Computing disparity maps
Project description
# default_exp disparity
Disparity
# export
import numba
import numpy as np
from camera_calib.utils import *
import re
from pathlib import Path
import matplotlib.pyplot as plt
import torch
Utilities
def _parse_name(name_img):
match = re.match(r'''SERIAL_(?P<serial>.*)_
DATETIME_(?P<date>.*)_
CAM_(?P<cam>.*)_
FRAMEID_(?P<frameid>.*)_
COUNTER_(?P<counter>.*).png''',
name_img,
re.VERBOSE)
return match.groupdict()
def _get_imgs(dir_imgs):
imgs = []
for file_img in dir_imgs.glob('*.png'):
dict_group = _parse_name(file_img.name)
img = api.File16bitImg(file_img)
img.idx_cam = int(dict_group['cam'])-1
img.idx_cb = int(dict_group['counter'])-1
imgs.append(img)
return imgs
def _print_imgs(imgs):
for img in imgs: print(f'{img.name} - cam: {img.idx_cam} - cb: {img.idx_cb}')
Compute disparity map
First, we need to calibrate, then rectify, and then we can compute disparity maps.
Calibrate
import camera_calib.api as api
imgs = _get_imgs(Path('data/calib'))
_print_imgs(imgs)
SERIAL_19061245_DATETIME_2020-08-16-16:58:39-927381_CAM_1_FRAMEID_0_COUNTER_1 - cam: 0 - cb: 0
SERIAL_16276941_DATETIME_2020-08-16-16:58:53-756240_CAM_2_FRAMEID_0_COUNTER_2 - cam: 1 - cb: 1
SERIAL_16276941_DATETIME_2020-08-16-16:58:39-927424_CAM_2_FRAMEID_0_COUNTER_1 - cam: 1 - cb: 0
SERIAL_16276941_DATETIME_2020-08-16-16:59:05-367688_CAM_2_FRAMEID_0_COUNTER_3 - cam: 1 - cb: 2
SERIAL_19061245_DATETIME_2020-08-16-16:59:59-403047_CAM_1_FRAMEID_0_COUNTER_4 - cam: 0 - cb: 3
SERIAL_16276941_DATETIME_2020-08-16-17:00:14-283298_CAM_2_FRAMEID_0_COUNTER_5 - cam: 1 - cb: 4
SERIAL_19061245_DATETIME_2020-08-16-16:58:53-756222_CAM_1_FRAMEID_0_COUNTER_2 - cam: 0 - cb: 1
SERIAL_16276941_DATETIME_2020-08-16-16:59:59-403092_CAM_2_FRAMEID_0_COUNTER_4 - cam: 1 - cb: 3
SERIAL_19061245_DATETIME_2020-08-16-16:59:05-367645_CAM_1_FRAMEID_0_COUNTER_3 - cam: 0 - cb: 2
SERIAL_19061245_DATETIME_2020-08-16-17:00:14-283252_CAM_1_FRAMEID_0_COUNTER_5 - cam: 0 - cb: 4
h_cb = 50.8
w_cb = 50.8
h_f = 42.672
w_f = 42.672
num_c_h = 16
num_c_w = 16
spacing_c = 2.032
cb_geom = api.CbGeom(h_cb, w_cb,
api.CpCSRGrid(num_c_h, num_c_w, spacing_c),
api.FmCFPGrid(h_f, w_f))
file_model = Path('models/dot_vision_checker.pth')
detector = api.DotVisionCheckerDLDetector(file_model)
refiner = api.OpenCVCheckerRefiner(hw_min=5, hw_max=15, cutoff_it=20, cutoff_norm=1e-3)
calib = api.multi_calib(imgs, cb_geom, detector, refiner)
Refining control points for: SERIAL_19061245_DATETIME_2020-08-16-16:58:39-927381_CAM_1_FRAMEID_0_COUNTER_1...
Refining control points for: SERIAL_19061245_DATETIME_2020-08-16-16:59:59-403047_CAM_1_FRAMEID_0_COUNTER_4...
Refining control points for: SERIAL_19061245_DATETIME_2020-08-16-16:58:53-756222_CAM_1_FRAMEID_0_COUNTER_2...
Refining control points for: SERIAL_19061245_DATETIME_2020-08-16-16:59:05-367645_CAM_1_FRAMEID_0_COUNTER_3...
Refining control points for: SERIAL_19061245_DATETIME_2020-08-16-17:00:14-283252_CAM_1_FRAMEID_0_COUNTER_5...
Refining single parameters...
- Iteration: 000 - Norm: 0.05166 - Loss: 40.43536
- Iteration: 001 - Norm: 0.05871 - Loss: 27.05900
- Iteration: 002 - Norm: 0.10641 - Loss: 16.14479
- Iteration: 003 - Norm: 0.52404 - Loss: 9.86104
- Iteration: 004 - Norm: 0.70472 - Loss: 5.50705
- Iteration: 005 - Norm: 0.15078 - Loss: 5.39712
- Iteration: 006 - Norm: 0.02470 - Loss: 5.38315
- Iteration: 007 - Norm: 0.01638 - Loss: 5.37218
- Iteration: 008 - Norm: 0.00468 - Loss: 5.37189
- Iteration: 009 - Norm: 0.13333 - Loss: 5.36454
- Iteration: 010 - Norm: 0.00423 - Loss: 5.36439
- Iteration: 011 - Norm: 28.76676 - Loss: 3.86312
- Iteration: 012 - Norm: 17.23997 - Loss: 3.17776
- Iteration: 013 - Norm: 0.00853 - Loss: 3.17776
- Iteration: 014 - Norm: 0.00000 - Loss: 3.17776
Refining control points for: SERIAL_16276941_DATETIME_2020-08-16-16:58:53-756240_CAM_2_FRAMEID_0_COUNTER_2...
