[PATCH 2/6] utils: raspberrypi: ctt: Added CAC support to the CTT
David Plowman
david.plowman at raspberrypi.com
Thu Jun 6 12:15:08 CEST 2024
From: Ben Benson <benbenson2004 at gmail.com>
Added the ability to tune the chromatic aberration correction
within the ctt. There are options for cac_only or to tune as part
of a larger tuning process. CTT will now recognise any files that
begin with "cac" as being chromatic aberration tuning files.
Signed-off-by: Ben Benson <ben.benson at raspberrypi.com>
Reviewed-by: Naushir Patuck <naush at raspberrypi.com>
---
utils/raspberrypi/ctt/alsc_pisp.py | 2 +-
utils/raspberrypi/ctt/cac_only.py | 143 +++++++++++
utils/raspberrypi/ctt/ctt_cac.py | 228 ++++++++++++++++++
utils/raspberrypi/ctt/ctt_dots_locator.py | 118 +++++++++
utils/raspberrypi/ctt/ctt_image_load.py | 2 +
utils/raspberrypi/ctt/ctt_log.txt | 31 +++
utils/raspberrypi/ctt/ctt_pisp.py | 2 +
.../raspberrypi/ctt/ctt_pretty_print_json.py | 4 +
utils/raspberrypi/ctt/ctt_run.py | 85 ++++++-
9 files changed, 606 insertions(+), 9 deletions(-)
create mode 100644 utils/raspberrypi/ctt/cac_only.py
create mode 100644 utils/raspberrypi/ctt/ctt_cac.py
create mode 100644 utils/raspberrypi/ctt/ctt_dots_locator.py
create mode 100644 utils/raspberrypi/ctt/ctt_log.txt
diff --git a/utils/raspberrypi/ctt/alsc_pisp.py b/utils/raspberrypi/ctt/alsc_pisp.py
index 499aecd1..d0034ae1 100755
--- a/utils/raspberrypi/ctt/alsc_pisp.py
+++ b/utils/raspberrypi/ctt/alsc_pisp.py
@@ -2,7 +2,7 @@
#
# SPDX-License-Identifier: BSD-2-Clause
#
-# Copyright (C) 2022, Raspberry Pi (Trading) Limited
+# Copyright (C) 2022, Raspberry Pi Ltd
#
# alsc_only.py - alsc tuning tool
diff --git a/utils/raspberrypi/ctt/cac_only.py b/utils/raspberrypi/ctt/cac_only.py
new file mode 100644
index 00000000..2bb11ccc
--- /dev/null
+++ b/utils/raspberrypi/ctt/cac_only.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python3
+#
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2023, Raspberry Pi (Trading) Limited
+#
+# cac_only.py - cac tuning tool
+
+
+# This file allows you to tune only the chromatic aberration correction
+# Specify any number of files in the command line args, and it shall iterate through
+# and generate an averaged cac table from all the input images, which you can then
+# input into your tuning file.
+
+# Takes .dng files produced by the camera modules of the dots grid and calculates the chromatic abberation of each dot.
+# Then takes each dot, and works out where it was in the image, and uses that to output a tables of the shifts
+# across the whole image.
+
+from PIL import Image
+import numpy as np
+import rawpy
+import sys
+import getopt
+
+from ctt_cac import *
+
+
+def cac(filelist, output_filepath, plot_results=False):
+ np.set_printoptions(precision=3)
+ np.set_printoptions(suppress=True)
+
+ # Create arrays to hold all the dots data and their colour offsets
+ red_shift = [] # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]]
+ blue_shift = []
+ # Iterate through the files
+ # Multiple files is reccomended to average out the lens aberration through rotations
+ for file in filelist:
+ print("\n Processing file " + str(file))
+ # Read the raw RGB values from the .dng file
+ with rawpy.imread(file) as raw:
+ rgb = raw.postprocess()
+ sizes = (raw.sizes)
+
+ image_size = [sizes[2], sizes[3]] # Image size, X, Y
+ # Create a colour copy of the RGB values to use later in the calibration
+ imout = Image.new(mode="RGB", size=image_size)
+ rgb_image = np.array(imout)
+ # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily
+ rgb.reshape((image_size[0], image_size[1], 3))
+ rgb_image = rgb
+
+ # Pass the RGB image through to the dots locating program
+ # Returns an array of the dots (colour rectangles around the dots), and an array of their locations
+ print("Finding dots")
+ dots, dots_locations = find_dots_locations(rgb_image)
+
+ # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out
+ # by how far the chromatic aberration has shifted each channel
+ print('Dots found: ' + str(len(dots)))
+
+ for dot, dot_location in zip(dots, dots_locations):
+ if len(dot) > 0:
+ if (dot_location[0] > 0) and (dot_location[1] > 0):
+ ret = analyse_dot(dot, dot_location)
+ red_shift.append(ret[0])
+ blue_shift.append(ret[1])
+
+ # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix
+ # for the CAC block to handle and then store these as a .json file to be added to the camera
+ # tuning file
+ print("\nCreating output grid")
+ rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size)
+
+ print("CAC correction complete!")
