# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# ctt_awb.py - camera tuning tool for AWB
from ctt_image_load import *
import matplotlib.pyplot as plt
from bisect import bisect_left
from scipy.optimize import fmin
"""
obtain piecewise linear approximation for colour curve
"""
def awb(Cam, cal_cr_list, cal_cb_list, plot):
imgs = Cam.imgs
"""
condense alsc calibration tables into one dictionary
"""
if cal_cr_list is None:
colour_cals = None
else:
colour_cals = {}
for cr, cb in zip(cal_cr_list, cal_cb_list):
cr_tab = cr['table']
cb_tab = cb['table']
"""
normalise tables so min value is 1
"""
cr_tab = cr_tab/np.min(cr_tab)
cb_tab = cb_tab/np.min(cb_tab)
colour_cals[cr['ct']] = [cr_tab, cb_tab]
"""
obtain data from greyscale macbeth patches
"""
rb_raw = []
rbs_hat = []
for Img in imgs:
Cam.log += '\nProcessing '+Img.name
"""
get greyscale patches with alsc applied if alsc enabled.
Note: if alsc is disabled then colour_cals will be set to None and the
function will just return the greyscale patches
"""
r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals)
"""
calculate ratio of r, b to g
"""
r_g = np.mean(r_patchs/g_patchs)
b_g = np.mean(b_patchs/g_patchs)
Cam.log += '\n r : {:.4f} b : {:.4f}'.format(r_g, b_g)
"""
The curve tends to be better behaved in so-called hatspace.
R, B, G represent the individual channels. The colour curve is plotted in
r, b space, where:
r = R/G
b = B/G
This will be referred to as dehatspace... (sorry)
Hatspace is defined as:
r_hat = R/(R+B+G)
b_hat = B/(R+B+G)
To convert from dehatspace to hastpace (hat operation):
r_hat = r/(1+r+b)
b_hat = b/(1+r+b)
To convert from hatspace to dehatspace (dehat operation):
r = r_hat/(1-r_hat-b_hat)
b = b_hat/(1-r_hat-b_hat)
Proof is left as an excercise to the reader...
Throughout the code, r and b are sometimes referred to as r_g and b_g
as a reminder that they are ratios
"""
r_g_hat = r_g/(1+r_g+b_g)
b_g_hat = b_g/(1+r_g+b_g)
Cam.log += '\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat)
rbs_hat.append((r_g_hat, b_g_hat, Img.col))
rb_raw.append((r_g, b_g))
Cam.log += '\n'
Cam.log += '\nFinished processing images'
"""
sort all lits simultaneously by r_hat
"""
rbs_zip = list(zip(rbs_hat, rb_raw))
rbs_zip.sort(key=lambda x: x[0][0])
rbs_hat, rb_raw = list(zip(*rbs_zip))
"""
unzip tuples ready for processing
"""
rbs_hat = list(zip(*rbs_hat))
rb_raw = list(zip(*rb_raw))
"""
fit quadratic fit to r_g hat and b_g_hat
"""
a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2)
Cam.log += '\nFit quadratic curve in hatspace'
"""
the algorithm now approximates the shortest distance from each point to the
curve in dehatspace. Since the fit is done in hatspace, it is easier to
find the actual shortest distance in hatspace and use the projection back
into dehatspace as an overestimate.
The distance will be used for two things:
1) In the case that colour temperature does not strictly decrease with
increasing r/g, the closest point to the line will be chosen out of an
increasing pair of colours.
2) To calculate transverse negative an dpositive, the maximum positive
and negative distance from the line are chosen. This benefits from the
overestimate as the transverse pos/neg are upper bound values.
