def estimate_overall_ref_model(colmap_folder,min_track_len,min_tri_angle,max_tri_angle,image_path):
print 'Loading COLMAP data'
scene_manager = SceneManager(colmap_folder)
scene_manager.load_cameras()
scene_manager.load_images()
images=['frame0859.jpg', 'frame0867.jpg', 'frame0875.jpg', 'frame0883.jpg', 'frame0891.jpg']
_L=np.array([])
_r=np.array([])
_ndotl=np.array([])
for image_name in images:
image_id = scene_manager.get_image_id_from_name(image_name)
image = scene_manager.images[image_id]
camera = scene_manager.get_camera(image.camera_id)
# image pose
R = util.quaternion_to_rotation_matrix(image.qvec)
print 'Loading 3D points'
scene_manager.load_points3D()
scene_manager.filter_points3D(min_track_len,
min_tri_angle=min_tri_angle, max_tri_angle=max_tri_angle,
image_list=set([image_id]))
points3D, points2D = scene_manager.get_points3D(image_id)
points3D = points3D.dot(R.T) + image.tvec[np.newaxis,:]
# need to remove redundant points
# http://stackoverflow.com/questions/16970982/find-unique-rows-in-numpy-array
points2D_view = np.ascontiguousarray(points2D).view(
np.dtype((np.void, points2D.dtype.itemsize * points2D.shape[1])))
_, idx = np.unique(points2D_view, return_index=True)
points2D, points3D = points2D[idx], points3D[idx]
# further rule out any points too close to the image border (for bilinear
# interpolation)
mask = (
(points2D[:,0] < camera.width - 1) & (points2D[:,1] < camera.height - 1))
points2D, points3D = points2D[mask], points3D[mask]
points2D_image = points2D.copy() # coordinates in image space
points2D = np.hstack((points2D, np.ones((points2D.shape[0], 1))))
points2D = points2D.dot(np.linalg.inv(camera.get_camera_matrix()).T)[:,:2]
print len(points3D), 'total points'
# load image
#image_file = scene_manager.image_path + image.name
image_file = image_path + image.name
im_rgb = skimage.img_as_float(skimage.io.imread(image_file)) # color image
L = skimage.color.rgb2lab(im_rgb)[:,:,0] * 0.01
L = np.maximum(L, 1e-6) # unfortunately, can't have black pixels, since we
# divide by L
# initial values on unit sphere
x, y = camera.get_image_grid()
print 'Computing nearest neighbors'
kdtree = KDTree(points2D)
weights, nn_idxs = kdtree.query(np.c_[x.ravel(),y.ravel()], KD_TREE_KNN)
weights = weights.reshape(camera.height, camera.width, KD_TREE_KNN)
nn_idxs = nn_idxs.reshape(camera.height, camera.width, KD_TREE_KNN)
# turn distances into weights for the nearest neighbors
np.exp(-weights, weights) # in-place
# create initial surface on unit sphere
S0 = np.dstack((x, y, np.ones_like(x)))
S0 /= np.linalg.norm(S0, axis=-1)[:,:,np.newaxis]
r_fixed = np.linalg.norm(points3D, axis=-1) # fixed 3D depths
S = S0.copy()
z = S0[:,:,2]
S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
points2D_image, r_fixed, util.generate_surface(camera, z))
z_est = np.maximum(S[:,:,2], 1e-6)
S = util.generate_surface(camera, z_est)
r = np.linalg.norm(S, axis=-1)
ndotl = util.calculate_ndotl(camera, S)
if _L.size == 0:
_L=L.ravel()
_r=r.ravel()
_ndotl=ndotl.ravel()
else:
_L=np.append(_L,L.ravel())
_r=np.append(_r,r.ravel())
_ndotl=np.append(_ndotl,ndotl.ravel())
#
falloff, model_params, residual = fit_reflectance_model(sfs.POWER_MODEL,
_L, _r, _ndotl, False, 5)
import pdb;pdb.set_trace();
def run_iterative_sfs(out_file, model_type, fit_falloff, *extra_params):
#if os.path.exists(out_file + '_z.bin'): # don't re-run existing results
# return
if model_type == sfs.LAMBERTIAN_MODEL:
model_name = 'LAMBERTIAN_MODEL'
