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authorJacopo Mondi <jacopo@jmondi.org>2019-10-22 01:52:08 +0200
committerLaurent Pinchart <laurent.pinchart@ideasonboard.com>2019-11-20 21:47:49 +0200
commit69bb53aca1cdb5861fd19b3b1345d20e4139e938 (patch)
treeb2ecc0b5ffc2c898b32993035da276e8e3c0ef9c /test/log/meson.build
parent4e5e352f3b43fed96987a78eae90f144f26ed7a1 (diff)
libcamera: Add ByteStreamBuffer
The ByteStreamBuffer class wraps a memory area, expected to be allocated by the user of the class and provides operations to perform sequential access in read and write modes. Signed-off-by: Jacopo Mondi <jacopo@jmondi.org> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
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generated by cgit v1.2.1 (git 2.18.0) at 2024-12-23 22:29:09 +0000
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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')