def test_thumbnail_gamma_effect():
img = np.zeros((1536, 2048))
j = 1536 // 2 + 8
I = np.array(tuple(range(8, 2048, 32))[:-1], dtype='int')
mag = I[-1] * 0.85
img[j - 8:j + 8, I] = np.array(I) * (mag / I[-1])
img = ImageProc.apply_point_spread_fn(img, 0.4)
print('max: %d' % np.max(img))
img = np.clip(img, 0, 1023).astype('uint16')
plt.imshow(img)
plt.show()
thb_real = cv2.resize(img,
None,
fx=1 / 16,
fy=1 / 16,
interpolation=cv2.INTER_AREA)
plt.imshow(thb_real)
plt.show()
img_gamma = ImageProc.adjust_gamma(img, 2.2, 0.1, max_val=1023)
thb_gamma = cv2.resize(img_gamma,
None,
fx=1 / 16,
fy=1 / 16,
interpolation=cv2.INTER_AREA)
thb = ImageProc.adjust_gamma(thb_gamma, 2.2, 0.1, inverse=1, max_val=1023)
x = thb_real[j // 16, I // 16]
xf = img[j, I]
xfg = img_gamma[j, I]
yg = thb_gamma[j // 16, I // 16]
y = thb[j // 16, I // 16]
line = np.linspace(0, np.max(x))
plt.plot(x, y, 'x')
plt.plot(line, line)
gamma, gamma_break, max_val, scale = fit_gamma(x, y)
plt.plot(
line,
ImageProc.adjust_gamma(line, gamma, gamma_break, max_val=max_val) *
scale)
plt.show()
quit()
def output(img, show, maxval=1.0, gamma=1.0, outfile=None):
img = ImageProc.adjust_gamma(maxval * img / np.max(img) * 255,
gamma=gamma) / 255
cv2.imwrite(outfile or (sys.argv[1] if len(sys.argv) > 1 else 'test.png'),
(255 * img).astype('uint8'))
if show:
img_sc = cv2.resize(img, (700, 700))
cv2.imshow('test.png', img_sc)
return cv2.waitKey()
def show_image(self,
gain=1,
processed=False,
compare=False,
median_filter=False,
zero_bg=False,
save_as=None):
img = self.image.astype('float')
if processed:
if zero_bg:
img = np.clip(
img - np.min(img) - (0 if zero_bg is True else zero_bg), 0,
np.inf)
img *= gain
if median_filter:
img = cv2.medianBlur(img.astype('uint16'), median_filter)
img = ImageProc.color_correct(img,
self.applied_bgr_mx,
max_val=self.max_val)
img = ImageProc.adjust_gamma(img,
self.applied_gamma,
self.applied_gamma_break,
max_val=self.max_val)
else:
img = np.clip(img * gain, 0, 2**self.bits - 1)
img = ImageProc.change_color_depth(img, self.bits, 8).astype('uint8')
if save_as is not None:
cv2.imwrite(save_as, img)
s = self.image.shape
if compare:
img = np.hstack((self.raw_image.astype(img.dtype),
np.ones((s[0], 1, s[2]), dtype=img.dtype), img))
sc = 1
plt.imshow(np.flip(img, axis=2))
plt.show()
return img, sc
def write_img(raw_imgs, outfile):
imgs = []
for raw in raw_imgs:
img = ImageProc.change_color_depth(raw.astype('float'), 8, bits)
img = ImageProc.adjust_gamma(img,
gamma,
gamma_break=gamma_break,
inverse=True,
max_val=max_val)
if bgr_cc_mx is not None:
img = ImageProc.color_correct(img,
bgr_cc_mx,
inverse=True,
max_val=max_val)
imgs.append(np.expand_dims(img, axis=0))
if len(imgs) == 1:
imgs = imgs[0]
stacked = np.stack(imgs, axis=0)
reduced = np.median(stacked, axis=0) if len(imgs) > 2 else np.min(
stacked, axis=0)
bg_img = np.round(reduced).squeeze().astype('uint16')
cv2.imwrite(outfile, bg_img, (cv2.CV_16U, ))
def detect_moon(self):
x, y = tuple(int(v) for v in self.moon_loc)
win = self.image[y - 24:y + 25, x - 29:x + 30, :]
if 0:
# TODO: what transformation would make this work?
win = ImageProc.adjust_gamma(win / 662 * 1023,
2.2,
inverse=1,
max_val=1023)
h, w, s, c = *win.shape[0:2], 19, 18
mask = np.zeros((h, w), dtype='uint8')
mask[(h // 2 - s):(h // 2 + s + 1),
(w // 2 - s):(w // 2 + s +
1)] = ImageProc.bsphkern(s * 2 + 1).astype('uint8')
mask[0:c, :] = 0
if SHOW_MEASURES:
mask_img = (mask.reshape(
(h, w, 1)) * np.array([255, 0, 0]).reshape(
(1, 1, 3))).astype('uint8')
win_img = np.clip(win, 0, 255).astype('uint8')
plt.imshow(np.flip(ImageProc.merge((mask_img, win_img)), axis=2))
plt.show()
mask = mask.flatten().astype('bool')
n = np.sum(mask)
measures = []
for i, cam in enumerate(self.cam):
raw_du = np.sum(win[:, :, i].flatten()[mask])
bg_du = np.mean(win[:, :, i].flatten()[np.logical_not(mask)])
du_count = raw_du - bg_du * n
measures.append(MoonMeasure(self, i, du_count))
self.measures = measures
return measures
def solve_pnp(self,
orig_sce_img,
outfile,
feat=ORB,
use_feature_db=False,
adjust_sc_rot=False,
add_noise=False,
scale_cam_img=False,
vary_scale=False,
match_mask_params=None,
verbose=1,
**kwargs):
# set max mem usable by reference features, scene features use rest of MAX_WORK_MEM
ref_max_mem = KeypointAlgo.FDB_MAX_MEM if use_feature_db else KeypointAlgo.MAX_WORK_MEM / 2
sm = self.system_model
self._ransac_err = KeypointAlgo.DEF_RANSAC_ERROR
self._render_z = -sm.min_med_distance
init_z = kwargs.get('init_z', self._render_z)
ref_img_sc = min(
1, self._render_z / init_z) * (sm.view_width if scale_cam_img else
self._cam.width) / sm.view_width
self.extra_values = None
if outfile is not None:
self.debug_filebase = outfile + (self.DEBUG_IMG_POSTFIX
if isinstance(orig_sce_img, str)
else '')
if self.est_real_ast_orient:
# so that can track rotation of 67P
sm.reset_to_real_vals()
if use_feature_db and self._fdb_helper is None:
from visnav.algo.fdbgen import FeatureDatabaseGenerator
self._fdb_helper = FeatureDatabaseGenerator(
self.system_model, self.render_engine, self.obj_idx)
# maybe load scene image
if isinstance(orig_sce_img, str):
orig_sce_img = self.load_target_image(orig_sce_img)
if add_noise:
self._shape_model_rng = np.max(
np.ptp(sm.asteroid.real_shape_model.vertices, axis=0))
self.timer = Stopwatch()
self.timer.start()
if use_feature_db:
if KeypointAlgo.FDB_REAL:
# find correct set of keypoints & descriptors from features db
ref_desc, ref_kp_3d, ref_kp, ref_img = self._query_fdb(feat)
else:
# calculate on-the-fly exactly the same features that would be returned from a feature db
ref_desc, ref_kp_3d, ref_kp, ref_img = self._fake_fdb(feat)
else:
# render model image
ref_img, depth_result = self.render_ref_img(ref_img_sc)
if False:
# normalize ref_img to match sce_img
ref_img = ImageProc.equalize_brightness(ref_img,
orig_sce_img,
percentile=99.999,
image_gamma=1.8)
if False:
gamma = 1.0 / 1.8
ref_img = ImageProc.adjust_gamma(ref_img, gamma)
orig_sce_img = ImageProc.adjust_gamma(orig_sce_img, gamma)
# get keypoints and descriptors
ee = sm.pixel_extent(abs(self._render_z))
ref_kp, ref_desc, self._latest_detector = KeypointAlgo.detect_features(