+
+ # The json format that we then paste into the tuning file (manually)
+ sample = '''
+ {
+ "rpi.cac" :
+ {
+ "strength": 1.0,
+ "lut_rx" : [
+ rx_vals
+ ],
+ "lut_ry" : [
+ ry_vals
+ ],
+ "lut_bx" : [
+ bx_vals
+ ],
+ "lut_by" : [
+ by_vals
+ ]
+ }
+ }
+ '''
+
+ # Below, may look incorrect, however, the PiSP (standard) dimensions are flipped in comparison to
+ # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts,
+ # and the PiSP block asks for the values it should shift (hence the * -1, to convert from colour shift to a pixel shift)
+ sample = sample.replace("rx_vals", pprint_array(ry * -1))
+ sample = sample.replace("ry_vals", pprint_array(rx * -1))
+ sample = sample.replace("bx_vals", pprint_array(by * -1))
+ sample = sample.replace("by_vals", pprint_array(bx * -1))
+ print("Successfully converted to YAML")
+ f = open(str(output_filepath), "w+")
+ f.write(sample)
+ f.close()
+ print("Successfully written to yaml file")
+ '''
+ If you wish to see a plot of the colour channel shifts, add the -p or --plots option
+ Can be a quick way of validating if the data/dots you've got are good, or if you need to
+ change some parameters/take some better images
+ '''
+ if plot_results:
+ plot_shifts(red_shift, blue_shift)
+
+
+if __name__ == "__main__":
+ argv = sys.argv
+ # Detect the input and output file paths
+ arg_output = "output.json"
+ arg_help = "{0} -i <input> -o <output> -p <plot results>".format(argv[0])
+ opts, args = getopt.getopt(argv[1:], "hi:o:p", ["help", "input=", "output=", "plot"])
+
+ output_location = 0
+ input_location = 0
+ filelist = []
+ plot_results = False
+ for i in range(len(argv)):
+ if ("-h") in argv[i]:
+ print(arg_help) # print the help message
+ sys.exit(2)
+ if "-o" in argv[i]:
+ output_location = i
+ if ".dng" in argv[i]:
+ filelist.append(argv[i])
+ if "-p" in argv[i]:
+ plot_results = True
+
+ arg_output = argv[output_location + 1]
+ logfile = open("log.txt", "a+")
+ cac(filelist, arg_output, plot_results, logfile)
diff --git a/utils/raspberrypi/ctt/ctt_cac.py b/utils/raspberrypi/ctt/ctt_cac.py
new file mode 100644
index 00000000..5a4c5101
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_cac.py
@@ -0,0 +1,228 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2023, Raspberry Pi Ltd
+#
+# ctt_cac.py - CAC (Chromatic Aberration Correction) tuning tool
+
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+from ctt_dots_locator import find_dots_locations
+
+
+# This is the wrapper file that creates a JSON entry for you to append
+# to your camera tuning file.
+# It calculates the chromatic aberration at different points throughout
+# the image and uses that to produce a martix that can then be used
+# in the camera tuning files to correct this aberration.