Refining control points for: SERIAL_16276941_DATETIME_2020-08-16-16:58:39-927424_CAM_2_FRAMEID_0_COUNTER_1...
Refining control points for: SERIAL_16276941_DATETIME_2020-08-16-16:59:05-367688_CAM_2_FRAMEID_0_COUNTER_3...
Refining control points for: SERIAL_16276941_DATETIME_2020-08-16-17:00:14-283298_CAM_2_FRAMEID_0_COUNTER_5...
Refining control points for: SERIAL_16276941_DATETIME_2020-08-16-16:59:59-403092_CAM_2_FRAMEID_0_COUNTER_4...
Refining single parameters...
- Iteration: 000 - Norm: 0.04648 - Loss: 33.60078
- Iteration: 001 - Norm: 0.03970 - Loss: 26.93946
- Iteration: 002 - Norm: 0.04580 - Loss: 24.62314
- Iteration: 003 - Norm: 0.14923 - Loss: 21.53392
- Iteration: 004 - Norm: 0.48321 - Loss: 13.00884
- Iteration: 005 - Norm: 0.00873 - Loss: 12.83886
- Iteration: 006 - Norm: 0.65804 - Loss: 8.40447
- Iteration: 007 - Norm: 0.10469 - Loss: 8.11526
- Iteration: 008 - Norm: 0.09923 - Loss: 8.02833
- Iteration: 009 - Norm: 0.09764 - Loss: 7.97387
- Iteration: 010 - Norm: 0.18271 - Loss: 7.92175
- Iteration: 011 - Norm: 9.62275 - Loss: 7.45953
- Iteration: 012 - Norm: 37.67393 - Loss: 5.66647
- Iteration: 013 - Norm: 1.13521 - Loss: 5.66112
- Iteration: 014 - Norm: 0.01024 - Loss: 5.66107
- Iteration: 015 - Norm: 142.51304 - Loss: 3.52503
- Iteration: 016 - Norm: 0.29318 - Loss: 3.52483
- Iteration: 017 - Norm: 0.00000 - Loss: 3.52483
Refining multi parameters...
- Iteration: 000 - Norm: 0.00633 - Loss: 365.50887
- Iteration: 001 - Norm: 0.03670 - Loss: 251.90410
- Iteration: 002 - Norm: 0.03891 - Loss: 191.50267
- Iteration: 003 - Norm: 0.03330 - Loss: 169.51325
- Iteration: 004 - Norm: 0.14800 - Loss: 115.86002
- Iteration: 005 - Norm: 0.03182 - Loss: 99.71262
- Iteration: 006 - Norm: 0.09812 - Loss: 78.23173
- Iteration: 007 - Norm: 0.12045 - Loss: 50.32390
- Iteration: 008 - Norm: 0.02356 - Loss: 45.52809
- Iteration: 009 - Norm: 0.00880 - Loss: 44.19760
- Iteration: 010 - Norm: 0.03753 - Loss: 40.63650
- Iteration: 011 - Norm: 0.03345 - Loss: 37.87511
- Iteration: 012 - Norm: 0.01658 - Loss: 36.71647
- Iteration: 013 - Norm: 0.02828 - Loss: 34.62113
- Iteration: 014 - Norm: 0.05827 - Loss: 30.72548
- Iteration: 015 - Norm: 0.00558 - Loss: 30.46024
- Iteration: 016 - Norm: 0.00377 - Loss: 30.32508
- Iteration: 017 - Norm: 0.01700 - Loss: 29.78092
- Iteration: 018 - Norm: 0.00733 - Loss: 29.58850
- Iteration: 019 - Norm: 0.16740 - Loss: 25.31908
- Iteration: 020 - Norm: 0.02405 - Loss: 24.72751
- Iteration: 021 - Norm: 0.00133 - Loss: 24.69757
- Iteration: 022 - Norm: 0.00339 - Loss: 24.62627
- Iteration: 023 - Norm: 0.00355 - Loss: 24.59673
- Iteration: 024 - Norm: 0.04353 - Loss: 24.94890
- Iteration: 025 - Norm: 0.04560 - Loss: 24.23809
- Iteration: 026 - Norm: 0.04802 - Loss: 24.02517
- Iteration: 027 - Norm: 0.00035 - Loss: 24.02463
- Iteration: 028 - Norm: 0.00095 - Loss: 24.02313
- Iteration: 029 - Norm: 0.07848 - Loss: 23.82852
- Iteration: 030 - Norm: 0.06296 - Loss: 23.67669
- Iteration: 031 - Norm: 0.00122 - Loss: 23.67818
- Iteration: 032 - Norm: 0.01764 - Loss: 23.65771
- Iteration: 033 - Norm: 0.53306 - Loss: 23.11532
- Iteration: 034 - Norm: 0.00389 - Loss: 23.11467
- Iteration: 035 - Norm: 0.00026 - Loss: 23.11465
- Iteration: 036 - Norm: 0.00855 - Loss: 23.11144
- Iteration: 037 - Norm: 0.04346 - Loss: 23.09543
- Iteration: 038 - Norm: 0.00329 - Loss: 23.09395
- Iteration: 039 - Norm: 0.00014 - Loss: 23.09395
- Iteration: 040 - Norm: 0.00550 - Loss: 23.09309
- Iteration: 041 - Norm: 0.11960 - Loss: 23.05538
- Iteration: 042 - Norm: 0.21922 - Loss: 22.99648
- Iteration: 043 - Norm: 0.00130 - Loss: 22.99645
- Iteration: 044 - Norm: 0.00077 - Loss: 22.99642
- Iteration: 045 - Norm: 0.04483 - Loss: 22.98557
- Iteration: 046 - Norm: 7.57304 - Loss: 21.17649
- Iteration: 047 - Norm: 10.45625 - Loss: 18.59859
- Iteration: 048 - Norm: 0.76570 - Loss: 18.39438
