def camera_image_to_eye_image(subj,transform_src_file,fixation_src_file,ipd_cm,frames,targets,drift,parallel_input, fixed_fixations=''):
'''transforms left camera image into left and right eye images
'''
fixation_src_mat = parallel_input
if fixed_fixations and not fixed_fixations.startswith('_'):
fixed_fixations = '_'+fixed_fixations
if drift == 0:
dst_dir = '../data/' + subj + fixed_fixations + '/' + subj + '_' + fixation_src_mat + '_eye_images_transform' + transform_src_file.split('_')[3].strip('.npz') + '/'
elif drift == 1:
dst_dir = '../data/' + subj + fixed_fixations + 'drift/' + subj + 'drift_' + fixation_src_mat + '_eye_images_transform' + transform_src_file.split('_')[3].strip('.npz') + '/'
# Normal source directory operation
if targets == 1:
dst_dir = '/Volumes/Macintosh HD 2/cameras2/3d_reprojection' + dst_dir[2:] # put targets directly on second HD
depth_src_dir = '/Volumes/Macintosh HD 2/cameras2/disparity_estimation/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_disparity_maps/three_d_denoised/'
else:
depth_src_dir = '../../disparity_estimation/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_disparity_maps/three_d_denoised/'
if targets == 1:
image_src_dir = '/Volumes/Macintosh HD 2/cameras2/image_rectification/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_frames_rect/'
else:
image_src_dir = '../../image_rectification/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_frames_rect/'
#create destination directory if it doesn't exist
if not os.path.exists(string.join(string.split(dst_dir,'/')[0:-2],'/')):
os.mkdir(string.join(string.split(dst_dir,'/')[0:-2],'/'))
if not os.path.exists(dst_dir):
camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir)
if not os.path.exists(dst_dir+'10deg/'):
camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir+'10deg/')
#if not os.path.exists(dst_dir+'10degfull/'):
# camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir+'10degfull/')
#save parameters into text file in destination directory
camera_to_eye_utils_parallel.save_reprojection_parameters(dst_dir,transform_src_file,depth_src_dir,image_src_dir,fixation_src_file)
#load in fixation location at each frame
fixation_mat = scipy.io.loadmat(fixation_src_file)
if fixation_src_mat.find('_orig') > -1 or fixation_src_mat.find('_cont') > -1:
fixation_src_mat = fixation_src_mat[0:-5]
if targets == 0:
if fixation_src_mat == 'task_walk_1':
fixation_info = fixation_mat['walk_1']
elif fixation_src_mat == 'task_walk_2':
fixation_info = fixation_mat['walk_2']
elif fixation_src_mat == 'task_walk_3':
fixation_info = fixation_mat['walk_3']
else:
fixation_info = fixation_mat['task']
#fixation_info = fixation_mat['walk_1']
else:
fixation_info = fixation_mat[fixation_src_mat]
fixation_coords_le = fixation_info[:,(4,5,6)]
fixation_coords_re = fixation_info[:,(7,8,9)]
fixation_coords_cyclo = fixation_info[:,(1,2,3)]
#and the ground truth target locations in cyclopean coordinates
target_coords = fixation_info[:,(10,11,12)]
target_tilt_slant = fixation_info[:,(13,14)]
#and flags
eye_flag = fixation_info[:,(15)]
#eye flags: 1 = found fixation, 2 = blink, 3 = saccade, 4 = missing data, to add 5 = not full frame
#load in pixel locations of targets in camera frames if present
if os.path.isfile(image_src_dir + 'E_coords.pydict'):
target_camera_pixels = numpy.load(image_src_dir + 'E_coords.pydict')
#load in transformation from cyclopean-eye-to-camera
#and calculate inverse rotation and translation for camera-to-cyclopean-eye
R_eye_to_cam,R_cam_to_eye,tvec_eye_to_cam,tvec_cam_to_eye,intrinsic_mat = camera_to_eye_utils_parallel.load_and_invert_transforms(transform_src_file)
#left camera intrinsics
focal_length_y = intrinsic_mat[1][1]
focal_length_x = intrinsic_mat[0][0]
center_y = intrinsic_mat[1][2]
center_x = intrinsic_mat[0][2]
#eye intrinsics (550x550 pixel images cover 25x25deg field of view), 3.5mm fl 583 pixel units
intrinsic_eye = numpy.zeros( (3,3) )
intrinsic_eye[0,0] = 583
intrinsic_eye[1,1] = 583
intrinsic_eye[0,2] = 275
intrinsic_eye[1,2] = 275
intrinsic_eye[2,2] = 1.0
dist_coeffs_eye = numpy.zeros( (5,1) )
#create lookup table for conversion between a pixel in the eye and the helmholtz azimuth and elevation
HH_table = camera_to_eye_utils_parallel.create_HH_table(intrinsic_eye)
#iterate through source directory of 3d coordinates and grab desired frames
imlist = os.listdir(depth_src_dir)
if frames[0] > 0 or frames[1] > 0:
imlist = imlist[frames[0]:frames[1]]
#initialize matrix with flags to identify status of each frame
frame_status_mat = numpy.zeros((len(imlist),2)) #like eye flags, but for non-target trials
frame_cnt = 0
#allocate error matrix if using targets
if targets == 1:
error_mat = numpy.ones((len(imlist),63))*numpy.nan
cyclo_fix_is_nan = 0
no_depth_at_cyclo = 0
#for each frame matrix of 3d camera coordinates, calculate eye images and disparity maps
for im in imlist:
#make sure necessary files exist
if os.path.isfile(depth_src_dir + im) and os.path.isfile(image_src_dir + 'rect_cam1_frame_' + im.split('_frame_')[1].strip('.npy') + '.bmp'):
#frame number
frame = int(im.split('_frame_')[1].strip('.npy'))
#print str(frame)
#frame status
frame_status_mat[frame_cnt,0] = frame
frame_status_mat[frame_cnt,1] = eye_flag[frame]
if targets == 1:
#add target pts and store error info if present
error_mat[frame_cnt,0] = frame #frame number
error_mat[frame_cnt,(1,2,3)] = target_coords[frame,:] #target cyclopean x,y,z
error_mat[frame_cnt,4] = target_tilt_slant[frame,0] #tilt
error_mat[frame_cnt,5] = target_tilt_slant[frame,1] #slant
error_mat[frame_cnt,6] = int(fixation_src_mat.split('_')[2]) #which distance repeat
#check the eye flag for this frame. If this frame is not a fixation or saccade ( i.e. a blink or missing data) skip reprojection
if frame_status_mat[frame_cnt,1] == 2 or frame_status_mat[frame_cnt,1] == 4:
if frame_status_mat[frame_cnt,1] == 2:
#blink: images are all black
lefteye_image,righteye_image,cycloeye_image,disparity_image,vdisparity_image, \
disparity_mat,vdisparity_mat,depth_mat,disparity_cyclo_image,vdisparity_cyclo_image, \
disparity_cyclo_mat, vdisparity_cyclo_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