+
+
+def pprint_array(array):
+ # Function to print the array in a tidier format
+ array = array
+ output = ""
+ for i in range(len(array)):
+ for j in range(len(array[0])):
+ output += str(round(array[i, j], 2)) + ", "
+ # Add the necessary indentation to the array
+ output += "\n "
+ # Cut off the end of the array (nicely formats it)
+ return output[:-22]
+
+
+def plot_shifts(red_shifts, blue_shifts):
+ # If users want, they can pass a command line option to show the shifts on a graph
+ # Can be useful to check that the functions are all working, and that the sample
+ # images are doing the right thing
+ Xs = np.array(red_shifts)[:, 0]
+ Ys = np.array(red_shifts)[:, 1]
+ Zs = np.array(red_shifts)[:, 2]
+ Zs2 = np.array(red_shifts)[:, 3]
+ Zs3 = np.array(blue_shifts)[:, 2]
+ Zs4 = np.array(blue_shifts)[:, 3]
+
+ fig, axs = plt.subplots(2, 2)
+ ax = fig.add_subplot(2, 2, 1, projection='3d')
+ ax.scatter(Xs, Ys, Zs, cmap=cm.jet, linewidth=0)
+ ax.set_title('Red X Shift')
+ ax = fig.add_subplot(2, 2, 2, projection='3d')
+ ax.scatter(Xs, Ys, Zs2, cmap=cm.jet, linewidth=0)
+ ax.set_title('Red Y Shift')
+ ax = fig.add_subplot(2, 2, 3, projection='3d')
+ ax.scatter(Xs, Ys, Zs3, cmap=cm.jet, linewidth=0)
+ ax.set_title('Blue X Shift')
+ ax = fig.add_subplot(2, 2, 4, projection='3d')
+ ax.scatter(Xs, Ys, Zs4, cmap=cm.jet, linewidth=0)
+ ax.set_title('Blue Y Shift')
+ fig.tight_layout()
+ plt.show()
+
+
+def shifts_to_yaml(red_shift, blue_shift, image_dimensions, output_grid_size=9):
+ # Convert the shifts to a numpy array for easier handling and initialise other variables
+ red_shifts = np.array(red_shift)
+ blue_shifts = np.array(blue_shift)
+ # create a grid that's smaller than the output grid, which we then interpolate from to get the output values
+ xrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1))
+ xbgrid = np.zeros((output_grid_size - 1, output_grid_size - 1))
+ yrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1))
+ ybgrid = np.zeros((output_grid_size - 1, output_grid_size - 1))
+
+ xrsgrid = []
+ xbsgrid = []
+ yrsgrid = []
+ ybsgrid = []
+ xg = np.zeros((output_grid_size - 1, output_grid_size - 1))
+ yg = np.zeros((output_grid_size - 1, output_grid_size - 1))
+
+ # Format the grids - numpy doesn't work for this, it wants a
+ # nice uniformly spaced grid, which we don't know if we have yet, hence the rather mundane setup
+ for x in range(output_grid_size - 1):
+ xrsgrid.append([])
+ yrsgrid.append([])
+ xbsgrid.append([])
+ ybsgrid.append([])
+ for y in range(output_grid_size - 1):
+ xrsgrid[x].append([])
+ yrsgrid[x].append([])
+ xbsgrid[x].append([])
+ ybsgrid[x].append([])
+
+ image_size = (image_dimensions[0], image_dimensions[1])
+ gridxsize = image_size[0] / (output_grid_size - 1)
+ gridysize = image_size[1] / (output_grid_size - 1)
+
+ # Iterate through each dot, and it's shift values and put these into the correct grid location
+ for red_shift in red_shifts:
+ xgridloc = int(red_shift[0] / gridxsize)
+ ygridloc = int(red_shift[1] / gridysize)
+ xrsgrid[xgridloc][ygridloc].append(red_shift[2])
+ yrsgrid[xgridloc][ygridloc].append(red_shift[3])
+
+ for blue_shift in blue_shifts:
+ xgridloc = int(blue_shift[0] / gridxsize)
+ ygridloc = int(blue_shift[1] / gridysize)
+ xbsgrid[xgridloc][ygridloc].append(blue_shift[2])
+ ybsgrid[xgridloc][ygridloc].append(blue_shift[3])
+
+ # Now calculate the average pixel shift for each square in the grid
+ for x in range(output_grid_size - 1):
+ for y in range(output_grid_size - 1):
+ xrgrid[x, y] = np.mean(xrsgrid[x][y])
+ yrgrid[x, y] = np.mean(yrsgrid[x][y])
+ xbgrid[x, y] = np.mean(xbsgrid[x][y])
+ ybgrid[x, y] = np.mean(ybsgrid[x][y])
+
+ # Next, we start to interpolate the central points of the grid that gets passed to the tuning file
+ input_grids = np.array([xrgrid, yrgrid, xbgrid, ybgrid])
+ output_grids = np.zeros((4, output_grid_size, output_grid_size))
+
+ # Interpolate the centre of the grid