- Iteration: 049 - Norm: 0.01591 - Loss: 18.39388
- Iteration: 050 - Norm: 0.01743 - Loss: 18.39040
- Iteration: 051 - Norm: 4.64610 - Loss: 17.20502
- Iteration: 052 - Norm: 2.02334 - Loss: 16.86937
- Iteration: 053 - Norm: 0.00002 - Loss: 16.86937
- Iteration: 054 - Norm: 0.02372 - Loss: 16.86879
- Iteration: 055 - Norm: 2.26827 - Loss: 16.59826
- Iteration: 056 - Norm: 29.46264 - Loss: 13.48704
- Iteration: 057 - Norm: 2.88659 - Loss: 13.25918
- Iteration: 058 - Norm: 0.04408 - Loss: 13.25897
- Iteration: 059 - Norm: 0.00140 - Loss: 13.25896
- Iteration: 060 - Norm: 0.00002 - Loss: 13.25896
- Iteration: 061 - Norm: 0.00000 - Loss: 13.25896
api.plot_residuals(calib);
api.plot_extrinsics(calib);
api.save(calib, 'data/calib/calib.pth')
Freeze above and just load
calib = api.load('data/calib/calib.pth')
Rectify
from image_rect import image_rect
imgs = _get_imgs(Path('data/scene1'))
_print_imgs(imgs)
SERIAL_19061245_DATETIME_2020-08-16-17:05:28-278345_CAM_1_FRAMEID_0_COUNTER_1 - cam: 0 - cb: 0
SERIAL_16276941_DATETIME_2020-08-16-17:05:28-278389_CAM_2_FRAMEID_0_COUNTER_1 - cam: 1 - cb: 0
[img1] = [img for img in imgs if img.idx_cam == 0]
[img2] = [img for img in imgs if img.idx_cam == 1]
img1.name, img2.name
('SERIAL_19061245_DATETIME_2020-08-16-17:05:28-278345_CAM_1_FRAMEID_0_COUNTER_1',
'SERIAL_16276941_DATETIME_2020-08-16-17:05:28-278389_CAM_2_FRAMEID_0_COUNTER_1')
rect = image_rect.rectify(calib)
with torch.no_grad():
arr1_r = image_rect.rect_img(img1, rect)
arr2_r = image_rect.rect_img(img2, rect)
_, axs = plt.subplots(1, 2, figsize=(20,15))
axs[0].imshow(arr1_r, cmap='gray')
axs[1].imshow(arr2_r, cmap='gray')
<matplotlib.image.AxesImage at 0x7f3c7f91ad10>
Disparity
Do initial resize to make processing faster; also note that I'm doing the rest in numba/numpy since a lot of nested for loops are involved.
NOTE: numba does not yet support classes with inheritance yet
arr1, arr2 = [imresize(torch2np(arr), shape(arr)/4) for arr in [arr1_r, arr2_r]]
_, axs = plt.subplots(1, 2, figsize=(10,10))
for arr, ax in zip([arr1, arr2], axs): ax.imshow(arr)
Basic block matching
sum of absolute difference is a common loss function for disparity maps
# export
@numba.jit(nopython=True)
def SAD(arr1, arr2):
l = 0
for i in range(arr1.shape[0]):
for j in range(arr2.shape[1]):
l += abs(arr1[i,j] - arr2[i,j])
return l
rect_loss_p will compute the loss for a single point and store the losses in a buffer
# export
@numba.jit(nopython=True)
def rect_loss_p(arr1, arr2, x, y, min_disp, max_disp, hw, loss, buf_loss):
h_arr, w_arr = arr1.shape
l_t, t_t, r_t, b_t = max(x-hw, 0), max(y-hw, 0), min(x+hw, w_arr-1), min(y+hw, h_arr-1)
h_t, w_t = b_t-t_t+1, r_t-l_t+1
for j in range(min_disp, max_disp+1):
if (j+l_t >= 0) and (j+r_t < w_arr): # Template in bounds
buf_loss[j-min_disp] = loss(arr1[t_t:t_t+h_t, l_t:l_t+w_t], arr2[t_t:t_t+h_t, j+l_t:j+l_t+w_t])
else: # Template out of bound
buf_loss[j-min_disp] = np.inf
argmin_int is the integer argument minimum; int suffix is only used to distinguish between subpixel minimum, which is used later.
# export
@numba.jit(nopython=True)
def argmin_int(arr): return np.argmin(arr)
Test out getting the loss for an example point
def _debug_rect_loss_p(x, y):
buf_loss = np.empty(max_disp-min_disp+1)
rect_loss_p(arr1, arr2, x, y, min_disp, max_disp, hw, loss, buf_loss)
disp = argmin(buf_loss) + min_disp
_, axs = plt.subplots(1, 2, figsize=(10,10))
axs[0].imshow(arr1)
axs[0].plot(x, y, 'rs')
axs[1].imshow(arr2)
axs[1].plot(x+disp, y, 'rs')
hw = 15
min_disp = -15
max_disp = 15
loss = SAD
argmin = argmin_int
_debug_rect_loss_p(x=60, y=75)
rect_loss_l will compute the loss for an entire line. Note that an initial disparity map guess can also be input; if this is the case, then the disparity range will be centered around this disparity value instead of zero.