# lefteye_image,righteye_image,disparity_image,vdisparity_image,disparity_mat,vdisparity_mat,depth_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
elif frame_status_mat[frame_cnt,1] == 4:
#missing data: images are all grey
lefteye_image,righteye_image,cycloeye_image,disparity_image,vdisparity_image, \
disparity_mat,vdisparity_mat,depth_mat,disparity_cyclo_image,vdisparity_cyclo_image, \
disparity_cyclo_mat, vdisparity_cyclo_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
# lefteye_image,righteye_image,disparity_image,vdisparity_image,disparity_mat,vdisparity_mat,depth_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
lefteye_image = lefteye_image+127
righteye_image = righteye_image+127
disparity_image = disparity_image+127
vdisparity_image = vdisparity_image+127
cycloeye_image = cycloeye_image+127
disparity_cyclo_image = disparity_cyclo_image+127
vdisparity_cyclo_image = vdisparity_cyclo_image+127
#save files
#print "No fixation, Saving files...\n"
cv.SaveImage(dst_dir + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image.copy()))
cv.SaveImage(dst_dir + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image.copy()))
# cv.SaveImage(dst_dir + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image.copy()))
cv.SaveImage(dst_dir + 'cycloeye/cycloeye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(cycloeye_image.copy()))
cv.SaveImage(dst_dir + 'disparity_cycloeye_image/disparity_cycloeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_cyclo_image.copy()))
# numpy.save(dst_dir + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat)
#cv.SaveImage(dst_dir + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image.copy()))
# numpy.save(dst_dir + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat)
#numpy.save(dst_dir + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat)
#increment frame cnt
frame_cnt += 1
continue
#load camera 3d coordinates
camera_coords = numpy.load(depth_src_dir + im)
camera_coords = camera_coords.astype('float64')
camera_coords_orig = camera_coords.copy()
#remove noisy edges
camera_coords = camera_to_eye_utils_parallel.remove_image_edges(camera_coords,25)
if targets == 1:
#grab ground truth target location in cyclopean coordinates
target_point = camera_to_eye_utils_parallel.ground_truth_target_cyclopean_coords(target_coords,frame)
#grab camera pixel target location in camera coordinates
target_camera_coords = camera_to_eye_utils_parallel.camera_target_camera_coords(target_point,target_camera_pixels,camera_coords,center_x,center_y,focal_length_x,focal_length_y,frame)
#for points with inf distance, convert to just very very large distance
inf_coords = numpy.zeros((numpy.where(camera_coords[:,:,2] == numpy.inf)[0].size,3))
inf_coords[:,0] = -1e100 * (-(center_x-numpy.where(camera_coords[:,:,2] == numpy.inf)[1])/focal_length_x)
inf_coords[:,1] = -1e100 * (-(center_y-numpy.where(camera_coords[:,:,2] == numpy.inf)[0])/focal_length_y)
inf_coords[:,2] = -1e100*numpy.ones((1,numpy.where(camera_coords[:,:,2] == numpy.inf)[0].size))
camera_coords[numpy.where(camera_coords[:,:,2] == numpy.inf)] = inf_coords
#for points at 0,0,0 placeholder replace with nans
camera_coords[numpy.where(camera_coords[:,:,2] == 0)] = numpy.nan
#reshape camera coords into 2d array with each column an x,y,z point
camera_mat = numpy.concatenate([numpy.reshape(camera_coords[:,:,0],(1,307200)),numpy.reshape(camera_coords[:,:,1],(1,307200)),numpy.reshape(camera_coords[:,:,2],(1,307200))])
#grab fixation pt in cyclopean coords for each eye
fixation_point_le = fixation_coords_le[frame,:]
fixation_point_re = fixation_coords_re[frame,:]
fixation_point_cyclo = fixation_coords_cyclo[frame, :]
# rarely, the recorded cyclopean fixation point is nan
# whenever this is the case, we simply average the left and right fixation points to approximate
if numpy.isnan(fixation_point_cyclo).all():
fixation_point_cyclo = (fixation_point_le + fixation_point_re) / 2.
cyclo_fix_is_nan += 1
if not targets: # we want to use the eyetracker info for the targets due to the uniformity of the screen
head_centered_3d_coords = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye,(3,1)))
cyclopean_helmholtz_coords = camera_to_eye_utils_parallel.fixation_to_Helmholtz(numpy.append(numpy.reshape(fixation_point_cyclo,(3,1)),numpy.reshape(fixation_point_cyclo,(3,1)),axis=1), 0, G=0.8)
R_cyclopean = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=cyclopean_helmholtz_coords[0,0], theta=cyclopean_helmholtz_coords[1,0], psi=cyclopean_helmholtz_coords[2,0])
R_cyclopean = numpy.asarray(R_cyclopean)
t_cyclopean = numpy.array([[0.],
[0],
[0]])
cycloeye_3d_coords = R_cyclopean.T.dot(head_centered_3d_coords + t_cyclopean)
# print numpy.nanmin(numpy.abs(cycloeye_3d_coords[0,:]))
pts_near_fovea_cycloeye = camera_to_eye_utils_parallel.find_pts_near_fovea(cycloeye_3d_coords)
pts_near_fovea_head = R_cyclopean.dot(pts_near_fovea_cycloeye) # rotate back to head-centered
with open(dst_dir + 'cyclopts_info/{0}.txt'.format(frame), mode='w') as f:
f.write("head-centered foveal points\n{0}\n\ncyclopean eye foveal points\n{1}\n\nstd:\n{2}\n{3} \
\nmean:\n{4}\n{5}\n\nz std in diopters:\n{6:.3}\n{7:.2}\n\nz mean in diopters:\n{8:.2}\n{9:.2}".format(pts_near_fovea_head, pts_near_fovea_cycloeye,
numpy.nanstd(pts_near_fovea_head, axis=1),
numpy.nanstd(pts_near_fovea_cycloeye, axis=1),
numpy.nanmean(pts_near_fovea_head, axis=1),
numpy.nanmean(pts_near_fovea_cycloeye, axis=1),
numpy.nanstd(1/(pts_near_fovea_head[2,:]/100)),
numpy.nanstd(1/(pts_near_fovea_cycloeye[2,:]/100)),
numpy.nanmean(1/(pts_near_fovea_head[2,:]/100)),
numpy.nanmean(1/(pts_near_fovea_cycloeye[2,:]/100))))
fixation_point_le = numpy.nanmean(pts_near_fovea_head, axis=1)
# print fixation_point_le
fixation_point_re = numpy.copy(fixation_point_le)
fixation_point_cyclo = numpy.copy(fixation_point_le) #all head-centered points now overlap
if numpy.isnan(fixation_point_le).any():
no_depth_at_cyclo += 1
frame_cnt += 1
continue
# proceed as usual
#---------------BEGIN ORIGNAL REROJECTION COMPUTATION--------------
#print 'Reprojecting points...'