ref_img,
feat,
maxmem=ref_max_mem,
max_feats=KeypointAlgo.MAX_FEATURES,
for_ref=True,
expected_pixel_extent=ee)
if BATCH_MODE and self.debug_filebase:
# save start situation in log archive
self.timer.stop()
img1 = cv2.resize(orig_sce_img, (sm.view_width, sm.view_height))
img2 = cv2.resize(ref_img, (sm.view_width, sm.view_height))
cv2.imwrite(self.debug_filebase + 'a.png',
np.concatenate((img1, img2), axis=1))
if DEBUG:
cv2.imshow('compare', np.concatenate((img1, img2), axis=1))
self.timer.start()
# AKAZE, SIFT, SURF are truly scale invariant, couldnt get ORB to work as good
vary_scale = vary_scale if feat == self.ORB else False
if len(ref_kp) < KeypointAlgo.MIN_FEATURES:
raise PositioningException(
'Too few (%d) reference features found' % (len(ref_kp), ))
ok = False
for i in range(self.MAX_SCENE_SCALE_STEPS):
try:
# resize scene image if necessary
sce_img_sc = (sm.view_width
if scale_cam_img else self._cam.width
) / self._cam.width / self.SCENE_SCALE_STEP**i
if np.isclose(sce_img_sc, 1):
sce_img = orig_sce_img
else:
sce_img = cv2.resize(orig_sce_img,
None,
fx=sce_img_sc,
fy=sce_img_sc,
interpolation=cv2.INTER_AREA)
# detect features in scene image
sce_max_mem = KeypointAlgo.MAX_WORK_MEM - (
KeypointAlgo.BYTES_PER_FEATURE[feat] + 12) * len(ref_desc)
ee = sm.pixel_extent(abs(match_mask_params[2])
) if match_mask_params is not None else 0
sce_kp, sce_desc, self._latest_detector = KeypointAlgo.detect_features(
sce_img,
feat,
maxmem=sce_max_mem,
max_feats=KeypointAlgo.MAX_FEATURES,
expected_pixel_extent=ee)
if len(sce_kp) < KeypointAlgo.MIN_FEATURES:
raise PositioningException(
'Too few (%d) scene features found' % (len(sce_kp), ))
# match descriptors
try:
mask = None
if match_mask_params is not None:
mask = KeypointAlgo.calc_match_mask(
sm, sce_kp, ref_kp, self._render_z, sce_img_sc,
ref_img_sc, *match_mask_params)
matches = KeypointAlgo.match_features(
sce_desc,
ref_desc,
self._latest_detector.defaultNorm(),
mask=mask,
method='brute')
error = None
except PositioningException as e:
matches = []
error = e
# debug by drawing matches
if verbose > 0 and (not BATCH_MODE or DEBUG):
logger.info('matches: %s/%s' %
(len(matches), min(len(sce_kp), len(ref_kp))))
if verbose > 1:
self._draw_matches(sce_img,
sce_img_sc,
sce_kp,
ref_img,
ref_img_sc,
ref_kp,
matches,
pause=False,
show=DEBUG)
if error is not None:
raise error
# select matched scene feature image coordinates
sce_kp_2d = np.array([
tuple(np.divide(sce_kp[m.queryIdx].pt, sce_img_sc))
for m in matches
],
dtype='float')
# prepare reference feature 3d coordinates (for only matched features)
if use_feature_db:
ref_kp_3d = ref_kp_3d[[m.trainIdx for m in matches], :]
if add_noise:
# add noise to noiseless 3d ref points from fdb
self.timer.stop()
ref_kp_3d, self.sm_noise, _ = tools.points_with_noise(
ref_kp_3d,
only_z=True,
noise_lv=SHAPE_MODEL_NOISE_LV[add_noise],
max_rng=self._shape_model_rng)
self.timer.start()
else:
# get feature 3d points using 3d model
ref_kp_3d = KeypointAlgo.inverse_project(
sm, [ref_kp[m.trainIdx].pt for m in matches],
depth_result, self._render_z, ref_img_sc)