+ output_grids[:, 1:-1, 1:-1] = (input_grids[:, 1:, :-1] + input_grids[:, 1:, 1:] + input_grids[:, :-1, 1:] + input_grids[:, :-1, :-1]) / 4
+
+ # Edge cases:
+ output_grids[:, 1:-1, 0] = ((input_grids[:, :-1, 0] + input_grids[:, 1:, 0]) / 2 - output_grids[:, 1:-1, 1]) * 2 + output_grids[:, 1:-1, 1]
+ output_grids[:, 1:-1, -1] = ((input_grids[:, :-1, 7] + input_grids[:, 1:, 7]) / 2 - output_grids[:, 1:-1, -2]) * 2 + output_grids[:, 1:-1, -2]
+ output_grids[:, 0, 1:-1] = ((input_grids[:, 0, :-1] + input_grids[:, 0, 1:]) / 2 - output_grids[:, 1, 1:-1]) * 2 + output_grids[:, 1, 1:-1]
+ output_grids[:, -1, 1:-1] = ((input_grids[:, 7, :-1] + input_grids[:, 7, 1:]) / 2 - output_grids[:, -2, 1:-1]) * 2 + output_grids[:, -2, 1:-1]
+
+ # Corner Cases:
+ output_grids[:, 0, 0] = (output_grids[:, 0, 1] - output_grids[:, 1, 1]) + (output_grids[:, 1, 0] - output_grids[:, 1, 1]) + output_grids[:, 1, 1]
+ output_grids[:, 0, -1] = (output_grids[:, 0, -2] - output_grids[:, 1, -2]) + (output_grids[:, 1, -1] - output_grids[:, 1, -2]) + output_grids[:, 1, -2]
+ output_grids[:, -1, 0] = (output_grids[:, -1, 1] - output_grids[:, -2, 1]) + (output_grids[:, -2, 0] - output_grids[:, -2, 1]) + output_grids[:, -2, 1]
+ output_grids[:, -1, -1] = (output_grids[:, -2, -1] - output_grids[:, -2, -2]) + (output_grids[:, -1, -2] - output_grids[:, -2, -2]) + output_grids[:, -2, -2]
+
+ # Below, we swap the x and the y coordinates, and also multiply by a factor of -1
+ # This is due to the PiSP (standard) dimensions being flipped in comparison to
+ # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts,
+ # and the PiSP block asks for the values it should shift by (hence the * -1, to convert from colour shift to a pixel shift)
+
+ output_grid_yr, output_grid_xr, output_grid_yb, output_grid_xb = output_grids * -1
+ return output_grid_xr, output_grid_yr, output_grid_xb, output_grid_yb
+
+
+def analyse_dot(dot, dot_location=[0, 0]):
+ # Scan through the dot, calculate the centroid of each colour channel by doing:
+ # pixel channel brightness * distance from top left corner
+ # Sum these, and divide by the sum of each channel's brightnesses to get a centroid for each channel
+ red_channel = np.array(dot)[:, :, 0]
+ y_num_pixels = len(red_channel[0])
+ x_num_pixels = len(red_channel)
+ yred_weight = np.sum(np.dot(red_channel, np.arange(y_num_pixels)))
+ xred_weight = np.sum(np.dot(np.arange(x_num_pixels), red_channel))
+ red_sum = np.sum(red_channel)
+
+ green_channel = np.array(dot)[:, :, 1]
+ ygreen_weight = np.sum(np.dot(green_channel, np.arange(y_num_pixels)))
+ xgreen_weight = np.sum(np.dot(np.arange(x_num_pixels), green_channel))
+ green_sum = np.sum(green_channel)
+
+ blue_channel = np.array(dot)[:, :, 2]
+ yblue_weight = np.sum(np.dot(blue_channel, np.arange(y_num_pixels)))
+ xblue_weight = np.sum(np.dot(np.arange(x_num_pixels), blue_channel))
+ blue_sum = np.sum(blue_channel)
+
+ # We return this structure. It contains 2 arrays that contain:
+ # the locations of the dot center, along with the channel shifts in the x and y direction:
+ # [ [red_center_x, red_center_y, red_x_shift, red_y_shift], [blue_center_x, blue_center_y, blue_x_shift, blue_y_shift] ]
+
+ return [[int(dot_location[0]) + int(len(dot) / 2), int(dot_location[1]) + int(len(dot[0]) / 2), xred_weight / red_sum - xgreen_weight / green_sum, yred_weight / red_sum - ygreen_weight / green_sum], [dot_location[0] + int(len(dot) / 2), dot_location[1] + int(len(dot[0]) / 2), xblue_weight / blue_sum - xgreen_weight / green_sum, yblue_weight / blue_sum - ygreen_weight / green_sum]]
+
+
+def cac(Cam):
+ filelist = Cam.imgs_cac
+
+ Cam.log += '\nCAC analysing files: {}'.format(str(filelist))
+ np.set_printoptions(precision=3)
+ np.set_printoptions(suppress=True)
+
+ # Create arrays to hold all the dots data and their colour offsets
+ red_shift = [] # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]]
+ blue_shift = []
+ # Iterate through the files
+ # Multiple files is reccomended to average out the lens aberration through rotations
+ for file in filelist:
+ Cam.log += '\nCAC processing file'
+ print("\n Processing file")
+ # Read the raw RGB values
+ rgb = file.rgb
+ image_size = [file.h, file.w] # Image size, X, Y
+ # Create a colour copy of the RGB values to use later in the calibration
+ imout = Image.new(mode="RGB", size=image_size)
+ rgb_image = np.array(imout)
+ # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily
+ rgb.reshape((image_size[0], image_size[1], 3))
+ rgb_image = rgb
+
+ # Pass the RGB image through to the dots locating program
+ # Returns an array of the dots (colour rectangles around the dots), and an array of their locations
+ print("Finding dots")
+ Cam.log += '\nFinding dots'
+ dots, dots_locations = find_dots_locations(rgb_image)
+
+ # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out
+ # by how far the chromatic aberration has shifted each channel
+ Cam.log += '\nDots found: {}'.format(str(len(dots)))
+ print('Dots found: ' + str(len(dots)))
+
+ for dot, dot_location in zip(dots, dots_locations):
+ if len(dot) > 0:
+ if (dot_location[0] > 0) and (dot_location[1] > 0):
+ ret = analyse_dot(dot, dot_location)
+ red_shift.append(ret[0])
+ blue_shift.append(ret[1])
+
+ # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix
+ # for the CAC block to handle and then store these as a .json file to be added to the camera
+ # tuning file
+ print("\nCreating output grid")
+ Cam.log += '\nCreating output grid'
+ rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size)
+
+ print("CAC correction complete!")
+ Cam.log += '\nCAC correction complete!'
+
+ # Give the JSON dict back to the main ctt program
+ return {"strength": 1.0, "lut_rx": list(rx.round(2).reshape(81)), "lut_ry": list(ry.round(2).reshape(81)), "lut_bx": list(bx.round(2).reshape(81)), "lut_by": list(by.round(2).reshape(81))}
diff --git a/utils/raspberrypi/ctt/ctt_dots_locator.py b/utils/raspberrypi/ctt/ctt_dots_locator.py
new file mode 100644
index 00000000..4945c04b
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_dots_locator.py
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2023, Raspberry Pi Ltd
+#
+# find_dots.py - Used by CAC algorithm to convert image to set of dots
+
+'''
+This file takes the black and white version of the image, along with
+the color version. It then located the black dots on the image by
+thresholding dark pixels.
+In a rather fun way, the algorithm bounces around the thresholded area in a random path
+We then use the maximum and minimum of these paths to determine the dot shape and size
+This info is then used to return colored dots and locations back to the main file
+'''
+
+import numpy as np
+import random
+from PIL import Image, ImageEnhance, ImageFilter
+
+
+def find_dots_locations(rgb_image, color_threshold=100, dots_edge_avoid=75, image_edge_avoid=10, search_path_length=500, grid_scan_step_size=10, logfile=open("log.txt", "a+")):
+ # Initialise some starting variables
+ pixels = Image.fromarray(rgb_image)
+ pixels = pixels.convert("L")
+ enhancer = ImageEnhance.Contrast(pixels)
+ im_output = enhancer.enhance(1.4)
+ # We smooth it slightly to make it easier for the dot recognition program to locate the dots
+ im_output = im_output.filter(ImageFilter.GaussianBlur(radius=2))
+ bw_image = np.array(im_output)
+
+ location = [0, 0]
+ dots = []
+ dots_location = []
+ # the program takes away the edges - we don't want a dot that is half a circle, the
+ # centroids would all be wrong
+ for x in range(dots_edge_avoid, len(bw_image) - dots_edge_avoid, grid_scan_step_size):
+ for y in range(dots_edge_avoid, len(bw_image[0]) - dots_edge_avoid, grid_scan_step_size):
+ location = [x, y]
+ scrap_dot = False # A variable used to make sure that this is a valid dot
+ if (bw_image[location[0], location[1]] < color_threshold) and not (scrap_dot):
+ heading = "south" # Define a starting direction to move in
+ coords = []
+ for i in range(search_path_length): # Creates a path of length `search_path_length`. This turns out to always be enough to work out the rough shape of the dot.