# export
@numba.jit(nopython=True)
def rect_loss_l(arr1, arr2, y, r_disp, hw, loss, buf_loss, arr_disp_init=None):
h_arr, w_arr = arr1.shape
for i in range(w_arr):
disp_init = 0 if arr_disp_init is None else arr_disp_init[y, i]
min_disp, max_disp = [disp + disp_init for disp in r_disp]
rect_loss_p(arr1, arr2, i, y, min_disp, max_disp, hw, loss, buf_loss[i])
def _debug_rect_loss_l(y):
buf_loss = np.empty((arr1.shape[1], r_disp[1]-r_disp[0]+1))
rect_loss_l(arr1, arr2, y, r_disp, hw, loss, buf_loss)
_, ax = plt.subplots(1, 1, figsize=(10,10))
ax.imshow(buf_loss.T)
return buf_loss
r_disp = (-15, 15)
_debug_rect_loss_l(60);
min_path_int will compute the path from left to right of transposed loss buffer using the minimum value in each column.
# export
@numba.jit(nopython=True)
def min_path_int(arr_loss, buf_path):
for i in range(len(arr_loss)):
buf_path[i] = argmin_int(arr_loss[i])
rect_match_arr_min_path will compute a disparity map. It takes an input min_path function which, when given a loss buffer, will compute the best path across it; this will make more sense when we use dynamic programming. arr_disp_init is an initial guess for the disparity map; this will make more sense when we do the image pyramids.
Note that this seems to be the level where multi-threading makes sense; it's not too fine grained where overhead will slow things down and it's not too grainular such that a single thread can cause a long delay.
# export
@numba.jit(nopython=True, parallel=True)
def rect_match_arr_min_path(arr1, arr2, r_disp, hw, loss, min_path, arr_disp_init=None):
h_arr, w_arr = arr1.shape[0], arr1.shape[1]
arr_disp = np.empty((h_arr, w_arr))
for i in numba.prange(h_arr):
buf_loss = np.empty((arr1.shape[1], r_disp[1]-r_disp[0]+1))
rect_loss_l(arr1, arr2, i, r_disp, hw, loss, buf_loss, arr_disp_init)
min_path(buf_loss, arr_disp[i]) # range offset and initial disparity need to be applied after
arr_disp[i] += r_disp[0]
if arr_disp_init is not None: arr_disp[i] += arr_disp_init[i]
return arr_disp
# export
def make_rect_match_arr_min_path(r_disp, hw, loss, min_path):
@numba.jit(nopython=True)
def rect_match_arr(arr1, arr2, arr_disp_init=None):
return rect_match_arr_min_path(arr1, arr2, r_disp, hw, loss, min_path, arr_disp_init)
return rect_match_arr
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_int)
arr_disp = rect_match_arr(arr1, arr2)
Do it again so numba will compile and run faster.
arr_disp = rect_match_arr(arr1, arr2)
~200 ms is not bad. This could be realtime-ish performance for this image resolution.
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=min_disp, vmax=max_disp)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=min_disp, vmax=max_disp, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c66f9a390>
As to be expected this doesn't look great; lets debug some problem areas
_debug_rect_loss_p(125, 60)
There is confusion with similar patterns. Note the found point on the right image is 3 stripes other rather than 2 on the left image.
_debug_rect_loss_p(125, 25)
Glare causes an issue; note the point on the right image is aligned to the glare instead of where it should be
_debug_rect_loss_p(45, 100)
This is actually wrong since the left part of the object is not visible to the right camera. It's more aligned to the side of the object rather than its actual location.
_debug_rect_loss_p(60, 125)
This might be due to the fact that sub images are not normalized (mean subtracted and divided by std-dev) before being compared.
Subpixel block matching
argmin_sub uses a single newton's iteration to find the root of the derivate (i.e. the minima). The update is the first derivative divided by the second derivative at the integer minimum location.
# export
@numba.jit(nopython=True)
def argmin_sub(arr):
idx_min = argmin_int(arr)
if 1 <= idx_min <= len(arr)-2:
delta_idx = ((arr[idx_min+1]-arr[idx_min-1])/2)/(arr[idx_min+1]-2*arr[idx_min]+arr[idx_min-1])
if np.isnan(delta_idx): delta_idx = 0
if delta_idx < -1: delta_idx = -1
if delta_idx > 1: delta_idx = 1
idx_min = idx_min - delta_idx
return idx_min
# export
@numba.jit(nopython=True)
def min_path_sub(arr_loss, buf_path):
for i in range(len(arr_loss)):
buf_path[i] = argmin_sub(arr_loss[i])
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_sub)
arr_disp = rect_match_arr(arr1, arr2)
arr_disp = rect_match_arr(arr1, arr2)
Again, around ~200 ms, the subpixel stuff doesn't add much overhead
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c666fc910>
Looks smoother near the center of the object.
Dynamic programming
The goal of dynamic programming is to find the shortest path from left to right in the following array:
arr_loss = _debug_rect_loss_l(75)
But with an added smoothness contraint. This will be in the form of a penalty for going "up" and "down" and also a max change between neighboring columns. The hope is that, in the above, the path taken will not skip down near the ~125 column, but will instead continue smoothly above it, because doing so would incur a pentalty.