if KeypointAlgo.DISCARD_OFF_OBJECT_FEATURES:
I = np.where(np.logical_not(np.isnan(ref_kp_3d[:, 0])))[0]
if len(I) < self.MIN_FEATURES:
raise PositioningException('Too few matches found')
sce_kp_2d = sce_kp_2d[I, :]
ref_kp_3d = ref_kp_3d[I, :]
matches = [matches[i] for i in I]
# finally solve pnp with ransac
rvec, tvec, inliers = KeypointAlgo.solve_pnp_ransac(
sm, sce_kp_2d, ref_kp_3d, self._ransac_err)
# debug by drawing inlier matches
if verbose > 1:
self._draw_matches(sce_img,
sce_img_sc,
sce_kp,
ref_img,
ref_img_sc,
ref_kp,
[matches[i[0]] for i in inliers],
label='c) inliers',
pause=self._pause)
inlier_count = self.count_inliers(sce_kp, ref_kp, matches,
inliers)
if verbose > 0:
logger.info('inliers: %s/%s, ' %
(inlier_count, len(matches)))
if inlier_count < KeypointAlgo.MIN_FEATURES:
raise PositioningException(
'RANSAC algorithm was left with too few inliers')
# dont try again if found enough inliers
ok = True
break
except PositioningException as e:
if not vary_scale:
raise e
# maybe try again using scaled down scene image
if not ok:
raise PositioningException(
'Not enough inliers even if tried scaling scene image down x%.1f'
% (1 / sce_img_sc))
elif vary_scale:
logger.info('success at x%.1f' % (1 / sce_img_sc))
self.timer.stop()
# set model params to solved pose & pos
self._set_sc_from_ast_rot_and_trans(rvec,
tvec,
self.latest_discretization_err_q,
rotate_sc=adjust_sc_rot)
# debugging
if verbose > 0 and (not BATCH_MODE or DEBUG):
rp_err = KeypointAlgo.reprojection_error(self._cam, sce_kp_2d,
ref_kp_3d, inliers, rvec,
tvec)
sh_err = sm.calc_shift_err()
logger.info(
'repr-err: %.2f, rel-rot-err: %.2f°, dist-err: %.2f%%, lat-err: %.2f%%, shift-err: %.1fm'
% (
rp_err,
math.degrees(sm.rel_rot_err()),
sm.dist_pos_err() * 100,
sm.lat_pos_err() * 100,
sh_err * 1000,
))
# save result image
if BATCH_MODE and self.debug_filebase:
# save result in log archive
res_img = self.render(shadows=self.RENDER_SHADOWS)
sce_img = cv2.resize(orig_sce_img, tuple(np.flipud(res_img.shape)))
cv2.imwrite(self.debug_filebase + 'd.png',
np.concatenate((sce_img, res_img), axis=1))
def est_refl_model(hapke=True, iters=1, init_noise=0.0, verbose=True):
sm = RosettaSystemModel()
imgsize = (512, 512)
imgs = {
'ROS_CAM1_20140831T104353': 3.2, # 60, 3.2s
'ROS_CAM1_20140831T140853': 3.2, # 62, 3.2s
'ROS_CAM1_20140831T103933': 3.2, # 65, 3.2s
'ROS_CAM1_20140831T022253': 3.2, # 70, 3.2s
'ROS_CAM1_20140821T100719': 2.8, # 75, 2.8s
'ROS_CAM1_20140821T200718': 2.0, # 80, 2.0s
'ROS_CAM1_20140822T113854': 2.0, # 85, 2.0s
'ROS_CAM1_20140823T021833': 2.0, # 90, 2.0s
'ROS_CAM1_20140819T120719': 2.0, # 95, 2.0s
'ROS_CAM1_20140824T021833': 2.8, # 100, 2.8s
'ROS_CAM1_20140824T020853': 2.8, # 105, 2.8s
'ROS_CAM1_20140824T103934': 2.8, # 110, 2.8s
'ROS_CAM1_20140818T230718': 2.0, # 113, 2.0s
'ROS_CAM1_20140824T220434': 2.8, # 120, 2.8s
'ROS_CAM1_20140828T020434': 2.8, # 137, 2.8s
'ROS_CAM1_20140827T140434': 3.2, # 145, 3.2s
'ROS_CAM1_20140827T141834': 3.2, # 150, 3.2s
'ROS_CAM1_20140827T061834': 3.2, # 155, 3.2s
'ROS_CAM1_20140827T021834': 3.2, # 157, 3.2s