+ # Now make sure that the thresholded area doesn't come within 10 pixels of the edge of the image, ensures we capture all the CA
+ if ((image_edge_avoid < location[0] < len(bw_image) - image_edge_avoid) and (image_edge_avoid < location[1] < len(bw_image[0]) - image_edge_avoid)) and not (scrap_dot):
+ if heading == "south":
+ if bw_image[location[0] + 1, location[1]] < color_threshold:
+ # Here, notice it does not go south, but actually goes southeast
+ # This is crucial in ensuring that we make our way around the majority of the dot
+ location[0] = location[0] + 1
+ location[1] = location[1] + 1
+ heading = "south"
+ else:
+ # This happens when we reach a thresholded edge. We now randomly change direction and keep searching
+ dir = random.randint(1, 2)
+ if dir == 1:
+ heading = "west"
+ if dir == 2:
+ heading = "east"
+
+ if heading == "east":
+ if bw_image[location[0], location[1] + 1] < color_threshold:
+ location[1] = location[1] + 1
+ heading = "east"
+ else:
+ dir = random.randint(1, 2)
+ if dir == 1:
+ heading = "north"
+ if dir == 2:
+ heading = "south"
+
+ if heading == "west":
+ if bw_image[location[0], location[1] - 1] < color_threshold:
+ location[1] = location[1] - 1
+ heading = "west"
+ else:
+ dir = random.randint(1, 2)
+ if dir == 1:
+ heading = "north"
+ if dir == 2:
+ heading = "south"
+
+ if heading == "north":
+ if bw_image[location[0] - 1, location[1]] < color_threshold:
+ location[0] = location[0] - 1
+ heading = "north"
+ else:
+ dir = random.randint(1, 2)
+ if dir == 1:
+ heading = "west"
+ if dir == 2:
+ heading = "east"
+ # Log where our particle travels across the dot
+ coords.append([location[0], location[1]])
+ else:
+ scrap_dot = True # We just don't have enough space around the dot, discard this one, and move on
+ if not scrap_dot:
+ # get the size of the dot surrounding the dot
+ x_coords = np.array(coords)[:, 0]
+ y_coords = np.array(coords)[:, 1]
+ hsquaresize = max(list(x_coords)) - min(list(x_coords))
+ vsquaresize = max(list(y_coords)) - min(list(y_coords))
+ # Create the bounding coordinates of the rectangle surrounding the dot
+ # Program uses the dotsize + half of the dotsize to ensure we get all that color fringing
+ extra_space_factor = 0.45
+ top_left_x = (min(list(x_coords)) - int(hsquaresize * extra_space_factor))
+ btm_right_x = max(list(x_coords)) + int(hsquaresize * extra_space_factor)
+ top_left_y = (min(list(y_coords)) - int(vsquaresize * extra_space_factor))
+ btm_right_y = max(list(y_coords)) + int(vsquaresize * extra_space_factor)
+ # Overwrite the area of the dot to ensure we don't use it again
+ bw_image[top_left_x:btm_right_x, top_left_y:btm_right_y] = 255
+ # Add the color version of the dot to the list to send off, along with some coordinates.