# export
@numba.jit(nopython=True)
def _min_path_int_dp(arr_loss, buf_path, r_disp, max_change, penalty_disp):
buf_route = np.zeros(arr_loss.shape)
buf_move = np.empty((2*max_change+1, r_disp[1]-r_disp[0]+1))
buf_loss_prev = arr_loss[-1].copy() # Going backwards, this is initial optimal loss
for i in range(len(arr_loss)-2, -1, -1):
# Get loss of each move
buf_move[:] = np.inf
for j in range(-max_change, max_change+1):
idx_minl, idx_maxl = max( j,0), min(arr_loss.shape[1]+j,arr_loss.shape[1])
idx_minm, idx_maxm = max(-j,0), min(arr_loss.shape[1]-j,arr_loss.shape[1])
buf_move[j+max_change, idx_minm:idx_maxm] = buf_loss_prev[idx_minl:idx_maxl] + abs(j)*penalty_disp
# Get optimal move and store it
for j in range(buf_move.shape[1]):
idx_min = np.argmin(buf_move[:,j])
buf_route[i,j] = idx_min - max_change
buf_loss_prev[j] = arr_loss[i,j] + buf_move[idx_min, j] # total loss = loss + optimal move
# Gather path
buf_path[0] = np.argmin(buf_loss_prev)
for i in range(1, len(buf_route)):
buf_path[i] = buf_path[i-1] + buf_route[i-1, int(buf_path[i-1])]
# export
def make_min_path_int_dp(r_disp, max_change, penalty_disp):
@numba.jit(nopython=True)
def min_path(arr_loss, buf_path):
return _min_path_int_dp(arr_loss, buf_path, r_disp, max_change, penalty_disp)
return min_path
max_change = 3
penalty_disp = 2
min_path_int_dp = make_min_path_int_dp(r_disp, max_change, penalty_disp)
def _debug_min_path(min_path):
buf_path = np.empty(arr_loss.shape[0])
min_path(arr_loss, buf_path)
_, ax = plt.subplots(1, 1, figsize=(10,10))
ax.imshow(arr_loss.T)
plt.plot(buf_path, '-r')
_debug_min_path(min_path_int)
_debug_min_path(min_path_int_dp)
Dynamic programming punishes the jump near 125 and prevents it from happening... cool
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_int_dp)
arr_disp = rect_match_arr(arr1, arr2)
arr_disp = rect_match_arr(arr1, arr2)
Again, ~200 ms, not bad.
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c66d5aa50>
Definitely much smoother. The glare still causes problems though.
Sub pixel dynamic programming
I just basically replaced all argmins with argmin_sub and also replaced indexing with interp. This assumes smoothness between adjacent optimal paths and im not sure if its strictly correct, but it seems to work.
# export
@numba.jit(nopython=True)
def interp(arr, idx):
if idx < 0 or idx > len(arr)-1: val = np.nan
else:
idx_f = np.floor(idx)
if idx == idx_f: val = arr[int(idx_f)]
else: val = (idx_f+1-idx)*arr[int(idx_f)] + (idx-idx_f)*arr[int(idx_f)+1]
return val
arr = np.array([1,2,3])
assert_allclose(np.isnan(interp(arr, -0.5)), True)
assert_allclose( interp(arr, 0.0), 1.0)
assert_allclose( interp(arr, 0.5), 1.5)
assert_allclose( interp(arr, 1.0), 2.0)
assert_allclose( interp(arr, 1.5), 2.5)
assert_allclose( interp(arr, 2.0), 3.0)
assert_allclose(np.isnan(interp(arr, 2.5)), True)
# export
@numba.jit(nopython=True)
def _min_path_sub_dp(arr_loss, buf_path, r_disp, max_change, penalty_disp):
buf_route = np.zeros(arr_loss.shape)
buf_move = np.empty((2*max_change+1, r_disp[1]-r_disp[0]+1))
buf_loss_prev = arr_loss[-1].copy() # Going backwards, this is initial optimal loss
for i in range(len(arr_loss)-2, -1, -1):
# Get loss of each move
buf_move[:] = np.inf
for j in range(-max_change, max_change+1):
idx_minl, idx_maxl = max( j,0), min(arr_loss.shape[1]+j,arr_loss.shape[1])
idx_minm, idx_maxm = max(-j,0), min(arr_loss.shape[1]-j,arr_loss.shape[1])
buf_move[j+max_change, idx_minm:idx_maxm] = buf_loss_prev[idx_minl:idx_maxl] + abs(j)*penalty_disp
# Get optimal move and store it
for j in range(buf_move.shape[1]):
idx_min = argmin_sub(buf_move[:,j])
buf_route[i,j] = idx_min - max_change
buf_loss_prev[j] = arr_loss[i,j] + interp(buf_move[:, j], idx_min)
# Gather path
buf_path[0] = argmin_sub(buf_loss_prev)
for i in range(1, len(buf_route)):
buf_path[i] = buf_path[i-1] + interp(buf_route[i-1], buf_path[i-1])
# export
def make_min_path_sub_dp(r_disp, max_change, penalty_disp):
@numba.jit(nopython=True)
def min_path(arr_loss, buf_path):
return _min_path_sub_dp(arr_loss, buf_path, r_disp, max_change, penalty_disp)
return min_path
min_path_sub_dp = make_min_path_sub_dp(r_disp, max_change, penalty_disp)
_debug_min_path(min_path_sub_dp)
It's smooth now
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_sub_dp)
arr_disp = rect_match_arr(arr1, arr2)
arr_disp = rect_match_arr(arr1, arr2)
Still ~200 ms
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c6651cb10>
It's a little bit different from the integer version, but overall it looks similar and is smoother
Image pyramid
Try using an image pyramid with "telescoping" search. Note that I've kept the window size the same for each level. In the most reduced image, it will use a proportionally larger window to get the overall translation correct, then in larger images, the proportionally smaller window will localize better (in theory at least).