"""
"""
define fit function
"""
def f(x):
return a*x**2 + b*x + c
"""
iterate over points (R, B are x and y coordinates of points) and calculate
distance to line in dehatspace
"""
dists = []
for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])):
"""
define function to minimise as square distance between datapoint and
point on curve. Squaring is monotonic so minimising radius squared is
equivalent to minimising radius
"""
def f_min(x):
y = f(x)
return((x-R)**2+(y-B)**2)
"""
perform optimisation with scipy.optmisie.fmin
"""
x_hat = fmin(f_min, R, disp=0)[0]
y_hat = f(x_hat)
"""
dehat
"""
x = x_hat/(1-x_hat-y_hat)
y = y_hat/(1-x_hat-y_hat)
rr = R/(1-R-B)
bb = B/(1-R-B)
"""
calculate euclidean distance in dehatspace
"""
dist = ((x-rr)**2+(y-bb)**2)**0.5
"""
return negative if point is below the fit curve
"""
if (x+y) > (rr+bb):
dist *= -1
dists.append(dist)
Cam.log += '\nFound closest point on fit line to each point in dehatspace'
"""
calculate wiggle factors in awb. 10% added since this is an upper bound
"""
transverse_neg = - np.min(dists) * 1.1
transverse_pos = np.max(dists) * 1.1
Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
"""
set minimum transverse wiggles to 0.1 .
Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
is a suitable minimum that gives better results for lighting conditions not
within calibration dataset. Anything less will generalise poorly.
"""
if transverse_pos < 0.01:
transverse_pos = 0.01
Cam.log += '\nForced transverse pos to 0.01'
if transverse_neg < 0.01:
transverse_neg = 0.01
Cam.log += '\nForced transverse neg to 0.01'
"""
generate new b_hat values at each r_hat according to fit
"""
r_hat_fit = np.array(rbs_hat[0])
b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c
"""
transform from hatspace to dehatspace
"""
r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)
b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)
c_fit = np.round(rbs_hat[2], 0)
"""
round to 4dp
"""
r_fit = np.where((1000*r_fit) % 1 <= 0.05, r_fit+0.0001, r_fit)
r_fit = np.where((1000*r_fit) % 1 >= 0.95, r_fit-0.0001, r_fit)
b_fit = np.where((1000*b_fit) % 1 <= 0.05, b_fit+0.0001, b_fit)
b_fit = np.where((1000*b_fit) % 1 >= 0.95, b_fit-0.0001, b_fit)
r_fit = np.round(r_fit, 4)
b_fit = np.round(b_fit, 4)
"""
The following code ensures that colour temperature decreases with
increasing r/g
"""
"""
iterate backwards over list for easier indexing
"""
i = len(c_fit) - 1
while i > 0:
if c_fit[i] > c_fit[i-1]:
Cam.log += '\nColour temperature increase found\n'
Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1])
Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i])
"""
if colour temperature increases then discard point furthest from
the transformed fit (dehatspace)
"""
error_1 = abs(dists[i-1])
error_2 = abs(dists[i])
Cam.log += '\nDistances from fit:\n'
Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1)
Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2)
"""
find bad index
note that in python false = 0 and true = 1
"""
bad = i - (error_1 < error_2)
Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad])
Cam.log += 'it is furthest from fit'
"""
delete bad point
"""
r_fit = np.delete(r_fit, bad)
b_fit = np.delete(b_fit, bad)
c_fit = np.delete(c_fit, bad).astype(np.uint16)
"""
note that if a point has been discarded then the length has decreased
by one, meaning that decreasing the index by one will reassess the kept
point against the next point. It is therefore possible, in theory, for
two adjacent points to be discarded, although probably rare
"""
i -= 1
"""
return formatted ct curve, ordered by increasing colour temperature
"""
ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1]
Cam.log += '\nFinal CT curve:'
for i in range(len(ct_curve)//3):
j = 3*i
Cam.log += '\n ct: {} '.format(ct_curve[j])
Cam.log += ' r: {} '.format(ct_curve[j+1])
Cam.log += ' b: {} '.format(ct_curve[j+2])
"""
plotting code for debug
"""
if plot:
x = np.linspace(np.min(rbs_hat[0]), np.max(rbs_hat[0]), 100)
y = a*x**2 + b*x + c
plt.subplot(2, 1, 1)
plt.title('hatspace')
plt.plot(rbs_hat[0], rbs_hat[1], ls='--', color='blue')
plt.plot(x, y, color='green', ls='-')
plt.scatter(rbs_hat[0], rbs_hat[1], color='red')
for i, ct in enumerate(rbs_hat[2]):
plt.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i]))
plt.xlabel('$\\hat{r}$')
plt.ylabel('$\\hat{b}$')
"""
optional set axes equal to shortest distance so line really does
looks perpendicular and everybody is happy
"""
# ax = plt.gca()
# ax.set_aspect('equal')
plt.grid()
plt.subplot(2, 1, 2)
plt.title('dehatspace - indoors?')