#calculate R and t for each eye to fixation pt
R_left,t_left,R_right,t_right, R_cyclopean, t_cyclopean = camera_to_eye_utils_parallel.find_eyes_Rt(numpy.matrix(numpy.append(numpy.reshape(fixation_point_le,(3,1)),numpy.reshape(fixation_point_re,(3,1)),axis=1)),ipd_cm)
try:
helmholtz_coords = camera_to_eye_utils_parallel.fixation_to_Helmholtz(numpy.append(numpy.reshape(fixation_point_le,(3,1)),numpy.reshape(fixation_point_re,(3,1)),axis=1), ipd_cm, G=0.8)
cyclopean_helmholtz_coords = camera_to_eye_utils_parallel.fixation_to_Helmholtz(numpy.append(numpy.reshape(fixation_point_cyclo,(3,1)),numpy.reshape(fixation_point_cyclo,(3,1)),axis=1), 0, G=0.8)
except AssertionError:
print 'ERROR in frame', frame, 'from data set', fixation_src_mat
raise
R_left = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=helmholtz_coords[0,0], theta=helmholtz_coords[1,0], psi=helmholtz_coords[2,0])
R_right = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=helmholtz_coords[0,1], theta=helmholtz_coords[1,1], psi=helmholtz_coords[2,1])
R_cyclopean = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=cyclopean_helmholtz_coords[0,0], theta=cyclopean_helmholtz_coords[1,0], psi=cyclopean_helmholtz_coords[2,0])
#cast as arrays
R_left = numpy.asarray(R_left)
R_right = numpy.asarray(R_right)
t_left = numpy.asarray(t_left)
t_right = numpy.asarray(t_right)
R_cyclopean = numpy.asarray(R_cyclopean)
t_cyclopean = numpy.asarray(t_cyclopean)
#transform camera coordinates into cyclopean coordinates, then into left and right eye coordinates
head_centered_3d_coords = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye,(3,1)))
# righteye_3d_coords_cyclo = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye,(3,1)))
# cycloeye_3d_coords_cyclo = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye, (3,1)))
#----------------END ORIGNAL REPORJECTION COMPUTATION--------------
lefteye_3d_coords = R_left.T.dot(head_centered_3d_coords + t_left)
righteye_3d_coords = R_right.T.dot(head_centered_3d_coords + t_right)
cycloeye_3d_coords = R_cyclopean.T.dot(head_centered_3d_coords + t_cyclopean)
#store cyclopean radial distance
cyclopean_distance = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,head_centered_3d_coords,calc_radial_distance=1)
#project 3d points to pixels
righteye_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_3d_coords)
lefteye_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_3d_coords)
cycloeye_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye, cycloeye_3d_coords)
#and then project 3d points to degrees in Helmholtz
righteye_HH = camera_to_eye_utils_parallel.convert_pixel_loc_to_HH_opt(righteye_pixels, intrinsic_eye)
lefteye_HH = camera_to_eye_utils_parallel.convert_pixel_loc_to_HH_opt(lefteye_pixels, intrinsic_eye)
cycloeye_HH = camera_to_eye_utils_parallel.convert_pixel_loc_to_HH_opt(cycloeye_pixels, intrinsic_eye)
#get coords of fixation pt
lefteye_fixation = R_left.T.dot(numpy.reshape(fixation_point_le,(3,1)) + t_left)
righteye_fixation = R_right.T.dot(numpy.reshape(fixation_point_re,(3,1)) + t_right)
cycloeye_fixation = R_cyclopean.T.dot(numpy.reshape(fixation_point_cyclo,(3,1)) + t_cyclopean)
print frame
print lefteye_fixation
print righteye_fixation
print cycloeye_fixation
print
#project fixation pt to pixels
lefteye_fixation_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_fixation)
righteye_fixation_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_fixation)
cycloeye_fixation_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye, cycloeye_fixation)
if targets == 1:
#get coords of GT target
lefteye_target = R_left.T.dot(numpy.reshape(target_point,(3,1)) + t_left)
righteye_target = R_right.T.dot(numpy.reshape(target_point,(3,1)) + t_right)
cycloeye_target = R_cyclopean.T.dot(numpy.reshape(target_point,(3,1)) + t_cyclopean)
#project GT target to pixels
lefteye_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_target)
righteye_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_target)
cycloeye_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye, cycloeye_target)
#get coords of camera target pt
#first transform from camera coordinates into cyclopean coordinates, then into left and right eye coordinates
lefteye_target_camera_cyclo = R_cam_to_eye.dot(numpy.reshape(target_camera_coords,(3,1)) + numpy.reshape(tvec_cam_to_eye,(3,1)))
righteye_target_camera_cyclo = R_cam_to_eye.dot(numpy.reshape(target_camera_coords,(3,1)) + numpy.reshape(tvec_cam_to_eye,(3,1)))
cycloeye_target_camera_cyclo = R_cam_to_eye.dot(numpy.reshape(target_camera_coords, (3,1)) + numpy.reshape(tvec_cam_to_eye, (3,1)))
lefteye_target_camera = R_left.T.dot(lefteye_target_camera_cyclo + t_left)
righteye_target_camera = R_right.T.dot(righteye_target_camera_cyclo + t_right)
cycloeye_target_camera = R_cyclopean.T.dot(cycloeye_target_camera_cyclo + t_cyclopean)
#project camera target to pixels
# print 'left'
# print tvec_cam_to_eye
#print 'right'
#print righteye_target_camera_cyclo
lefteye_camera_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_target_camera)
righteye_camera_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_target_camera)
cycloeye_camera_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye, cycloeye_target_camera)
ground_truth_target_camera_coords = R_eye_to_cam.dot(numpy.reshape(target_point,(3,1)) + numpy.reshape(tvec_eye_to_cam,(3,1)))
#create new eye images
lefteye_image,righteye_image,cycloeye_image,disparity_image,vdisparity_image, \
disparity_mat,vdisparity_mat,depth_mat,disparity_cyclo_image,vdisparity_cyclo_image, \
disparity_cyclo_mat, vdisparity_cyclo_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
#load in camera image
cam_image = cv2.imread(image_src_dir + 'rect_cam1_frame_' + im.split('_frame_')[1].strip('.npy') + '.bmp', flags=0)
#print "Filling eye images..."