elif model_type == sfs.OREN_NAYAR_MODEL:
model_name = 'OREN_NAYAR_MODEL'
elif model_type == sfs.PHONG_MODEL:
model_name = 'PHONG_MODEL'
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_name = 'COOK_TORRANCE_MODEL'
elif model_type == sfs.POWER_MODEL:
model_name = 'POWER_MODEL'
if model_type == sfs.POWER_MODEL:
print '%s_%i' % (model_name, extra_params[0])
else:
print model_name
S = S0.copy()
z = S0[:, :, 2]
for iteration in xrange(MAX_NUM_ITER):
print 'Iteration', iteration
z_old = z
if get_initial_warp:
#z_gt=util.load_point_ply('C:\\Users\\user\\Documents\\UNC\\Research\\ColonProject\\code\\Rui\\SFS_CPU\\frame0859.jpg_gt.ply')
# warp to 3D points
if iteration > -1:
S, r_ratios = nearest_neighbor_warp(
weights, nn_idxs, points2D_image, r_fixed,
util.generate_surface(camera, z))
z_est = np.maximum(S[:, :, 2], 1e-6)
S = util.generate_surface(camera, z_est)
else:
z_est = z
S = util.generate_surface(camera, z_est)
#util.save_sfs_ply('warp' + '.ply', S, im_rgb)
#util.save_xyz('test.xyz',points3D);
#z=z_est
#break
#z_est=z
else:
#import pdb;pdb.set_trace()
#z_est = extract_depth_map(camera,ref_surf_name,R,image)
z_est = np.fromfile(
'C:\Users\user\Documents\UNC\Research\ColonProject\code\SFS_Program_from_True\endo_evaluation\gt_surfaces\\frame0859.jpg.bin',
dtype=np.float32).reshape(camera.height,
camera.width) / WORLD_SCALE
z_est = z_est.astype(float)
#S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
# points2D_image, r_fixed, util.generate_surface(camera, z_est))
z_est = np.maximum(z_est[:, :], 1e-6)
#Sworld = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
S = util.generate_surface(camera, z_est)
#util.save_sfs_ply('test' + '.ply', S, im_rgb)
#util.save_sfs_ply(out_file + '_warp_%i.ply' % iteration, Sworld, im_rgb)
#import pdb;pdb.set_trace()
# if we need to, make corrections for non-positive depths
#S = util.generate_surface(camera, z_est)
mask = (z_est < INIT_Z)
specular_mask = (L < 0.8)
dark_mask = (L > 0.1)
_mask = np.logical_and(specular_mask, mask)
_mask = np.logical_and(_mask, dark_mask)
# fit reflectance model
r = np.linalg.norm(S, axis=-1)
ndotl = util.calculate_ndotl(camera, S)
falloff, model_params, residual = fit_reflectance_model(
model_type, L[_mask], r.ravel(), r[_mask], ndotl.ravel(),
ndotl[_mask], fit_falloff, camera.width, camera.height,
*extra_params)
#r = np.linalg.norm(S[specular_mask], axis=-1)
#import pdb;pdb.set_trace()
#model_params=np.array([26.15969874,-27.674055,-12.52426,7.579855,21.9768004,24.3911142,-21.7282996,-19.850894,-11.62229,-4.837014])
#model_params=np.array([-19.4837,-490.4796,812.4527,-426.09107,139.2602,351.8061,-388.1591,875.5013,-302.4748,-414.4384])
#falloff = 1.2
#ndotl = util.calculate_ndotl(camera, S)[specular_mask]
#falloff, model_params, residual = fit_reflectance_model(model_type,
# L[specular_mask], r, ndotl, fit_falloff, *extra_params)
#r = np.linalg.norm(S[neighborhood_mask], axis=-1)
#ndotl = util.calculate_ndotl(camera, S)[neighborhood_mask]
#falloff, model_params, residual = fit_reflectance_model(model_type,
# L[neighborhood_mask], r, ndotl, fit_falloff, *extra_params)
# lambda values reflect our confidence in the current surface: 0
# corresponds to only using SFS at a pixel, 1 corresponds to equally
# weighting SFS and the current estimate, and larger values increasingly
# favor using only the current estimate
rdiff = np.abs(r_fixed - get_estimated_r(S, points2D_image))
w = np.log10(r_fixed) - np.log10(rdiff) - np.log10(2.)