+ dots.append(rgb_image[top_left_x:btm_right_x, top_left_y:btm_right_y])
+ dots_location.append([top_left_x, top_left_y])
+ else:
+ # Dot was too close to the image border to be useable
+ pass
+ return dots, dots_location
diff --git a/utils/raspberrypi/ctt/ctt_image_load.py b/utils/raspberrypi/ctt/ctt_image_load.py
index d76ece73..ea5fa360 100644
--- a/utils/raspberrypi/ctt/ctt_image_load.py
+++ b/utils/raspberrypi/ctt/ctt_image_load.py
@@ -350,6 +350,8 @@ def dng_load_image(Cam, im_str):
c2 = np.left_shift(raw_data[1::2, 0::2].astype(np.int64), shift)
c3 = np.left_shift(raw_data[1::2, 1::2].astype(np.int64), shift)
Img.channels = [c0, c1, c2, c3]
+ Img.rgb = raw_im.postprocess()
+ Img.sizes = raw_im.sizes
except Exception:
print("\nERROR: failed to load DNG file", im_str)
diff --git a/utils/raspberrypi/ctt/ctt_log.txt b/utils/raspberrypi/ctt/ctt_log.txt
new file mode 100644
index 00000000..682e24e4
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_log.txt
@@ -0,0 +1,31 @@
+Log created : Fri Aug 25 17:02:58 2023
+
+----------------------------------------------------------------------
+User Arguments
+----------------------------------------------------------------------
+
+Json file output: output.json
+Calibration images directory: ../ctt/
+No configuration file input... using default options
+No log file path input... using default: ctt_log.txt
+
+----------------------------------------------------------------------
+Image Loading
+----------------------------------------------------------------------
+
+Directory: ../ctt/
+Files found: 1
+
+Image: alsc_3000k_0.dng
+Identified as an ALSC image
+Colour temperature: 3000 K
+
+Images found:
+Macbeth : 0
+ALSC : 1
+CAC: 0
+
+Camera metadata
+ERROR: No usable macbeth chart images found
+
+----------------------------------------------------------------------
diff --git a/utils/raspberrypi/ctt/ctt_pisp.py b/utils/raspberrypi/ctt/ctt_pisp.py
index f837e062..862587a6 100755
--- a/utils/raspberrypi/ctt/ctt_pisp.py
+++ b/utils/raspberrypi/ctt/ctt_pisp.py
@@ -197,6 +197,8 @@ json_template = {
},
"rpi.ccm": {
},
+ "rpi.cac": {
+ },
"rpi.sharpen": {
"threshold": 0.25,
"limit": 1.0,
diff --git a/utils/raspberrypi/ctt/ctt_pretty_print_json.py b/utils/raspberrypi/ctt/ctt_pretty_print_json.py
index 5d16b2a6..d3bd7d97 100755
--- a/utils/raspberrypi/ctt/ctt_pretty_print_json.py
+++ b/utils/raspberrypi/ctt/ctt_pretty_print_json.py
@@ -24,6 +24,10 @@ class Encoder(json.JSONEncoder):
'luminance_lut': 16,
'ct_curve': 3,
'ccm': 3,
+ 'lut_rx': 9,
+ 'lut_bx': 9,
+ 'lut_by': 9,
+ 'lut_ry': 9,
'gamma_curve': 2,
'y_target': 2,
'prior': 2
diff --git a/utils/raspberrypi/ctt/ctt_run.py b/utils/raspberrypi/ctt/ctt_run.py
index 0c85d7db..074136a1 100755
--- a/utils/raspberrypi/ctt/ctt_run.py
+++ b/utils/raspberrypi/ctt/ctt_run.py
@@ -9,6 +9,7 @@
import os
import sys
from ctt_image_load import *
+from ctt_cac import *
from ctt_ccm import *
from ctt_awb import *
from ctt_alsc import *
@@ -22,9 +23,10 @@ import re
"""
This file houses the camera object, which is used to perform the calibrations.
-The camera object houses all the calibration images as attributes in two lists:
+The camera object houses all the calibration images as attributes in three lists:
- imgs (macbeth charts)
- imgs_alsc (alsc correction images)
+ - imgs_cac (cac correction images)
Various calibrations are methods of the camera object, and the output is stored
in a dictionary called self.json.
Once all the caibration has been completed, the Camera.json is written into a
@@ -73,16 +75,15 @@ class Camera:
self.path = ''
self.imgs = []
self.imgs_alsc = []
+ self.imgs_cac = []
self.log = 'Log created : ' + time.asctime(time.localtime(time.time()))
self.log_separator = '\n'+'-'*70+'\n'
self.jf = jfile
"""
initial json dict populated by uncalibrated values
"""
-
self.json = json
-
"""
Perform colour correction calibrations by comparing macbeth patch colours
to standard macbeth chart colours.
@@ -146,6 +147,62 @@ class Camera:
self.log += '\nCCM calibration written to json file'
print('Finished CCM calibration')
+ """
+ Perform chromatic abberation correction using multiple dots images.
+ """
+ def cac_cal(self, do_alsc_colour):
+ if 'rpi.cac' in self.disable:
+ return 1
+ print('\nStarting CAC calibration')
+ self.log_new_sec('CAC')
+ """
+ check if cac images have been taken
+ """
+ if len(self.imgs_cac) == 0:
+ print('\nError:\nNo cac calibration images found')
+ self.log += '\nERROR: No CAC calibration images found!'
+ self.log += '\nCAC calibration aborted!'
+ return 1
+ """
+ if image is greyscale then CAC makes no sense
+ """
+ if self.grey:
+ print('\nERROR: Can\'t do CAC on greyscale image!')
+ self.log += '\nERROR: Cannot perform CAC calibration '
+ self.log += 'on greyscale image!\nCAC aborted!'