plt.plot(r_fit, b_fit, color='blue')
plt.scatter(rb_raw[0], rb_raw[1], color='green')
plt.scatter(r_fit, b_fit, color='red')
for i, ct in enumerate(c_fit):
plt.annotate(str(ct), (r_fit[i], b_fit[i]))
plt.xlabel('$r$')
plt.ylabel('$b$')
"""
optional set axes equal to shortest distance so line really does
looks perpendicular and everybody is happy
"""
# ax = plt.gca()
# ax.set_aspect('equal')
plt.subplots_adjust(hspace=0.5)
plt.grid()
plt.show()
"""
end of plotting code
"""
return(ct_curve, np.round(transverse_pos, 5), np.round(transverse_neg, 5))
"""
obtain greyscale patches and perform alsc colour correction
"""
def get_alsc_patches(Img, colour_cals, grey=True):
"""
get patch centre coordinates, image colour and the actual
patches for each channel, remembering to subtract blacklevel
If grey then only greyscale patches considered
"""
if grey:
cen_coords = Img.cen_coords[3::4]
col = Img.col
patches = [np.array(Img.patches[i]) for i in Img.order]
r_patchs = patches[0][3::4] - Img.blacklevel_16
b_patchs = patches[3][3::4] - Img.blacklevel_16
"""
note two green channels are averages
"""
g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16
else:
cen_coords = Img.cen_coords
col = Img.col
patches = [np.array(Img.patches[i]) for i in Img.order]
r_patchs = patches[0] - Img.blacklevel_16
b_patchs = patches[3] - Img.blacklevel_16
g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16
if colour_cals is None:
return r_patchs, b_patchs, g_patchs
"""
find where image colour fits in alsc colour calibration tables
"""
cts = list(colour_cals.keys())
pos = bisect_left(cts, col)
"""
if img colour is below minimum or above maximum alsc calibration colour, simply
pick extreme closest to img colour
"""
if pos % len(cts) == 0:
"""
this works because -0 = 0 = first and -1 = last index
"""
col_tabs = np.array(colour_cals[cts[-pos//len(cts)]])
"""
else, perform linear interpolation between existing alsc colour
calibration tables
"""
else:
bef = cts[pos-1]
aft = cts[pos]
da = col-bef
db = aft-col
bef_tabs = np.array(colour_cals[bef])
aft_tabs = np.array(colour_cals[aft])
col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db)
col_tabs = np.reshape(col_tabs, (2, 12, 16))
"""
calculate dx, dy used to calculate alsc table
"""
w, h = Img.w/2, Img.h/2
dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))
"""
make list of pairs of gains for each patch by selecting the correct value
in alsc colour calibration table
"""
patch_gains = []
for cen in cen_coords:
x, y = cen[0]//dx, cen[1]//dy
# We could probably do with some better spatial interpolation here?
col_gains = (col_tabs[0][y][x], col_tabs[1][y][x])
patch_gains.append(col_gains)
"""
multiply the r and b channels in each patch by the respective gain, finally
performing the alsc colour correction
"""
for i, gains in enumerate(patch_gains):
r_patchs[i] = r_patchs[i] * gains[0]
b_patchs[i] = b_patchs[i] * gains[1]
"""
return greyscale patches, g channel and correct r, b channels
"""
return r_patchs, b_patchs, g_patchs
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#!/usr/bin/env python3
# SPDX-License-Identifier: BSD-3-Clause
# Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com>
# A simple libcamera capture example
#
# This is a python version of simple-cam from:
# https://git.libcamera.org/libcamera/simple-cam.git
#
# \todo Move to simple-cam repository when the Python API has stabilized more
import libcamera as libcam
import selectors
import sys
import time
TIMEOUT_SEC = 3
def handle_camera_event(cm):
# cm.get_ready_requests() returns the ready requests, which in our case
# should almost always return a single Request, but in some cases there
# could be multiple or none.
reqs = cm.get_ready_requests()
# Process the captured frames
for req in reqs:
process_request(req)
def process_request(request):
global camera
print()
print(f'Request completed: {request}')
