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author	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2022-10-19 02:52:22 +0300
committer	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2022-10-19 22:58:52 +0300
commit	376adeb7b7e0b75f2fa90b890fe0203d1e268cdf (patch)
tree	db22313f190aa098304fe614a1f9eb4bfa8f2e8d /src/py/examples
parent	f08b18ebe9b650573a78574dc67fad95cb5c60a4 (diff)

ipa: rkisp1: Downgrade sensor controls range message to Debug

There's no need to print the exposure and gain control ranges as an Info message. Downgrade it to Debug. While at it, print the ranges using the "[min, max]" syntax. Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: Jacopo Mondi <jacopo@jmondi.org>

Diffstat (limited to 'src/py/examples')

0 files changed, 0 insertions, 0 deletions

.format(shutter_speed) Cam.log += '\nGain = {}'.format(gain) Cam.log += '\nAperture = {}'.format(aperture) patches = [Img.patches[i] for i in Img.order] channels = [Img.channels[i] for i in Img.order] return lux_calc(Cam, Img, patches, channels), shutter_speed, gain """ perform lux calibration on bayer channels """ def lux_calc(Cam, Img, patches, channels): """ find means color channels on grey patches """ ap_r = np.mean(patches[0][3::4]) ap_g = (np.mean(patches[1][3::4])+np.mean(patches[2][3::4]))/2 ap_b = np.mean(patches[3][3::4]) Cam.log += '\nAverage channel values on grey patches:' Cam.log += '\nRed = {:.0f} Green = {:.0f} Blue = {:.0f}'.format(ap_r, ap_b, ap_g) # print(ap_r, ap_g, ap_b) """ calculate channel gains """ gr = ap_g/ap_r gb = ap_g/ap_b Cam.log += '\nChannel gains: Red = {:.3f} Blue = {:.3f}'.format(gr, gb) """ find means color channels on image and scale by gain note greens are averaged together (treated as one channel) """ a_r = np.mean(channels[0])*gr a_g = (np.mean(channels[1])+np.mean(channels[2]))/2 a_b = np.mean(channels[3])*gb Cam.log += '\nAverage channel values over entire image scaled by channel gains:' Cam.log += '\nRed = {:.0f} Green = {:.0f} Blue = {:.0f}'.format(a_r, a_b, a_g) # print(a_r, a_g, a_b) """ Calculate y with top row of yuv matrix """ y = 0.299*a_r + 0.587*a_g + 0.114*a_b Cam.log += '\nY value calculated: {}'.format(int(y)) # print(y) return int(y)