#for each pixel in the camera image, relocate it to the eye images
lefteye_image = camera_to_eye_utils_parallel.create_eye_image(lefteye_pixels,cam_image,lefteye_image)
righteye_image = camera_to_eye_utils_parallel.create_eye_image(righteye_pixels,cam_image,righteye_image)
cycloeye_image = camera_to_eye_utils_parallel.create_eye_image(cycloeye_pixels, cam_image, cycloeye_image)
disparity_mat,vdisparity_mat,disparity_cyclo_mat, vdisparity_cyclo_mat = camera_to_eye_utils_parallel.create_angular_disparity_mats(lefteye_pixels,lefteye_HH,
righteye_pixels,righteye_HH,
cycloeye_pixels,
disparity_mat,vdisparity_mat,
disparity_cyclo_mat, vdisparity_cyclo_mat)
#disparity_mat,vdisparity_mat = camera_to_eye_utils_parallel.create_disparity_mats(lefteye_pixels,righteye_pixels,disparity_mat,vdisparity_mat)
#for each radial distance in the camera image, relocate it to the left eye
depth_mat = camera_to_eye_utils_parallel.create_depth_image(cycloeye_pixels,cyclopean_distance,depth_mat)
depth_mat = depth_mat[:,:,3]
#make disparity image
disparity_image = camera_to_eye_utils_parallel.make_colormap_disparity(disparity_image,disparity_mat)
vdisparity_image = camera_to_eye_utils_parallel.make_colormap_disparity(vdisparity_image,vdisparity_mat)
disparity_cyclo_image = camera_to_eye_utils_parallel.make_colormap_disparity(disparity_cyclo_image,disparity_cyclo_mat)
vdisparity_cyclo_image = camera_to_eye_utils_parallel.make_colormap_disparity(vdisparity_cyclo_image,vdisparity_cyclo_mat)
#add yellow fixation pt
#righteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((righteye_image,righteye_image,righteye_image)),righteye_fixation_pixels)
#lefteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((lefteye_image,lefteye_image,lefteye_image)),lefteye_fixation_pixels)
#disparity_image = camera_to_eye_utils_parallel.draw_fixation_point(disparity_image,lefteye_fixation_pixels)
#vdisparity_image = camera_to_eye_utils_parallel.draw_fixation_point(vdisparity_image,lefteye_fixation_pixels)
if targets == 1:
#add yellow fixation pt
righteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((righteye_image,righteye_image,righteye_image)),righteye_fixation_pixels)
lefteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((lefteye_image,lefteye_image,lefteye_image)),lefteye_fixation_pixels)
cycloeye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((cycloeye_image,cycloeye_image,cycloeye_image)),cycloeye_fixation_pixels)
disparity_image = camera_to_eye_utils_parallel.draw_fixation_point(disparity_image,lefteye_fixation_pixels)
disparity_cyclo_image = camera_to_eye_utils_parallel.draw_fixation_point(disparity_cyclo_image,cycloeye_fixation_pixels)
#add target pts
#if numpy.isnan(target_point[2]) == 0:
# #cyan is projection of ground truth target to eyes
# righteye_image = draw_fixation_point(righteye_image,righteye_target_pixels,rgb=(0,255,255))
# lefteye_image = draw_fixation_point(lefteye_image,lefteye_target_pixels,rgb=(0,255,255))
if numpy.isnan(target_camera_coords[2]) == 0:
#magenta is projection of target pixels in camera to eyes
righteye_image = camera_to_eye_utils_parallel.draw_fixation_point(righteye_image,righteye_camera_target_pixels,rgb=(255,0,255))
lefteye_image = camera_to_eye_utils_parallel.draw_fixation_point(lefteye_image,lefteye_camera_target_pixels,rgb=(255,0,255))
cycloeye_image = camera_to_eye_utils_parallel.draw_fixation_point(cycloeye_image,cycloeye_camera_target_pixels,rgb=(255,0,255))
#save files
#print "Saving files...\n"
cv.SaveImage(dst_dir + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image.copy()))
cv.SaveImage(dst_dir + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image.copy()))
cv.SaveImage(dst_dir + 'cycloeye/cycloeye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(cycloeye_image.copy()))
# cv.SaveImage(dst_dir + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image.copy()))
# numpy.save(dst_dir + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat)
cv.SaveImage(dst_dir + 'disparity_cycloeye_image/disparity_cycloeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_cyclo_image.copy()))
# cv.SaveImage(dst_dir + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image.copy()))
cv.SaveImage(dst_dir + 'vdisparity_cycloeye_image/vdisparity_cycloeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_cyclo_image.copy()))
# numpy.save(dst_dir + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat)
# numpy.save(dst_dir + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat)
#save truncated files (10deg circular window for analysis)
lefteye_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(lefteye_image,intrinsic_eye[0,2])
righteye_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(righteye_image,intrinsic_eye[0,2])
cycloeye_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(cycloeye_image,intrinsic_eye[0,2])
# disparity_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_image,intrinsic_eye[0,2])
# vdisparity_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_image,intrinsic_eye[0,2])
# disparity_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_mat,intrinsic_eye[0,2])
# vdisparity_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_mat,intrinsic_eye[0,2])
disparity_cyclo_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_cyclo_image,intrinsic_eye[0,2])
vdisparity_cyclo_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_cyclo_image,intrinsic_eye[0,2])
disparity_cyclo_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_cyclo_mat,intrinsic_eye[0,2])
vdisparity_cyclo_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_cyclo_mat,intrinsic_eye[0,2])
depth_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(depth_mat,intrinsic_eye[0,2])
HH_table_10deg = camera_to_eye_utils_parallel.apply_circle_mask(HH_table,intrinsic_eye[0,2])
cv.SaveImage(dst_dir + '10deg/' + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image_10deg.copy()))
cv.SaveImage(dst_dir + '10deg/' + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image_10deg.copy()))
cv.SaveImage(dst_dir + '10deg/' + 'cycloeye/cycloeye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(cycloeye_image_10deg.copy()))