# export
def rect_match_pyr(arr1, arr2, rect_match_arr, steps=3):
if not np.all(shape(arr1) % 2**steps == 0): raise RuntimeError('Shape must be divisible by 2^steps')
def _get_pyr(arr):
arr_pyr = [arr]
for idx in range(steps-1):
arr_pyr.append(imresize(arr_pyr[-1], shape(arr_pyr[-1])/2))
return arr_pyr
arr1_pyr, arr2_pyr = [_get_pyr(arr) for arr in [arr1, arr2]]
arr_disp = None
for idx in range(steps-1,-1,-1):
arr1, arr2 = arr1_pyr[idx], arr2_pyr[idx]
if arr_disp is not None:
arr_disp = imresize(2*arr_disp, 2*shape(arr_disp)) # Remember to multiply disparities by 2
arr_disp = np.round(arr_disp).astype(np.long) # Must be integer
arr_disp = rect_match_arr(arr1, arr2, arr_disp)
return arr_disp
r_disp = (-5,5)
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_sub)
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
~100 ms, could probably optimize more but its fast
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c640bbd50>
min_path_sub_dp = make_min_path_sub_dp(r_disp, max_change, penalty_disp)
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path_sub_dp)
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
~150 ms, a little slower but still pretty fast
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c523c3d10>
SGM
Semi global matching is another popular method thats fast but has more smoothness constraints than dynamic programming (which is restricted to rows). A lot of SGM implementations I've seen usually have a fixed number of directions (like 8 cardinal directions) which are hard coded. I want to be able to input any direction and see what the output disparity map looks like. I've attempted to do this by implementing a line_loop which will iterate over an array in non-overlapping lines.
Line loop
np.isclose hasnt been implemented yet
# export
@numba.jit(nopython=True)
def isclose(x, y, atol=1e-8): return abs(x-y) < atol
np.clip hasnt been implemented yet
# export
@numba.jit(nopython=True)
def clip(x, min_x, max_x): return max(min(x, max_x), min_x)
line_loop will iterate over an arr line by line in a non-overlapping and full/unique manner given an input theta.
# export
@numba.jit(nopython=True)
def line_loop(arr, theta, callback):
h, w = arr.shape[0:2]
# Get dx, dy
dx, dy = np.cos(theta), np.sin(theta)
if isclose(dx, 0): dx, dy = 0, np.sign(dy)*h
elif isclose(dy, 0): dx, dy = np.sign(dx)*w, 0
else:
sf = min(abs(dx), abs(dy))
dx, dy = np.round(dx/sf), np.round(dy/sf)
dx, dy = clip(dx, 1-w, w-1), clip(dy, 1-h, h-1)
dx, dy = int(dx), int(dy)
# Get increments
if abs(dx) > abs(dy): l, dx_l, dy_l, dx_c, dy_c = abs(dx), np.sign(dx), 0, 0, np.sign(dy)
else: l, dx_l, dy_l, dx_c, dy_c = abs(dy), 0, np.sign(dy), np.sign(dx), 0
# Get initial p0
if 0 <= dx < w and 0 < dy <= h: x0, y0 = w-1, 0
elif 0 < dx <= w and 0 >= dy > -h: x0, y0 = 0, 0
elif 0 >= dx > -w and 0 > dy >= -h: x0, y0 = 0, h-1
elif 0 > dx >= -w and 0 <= dy < h: x0, y0 = w-1, h-1
else: raise RuntimeError('Invalid dx, dy')
# Do line iterations
it_p, num_p = 0, h*w # Iterate until it_p == num_p
while it_p < num_p:
x, y, started = x0, y0, False # Start tracing line at p0
while True:
for i in range(l):
if (0 <= x < w) and (0 <= y < h):
if not started: callback.start_line(arr, x, y); started=True
else: callback.in_line(arr, x, y)
it_p += 1 # Point increment
x += dx_l; y += dy_l # Line increment
x += dx_c; y += dy_c # Change increment
if not ((0 <= x < w) and (0 <= y < h)): break # Lines goes out of array, so end this line
# Shift start of line based on current p0
if x0 >= w-1 and y0 >= 1: y0 -= max(abs(dy), 1)
elif x0 >= 1 and y0 <= 0: x0 -= max(abs(dx), 1)
elif x0 <= 0 and y0 <= h-2: y0 += max(abs(dy), 1)
elif x0 <= w-2 and y0 >= h-1: x0 += max(abs(dx), 1)
else: raise RuntimeError('Invalid x0, y0')
Line loop tests
Use callbacks to test
@numba.experimental.jitclass([('it', numba.int32)])
class callback_itfill(object):
def __init__(self): self.it = -1
def start_line(self, arr, x, y): self.it += 1; arr[y, x] = self.it
def in_line(self, arr, x, y): self.it += 1; arr[y, x] = self.it
@numba.experimental.jitclass([('line', numba.int32)])
class callback_linefill(object):
def __init__(self): self.line = -1
def start_line(self, arr, x, y): self.line += 1; arr[y, x] = self.line
def in_line(self, arr, x, y): arr[y, x] = self.line
@numba.experimental.jitclass([('line', numba.int32)])
class callback_startfill(object):
def __init__(self): self.line = -1
def start_line(self, arr, x, y): self.line += 1; arr[y, x] = self.line
def in_line(self, arr, x, y): pass
Do 16 cardinal directions
arr = np.full((3, 4), -1)
theta = (0/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 0, -1, -1, -1],
[ 1, -1, -1, -1],
[ 2, -1, -1, -1]]))
theta = (1/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 4, 1, 2, 0],
[ 8, 5, 6, 3],
[11, 9, 10, 7]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[2, 1, 1, 0],
[3, 2, 2, 1],
[4, 3, 3, 2]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 2, 1, -1, 0],
[ 3, -1, -1, -1],
[ 4, -1, -1, -1]]))
theta = (2/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 6, 3, 1, 0],