'ROS_CAM1_20140826T221834': 2.8, # 160, 2.8s
}
target_exposure = np.min(list(imgs.values()))
for img, exposure in imgs.items():
real = cv2.imread(
os.path.join(sm.asteroid.image_db_path, img + '_P.png'),
cv2.IMREAD_GRAYSCALE)
real = ImageProc.adjust_gamma(real, 1 / 1.8)
#dark_px_lim = np.percentile(real, 0.1)
#dark_px = np.mean(real[real<=dark_px_lim])
real = cv2.resize(real, imgsize)
# remove dark pixel intensity and normalize based on exposure
#real = real - dark_px
#real *= (target_exposure / exposure)
imgs[img] = real
re = RenderEngine(*imgsize, antialias_samples=0)
obj_idx = re.load_object(sm.asteroid.hires_target_model_file, smooth=False)
ab = AlgorithmBase(sm, re, obj_idx)
model = RenderEngine.REFLMOD_HAPKE if hapke else RenderEngine.REFLMOD_LUNAR_LAMBERT
defs = RenderEngine.REFLMOD_PARAMS[model]
if hapke:
# L, th, w, b (scattering anisotropy), c (scattering direction from forward to back), B0, hs
#real_ini_x = [515, 16.42, 0.3057, 0.8746]
sppf_n = 2
real_ini_x = defs[:2] + defs[3:3 + sppf_n]
scales = np.array((500, 20, 3e-1, 3e-1))[:2 + sppf_n]
else:
ll_poly = 5
#real_ini_x = np.array(defs[:7])
real_ini_x = np.array(
(9.95120e-01, -6.64840e-03, 3.96267e-05, -2.16773e-06, 2.08297e-08,
-5.48768e-11, 1)) # theta=20
real_ini_x = np.hstack((real_ini_x[0:ll_poly + 1], (real_ini_x[-1], )))
scales = np.array((3e-03, 2e-05, 1e-06, 1e-08, 5e-11, 1))
scales = np.hstack((scales[0:ll_poly], (scales[-1], )))
def set_params(x):
if hapke:
# optimize J, th, w, b, (c), B_SH0, hs
xsc = list(np.array(x) * scales)
vals = xsc[:2] + [defs[2]] + xsc[2:] + defs[len(xsc) + 1:]
else:
vals = [1] + list(np.array(x)[:-1] * scales[:-1]) + [0] * (
5 - ll_poly) + [x[-1] * scales[-1], 0, 0, 0]
RenderEngine.REFLMOD_PARAMS[model] = vals
# debug 1: real vs synth, 2: err img, 3: both
def costfun(x, debug=0, verbose=True):
set_params(x)
err = 0
for file, real in imgs.items():
lblloader.load_image_meta(
os.path.join(sm.asteroid.image_db_path, file + '.LBL'), sm)
sm.swap_values_with_real_vals()
synth2 = ab.render(shadows=True, reflection=model, gamma=1)
err_img = (synth2.astype('float') - real)**2
lim = np.percentile(err_img, 99)
err_img[err_img > lim] = 0
err += np.mean(err_img)
if debug:
if debug % 2:
cv2.imshow(
'real vs synthetic',
np.concatenate((real.astype('uint8'), 255 * np.ones(
(real.shape[0], 1), dtype='uint8'), synth2),
axis=1))
if debug > 1:
err_img = err_img**0.2
cv2.imshow('err', err_img / np.max(err_img))
cv2.waitKey()
err /= len(imgs)
if verbose:
print('%s => %f' %
(', '.join(['%.4e' % i for i in np.array(x) * scales]), err))
return err
best_x = None
best_err = float('inf')
for i in range(iters):
if hapke:
ini_x = tuple(real_ini_x + init_noise *
np.random.normal(0, 1, (len(scales), )) * scales)
else:
ini_x = tuple(real_ini_x[1:-1] / real_ini_x[0] + init_noise *
np.random.normal(0, 1, (len(scales) - 1, )) *
scales[:-1]) + (real_ini_x[-1] * real_ini_x[0], )
if verbose:
print('\n\n\n==== i:%d ====\n' % i)
res = minimize(
costfun,
tuple(ini_x / scales),
args=(0, verbose),
#method="BFGS", options={'maxiter': 10, 'eps': 1e-3, 'gtol': 1e-3})
method="Nelder-Mead",