# cv.SaveImage(dst_dir + '10deg/' + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image_10deg.copy()))
# numpy.save(dst_dir + '10deg/' + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat_10deg)
# cv.SaveImage(dst_dir + '10deg/' + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image_10deg.copy()))
# numpy.save(dst_dir + '10deg/' + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat_10deg)
cv.SaveImage(dst_dir + '10deg/' + 'disparity_cycloeye_image/disparity_cycloeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_cyclo_image_10deg.copy()))
numpy.save(dst_dir + '10deg/' + 'disparity_cycloeye/disparity_cycloeye_frame_' + im.split('_frame_')[1], disparity_cyclo_mat_10deg)
cv.SaveImage(dst_dir + '10deg/' + 'vdisparity_cycloeye_image/vdisparity_cycloeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_cyclo_image_10deg.copy()))
numpy.save(dst_dir + '10deg/' + 'vdisparity_cycloeye/vdisparity_cylcoeye_frame_' + im.split('_frame_')[1], vdisparity_cyclo_mat_10deg)
numpy.save(dst_dir + '10deg/' + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat_10deg)
if not os.path.exists(dst_dir + '10deg/HH_coords_of_eye_sensor_pixels.npy'):
numpy.save(dst_dir + '10deg/HH_coords_of_eye_sensor_pixels.npy', HH_table_10deg)
#check for missing edges
#if any edges contain only nans, the data is missing, add flag
if numpy.any(numpy.isnan(disparity_cyclo_mat_10deg[0:207,1])==0) == False or numpy.any(numpy.isnan(disparity_cyclo_mat_10deg[0:207,-2])==0) == False or numpy.any(numpy.isnan(disparity_cyclo_mat_10deg[1,0:207])==0) == False or numpy.any(numpy.isnan(disparity_cyclo_mat_10deg[-2,0:207])==0) == False:
frame_status_mat[frame_cnt,1] = 5
#separately save only frames with no missing edges
#else:
# cv.SaveImage(dst_dir + '10degfull/' + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image_10deg.copy()))
# cv.SaveImage(dst_dir + '10degfull/' + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image_10deg.copy()))
# cv.SaveImage(dst_dir + '10degfull/' + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image_10deg.copy()))
# numpy.save(dst_dir + '10degfull/' + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat_10deg)
# cv.SaveImage(dst_dir + '10degfull/' + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image_10deg.copy()))
# numpy.save(dst_dir + '10degfull/' + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat_10deg)
if targets == 1:
#store error info
error_mat[frame_cnt,(7,8)] = lefteye_fixation_pixels #should just be center of the sensor (275,275)
error_mat[frame_cnt,(9,10)] = righteye_fixation_pixels
error_mat[frame_cnt,(11,12)] = cycloeye_fixation_pixels
error_mat[frame_cnt,(13,14,15)] = lefteye_fixation.squeeze() # 3d position of fixation point in each eye's coordinate systems
error_mat[frame_cnt,(16,17,18)] = righteye_fixation.squeeze()
error_mat[frame_cnt,(19,20,21)] = cycloeye_fixation.squeeze()
error_mat[frame_cnt,(22,23)] = lefteye_target_pixels #projection of ground truth target from camera to eye
error_mat[frame_cnt,(24,25)] = righteye_target_pixels
error_mat[frame_cnt,(26,27)] = cycloeye_target_pixels
error_mat[frame_cnt,(28,29,30)] = lefteye_target.squeeze() # 3d coords of ground truth target
error_mat[frame_cnt,(31,32,33)] = righteye_target.squeeze()
error_mat[frame_cnt,(34,35,36)] = cycloeye_target.squeeze()
error_mat[frame_cnt,(37,38)] = lefteye_camera_target_pixels #projection of camera target to eye
error_mat[frame_cnt,(39,40)] = righteye_camera_target_pixels
error_mat[frame_cnt,(41,42)] = cycloeye_camera_target_pixels
error_mat[frame_cnt,(43,44,45)] = lefteye_target_camera.squeeze() #3d coords of camera target
error_mat[frame_cnt,(46,47,48)] = righteye_target_camera.squeeze()
error_mat[frame_cnt,(49,50,51)] = cycloeye_target_camera.squeeze()
error_mat[frame_cnt,(52,53,54)] = lefteye_target_camera_cyclo.squeeze() # camera target in head centered coords (compare to target_point)
error_mat[frame_cnt,(55,56,57)] = ground_truth_target_camera_coords.squeeze() # ground truth target in camera coords
error_mat[frame_cnt,(58,59,60)] = target_camera_coords # camera target in camera coords
#disparity of target pixel
if not numpy.isnan(lefteye_camera_target_pixels[0]):
# check that x-values are between 0 and +550 and y-values are between 0 and -550
if lefteye_camera_target_pixels[0] < 0 or lefteye_camera_target_pixels[0] > 550 or \
lefteye_camera_target_pixels[1] < 0 or lefteye_camera_target_pixels[1] > 550:
print "Yikes, these pixels are out of bounds! We're setting the disparity to NaN."
print lefteye_camera_target_pixels
print ''
camera_target_disparity = numpy.nan
else:
camera_target_disparity = disparity_mat[-cycloeye_camera_target_pixels[1],cycloeye_camera_target_pixels[0]]
error_mat[frame_cnt,(61)] = camera_target_disparity
#eye flag
error_mat[frame_cnt,(62)] = frame_status_mat[frame_cnt,1]
numpy.save(dst_dir + subj + fixed_fixations + '_' + fixation_src_mat + '_target_errors.npy', error_mat)
numpy.save(dst_dir + subj + fixed_fixations + '_' + fixation_src_mat + '_frame_status.npy' , frame_status_mat)
#increment frame cnt
frame_cnt += 1
# TODO Convert this to a message, print it and save it as a text file
results_dict = {'subj':subj, 'fixation_src_mat':fixation_src_mat,
'total_frames':len(frame_status_mat),
'good_fixations':(len(frame_status_mat[frame_status_mat == 1])),
'percent_good_fixations':numpy.true_divide(len(frame_status_mat[frame_status_mat == 1]),len(frame_status_mat))*100,
'blinks':(len(frame_status_mat[frame_status_mat == 2])),
'percent_blinks':(numpy.true_divide(len(frame_status_mat[frame_status_mat == 2]),len(frame_status_mat))*100),
'saccades':(len(frame_status_mat[frame_status_mat == 3])),
'percent_saccades':(numpy.true_divide(len(frame_status_mat[frame_status_mat == 3]),len(frame_status_mat))*100),
'missing_data':(len(frame_status_mat[frame_status_mat == 4])),
'percent_missing_data':(numpy.true_divide(len(frame_status_mat[frame_status_mat == 4]),len(frame_status_mat))*100),
'not_full_frame':(len(frame_status_mat[frame_status_mat == 5])),
'percent_not_full_frame':(numpy.true_divide(len(frame_status_mat[frame_status_mat == 5]),len(frame_status_mat))*100),
'nans':(len(frame_status_mat[numpy.isnan(frame_status_mat)])),
'percent_nans':(numpy.true_divide(len(frame_status_mat[numpy.isnan(frame_status_mat)]),len(frame_status_mat))*100),