[ 9, 7, 4, 2],
[11, 10, 8, 5]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[3, 2, 1, 0],
[4, 3, 2, 1],
[5, 4, 3, 2]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 3, 2, 1, 0],
[ 4, -1, -1, -1],
[ 5, -1, -1, -1]]))
theta = (3/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 8, 5, 2, 0],
[ 9, 6, 3, 1],
[11, 10, 7, 4]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[3, 2, 1, 0],
[3, 2, 1, 0],
[4, 3, 2, 1]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 3, 2, 1, 0],
[-1, -1, -1, -1],
[ 4, -1, -1, -1]]))
theta = (4/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 9, 6, 3, 0],
[10, 7, 4, 1],
[11, 8, 5, 2]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[3, 2, 1, 0],
[3, 2, 1, 0],
[3, 2, 1, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 3, 2, 1, 0],
[-1, -1, -1, -1],
[-1, -1, -1, -1]]))
theta = (5/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[10, 7, 4, 1],
[11, 8, 5, 2],
[ 9, 6, 3, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[4, 3, 2, 1],
[4, 3, 2, 1],
[3, 2, 1, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 4, 3, 2, 1],
[-1, -1, -1, -1],
[-1, -1, -1, 0]]))
theta = (6/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[11, 9, 6, 3],
[10, 7, 4, 1],
[ 8, 5, 2, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[5, 4, 3, 2],
[4, 3, 2, 1],
[3, 2, 1, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 5, 4, 3, 2],
[-1, -1, -1, 1],
[-1, -1, -1, 0]]))
theta = (7/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[11, 10, 7, 6],
[ 9, 8, 3, 2],
[ 5, 4, 1, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[3, 3, 2, 2],
[2, 2, 1, 1],
[1, 1, 0, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, 3, -1, 2],
[-1, -1, -1, 1],
[-1, -1, -1, 0]]))
theta = (8/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[11, 10, 9, 8],
[ 7, 6, 5, 4],
[ 3, 2, 1, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[2, 2, 2, 2],
[1, 1, 1, 1],
[0, 0, 0, 0]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, -1, -1, 2],
[-1, -1, -1, 1],
[-1, -1, -1, 0]]))
theta = (9/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 7, 10, 9, 11],
[ 3, 6, 5, 8],
[ 0, 2, 1, 4]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[2, 3, 3, 4],
[1, 2, 2, 3],
[0, 1, 1, 2]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, -1, -1, 4],
[-1, -1, -1, 3],
[ 0, -1, 1, 2]]))
theta = (10/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 5, 8, 10, 11],
[ 2, 4, 7, 9],
[ 0, 1, 3, 6]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[2, 3, 4, 5],
[1, 2, 3, 4],
[0, 1, 2, 3]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, -1, -1, 5],
[-1, -1, -1, 4],
[ 0, 1, 2, 3]]))
theta = (11/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 4, 7, 10, 11],
[ 1, 3, 6, 9],
[ 0, 2, 5, 8]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[1, 2, 3, 4],
[0, 1, 2, 3],
[0, 1, 2, 3]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, -1, -1, 4],
[-1, -1, -1, -1],
[ 0, 1, 2, 3]]))
theta = (12/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 2, 5, 8, 11],
[ 1, 4, 7, 10],
[ 0, 3, 6, 9]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[-1, -1, -1, -1],
[-1, -1, -1, -1],
[ 0, 1, 2, 3]]))
theta = (13/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 0, 3, 6, 9],
[ 2, 5, 8, 11],
[ 1, 4, 7, 10]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[0, 1, 2, 3],
[1, 2, 3, 4],
[1, 2, 3, 4]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 0, -1, -1, -1],
[-1, -1, -1, -1],
[ 1, 2, 3, 4]]))
theta = (14/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 0, 2, 5, 8],
[ 1, 4, 7, 10],
[ 3, 6, 9, 11]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[0, 1, 2, 3],
[1, 2, 3, 4],
[2, 3, 4, 5]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 0, -1, -1, -1],
[ 1, -1, -1, -1],
[ 2, 3, 4, 5]]))
theta = (15/8)*np.pi
arr[:] = -1; line_loop(arr, theta, callback_itfill())
assert_allclose(arr, np.array([[ 0, 1, 4, 5],
[ 2, 3, 8, 9],
[ 6, 7, 10, 11]]))
arr[:] = -1; line_loop(arr, theta, callback_linefill())
assert_allclose(arr, np.array([[0, 0, 1, 1],
[1, 1, 2, 2],
[2, 2, 3, 3]]))
arr[:] = -1; line_loop(arr, theta, callback_startfill())
assert_allclose(arr, np.array([[ 0, -1, -1, -1],
[ 1, -1, -1, -1],
[ 2, -1, 3, -1]]))
SGM implementation
First, we need to precompute the disparity losses for the entire array; this buffer can be quite large for large images
# export
@numba.jit(nopython=True, parallel=True)
def rect_loss_arr(arr1, arr2, r_disp, hw, loss, buf_loss, arr_disp_init=None):
h_arr, w_arr = arr1.shape
for i in numba.prange(h_arr):
rect_loss_l(arr1, arr2, i, r_disp, hw, loss, buf_loss[i], arr_disp_init)
# export
@numba.experimental.jitclass([('buf_accum', numba.float64[:,:,:]),
('buf_move', numba.float64[:,:]),
('x_prev', numba.int32),
('y_prev', numba.int32),
('max_change', numba.int32),
('penalty_disp', numba.int32)])
class callback_sgm(object):
def __init__(self, sz, r_disp, max_change, penalty_disp):
self.buf_accum = np.empty((sz[0], sz[1], r_disp[1]-r_disp[0]+1))
self.buf_move = np.empty((2*max_change+1, r_disp[1]-r_disp[0]+1))
