")
f.write("csize 2\n")
f.write("threshold 0.7\n")
f.write("wsize 7\n")
f.write("minImageNum 3\n")
f.write("CPU 8\n")
f.write("setEdge 0\n")
f.write("useBound 0\n")
f.write("useVisData {}\n".format(int(image_graph is not None)))
f.write("sequence -1\n")
f.write("timages -1 0 %d\n" % len(shot_index))
f.write("oimages 0\n")
