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#!/usr/bin/env python3
r'''Checks the C and Python implementations of stereo_range()
This test needs external data, so it isn't included in the enabled-by-default set
'''
import sys
import numpy as np
import numpysane as nps
import os
testdir = os.path.dirname(os.path.realpath(__file__))
# I import the LOCAL mrcal since that's what I'm testing
sys.path[:0] = f"{testdir}/..",
import mrcal
import testutils
import cv2
# This test needs data external to this repository. Can be downloaded like this:
if False:
import subprocess
subprocess.check_output("wget -O /tmp/0.cameramodel https://mrcal.secretsauce.net/external/2022-11-05--dtla-overpass--samyang--alpha7/stereo/0.cameramodel && " +
"wget -O /tmp/1.cameramodel https://mrcal.secretsauce.net/external/2022-11-05--dtla-overpass--samyang--alpha7/stereo/1.cameramodel && "
"wget -O /tmp/0.jpg https://mrcal.secretsauce.net/external/2022-11-05--dtla-overpass--samyang--alpha7/stereo/0.jpg && "
"wget -O /tmp/1.jpg https://mrcal.secretsauce.net/external/2022-11-05--dtla-overpass--samyang--alpha7/stereo/1.jpg",
shell = True)
model_filenames=("/tmp/0.cameramodel",
"/tmp/1.cameramodel")
image_filenames=("/tmp/0.jpg",
"/tmp/1.jpg")
pixels_per_deg_az = -.25
pixels_per_deg_el = -.25
az_fov_deg = dict(LENSMODEL_LATLON = 160,
LENSMODEL_PINHOLE = 120)
el_fov_deg = 60
az0_deg = 0
el0_deg = 0
try:
models = [mrcal.cameramodel(f) \
for f in model_filenames]
except FileNotFoundError:
print("Data not found. Commands to download appear at the top of this script")
sys.exit(1)
images = [mrcal.load_image(f, bits_per_pixel = 8, channels = 1) \
for f in image_filenames]
clahe = cv2.createCLAHE()
clahe.setClipLimit(8)
images = [ clahe.apply(image) for image in images ]
# This is a hard-coded property of the OpenCV StereoSGBM implementation
disparity_scale = 16
q = np.array(((30, 100),
(300, 200),
(1000, 400),
(500, 300),
(80, 500),
(400, 350),
(900, 350),
(1100, 350),
(1400, 350),
(400, 450),
(900, 450),
(1100, 450),
(1400, 450),
(1300, 450)))
for rectification_model in ('LENSMODEL_LATLON',
'LENSMODEL_PINHOLE',
):
models_rectified = \
mrcal.rectified_system(models,
az_fov_deg = az_fov_deg[rectification_model],
el_fov_deg = el_fov_deg,
az0_deg = az0_deg,
el0_deg = el0_deg,
pixels_per_deg_az = pixels_per_deg_az,
pixels_per_deg_el = pixels_per_deg_el,
rectification_model = rectification_model)
for disparity_min in (0,10,):
disparity_max = 128
# round to nearest multiple of disparity_scale. The OpenCV StereoSGBM
# implementation requires this
num_disparities = disparity_max - disparity_min
num_disparities = disparity_scale*round(num_disparities/disparity_scale)
stereo_sgbm = \
cv2.StereoSGBM_create(minDisparity = disparity_min,
numDisparities = num_disparities,
P1 = 600,
P2 = 2400,
disp12MaxDiff = 1,
uniquenessRatio = 5,
speckleWindowSize = 100,
speckleRange = 2,
mode = cv2.StereoSGBM_MODE_SGBM)
rectification_maps = mrcal.rectification_maps(models, models_rectified)
images_rectified = [mrcal.transform_image(images[i],
rectification_maps[i]) \
for i in range(2)]
disparity = stereo_sgbm.compute(*images_rectified)
mask_valid = \
(disparity > 0) * \
(disparity >= disparity_min*disparity_scale) * \
(disparity <= disparity_max*disparity_scale)
ranges_dense = \
mrcal.stereo_range( disparity,
models_rectified,
disparity_scale = disparity_scale,
disparity_min = disparity_min)
mask_valid_ranges_dense = ranges_dense > 0
ranges_dense_python = \
mrcal.stereo._stereo_range_python( disparity,
models_rectified,
disparity_scale = disparity_scale,
disparity_min = disparity_min)
mask_valid_ranges_dense_pythion = ranges_dense_python > 0
ranges_sparse = \
mrcal.stereo_range( disparity[q[:,1], q[:,0]],
models_rectified,
qrect0 = q,
disparity_scale = disparity_scale,
disparity_min = disparity_min)
ranges_sparse_python = \
mrcal.stereo._stereo_range_python( disparity[q[:,1], q[:,0]],
models_rectified,
qrect0 = q,
disparity_scale = disparity_scale,
disparity_min = disparity_min)
testutils.confirm_equal( ranges_dense,
ranges_dense_python,
worstcase = True,
eps = 1e-3,
msg=f'Dense stereo_range() matches in C and Python: rectification_model={rectification_model} disparity_min={disparity_min}')
testutils.confirm_equal( ranges_sparse,
ranges_sparse_python,
worstcase = True,
eps = 1e-3,
msg=f'Sparse stereo_range() matches in C and Python: rectification_model={rectification_model} disparity_min={disparity_min}')
testutils.confirm_equal( ranges_sparse,
ranges_dense[q[:,1], q[:,0]],
worstcase = True,
eps = 1e-3,
msg=f'Sparse and dense stereo_range() match: rectification_model={rectification_model} disparity_min={disparity_min}')
testutils.confirm_equal( mask_valid_ranges_dense,
mask_valid,
msg=f'Dense stereo_range() invalid values handled correctly: rectification_model={rectification_model} disparity_min={disparity_min}')
testutils.confirm_equal( mask_valid_ranges_dense_pythion,
mask_valid,
msg=f'Dense stereo_range() in Python: invalid values handled correctly: rectification_model={rectification_model} disparity_min={disparity_min}')
testutils.confirm( np.all(ranges_dense[~mask_valid] == 0.),
msg=f'Dense stereo_range() values are all 0 in invalid areas: rectification_model={rectification_model} disparity_min={disparity_min}')
if False:
range_image_limits = (1,1000)
disparity_colored = mrcal.apply_color_map(disparity,
a_min = disparity_min*disparity_scale,
a_max = disparity_max*disparity_scale)
ranges_colored = mrcal.apply_color_map(ranges_dense,
a_min = range_image_limits[0],
a_max = range_image_limits[1])
mrcal.save_image("/tmp/disparity.png", disparity_colored)
mrcal.save_image("/tmp/range.png", ranges_colored)
print("Wrote /tmp/disparity.png and /tmp/range.png")
testutils.finish()
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