options={
'maxiter': 120,
'xtol': 1e-4,
'ftol': 1e-4
})
#method="COBYLA", options={'rhobeg': 1.0, 'maxiter': 200, 'disp': False, 'catol': 0.0002})
if not verbose:
print('%s => %f' %
(', '.join(['%.5e' % i
for i in np.array(res.x) * scales]), res.fun))
if res.fun < best_err:
best_err = res.fun
best_x = res.x
if verbose:
costfun(best_x, 3, verbose=True)
if hapke:
x = tuple(best_x * scales)
else:
x = (1, ) + tuple(best_x * scales)
if verbose:
p = np.linspace(0, 160, 100)
L = get_graph_L(20, p)
plt.plot(p, L, p, Lfun(x[:-1], p))
plt.show()
return x
def render(self,
obj_idxs,
rel_pos_v,
rel_rot_q,
light_v,
get_depth=False,
shadows=True,
textures=True,
gamma=1.0,
reflection=REFLMOD_LUNAR_LAMBERT,
flux_density=False):
obj_idxs = [obj_idxs] if isinstance(obj_idxs, int) else obj_idxs
rel_pos_v = np.array(rel_pos_v).reshape((-1, 3))
rel_rot_q = np.array(rel_rot_q).reshape((-1, ))
light_v = np.array(light_v)
assert len(obj_idxs) == rel_pos_v.shape[0] == rel_rot_q.shape[
0], 'obj_idxs, rel_pos_v and rel_rot_q dimensions dont match'
shadow_mvps = None
if shadows:
shadow_mvps = self._render_shadowmap(obj_idxs, rel_pos_v,
rel_rot_q, light_v)
self._fbo.use()
self._ctx.enable(moderngl.DEPTH_TEST)
self._ctx.enable(moderngl.CULL_FACE)
self._ctx.front_face = 'ccw' # cull back faces
self._ctx.clear(0, 0, 0, 0, float('inf'))
if shadows:
self._shadow_map.use(RenderEngine._LOC_SHADOW_MAP)
self._prog['shadow_map'].value = RenderEngine._LOC_SHADOW_MAP
for i, obj_idx in enumerate(obj_idxs):
self._set_params(obj_idx,
rel_pos_v[i],
rel_rot_q[i],
light_v,
shadow_mvps,
textures,
reflection,
prog=self._prog,
flux_density=flux_density)
self._objs[obj_idx].render()
if self._samples > 0:
self._ctx.copy_framebuffer(self._fbo2, self._fbo)
fbo = self._fbo2
dbo = self._dbo2
else:
fbo = self._fbo
dbo = self._dbo
data = np.frombuffer(fbo.read(components=1, alignment=1, dtype='f4'),
dtype='f4').reshape((self._height, self._width))
data = np.flipud(data)
if get_depth:
depth = np.frombuffer(dbo.read(alignment=1), dtype='f4').reshape(
(self._height, self._width))
depth = np.flipud(depth)
# normalize depth
if self._persp_proj:
# for perspective projection
a = -(self._frustum_far - self._frustum_near) / (
2.0 * self._frustum_far * self._frustum_near)
b = (self._frustum_far + self._frustum_near) / (
2.0 * self._frustum_far * self._frustum_near)
if self._frustum_far / self._frustum_near < 1e7:
depth = np.divide(1.0, (2.0 * a) * depth -
(a - b)) # 1/((2*X-1)*a+b)
else:
# up to difference of 1e14
depth = np.divide(1.0,
(2.0 * a) * depth.astype(np.float64) -
(a - b)).astype(np.float32)
else:
# for orthographic projection
# - depth is between 0 and 1
depth = depth * (self._frustum_far -
self._frustum_near) + self._frustum_near
# free memory to avoid memory leaks
if shadows:
self._shadow_map.release()
if flux_density:
data = data * flux_density
else:
data = np.clip(data * 255, 0, 255).astype('uint8')
if gamma != 1.0:
data = ImageProc.adjust_gamma(data, gamma)
return (data, depth) if get_depth else data
def costfn(p, x, y):
gamma, gamma_break, max_val, scale = tuple(map(abs, p))