'cyclo_fix_is_nan':cyclo_fix_is_nan, 'no_depth_at_cyclo':no_depth_at_cyclo}
msg = """{subj}-{fixation_src_mat}
total frames: {total_frames}
good fixations: {percent_good_fixations:.2}% ({good_fixations})
blinks: {percent_blinks:.2}% ({blinks})
saccades: {percent_saccades:.2}% ({saccades})
missing data: {percent_missing_data:.2}% (missing_data)
not full frame: {percent_not_full_frame:.2}% ({not_full_frame})
Nans?: {percent_nans:.2}% ({nans})
Number of cyclopean fixation points that are nan: {cyclo_fix_is_nan}
No depth info at cycloean fixation: {no_depth_at_cyclo}
""".format(**results_dict)
print msg
with open(dst_dir + 'results.txt', mode='w') as f:
f.write(msg)
def camera_image_to_eye_image(subj,transform_src_file,fixation_src_file,ipd_cm,frames,targets,drift,parallel_input, fixed_fixations=''):
'''transforms left camera image into left and right eye images
'''
fixation_src_mat = parallel_input
if fixed_fixations and not fixed_fixations.startswith('_'):
fixed_fixations = '_'+fixed_fixations
if drift == 0:
dst_dir = '../data/' + subj + fixed_fixations + '/' + subj + '_' + fixation_src_mat + '_eye_images_transform' + transform_src_file.split('_')[3].strip('.npz') + '/'
elif drift == 1:
dst_dir = '../data/' + subj + fixed_fixations + 'drift/' + subj + 'drift_' + fixation_src_mat + '_eye_images_transform' + transform_src_file.split('_')[3].strip('.npz') + '/'
# Normal source directory operation
depth_src_dir = '../../disparity_estimation/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_disparity_maps/three_d_denoised/'
image_src_dir = '../../image_rectification/data/' + subj + '/' + subj + '_' + fixation_src_mat + '_frames_rect/'
#create destination directory if it doesn't exist
if not os.path.exists(string.join(string.split(dst_dir,'/')[0:-2],'/')):
os.mkdir(string.join(string.split(dst_dir,'/')[0:-2],'/'))
if not os.path.exists(dst_dir):
camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir)
if not os.path.exists(dst_dir+'10deg/'):
camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir+'10deg/')
#if not os.path.exists(dst_dir+'10degfull/'):
# camera_to_eye_utils_parallel.make_destination_directory_tree(dst_dir+'10degfull/')
#save parameters into text file in destination directory
camera_to_eye_utils_parallel.save_reprojection_parameters(dst_dir,transform_src_file,depth_src_dir,image_src_dir,fixation_src_file)
#load in fixation location at each frame
fixation_mat = scipy.io.loadmat(fixation_src_file)
if fixation_src_mat.find('_orig') > -1 or fixation_src_mat.find('_cont') > -1:
fixation_src_mat = fixation_src_mat[0:-5]
if targets == 0:
if fixation_src_mat == 'task_walk_1':
fixation_info = fixation_mat['walk_1']
elif fixation_src_mat == 'task_walk_2':
fixation_info = fixation_mat['walk_2']
elif fixation_src_mat == 'task_walk_3':
fixation_info = fixation_mat['walk_3']
else:
fixation_info = fixation_mat['task']
#fixation_info = fixation_mat['walk_1']
else:
fixation_info = fixation_mat[fixation_src_mat]
fixation_coords_le = fixation_info[:,(4,5,6)]
fixation_coords_re = fixation_info[:,(7,8,9)]
#and the ground truth target locations in cyclopean coordinates
target_coords = fixation_info[:,(10,11,12)]
target_tilt_slant = fixation_info[:,(13,14)]
#and flags
eye_flag = fixation_info[:,(15)]
#eye flags: 1 = found fixation, 2 = blink, 3 = saccade, 4 = missing data, to add 5 = not full frame
#load in pixel locations of targets in camera frames if present
if os.path.isfile(image_src_dir + 'E_coords.pydict'):
target_camera_pixels = numpy.load(image_src_dir + 'E_coords.pydict')
#load in transformation from cyclopean-eye-to-camera
#and calculate inverse rotation and translation for camera-to-cyclopean-eye
R_eye_to_cam,R_cam_to_eye,tvec_eye_to_cam,tvec_cam_to_eye,intrinsic_mat = camera_to_eye_utils_parallel.load_and_invert_transforms(transform_src_file)
#left camera intrinsics
focal_length_y = intrinsic_mat[1][1]
focal_length_x = intrinsic_mat[0][0]
center_y = intrinsic_mat[1][2]
center_x = intrinsic_mat[0][2]
#eye intrinsics (550x550 pixel images cover 25x25deg field of view), 3.5mm fl 583 pixel units
intrinsic_eye = numpy.zeros( (3,3) )
intrinsic_eye[0,0] = 583
intrinsic_eye[1,1] = 583
intrinsic_eye[0,2] = 275
intrinsic_eye[1,2] = 275
intrinsic_eye[2,2] = 1.0
dist_coeffs_eye = numpy.zeros( (5,1) )
#iterate through source directory of 3d coordinates and grab desired frames
imlist = os.listdir(depth_src_dir)
if frames[0] > 0 or frames[1] > 0:
imlist = imlist[frames[0]:frames[1]]
#initialize matrix with flags to identify status of each frame
frame_status_mat = numpy.zeros((len(imlist),2)) #like eye flags, but for non-target trials
frame_cnt = 0
#allocate error matrix if using targets
if targets == 1:
error_mat = numpy.ones((len(imlist),21))*numpy.nan
#for each frame matrix of 3d camera coordinates, calculate eye images and disparity maps
for im in imlist:
#make sure necessary files exist
if os.path.isfile(depth_src_dir + im) and os.path.isfile(image_src_dir + 'rect_cam1_frame_' + im.split('_frame_')[1].strip('.npy') + '.bmp'):
#frame number
frame = int(im.split('_frame_')[1].strip('.npy'))
#print str(frame)
#frame status
frame_status_mat[frame_cnt,0] = frame
frame_status_mat[frame_cnt,1] = eye_flag[frame]
if targets == 1:
#add target pts and store error info if present
error_mat[frame_cnt,0] = frame #frame number
error_mat[frame_cnt,(1,2,3)] = target_coords[frame,:] #target cyclopean x,y,z
error_mat[frame_cnt,4] = target_tilt_slant[frame,0] #tilt
error_mat[frame_cnt,5] = target_tilt_slant[frame,1] #slant
error_mat[frame_cnt,6] = int(fixation_src_mat.split('_')[2]) #which distance repeat
#check the eye flag for this frame. If this frame is not a fixation or saccade ( i.e. a blink or missing data) skip reprojection
if frame_status_mat[frame_cnt,1] == 2 or frame_status_mat[frame_cnt,1] == 4:
if frame_status_mat[frame_cnt,1] == 2:
#blink: images are all black
lefteye_image,righteye_image,disparity_image,vdisparity_image,disparity_mat,vdisparity_mat,depth_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
elif frame_status_mat[frame_cnt,1] == 4:
#missing data: images are all grey
lefteye_image,righteye_image,disparity_image,vdisparity_image,disparity_mat,vdisparity_mat,depth_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
lefteye_image = lefteye_image+127
