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authorJean-Michel Hautbois <jeanmichel.hautbois@ideasonboard.com>2021-09-22 17:44:01 +0200
committerJean-Michel Hautbois <jeanmichel.hautbois@ideasonboard.com>2021-10-06 15:59:40 +0200
commit380b08754f10d6e03e96d13cac5be5e84e12835e (patch)
tree9d1424af9f8560fa102111dc57299afeda002a7d /Documentation/conf.py
parent03132d0ff9aaaa709b9db21c0e272ea8f51f3a7d (diff)
ipa: ipu3: awb: Use the line stride for the stats
The statistics buffer 'ipu3_uapi_awb_raw_buffer' stores the ImgU calculation results in a buffer aligned horizontally to a multiple of 4 cells. The AWB loop should take care of it to add the proper offset between lines and avoid any staircase effect. It is no longer required to pass the grid configuration context to the private functions called from process() which simplifies the code flow. Signed-off-by: Jean-Michel Hautbois <jeanmichel.hautbois@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Diffstat (limited to 'Documentation/conf.py')
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# 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))}