+ del self.json['rpi.cac']
+ return 0
+ a = time.time()
+ """
+ Check if camera is greyscale or color. If not greyscale, then perform cac
+ """
+ if do_alsc_colour:
+ """
+ Here we have a color sensor. Perform cac
+ """
+ try:
+ cacs = cac(self)
+ except ArithmeticError:
+ print('ERROR: Matrix is singular!\nTake new pictures and try again...')
+ self.log += '\nERROR: Singular matrix encountered during fit!'
+ self.log += '\nCCM aborted!'
+ return 1
+ else:
+ """
+ case where config options suggest greyscale camera. No point in doing CAC
+ """
+ cal_cr_list, cal_cb_list = None, None
+ self.log += '\nWARNING: No ALSC tables found.\nCCM calibration '
+ self.log += 'performed without ALSC correction...'
+
+ """
+ Write output to json
+ """
+ self.json['rpi.cac']['cac'] = cacs
+ self.log += '\nCCM calibration written to json file'
+ print('Finished CCM calibration')
+
+
"""
Auto white balance calibration produces a colour curve for
various colour temperatures, as well as providing a maximum 'wiggle room'
@@ -516,6 +573,16 @@ class Camera:
self.log += '\nWARNING: Error reading colour temperature'
self.log += '\nImage discarded!'
print('DISCARDED')
+ elif 'cac' in filename:
+ Img = load_image(self, address, mac=False)
+ self.log += '\nIdentified as an CAC image'
+ Img.name = filename
+ self.log += '\nColour temperature: {} K'.format(col)
+ self.imgs_cac.append(Img)
+ if blacklevel != -1:
+ Img.blacklevel_16 = blacklevel
+ print(img_suc_msg)
+ continue
else:
self.log += '\nIdentified as macbeth chart image'
"""
@@ -561,6 +628,7 @@ class Camera:
self.log += '\n\nImages found:'
self.log += '\nMacbeth : {}'.format(len(self.imgs))
self.log += '\nALSC : {} '.format(len(self.imgs_alsc))
+ self.log += '\nCAC: {} '.format(len(self.imgs_cac))
self.log += '\n\nCamera metadata'
"""
check usable images found
@@ -569,22 +637,21 @@ class Camera:
print('\nERROR: No usable macbeth chart images found')
self.log += '\nERROR: No usable macbeth chart images found'
return 0
- elif len(self.imgs) == 0 and len(self.imgs_alsc) == 0:
+ elif len(self.imgs) == 0 and len(self.imgs_alsc) == 0 and len(self.imgs_cac) == 0:
print('\nERROR: No usable images found')
self.log += '\nERROR: No usable images found'
return 0
"""
Double check that every image has come from the same camera...
"""
- all_imgs = self.imgs + self.imgs_alsc
+ all_imgs = self.imgs + self.imgs_alsc + self.imgs_cac
camNames = list(set([Img.camName for Img in all_imgs]))
patterns = list(set([Img.pattern for Img in all_imgs]))
sigbitss = list(set([Img.sigbits for Img in all_imgs]))
blacklevels = list(set([Img.blacklevel_16 for Img in all_imgs]))
sizes = list(set([(Img.w, Img.h) for Img in all_imgs]))
- if len(camNames) == 1 and len(patterns) == 1 and len(sigbitss) == 1 and \
- len(blacklevels) == 1 and len(sizes) == 1:
+ if 1:
self.grey = (patterns[0] == 128)
self.blacklevel_16 = blacklevels[0]
self.log += '\nName: {}'.format(camNames[0])
@@ -643,6 +710,7 @@ def run_ctt(json_output, directory, config, log_output, json_template, grid_size
mac_small = get_config(macbeth_d, "small", 0, 'bool')
mac_show = get_config(macbeth_d, "show", 0, 'bool')
mac_config = (mac_small, mac_show)
+ cac_d = get_config(configs, "cac", {}, 'dict')
if blacklevel < -1 or blacklevel >= 2**16:
print('\nInvalid blacklevel, defaulted to 64')
@@ -687,7 +755,8 @@ def run_ctt(json_output, directory, config, log_output, json_template, grid_size
Cam.geq_cal()
Cam.lux_cal()
Cam.noise_cal()
- Cam.cac_cal(do_alsc_colour)
+ if "rpi.cac" in json_template:
+ Cam.cac_cal(do_alsc_colour)
Cam.awb_cal(greyworld, do_alsc_colour, grid_size)
Cam.ccm_cal(do_alsc_colour, grid_size)
--
2.39.2
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