righteye_image = righteye_image+127
disparity_image = disparity_image+127
vdisparity_image = vdisparity_image+127
#save files
#print "No fixation, Saving files...\n"
cv.SaveImage(dst_dir + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image.copy()))
cv.SaveImage(dst_dir + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image.copy()))
cv.SaveImage(dst_dir + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image.copy()))
numpy.save(dst_dir + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat)
cv.SaveImage(dst_dir + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image.copy()))
numpy.save(dst_dir + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat)
#numpy.save(dst_dir + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat)
#increment frame cnt
frame_cnt += 1
continue
#load camera 3d coordinates
camera_coords = numpy.load(depth_src_dir + im)
camera_coords = camera_coords.astype('float64')
camera_coords_orig = camera_coords.copy()
#remove noisy edges
camera_coords = camera_to_eye_utils_parallel.remove_image_edges(camera_coords,25)
if targets == 1:
#grab ground truth target location in cyclopean coordinates
target_point = camera_to_eye_utils_parallel.ground_truth_target_cyclopean_coords(target_coords,frame)
#grab camera pixel target location in camera coordinates
target_camera_coords = camera_to_eye_utils_parallel.camera_target_camera_coords(target_point,target_camera_pixels,camera_coords,center_x,center_y,focal_length_x,focal_length_y,frame)
#for points with inf distance, convert to just very very large distance
inf_coords = numpy.zeros((numpy.where(camera_coords[:,:,2] == numpy.inf)[0].size,3))
inf_coords[:,0] = -1e100 * (-(center_x-numpy.where(camera_coords[:,:,2] == numpy.inf)[1])/focal_length_x)
inf_coords[:,1] = -1e100 * (-(center_y-numpy.where(camera_coords[:,:,2] == numpy.inf)[0])/focal_length_y)
inf_coords[:,2] = -1e100*numpy.ones((1,numpy.where(camera_coords[:,:,2] == numpy.inf)[0].size))
camera_coords[numpy.where(camera_coords[:,:,2] == numpy.inf)] = inf_coords
#for points at 0,0,0 placeholder replace with nans
camera_coords[numpy.where(camera_coords[:,:,2] == 0)] = numpy.nan
#reshape camera coords into 2d array with each column an x,y,z point
camera_mat = numpy.concatenate([numpy.reshape(camera_coords[:,:,0],(1,307200)),numpy.reshape(camera_coords[:,:,1],(1,307200)),numpy.reshape(camera_coords[:,:,2],(1,307200))])
#grab fixation pt in cyclopean coords for each eye
fixation_point_le = fixation_coords_le[frame,:]
fixation_point_re = fixation_coords_re[frame,:]
#print frame
#print fixation_point_le
#print fixation_point_re
#print 'Reprojecting points...'
#calculate R and t for each eye to fixation pt
R_left,t_left,R_right,t_right = camera_to_eye_utils_parallel.find_eyes_Rt(numpy.matrix(numpy.append(numpy.reshape(fixation_point_le,(3,1)),numpy.reshape(fixation_point_re,(3,1)),axis=1)),ipd_cm)
helmholtz_coords = camera_to_eye_utils_parallel.fixation_to_Helmholtz(numpy.append(numpy.reshape(fixation_point_le,(3,1)),numpy.reshape(fixation_point_re,(3,1)),axis=1), ipd_cm, G=0.8)
R_left = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=helmholtz_coords[0,0], theta=helmholtz_coords[1,0], psi=helmholtz_coords[2,0])
R_right = camera_to_eye_utils_parallel.R_from_Helmholtz(phi=helmholtz_coords[0,1], theta=helmholtz_coords[1,1], psi=helmholtz_coords[2,1])
#cast as arrays
R_left = numpy.asarray(R_left)
R_right = numpy.asarray(R_right)
t_left = numpy.asarray(t_left)
t_right = numpy.asarray(t_right)
#transform camera coordinates into cyclopean coordinates, then into left and right eye coordinates
lefteye_3d_coords_cyclo = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye,(3,1)))
righteye_3d_coords_cyclo = R_cam_to_eye.dot(camera_mat + numpy.reshape(tvec_cam_to_eye,(3,1)))
lefteye_3d_coords = R_left.T.dot(lefteye_3d_coords_cyclo + t_left)
righteye_3d_coords = R_right.T.dot(righteye_3d_coords_cyclo + t_right)
#store cyclopean radial distance
cyclopean_radial_distance = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_3d_coords_cyclo,calc_radial_distance=1)
#project 3d points to pixels
righteye_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_3d_coords)
lefteye_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_3d_coords)
#get coords of fixation pt
lefteye_fixation = R_left.T.dot(numpy.reshape(fixation_point_le,(3,1)) + t_left)
righteye_fixation = R_right.T.dot(numpy.reshape(fixation_point_re,(3,1)) + t_right)
#project fixation pt to pixels
lefteye_fixation_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_fixation)
righteye_fixation_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_fixation)
if targets == 1:
#get coords of GT target
lefteye_target = R_left.T.dot(numpy.reshape(target_point,(3,1)) + t_left)
righteye_target = R_right.T.dot(numpy.reshape(target_point,(3,1)) + t_right)
#project GT target to pixels
lefteye_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_target)
righteye_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_target)
#get coords of camera target pt
#first transform from camera coordinates into cyclopean coordinates, then into left and right eye coordinates
lefteye_target_camera_cyclo = R_cam_to_eye.dot(numpy.reshape(target_camera_coords,(3,1)) + numpy.reshape(tvec_cam_to_eye,(3,1)))
righteye_target_camera_cyclo = R_cam_to_eye.dot(numpy.reshape(target_camera_coords,(3,1)) + numpy.reshape(tvec_cam_to_eye,(3,1)))
lefteye_target_camera = R_left.T.dot(lefteye_target_camera_cyclo + t_left)
righteye_target_camera = R_right.T.dot(righteye_target_camera_cyclo + t_right)
#project camera target to pixels
# print 'left'
# print tvec_cam_to_eye
#print 'right'
#print righteye_target_camera_cyclo
lefteye_camera_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,lefteye_target_camera)
righteye_camera_target_pixels = camera_to_eye_utils_parallel.project_to_pixels(intrinsic_eye,righteye_target_camera)
#create new eye images
lefteye_image,righteye_image,disparity_image,vdisparity_image,disparity_mat,vdisparity_mat,depth_mat = camera_to_eye_utils_parallel.initialize_images_and_mats()
#load in camera image
cam_image = cv2.imread(image_src_dir + 'rect_cam1_frame_' + im.split('_frame_')[1].strip('.npy') + '.bmp', flags=0)
#print "Filling eye images..."
