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 analyze_aurora_img(img_file, get_bgr_cam):
debug = 1
n = 0
Frame.MISSING_BG_REMOVE_STRIPES = 0
bgr_cam = get_bgr_cam(thumbnail=False, estimated=1, final=1)
f = Frame.from_file(bgr_cam, img_file, img_file[:-4]+'.lbl', bg_offset=False, debug=debug)
if 0:
f.show_image(processed=True, save_as='C:/projects/s100imgs/processed-aurora.png')
img = f.image.astype('float')
if 0:
img = img - np.percentile(img, 5, axis=1).reshape((-1, 1, 3))
bg1 = (830, 1070), (1180, 1400)
# bg1 = (0, 900), (660, 1280)
# bg2 = (1560, 1050), (2048, 1350)
mean_bg = np.mean(img[bg1[0][1]:bg1[1][1], bg1[0][0]:bg1[1][0], :].reshape((-1, 3)), axis=0)
# mean_bg = np.mean(np.vstack((img[bg1[0][1]:bg1[1][1], bg1[0][0]:bg1[1][0], :].reshape((-1, 3)),
# img[bg2[0][1]:bg2[1][1], bg2[0][0]:bg2[1][0], :].reshape((-1, 3)))), axis=0)
img = img - mean_bg
# img = ImageProc.apply_point_spread_fn(img - mean_bg, 0.01)
# img = np.clip(img, 0, 1023).astype('uint16')
# img = cv2.medianBlur(img, 31)
# img = np.clip(img, 0, RAW_IMG_MAX_VALUE) / RAW_IMG_MAX_VALUE
n += 1
plt.figure(n)
imsh = (np.clip(img * 2 + RAW_IMG_MAX_VALUE * 0.3, 0, RAW_IMG_MAX_VALUE) / RAW_IMG_MAX_VALUE * 255).astype('uint8')
rd_y = (720, 700), (890, 720)
rd_r1 = (720, 830), (890, 870)
rd_r2 = (1080, 820), (1250, 860)
gr_r = (1280, 770), (1450, 795)
cv2.rectangle(imsh, bg1[0], bg1[1], (255, 0, 0), 2) # bg1
# cv2.rectangle(imsh, bg2[0], bg2[1], (255, 0, 0), 2) # bg2
cv2.rectangle(imsh, rd_y[0], rd_y[1], (0, 200, 200), 2) # yellow
cv2.rectangle(imsh, rd_r1[0], rd_r1[1], (0, 0, 255), 2) # red1
cv2.rectangle(imsh, rd_r2[0], rd_r2[1], (0, 0, 255), 2) # red2
cv2.rectangle(imsh, gr_r[0], gr_r[1], (0, 255, 0), 2) # green
plt.imshow(np.flip(imsh, axis=2))
plt.show()
def electrons(lam):
h = 6.626e-34 # planck constant (m2kg/s)
c = 3e8 # speed of light
return h * c / lam # energy per photon
blue = 427.8e-9
green = 557.7e-9
yellow = 589.3e-9
red = 630.0e-9
colors = (blue, green, yellow, red)
dus_per_rad = dict(zip(colors, ([], [], [], []))) # DUs per 1 W/m2/sr of radiance
coef = f.exposure * f.gain * RAW_IMG_MAX_VALUE
for wl in dus_per_rad.keys():
for cam in bgr_cam:
cgain = cam.gain * cam.emp_coef * cam.aperture_area
fn, _ = Camera.qeff_fn(tuple(cam.qeff_coefs), 350e-9, 1000e-9)
# W/m2/sr => phot/s/m2/sr => elec/s/m2/sr => DUs/sr
dus_per_rad[wl].append(1/electrons(wl) * fn(wl) * coef * cgain)
for wl in dus_per_rad.keys():
dus_per_rad[wl] = np.array(dus_per_rad[wl])
class Patch:
def __init__(self, name, rect, bands, mean=None, rad=None):
self.name, self.rect, self.bands, self.mean, self.rad = name, rect, bands, mean, rad
nt = lambda n, r, b: Patch(name=n, rect=r, bands=b)
patches = [
nt('Clean Red', rd_r1, (blue, green, red)),
nt('Strong Red', rd_r2, (blue, green, red)),
nt('Green', gr_r, (blue, green, red)),
nt('Sodium', rd_y, (blue, green, yellow)),
]
# pseudo inverse
for p in patches:
p.mean = np.mean(img[p.rect[0][1]:p.rect[1][1], p.rect[0][0]:p.rect[1][0], :].reshape((-1, 3)), axis=0)