lambdas = (np.sum(weights * w[nn_idxs], axis=-1) /
np.sum(weights, axis=-1))
lambdas = np.maximum(lambdas, 0.) # just in case
#
lambdas[~mask] = 0
#if iteration == 0: # don't use current estimated surface on first pass
#lambdas = np.zeros_like(z)
#else:
# r_ratios_postwarp = r_fixed / get_estimated_r(S, points2D_image)
# ratio_diff = np.abs(r_ratios_prewarp - r_ratios_postwarp)
# ratio_diff[ratio_diff == 0] = 1e-10 # arbitrarily high lambda
# feature_lambdas = 1. / ratio_diff
# lambdas = (np.sum(weights * feature_lambdas[nn_idxs], axis=-1) /
# np.sum(weights, axis=-1))
# run SFS
H_lut, dH_lut = compute_H_LUT(model_type, model_params,
NUM_H_LUT_BINS)
#import pdb;pdb.set_trace()
# run SFS
#H_lut = np.ascontiguousarray(H_lut.astype(np.float32))
#dH_lut = np.ascontiguousarray(dH_lut.astype(np.float32))
z = run_sfs(H_lut, dH_lut, camera, L, lambdas, z_est, model_type,
model_params, falloff, vmask,
use_image_weighted_derivatives)
# check for convergence
#diff = np.sum(np.abs(z_old[specular_mask] - z[specular_mask]))
#if diff < CONVERGENCE_THRESHOLD * camera.height * camera.width:
# break
# save the surface
#S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
#util.save_sfs_ply(out_file + '_%i.ply' % iteration, S, im_rgb)
else:
print 'DID NOT CONVERGE'
#import pdb;pdb.set_trace()
S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
util.save_sfs_ply(out_file + '.ply', S, im_rgb)
z.astype(np.float32).tofile(out_file + '_z.bin')
# save the surface
#S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
#S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
# points2D_image, r_fixed, util.generate_surface(camera, z))
#util.save_sfs_ply(out_file + '_warped.ply', S, im_rgb)
#z = np.maximum(S[:,:,2], 1e-6)
#z.astype(np.float32).tofile(out_file + '_warped_z.bin')
#reflectance_models.save_reflectance_model(out_file + '_reflectance.txt',
# model_name, residual, model_params, falloff, *extra_params)
print
def estimate_overall_ref_model(colmap_folder, min_track_len, min_tri_angle,
max_tri_angle, image_path):
print 'Loading COLMAP data'
scene_manager = SceneManager(colmap_folder)
scene_manager.load_cameras()
scene_manager.load_images()
images = [
'frame0859.jpg', 'frame0867.jpg', 'frame0875.jpg', 'frame0883.jpg',
'frame0891.jpg'
]
_L = np.array([])
_r = np.array([])
_ndotl = np.array([])
for image_name in images:
image_id = scene_manager.get_image_id_from_name(image_name)
image = scene_manager.images[image_id]
camera = scene_manager.get_camera(image.camera_id)
# image pose
R = util.quaternion_to_rotation_matrix(image.qvec)
print 'Loading 3D points'
scene_manager.load_points3D()
scene_manager.filter_points3D(min_track_len,
min_tri_angle=min_tri_angle,
max_tri_angle=max_tri_angle,
image_list=set([image_id]))
points3D, points2D = scene_manager.get_points3D(image_id)
points3D = points3D.dot(R.T) + image.tvec[np.newaxis, :]
# need to remove redundant points
# http://stackoverflow.com/questions/16970982/find-unique-rows-in-numpy-array
points2D_view = np.ascontiguousarray(points2D).view(
np.dtype((np.void, points2D.dtype.itemsize * points2D.shape[1])))
_, idx = np.unique(points2D_view, return_index=True)
points2D, points3D = points2D[idx], points3D[idx]
# further rule out any points too close to the image border (for bilinear
# interpolation)
mask = ((points2D[:, 0] < camera.width - 1) &
(points2D[:, 1] < camera.height - 1))
points2D, points3D = points2D[mask], points3D[mask]
points2D_image = points2D.copy() # coordinates in image space
points2D = np.hstack((points2D, np.ones((points2D.shape[0], 1))))
points2D = points2D.dot(np.linalg.inv(
