# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi (Trading) Limited
#
# ctt_ccm.py - camera tuning tool for CCM (colour correction matrix)
from ctt_image_load import *
from ctt_awb import get_alsc_patches
"""
takes 8-bit macbeth chart values, degammas and returns 16 bit
"""
def degamma(x):
x = x / ((2**8)-1)
x = np.where(x < 0.04045, x/12.92, ((x+0.055)/1.055)**2.4)
x = x * ((2**16)-1)
return x
"""
FInds colour correction matrices for list of images
"""
def ccm(Cam,cal_cr_list,cal_cb_list):
imgs = Cam.imgs
"""
standard macbeth chart colour values
"""
m_rgb = np.array([ # these are in sRGB
[116, 81, 67], # dark skin
[199, 147, 129], # light skin
[91, 122, 156], # blue sky
[90, 108, 64], # foliage
[130, 128, 176], # blue flower
[92, 190, 172], # bluish green
[224, 124, 47], # orange
[68, 91,170], # purplish blue
[198, 82, 97], # moderate red
[94, 58, 106], # purple
[159, 189, 63], # yellow green
[230, 162, 39], # orange yellow
[35, 63, 147], # blue
[67, 149, 74], # green
[180, 49, 57], # red
[238, 198, 20], # yellow
[193, 84, 151], # magenta
[0, 136, 170], # cyan (goes out of gamut)
[245, 245, 243], # white 9.5
[200, 202, 202], # neutral 8
[161, 163, 163], # neutral 6.5
[121, 121, 122], # neutral 5
[82, 84, 86], # neutral 3.5
[49, 49, 51] # black 2
])
"""
convert reference colours from srgb to rgb
"""
m_srgb = degamma(m_rgb)
"""
reorder reference values to match how patches are ordered
"""
m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24,3))
"""
reformat alsc correction tables or set colour_cals to None if alsc is
deactivated
"""
if cal_cr_list == 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]
"""
for each image, perform awb and alsc corrections.
Then calculate the colour correction matrix for that image, recording the
ccm and the colour tempertaure.
"""
ccm_tab = {}
for Img in imgs:
Cam.log += '\nProcessing image: ' + Img.name
"""
get macbeth patches with alsc applied if alsc enabled.
Note: if alsc is disabled then colour_cals will be set to None and no
the function will simply return the macbeth patches
"""
r,b,g = get_alsc_patches(Img,colour_cals,grey=False)
"""
do awb
Note: awb is done by measuring the macbeth chart in the image, rather
than from the awb calibration. This is done so the awb will be perfect
and the ccm matrices will be more accurate.
"""
r_greys,b_greys,g_greys = r[3::4],b[3::4],g[3::4]
r_g = np.mean(r_greys/g_greys)
b_g = np.mean(b_greys/g_greys)
r = r / r_g
b = b / b_g
"""
normalise brightness wrt reference macbeth colours and then average
each channel for each patch
"""
gain = np.mean(m_srgb)/np.mean((r,g,b))
Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain)
r = np.mean(gain*r,axis=1)
b = np.mean(gain*b,axis=1)
g = np.mean(gain*g,axis=1)
"""
calculate ccm matrix
"""
ccm = do_ccm(r,g,b,m_srgb)
"""
if a ccm has already been calculated for that temperature then don't
overwrite but save both. They will then be averaged later on
"""
if Img.col in ccm_tab.keys():
ccm_tab[Img.col].append(ccm)
else:
ccm_tab[Img.col] = [ccm]
Cam.log += '\n'
Cam.log += '\nFinished processing images'
"""
average any ccms that share a colour temperature
"""
for k,v in ccm_tab.items():
tab = np.mean(v,axis=0)
tab = np.where((10000*tab)%1<=0.05,tab+0.00001,tab)
tab = np.where((10000*tab)%1>=0.95,tab-0.00001,tab)
ccm_tab[k] = list(np.round(tab,5))
Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k)
"""
return all ccms with respective colour temperature in the correct format,
sorted by their colour temperature
"""
sorted_ccms = sorted(ccm_tab.items(),key=lambda kv: kv[0])
ccms = []
for i in sorted_ccms:
ccms.append({
'ct' : i[0],
'ccm' : i[1]
})
return ccms
"""
calculates the ccm for an individual image.
ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3
matrix, each row must add up to 1 in order to conserve greyness, simplifying
calculation.
Should you want to fit them in another space (e.g. LAB) we wish you the best of
luck and send us the code when you are done! :-)
"""
def do_ccm(r,g,b,m_srgb):
rb = r-b
gb = g-b
rb_2s = (rb*rb)
rb_gbs = (rb*gb)
gb_2s = (gb*gb)
r_rbs = ( rb * (m_srgb[...,0] - b) )
r_gbs = ( gb * (m_srgb[...,0] - b) )
g_rbs = ( rb * (m_srgb[...,1] - b) )
g_gbs = ( gb * (m_srgb[...,1] - b) )
b_rbs = ( rb * (m_srgb[...,2] - b) )
b_gbs = ( gb * (m_srgb[...,2] - b) )
"""
Obtain least squares fit
"""
rb_2 = np.sum(rb_2s)
gb_2 = np.sum(gb_2s)
rb_gb = np.sum(rb_gbs)
r_rb = np.sum(r_rbs)
r_gb = np.sum(r_gbs)
g_rb = np.sum(g_rbs)
g_gb = np.sum(g_gbs)
b_rb = np.sum(b_rbs)
b_gb = np.sum(b_gbs)
det = rb_2*gb_2 - rb_gb*rb_gb
"""
Raise error if matrix is singular...
