From 57b5210f37f4b6342abc9608740f946e6277eac1 Mon Sep 17 00:00:00 2001 From: Ben Benson Date: Fri, 25 Aug 2023 16:58:23 +0100 Subject: utils: raspberrypi: ctt: Added CAC support to the CTT 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 Reviewed-by: Naushir Patuck --- 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 + utils/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 (limited to 'utils/raspberrypi') 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 -o -p ".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 310c5e88..d37e9694 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 ebd1a1c5..a41b9925 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) -- cgit v1.2.1