camera.get_camera_matrix()).T)[:, :2]
print len(points3D), 'total points'
# load image
#image_file = scene_manager.image_path + image.name
image_file = image_path + image.name
im_rgb = skimage.img_as_float(
skimage.io.imread(image_file)) # color image
L = skimage.color.rgb2lab(im_rgb)[:, :, 0] * 0.01
L = np.maximum(
L, 1e-6) # unfortunately, can't have black pixels, since we
# divide by L
# initial values on unit sphere
x, y = camera.get_image_grid()
print 'Computing nearest neighbors'
kdtree = KDTree(points2D)
weights, nn_idxs = kdtree.query(np.c_[x.ravel(), y.ravel()],
KD_TREE_KNN)
weights = weights.reshape(camera.height, camera.width, KD_TREE_KNN)
nn_idxs = nn_idxs.reshape(camera.height, camera.width, KD_TREE_KNN)
# turn distances into weights for the nearest neighbors
np.exp(-weights, weights) # in-place
# create initial surface on unit sphere
S0 = np.dstack((x, y, np.ones_like(x)))
S0 /= np.linalg.norm(S0, axis=-1)[:, :, np.newaxis]
r_fixed = np.linalg.norm(points3D, axis=-1) # fixed 3D depths
S = S0.copy()
z = S0[:, :, 2]
S, r_ratios = nearest_neighbor_warp(weights, nn_idxs, points2D_image,
r_fixed,
util.generate_surface(camera, z))
z_est = np.maximum(S[:, :, 2], 1e-6)
S = util.generate_surface(camera, z_est)
r = np.linalg.norm(S, axis=-1)
ndotl = util.calculate_ndotl(camera, S)
if _L.size == 0:
_L = L.ravel()
_r = r.ravel()
_ndotl = ndotl.ravel()
else:
_L = np.append(_L, L.ravel())
_r = np.append(_r, r.ravel())
_ndotl = np.append(_ndotl, ndotl.ravel())
#
falloff, model_params, residual = fit_reflectance_model(
sfs.POWER_MODEL, _L, _r, _ndotl, False, 5)
import pdb
pdb.set_trace()
def run_iterative_sfs(out_file, model_type, fit_falloff, *extra_params):
#if os.path.exists(out_file + '_z.bin'): # don't re-run existing results
# return
if model_type == sfs.LAMBERTIAN_MODEL:
model_name = 'LAMBERTIAN_MODEL'
elif model_type == sfs.OREN_NAYAR_MODEL:
model_name = 'OREN_NAYAR_MODEL'
elif model_type == sfs.PHONG_MODEL:
model_name = 'PHONG_MODEL'
elif model_type == sfs.COOK_TORRANCE_MODEL:
model_name = 'COOK_TORRANCE_MODEL'
elif model_type == sfs.POWER_MODEL:
model_name = 'POWER_MODEL'
if model_type == sfs.POWER_MODEL:
print '%s_%i' % (model_name, extra_params[0])
else:
print model_name
S = S0.copy()
z = S0[:,:,2]
for iteration in xrange(MAX_NUM_ITER):
print 'Iteration', iteration
z_old = z
if get_initial_warp:
#z_gt=util.load_point_ply('C:\\Users\\user\\Documents\\UNC\\Research\\ColonProject\\code\\Rui\\SFS_CPU\\frame0859.jpg_gt.ply')
# warp to 3D points
if iteration>-1:
S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
points2D_image, r_fixed, util.generate_surface(camera, z))
z_est = np.maximum(S[:,:,2], 1e-6)
S=util.generate_surface(camera, z_est)
else:
z_est=z
S=util.generate_surface(camera, z_est)
#util.save_sfs_ply('warp' + '.ply', S, im_rgb)
#util.save_xyz('test.xyz',points3D);
#z=z_est
#break
#z_est=z
else:
#import pdb;pdb.set_trace()
#z_est = extract_depth_map(camera,ref_surf_name,R,image)
z_est=np.fromfile(
'C:\Users\user\Documents\UNC\Research\ColonProject\code\SFS_Program_from_True\endo_evaluation\gt_surfaces\\frame0859.jpg.bin', dtype=np.float32).reshape(
camera.height, camera.width)/WORLD_SCALE
z_est=z_est.astype(float)
#S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
# points2D_image, r_fixed, util.generate_surface(camera, z_est))
z_est = np.maximum(z_est[:,:], 1e-6)
#Sworld = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
S = util.generate_surface(camera, z_est)
#util.save_sfs_ply('test' + '.ply', S, im_rgb)
#util.save_sfs_ply(out_file + '_warp_%i.ply' % iteration, Sworld, im_rgb)
#import pdb;pdb.set_trace()