This shouldn't really happen with real data but if it does just take new
pictures and try again, not much else to be done unfortunately...
"""
if det < 0.001:
raise ArithmeticError
r_a = (gb_2*r_rb - rb_gb*r_gb)/det
r_b = (rb_2*r_gb - rb_gb*r_rb)/det
"""
Last row can be calculated by knowing the sum must be 1
"""
r_c = 1 - r_a - r_b
g_a = (gb_2*g_rb - rb_gb*g_gb)/det
g_b = (rb_2*g_gb - rb_gb*g_rb)/det
g_c = 1 - g_a - g_b
b_a = (gb_2*b_rb - rb_gb*b_gb)/det
b_b = (rb_2*b_gb - rb_gb*b_rb)/det
b_c = 1 - b_a - b_b
"""
format ccm
"""
ccm = [r_a,r_b,r_c,g_a,g_b,g_c,b_a,b_b,b_c]
return ccm
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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 capture example extending the simple-capture.py example:
# - Capture frames using events from multiple cameras
# - Listening events from stdin to exit the application
# - Memory mapping the frames and calculating CRC
import binascii
import libcamera as libcam
import libcamera.utils
import selectors
import sys
# A container class for our state per camera
class CameraCaptureContext:
idx: int
cam: libcam.Camera
reqs: list[libcam.Request]
mfbs: dict[libcam.FrameBuffer, libcamera.utils.MappedFrameBuffer]
def __init__(self, cam, idx):
self.idx = idx
self.cam = cam
# Acquire the camera for our use
cam.acquire()
# Configure the camera
cam_config = cam.generate_configuration([libcam.StreamRole.Viewfinder])
stream_config = cam_config.at(0)
cam.configure(cam_config)
stream = stream_config.stream
# Allocate the buffers for capture
allocator = libcam.FrameBufferAllocator(cam)
ret = allocator.allocate(stream)
assert ret > 0
num_bufs = len(allocator.buffers(stream))
print(f'cam{idx} ({cam.id}): capturing {num_bufs} buffers with {stream_config}')
# Create the requests and assign a buffer for each request
self.reqs = []
self.mfbs = {}
for i in range(num_bufs):
# Use the buffer index as the "cookie"
req = cam.create_request(idx)
buffer = allocator.buffers(stream)[i]
req.add_buffer(stream, buffer)
self.reqs.append(req)
# Save a mmapped buffer so we can calculate the CRC later
self.mfbs[buffer] = libcamera.utils.MappedFrameBuffer(buffer).mmap()
def uninit_camera(self):
# Stop the camera
self.cam.stop()
# Release the camera
self.cam.release()
# A container class for our state
class CaptureContext:
cm: libcam.CameraManager
camera_contexts: list[CameraCaptureContext] = []
def handle_camera_event(self):
# 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 = self.cm.get_ready_requests()
# Process the captured frames
for req in reqs:
self.handle_request(req)
return True
def handle_request(self, req: libcam.Request):
cam_ctx = self.camera_contexts[req.cookie]
buffers = req.buffers
assert len(buffers) == 1
# A ready Request could contain multiple buffers if multiple streams
# were being used. Here we know we only have a single stream,
# and we use next(iter()) to get the first and only buffer.
stream, fb = next(iter(buffers.items()))
# Use the MappedFrameBuffer to access the pixel data with CPU. We calculate
# the crc for each plane.
mfb = cam_ctx.mfbs[fb]
crcs = [binascii.crc32(p) for p in mfb.planes]
meta = fb.metadata
print('cam{:<6} seq {:<6} bytes {:10} CRCs {}'
.format(cam_ctx.idx,
meta.sequence,
'/'.join([str(p.bytes_used) for p in meta.planes]),
crcs))
# We want to re-queue the buffer we just handled. Instead of creating
# a new Request, we re-use the old one. We need to call req.reuse()
# to re-initialize the Request before queuing.
req.reuse()
cam_ctx.cam.queue_request(req)
def handle_key_event(self):
sys.stdin.readline()
print('Exiting...')
return False
def capture(self):
# Queue the requests to the camera
for cam_ctx in self.camera_contexts:
for req in cam_ctx.reqs:
cam_ctx.cam.queue_request(req)
# Use Selector to wait for events from the camera and from the keyboard
sel = selectors.DefaultSelector()
sel.register(sys.stdin, selectors.EVENT_READ, self.handle_key_event)
sel.register(self.cm.event_fd, selectors.EVENT_READ, lambda: self.handle_camera_event())
running = True
while running:
events = sel.select()
for key, mask in events:
# If the handler return False, we should exit
if not key.data():
running = False
def main():
cm = libcam.CameraManager.singleton()
ctx = CaptureContext()
ctx.cm = cm
for idx, cam in enumerate(cm.cameras):
cam_ctx = CameraCaptureContext(cam, idx)
ctx.camera_contexts.append(cam_ctx)
# Start the cameras
for cam_ctx in ctx.camera_contexts:
cam_ctx.cam.start()
ctx.capture()
for cam_ctx in ctx.camera_contexts:
cam_ctx.uninit_camera()
return 0
if __name__ == '__main__':
sys.exit(main())
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