self.x_prev, self.y_prev = -1, -1
self.max_change, self.penalty_disp = max_change, penalty_disp
def start_line(self, arr, x, y):
self.buf_accum[y, x] = arr[y, x] # Initialize
self.x_prev, self.y_prev = x, y
def in_line(self, arr, x, y):
# Get loss of each move
self.buf_move[:] = np.inf
for j in range(-self.max_change, self.max_change+1):
idx_minl, idx_maxl = max( j,0), min(self.buf_move.shape[1]+j,self.buf_move.shape[1])
idx_minm, idx_maxm = max(-j,0), min(self.buf_move.shape[1]-j,self.buf_move.shape[1])
self.buf_move[j+max_change, idx_minm:idx_maxm] = \
self.buf_accum[self.y_prev, self.x_prev, idx_minl:idx_maxl] + abs(j)*self.penalty_disp
# Get optimal cost and store it
for j in range(self.buf_move.shape[1]):
self.buf_accum[y, x, j] = arr[y, x, j] + np.min(self.buf_move[:, j])
self.x_prev, self.y_prev = x, y
Precompute loss array
r_disp = (-15, 15)
buf_loss = np.empty((arr1.shape[0], arr1.shape[1], r_disp[1]-r_disp[0]+1))
rect_loss_arr(arr1, arr2, r_disp, hw, loss, buf_loss)
Get SGM callback
callback = callback_sgm(arr1.shape, r_disp, max_change, penalty_disp)
Plot each direction
fig, axs = plt.subplots(4, 4, figsize=(20, 20))
for idx, ax in enumerate(axs.flatten()):
theta = (idx/8)*np.pi
line_loop(buf_loss, theta, callback)
ax.imshow(np.argmin(callback.buf_accum, axis=2))
ax.set_title(f'Theta: {theta}')
Make api for sgm
# export
@numba.jit(nopython=True, parallel=True)
def rect_match_arr_sgm(arr1, arr2, r_disp, hw, loss, max_change, penalty_disp, thetas, arr_disp_init=None):
h_arr, w_arr = arr1.shape[0], arr1.shape[1]
# Precompute losses
buf_loss = np.empty((h_arr, w_arr, r_disp[1]-r_disp[0]+1))
rect_loss_arr(arr1, arr2, r_disp, hw, loss, buf_loss, arr_disp_init)
# Do SGM accumulation
callback = callback_sgm(arr1.shape, r_disp, max_change, penalty_disp)
buf_accum = np.zeros((h_arr, w_arr, r_disp[1]-r_disp[0]+1))
for theta in thetas:
line_loop(buf_loss, theta, callback)
buf_accum += callback.buf_accum
# Get disparity map
arr_disp = np.empty((h_arr, w_arr))
for i in range(arr1.shape[0]):
for j in range(arr2.shape[1]):
arr_disp[i, j] = np.argmin(buf_accum[i, j]) + r_disp[0]
if arr_disp_init is not None: arr_disp += arr_disp_init
return arr_disp
# export
def make_rect_match_arr_sgm(r_disp, hw, loss, max_change, penalty_disp, thetas):
@numba.jit(nopython=True)
def rect_match_arr(arr1, arr2, arr_disp_init=None):
return rect_match_arr_sgm(arr1, arr2, r_disp, hw, loss, max_change, penalty_disp, thetas, arr_disp_init)
return rect_match_arr
hw = 15
num_directions = 16
thetas = np.linspace(0, 2*np.pi, num_directions+1)[:-1]
rect_match_arr = make_rect_match_arr_sgm(r_disp, hw, loss, max_change, penalty_disp, thetas)
arr_disp = rect_match_arr(arr1, arr2)
arr_disp = rect_match_arr(arr1, arr2)
I think this is slower primarily because input to the line looper is a class, so I think the callback isn't getting inlined which incurs more overhead, but not sure.
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c51ddc4d0>
Try pyramid
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
arr_disp = rect_match_pyr(arr1, arr2, rect_match_arr)
_, axs = plt.subplots(1, 2, figsize=(15,10))
axs[0].imshow(arr_disp, vmin=-15, vmax=15)
axs[1].imshow(arr1)
axs[1].imshow(arr_disp, vmin=-15, vmax=15, alpha=0.5)
<matplotlib.image.AxesImage at 0x7f3c518bda10>
It's slower... probably because overhead within the sgm call, so pyramiding doesn't help, and might look worse because smoothness contraints are only applied for new disparities added at the every level, so it won't be as smooth.
API
# export
class RectMatch:
def __init__(self, type_rect_match, hw=15, r_disp=(-15,15), loss=SAD, max_change=3, penalty_disp=2, steps=1):
if type_rect_match in ['int', 'sub', 'int_dp', 'sub_dp']:
if type_rect_match == 'int':
min_path = min_path_int
elif type_rect_match == 'sub':
min_path = min_path_sub
elif type_rect_match == 'int_dp':
min_path = make_min_path_int_dp(r_disp, max_change, penalty_disp)
elif type_rect_match == 'sub_dp':
min_path = make_min_path_sub_dp(r_disp, max_change, penalty_disp)
rect_match_arr = make_rect_match_arr_min_path(r_disp, hw, loss, min_path)
elif type_rect_match == 'sgm':
rect_match_arr = make_rect_match_arr_sgm(r_disp, hw, loss, max_change, penalty_disp, thetas)
else:
raise RuntimeError(f'Unrecognized min path type: {type_rect_match}')
self.rect_match_arr, self.steps = rect_match_arr, steps
def __call__(self, arr1, arr2):
return rect_match_pyr(arr1, arr2, self.rect_match_arr, self.steps)
types_rect_match = ['int', 'sub', 'int_dp', 'sub_dp', 'sgm']
rect_matchs = [RectMatch(type_rect_match) for type_rect_match in types_rect_match]
_, axs = plt.subplots(3, 2, figsize=(15,20))
for ax, rect_match, type_rect_match in zip(axs.ravel(), rect_matchs, types_rect_match):
ax.imshow(rect_match(arr1, arr2), vmin=-15, vmax=15)
ax.set_title(type_rect_match)
axs[2,1].set_visible(False)
Build
build_notebook()
<IPython.core.display.Javascript object>
Converted README.ipynb.
convert_notebook()
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