# if we need to, make corrections for non-positive depths
#S = util.generate_surface(camera, z_est)
mask = (z_est < INIT_Z)
specular_mask=(L<0.8)
dark_mask=(L>0.1)
_mask=np.logical_and(specular_mask,mask)
_mask=np.logical_and(_mask,dark_mask)
# fit reflectance model
r = np.linalg.norm(S, axis=-1)
ndotl = util.calculate_ndotl(camera, S)
falloff, model_params, residual = fit_reflectance_model(model_type,
L[_mask],r.ravel(), r[_mask],ndotl.ravel(), ndotl[_mask], fit_falloff,camera.width,camera.height, *extra_params)
#r = np.linalg.norm(S[specular_mask], axis=-1)
#import pdb;pdb.set_trace()
#model_params=np.array([26.15969874,-27.674055,-12.52426,7.579855,21.9768004,24.3911142,-21.7282996,-19.850894,-11.62229,-4.837014])
#model_params=np.array([-19.4837,-490.4796,812.4527,-426.09107,139.2602,351.8061,-388.1591,875.5013,-302.4748,-414.4384])
#falloff = 1.2
#ndotl = util.calculate_ndotl(camera, S)[specular_mask]
#falloff, model_params, residual = fit_reflectance_model(model_type,
# L[specular_mask], r, ndotl, fit_falloff, *extra_params)
#r = np.linalg.norm(S[neighborhood_mask], axis=-1)
#ndotl = util.calculate_ndotl(camera, S)[neighborhood_mask]
#falloff, model_params, residual = fit_reflectance_model(model_type,
# L[neighborhood_mask], r, ndotl, fit_falloff, *extra_params)
# lambda values reflect our confidence in the current surface: 0
# corresponds to only using SFS at a pixel, 1 corresponds to equally
# weighting SFS and the current estimate, and larger values increasingly
# favor using only the current estimate
rdiff = np.abs(r_fixed - get_estimated_r(S, points2D_image))
w = np.log10(r_fixed) - np.log10(rdiff) - np.log10(2.)
lambdas = (np.sum(weights * w[nn_idxs], axis=-1) /
np.sum(weights, axis=-1))
lambdas = np.maximum(lambdas, 0.) # just in case
#
lambdas[~mask] = 0
#if iteration == 0: # don't use current estimated surface on first pass
#lambdas = np.zeros_like(z)
#else:
# r_ratios_postwarp = r_fixed / get_estimated_r(S, points2D_image)
# ratio_diff = np.abs(r_ratios_prewarp - r_ratios_postwarp)
# ratio_diff[ratio_diff == 0] = 1e-10 # arbitrarily high lambda
# feature_lambdas = 1. / ratio_diff
# lambdas = (np.sum(weights * feature_lambdas[nn_idxs], axis=-1) /
# np.sum(weights, axis=-1))
# run SFS
H_lut, dH_lut = compute_H_LUT(model_type, model_params, NUM_H_LUT_BINS)
#import pdb;pdb.set_trace()
# run SFS
#H_lut = np.ascontiguousarray(H_lut.astype(np.float32))
#dH_lut = np.ascontiguousarray(dH_lut.astype(np.float32))
z = run_sfs(H_lut,dH_lut,camera, L, lambdas, z_est, model_type, model_params, falloff,vmask,
use_image_weighted_derivatives)
# check for convergence
#diff = np.sum(np.abs(z_old[specular_mask] - z[specular_mask]))
#if diff < CONVERGENCE_THRESHOLD * camera.height * camera.width:
# break
# save the surface
#S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
#util.save_sfs_ply(out_file + '_%i.ply' % iteration, S, im_rgb)
else:
print 'DID NOT CONVERGE'
#import pdb;pdb.set_trace()
S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
util.save_sfs_ply(out_file + '.ply', S, im_rgb)
z.astype(np.float32).tofile(out_file + '_z.bin')
# save the surface
#S = util.generate_surface(camera, z)
#S = (S - image.tvec[np.newaxis,np.newaxis,:]).dot(R)
#S, r_ratios = nearest_neighbor_warp(weights, nn_idxs,
# points2D_image, r_fixed, util.generate_surface(camera, z))
#util.save_sfs_ply(out_file + '_warped.ply', S, im_rgb)
#z = np.maximum(S[:,:,2], 1e-6)
#z.astype(np.float32).tofile(out_file + '_warped_z.bin')
#reflectance_models.save_reflectance_model(out_file + '_reflectance.txt',
# model_name, residual, model_params, falloff, *extra_params)
print