diff = ImageProc.adjust_gamma(x, gamma, gamma_break,
max_val=max_val) * scale - y
diff = tools.pseudo_huber_loss(120, diff)
return np.sum(diff) if _USE_BFGS else diff
plt.show()
for hd in (
48737, 35468, 39801
): # Lambda Orionis (HD36861) Teff too high for model (37689K)
fname = r'C:\projects\s100imgs\spectra\%s.fits' % hd
fdat = fits.getdata(fname)
teff, logg, feh = [stars[hd][f] for f in (f_teff, f_logg, f_feh)]
if teff > 30000:
logg = max(logg, 4.0)
testf(fdat, teff, logg, feh or 0)
quit()
# cam = RosettaSystemModel(focused_attenuated=False).cam
cam = DidymosSystemModel(use_narrow_cam=True).cam
# cam_q = tools.rand_q(math.radians(180))
cam_q = quaternion.one
for i in range(100):
cam_q = tools.ypr_to_q(0, np.radians(1), 0) * cam_q
flux_density = Stars.flux_density(cam_q, cam)
img = cam.sense(flux_density, exposure=2, gain=2)
img = np.clip(img * 255, 0, 255).astype('uint8')
img = ImageProc.adjust_gamma(img, 1.8)
sc = min(768 / cam.width, 768 / cam.height)
cv2.imshow('stars', cv2.resize(img, None, fx=sc, fy=sc))
cv2.waitKey()
print('done')
def __init__(self,
cam,
gain,
exposure,
timestamp,
raw_image,
background_img,
bg_offset=0,
bits=8,
applied_gamma=1.0,
applied_gamma_break=0.0,
applied_bgr_mx=None,
debug=False):
self.id = Frame.CURRENT_ID
Frame.CURRENT_ID += 1
self.cam = [cam] if isinstance(cam, Camera) else cam
self.resize_scale = raw_image.shape[1] / self.cam[0].width
for c in self.cam:
c.height, c.width = raw_image.shape[:2]
self.bits = bits = int(bits)
self.gain = gain
self.exposure = exposure
self.timestamp = timestamp
self.raw_image = raw_image
self.applied_gamma = applied_gamma
self.applied_gamma_break = applied_gamma_break
self.applied_bgr_mx = applied_bgr_mx
self.debug = debug
img_bits = int(str(raw_image.dtype)[4:])
max_val = 2**img_bits - 1
img = raw_image.astype('float')
# NOTE: NanoCam has this, doesnt make sense in general!
operation_order = reversed((
'ex_gamma',
'depth',
'color',
'gamma',
))
for op in operation_order:
if op == 'depth' and img_bits != bits:
img = ImageProc.change_color_depth(img, img_bits, bits)
max_val = 2**bits - 1
if op == 'gamma' and applied_gamma != 1.0:
img = ImageProc.adjust_gamma(img,
applied_gamma,
gamma_break=applied_gamma_break,
inverse=True,
max_val=max_val)
if op == 'color' and applied_bgr_mx is not None:
img = ImageProc.color_correct(img,
applied_bgr_mx,
inverse=True,
max_val=max_val)
# if op == 'ex_gamma' and GAMMA_ADJUSTMENT:
# img = ImageProc.adjust_gamma(img, GAMMA_ADJUSTMENT, inverse=True, max_val=max_val)
self.background_img = background_img
if background_img is not None:
self.image = ImageProc.remove_bg(img,
background_img,
gain=1,
offset=bg_offset,
max_val=max_val)
elif self.MISSING_BG_REMOVE_STRIPES:
for k in range(img.shape[2]):
img[:, :, k] -= np.percentile(img[:, :, k], 50,
axis=0).reshape((1, -1))
img[:, :, k] -= np.percentile(img[:, :, k], 50,
axis=1).reshape((-1, 1))
img += bg_offset - np.min(img)
self.image = np.clip(img, 0, max_val)
else:
self.image = img
if bg_offset is not False:
self.image = np.round(self.image).astype('uint16')
self.measures = []