# When a request has completed, it is populated with a metadata control
# list that allows an application to determine various properties of
# the completed request. This can include the timestamp of the Sensor
# capture, or its gain and exposure values, or properties from the IPA
# such as the state of the 3A algorithms.
#
# To examine each request, print all the metadata for inspection. A custom
# application can parse each of these items and process them according to
# its needs.
requestMetadata = request.metadata
for id, value in requestMetadata.items():
print(f'\t{id.name} = {value}')
# Each buffer has its own FrameMetadata to describe its state, or the
# usage of each buffer. While in our simple capture we only provide one
# buffer per request, a request can have a buffer for each stream that
# is established when configuring the camera.
#
# This allows a viewfinder and a still image to be processed at the
# same time, or to allow obtaining the RAW capture buffer from the
# sensor along with the image as processed by the ISP.
buffers = request.buffers
for _, buffer in buffers.items():
metadata = buffer.metadata
# Print some information about the buffer which has completed.
print(f' seq: {metadata.sequence:06} timestamp: {metadata.timestamp} bytesused: ' +
'/'.join([str(p.bytes_used) for p in metadata.planes]))
# Image data can be accessed here, but the FrameBuffer
# must be mapped by the application
# Re-queue the Request to the camera.
request.reuse()
camera.queue_request(request)
# ----------------------------------------------------------------------------
# Camera Naming.
#
# Applications are responsible for deciding how to name cameras, and present
# that information to the users. Every camera has a unique identifier, though
# this string is not designed to be friendly for a human reader.
#
# To support human consumable names, libcamera provides camera properties
# that allow an application to determine a naming scheme based on its needs.
#
# In this example, we focus on the location property, but also detail the
# model string for external cameras, as this is more likely to be visible
# information to the user of an externally connected device.
#
# The unique camera ID is appended for informative purposes.
#
def camera_name(camera):
props = camera.properties
location = props.get(libcam.properties.Location, None)
if location == libcam.properties.LocationEnum.Front:
name = 'Internal front camera'
elif location == libcam.properties.LocationEnum.Back:
name = 'Internal back camera'
elif location == libcam.properties.LocationEnum.External:
name = 'External camera'
if libcam.properties.Model in props:
name += f' "{props[libcam.properties.Model]}"'
else:
name = 'Undefined location'
name += f' ({camera.id})'
return name
def main():
global camera
# --------------------------------------------------------------------
# Get the Camera Manager.
#
# The Camera Manager is responsible for enumerating all the Camera
# in the system, by associating Pipeline Handlers with media entities
# registered in the system.
#
# The CameraManager provides a list of available Cameras that
# applications can operate on.
#
# There can only be a single CameraManager within any process space.
cm = libcam.CameraManager.singleton()
# Just as a test, generate names of the Cameras registered in the
# system, and list them.
for camera in cm.cameras:
print(f' - {camera_name(camera)}')
# --------------------------------------------------------------------
# Camera
#
# Camera are entities created by pipeline handlers, inspecting the
# entities registered in the system and reported to applications
# by the CameraManager.
#
# In general terms, a Camera corresponds to a single image source
# available in the system, such as an image sensor.
#
# Application lock usage of Camera by 'acquiring' them.
# Once done with it, application shall similarly 'release' the Camera.
#
# As an example, use the first available camera in the system after
# making sure that at least one camera is available.
#
# Cameras can be obtained by their ID or their index, to demonstrate
# this, the following code gets the ID of the first camera; then gets
# the camera associated with that ID (which is of course the same as
# cm.cameras[0]).
if not cm.cameras:
print('No cameras were identified on the system.')
return -1
camera_id = cm.cameras[0].id
camera = cm.get(camera_id)
camera.acquire()
# --------------------------------------------------------------------
# Stream
|