px_sr = cam.pixel_solid_angle((p.rect[0][0]+p.rect[1][0])//2, (p.rect[0][1]+p.rect[1][1])//2)
E = np.hstack((dus_per_rad[p.bands[0]], dus_per_rad[p.bands[1]], dus_per_rad[p.bands[2]])) * px_sr
invE = np.linalg.inv(E.T.dot(E)).dot(E.T)
rad = invE.dot(p.mean) # radiance in W/m2/sr
# e = E.dot(rad)
# diff = (p.mean - e) * 100 / np.linalg.norm(p.mean)
p.rad = [''] * len(colors)
for i, b in enumerate(p.bands):
idx = colors.index(b)
p.rad[idx] = rad[i]
sep = '\t' if 1 else ' & '
le = '\n' if 1 else ' \\\\\n'
if 0:
print(sep.join(('Patch', 'Emission at', '', '', 'Red', 'Green', 'Blue')), end=le)
for name, irr, mean, model, diff in patches:
print(sep.join((name, '428 nm', ('%.3e' % irr[0]) if irr[0] else 'n/a', 'Mean',
*('%.1f' % m for m in np.flip(mean.flatten())))), end=le)
print(sep.join(('', '557.7 nm', ('%.3e' % irr[1]) if irr[1] else 'n/a', 'Modeled',
*('%.1f' % m for m in np.flip(model.flatten())))), end=le)
print(sep.join(('', '589 nm', ('%.3e' % irr[2]) if irr[2] else 'n/a', 'Diff. [%]',
*('%.1f' % m for m in np.flip(diff.flatten())))), end=le)
print(sep.join(('', '630 nm', ('%.3e' % irr[3]) if irr[3] else 'n/a', *(['']*4))), end=le)
else:
print(sep.join(('Patch', 'Red', 'Green', 'Blue', '428 nm', '557.7 nm', '589 nm', '630 nm')), end=le)
for p in patches:
# in kilo Rayleigh (kR) == 6330*1e9*lambda * W/m2/sr or 4*pi*10^(-10)*10^(-3) * photon flux
print(sep.join((p.name, *('%.1f' % m for m in np.flip(p.mean.flatten())),
*(tools.fixed_precision(r*4*np.pi*1e-13/electrons(colors[i]), 3, True) if r else '' # r*1e-13*4*np.pi
for i, r in enumerate(p.rad)))), end=le)
quit()
aurora = np.zeros_like(img)
for i, color in enumerate(colors):
# d/dx[(r-aw)'*(r-aw)] == 0
# => w == (r'*a)/(a'*a)
a = emission[color]
w = np.sum(img.reshape((-1, 3)) * a.T, axis=1) / sum(a**2)
e = (w*a).T
r = img.reshape((-1, 3)) - e
x = w / np.linalg.norm(r, axis=1)
# plt.figure(2)
# plt.imshow(w.reshape(img.shape[:2])/np.max(w))
# plt.title('weight (max=%f)' % np.max(w))
# plt.figure(3)
# plt.imshow(x.reshape(img.shape[:2])/np.max(x))
# plt.title('x (max=%f)' % np.max(x))
n += 1
plt.figure(n)
x[x < {red: 10, green: 6, yellow: 16}[color]] = 0
x[w < 100] = 0
xf = ImageProc.apply_point_spread_fn(x.reshape(img.shape[:2]), 0.03)
xf = cv2.medianBlur(xf.astype('uint16'), 11)
plt.imshow(xf / np.max(xf))
plt.title('Emission detection @ %.1fnm' % (color * 1e9))
e[xf.flatten() == 0, :] = (0, 0, 0)
aurora += e.reshape(img.shape)
# plt.figure(6)
# plt.imshow(np.flip(e.reshape(img.shape) / np.max(e), axis=2))
# plt.title('modeled aurora')
# plt.figure(7)
# plt.imshow(np.flip(r.reshape(img.shape)/np.max(r), axis=2))
# plt.title('residual')
# plt.show()
plt.figure(8)
plt.imshow(np.flip(aurora / np.max(aurora), axis=2))
plt.title('modeled aurora')
plt.show()
# TODO: translate rgb values to aurora (ir)radiance
# - following uses W/m2/sr for "in-band radiance"
# - https://www.osapublishing.org/DirectPDFAccess/A2F3D832-975A-1850-088634AAFCF21258_186134/ETOP-2009-ESB4.pdf?da=1&id=186134&uri=ETOP-2009-ESB4&seq=0&mobile=no
# - use pixel sr?
print('done')