#for each pixel in the camera image, relocate it to the eye images
lefteye_image = camera_to_eye_utils_parallel.create_eye_image(lefteye_pixels,cam_image,lefteye_image)
righteye_image = camera_to_eye_utils_parallel.create_eye_image(righteye_pixels,cam_image,righteye_image)
disparity_mat,vdisparity_mat = camera_to_eye_utils_parallel.create_disparity_mats(lefteye_pixels,righteye_pixels,disparity_mat,vdisparity_mat)
#for each radial distance in the camera image, relocate it to the left eye
depth_mat = camera_to_eye_utils_parallel.create_eye_image(lefteye_pixels,cyclopean_radial_distance,depth_mat,distance = 1)
#make disparity image
disparity_image = camera_to_eye_utils_parallel.make_colormap_disparity(disparity_image,disparity_mat)
vdisparity_image = camera_to_eye_utils_parallel.make_colormap_disparity(vdisparity_image,vdisparity_mat)
#add yellow fixation pt
righteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((righteye_image,righteye_image,righteye_image)),righteye_fixation_pixels)
lefteye_image = camera_to_eye_utils_parallel.draw_fixation_point(numpy.dstack((lefteye_image,lefteye_image,lefteye_image)),lefteye_fixation_pixels)
disparity_image = camera_to_eye_utils_parallel.draw_fixation_point(disparity_image,lefteye_fixation_pixels)
vdisparity_image = camera_to_eye_utils_parallel.draw_fixation_point(vdisparity_image,lefteye_fixation_pixels)
if targets == 1:
#add target pts
#if numpy.isnan(target_point[2]) == 0:
# #cyan is projection of ground truth target to eyes
# righteye_image = draw_fixation_point(righteye_image,righteye_target_pixels,rgb=(0,255,255))
# lefteye_image = draw_fixation_point(lefteye_image,lefteye_target_pixels,rgb=(0,255,255))
if numpy.isnan(target_camera_coords[2]) == 0:
#magenta is projection of target pixels in camera to eyes
righteye_image = camera_to_eye_utils_parallel.draw_fixation_point(righteye_image,righteye_camera_target_pixels,rgb=(255,0,255))
lefteye_image = camera_to_eye_utils_parallel.draw_fixation_point(lefteye_image,lefteye_camera_target_pixels,rgb=(255,0,255))
#save files
#print "Saving files...\n"
cv.SaveImage(dst_dir + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image.copy()))
cv.SaveImage(dst_dir + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image.copy()))
cv.SaveImage(dst_dir + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image.copy()))
numpy.save(dst_dir + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat)
cv.SaveImage(dst_dir + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image.copy()))
numpy.save(dst_dir + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat)
numpy.save(dst_dir + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat)
#save truncated files (10deg circular window for analysis)
lefteye_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(lefteye_image,intrinsic_eye[0,2])
righteye_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(righteye_image,intrinsic_eye[0,2])
#disparity_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_image,intrinsic_eye[0,2])
#vdisparity_image_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_image,intrinsic_eye[0,2])
disparity_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(disparity_mat,intrinsic_eye[0,2])
vdisparity_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(vdisparity_mat,intrinsic_eye[0,2])
depth_mat_10deg = camera_to_eye_utils_parallel.apply_circle_mask(depth_mat,intrinsic_eye[0,2])
cv.SaveImage(dst_dir + '10deg/' + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image_10deg.copy()))
cv.SaveImage(dst_dir + '10deg/' + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image_10deg.copy()))
#cv.SaveImage(dst_dir + '10deg/' + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image_10deg.copy()))
numpy.save(dst_dir + '10deg/' + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat_10deg)
#cv.SaveImage(dst_dir + '10deg/' + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image_10deg.copy()))
numpy.save(dst_dir + '10deg/' + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat_10deg)
numpy.save(dst_dir + '10deg/' + 'deptheye/deptheye_frame_' + im.split('_frame_')[1], depth_mat_10deg)
#check for missing edges
#if any edges contain only nans, the data is missing, add flag
if numpy.any(numpy.isnan(disparity_mat_10deg[0:207,1])==0) == False or numpy.any(numpy.isnan(disparity_mat_10deg[0:207,-2])==0) == False or numpy.any(numpy.isnan(disparity_mat_10deg[1,0:207])==0) == False or numpy.any(numpy.isnan(disparity_mat_10deg[-2,0:207])==0) == False:
frame_status_mat[frame_cnt,1] = 5
#separately save only frames with no missing edges
#else:
# cv.SaveImage(dst_dir + '10degfull/' + 'lefteye/lefteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(lefteye_image_10deg.copy()))
# cv.SaveImage(dst_dir + '10degfull/' + 'righteye/righteye_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(righteye_image_10deg.copy()))
# cv.SaveImage(dst_dir + '10degfull/' + 'disparityeye_image/disparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(disparity_image_10deg.copy()))
# numpy.save(dst_dir + '10degfull/' + 'disparityeye/disparityeye_frame_' + im.split('_frame_')[1], disparity_mat_10deg)
# cv.SaveImage(dst_dir + '10degfull/' + 'vdisparityeye_image/vdisparityeye_image_frame_' + im.split('_frame_')[1].strip('.npy') + '.png', cv.fromarray(vdisparity_image_10deg.copy()))
# numpy.save(dst_dir + '10degfull/' + 'vdisparityeye/vdisparityeye_frame_' + im.split('_frame_')[1], vdisparity_mat_10deg)
if targets == 1:
#store error info
error_mat[frame_cnt,(7,8)] = lefteye_fixation_pixels #should just be center of the sensor (275,275)
error_mat[frame_cnt,(9,10)] = lefteye_target_pixels #projection of ground truth target from camera to eye
error_mat[frame_cnt,(11,12)] = lefteye_camera_target_pixels #projection of camera target to eye
error_mat[frame_cnt,(13,14)] = righteye_fixation_pixels
error_mat[frame_cnt,(15,16)] = righteye_target_pixels
error_mat[frame_cnt,(17,18)] = righteye_camera_target_pixels
#disparity of target pixel
if not numpy.isnan(lefteye_camera_target_pixels[0]):
camera_target_disparity = disparity_mat[-lefteye_camera_target_pixels[1],lefteye_camera_target_pixels[0]]
error_mat[frame_cnt,(19)] = camera_target_disparity
#eye flag
error_mat[frame_cnt,(20)] = frame_status_mat[frame_cnt,1]
numpy.save(dst_dir + dst_dir.split('/')[2] + '_' + fixation_src_mat + '_target_errors.npy', error_mat)
numpy.save(dst_dir + dst_dir.split('/')[2] + '_' + fixation_src_mat + '_frame_status.npy' , frame_status_mat)
#increment frame cnt
frame_cnt += 1
print dst_dir.split('/')[2] + '-' + fixation_src_mat
print 'total frames: ' + str(len(frame_status_mat))
print 'good fixations:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[frame_status_mat == 1]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[frame_status_mat == 1]))) + ')'
print 'blinks:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[frame_status_mat == 2]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[frame_status_mat == 2]))) + ')'
print 'saccades:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[frame_status_mat == 3]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[frame_status_mat == 3]))) + ')'
print 'missing data:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[frame_status_mat == 4]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[frame_status_mat == 4]))) + ')'
print 'not full frame:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[frame_status_mat == 5]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[frame_status_mat == 5]))) + ')'
print 'Nans?:' + '%.2f' % (numpy.true_divide(len(frame_status_mat[numpy.isnan(frame_status_mat)]),len(frame_status_mat))*100) + '% (' + str((len(frame_status_mat[numpy.isnan(frame_status_mat)]))) + ')'