image_utils.py

import bpy
import os
import time


def get_orig_render_settings():
    rs = bpy.context.scene.render
    ims = rs.image_settings

    vs = bpy.context.scene.view_settings

    orig_settings = {
        'file_format': ims.file_format,
        'quality': ims.quality,
        'color_mode': ims.color_mode,
        'compression': ims.compression,
        'exr_codec': ims.exr_codec,
        'view_transform': vs.view_transform
    }
    return orig_settings


def set_orig_render_settings(orig_settings):
    rs = bpy.context.scene.render
    ims = rs.image_settings
    vs = bpy.context.scene.view_settings

    ims.file_format = orig_settings['file_format']
    ims.quality = orig_settings['quality']
    ims.color_mode = orig_settings['color_mode']
    ims.compression = orig_settings['compression']
    ims.exr_codec = orig_settings['exr_codec']

    vs.view_transform = orig_settings['view_transform']


def img_save_as(img, filepath='//', file_format='JPEG', quality=90, color_mode='RGB', compression=15,
                view_transform='Raw', exr_codec='DWAA'):
    '''Uses Blender 'save render' to save images - BLender isn't really able so save images with other methods correctly.'''

    ors = get_orig_render_settings()

    rs = bpy.context.scene.render
    vs = bpy.context.scene.view_settings

    ims = rs.image_settings
    ims.file_format = file_format
    ims.quality = quality
    ims.color_mode = color_mode
    ims.compression = compression
    ims.exr_codec = exr_codec
    vs.view_transform = view_transform

    img.save_render(filepath=bpy.path.abspath(filepath), scene=bpy.context.scene)

    set_orig_render_settings(ors)


def set_colorspace(img, colorspace):
    '''sets image colorspace, but does so in a try statement, because some people might actually replace the default
    colorspace settings, and it literally can't be guessed what these people use, even if it will mostly be the filmic addon.
    '''
    try:
        if colorspace == 'Non-Color':
            img.colorspace_settings.is_data = True
        else:
            img.colorspace_settings.name = colorspace
    except:
        print(f'Colorspace {colorspace} not found.')

def analyze_image_is_true_hdr(image):
    import numpy
    scene = bpy.context.scene
    ui_props = scene.blenderkitUI
    size = image.size
    imageWidth = size[0]
    imageHeight = size[1]
    tempBuffer = numpy.empty(imageWidth * imageHeight * 4, dtype=numpy.float32)
    image.pixels.foreach_get(tempBuffer)
    image.blenderkit.true_hdr = numpy.amax(tempBuffer) > 1.05

def generate_hdr_thumbnail():
    import numpy
    scene = bpy.context.scene
    ui_props = scene.blenderkitUI
    hdr_image = ui_props.hdr_upload_image  # bpy.data.images.get(ui_props.hdr_upload_image)

    base, ext = os.path.splitext(hdr_image.filepath)
    thumb_path = base + '.jpg'
    thumb_name = os.path.basename(thumb_path)

    max_thumbnail_size = 2048
    size = hdr_image.size
    ratio = size[0] / size[1]

    imageWidth = size[0]
    imageHeight = size[1]
    thumbnailWidth = min(size[0], max_thumbnail_size)
    thumbnailHeight = min(size[1], int(max_thumbnail_size / ratio))

    tempBuffer = numpy.empty(imageWidth * imageHeight * 4, dtype=numpy.float32)
    inew = bpy.data.images.new(thumb_name, imageWidth, imageHeight, alpha=False, float_buffer=False)

    hdr_image.pixels.foreach_get(tempBuffer)

    hdr_image.blenderkit.true_hdr = numpy.amax(tempBuffer) > 1.05

    inew.filepath = thumb_path
    set_colorspace(inew, 'Linear')
    inew.pixels.foreach_set(tempBuffer)

    bpy.context.view_layer.update()
    if thumbnailWidth < imageWidth:
        inew.scale(thumbnailWidth, thumbnailHeight)

    img_save_as(inew, filepath=inew.filepath)


def find_color_mode(image):
    if not isinstance(image, bpy.types.Image):
        raise (TypeError)
    else:
        depth_mapping = {
            8: 'BW',
            24: 'RGB',
            32: 'RGBA',  # can also be bw.. but image.channels doesn't work.
            96: 'RGB',
            128: 'RGBA',
        }
        return depth_mapping.get(image.depth, 'RGB')


def find_image_depth(image):
    if not isinstance(image, bpy.types.Image):
        raise (TypeError)
    else:
        depth_mapping = {
            8: '8',
            24: '8',
            32: '8',  # can also be bw.. but image.channels doesn't work.
            96: '16',
            128: '16',
        }
        return depth_mapping.get(image.depth, '8')


def can_erase_alpha(na):
    alpha = na[3::4]
    alpha_sum = alpha.sum()
    if alpha_sum == alpha.size:
        print('image can have alpha erased')
    # print(alpha_sum, alpha.size)
    return alpha_sum == alpha.size


def is_image_black(na):
    r = na[::4]
    g = na[1::4]
    b = na[2::4]

    rgbsum = r.sum() + g.sum() + b.sum()

    # print('rgb sum', rgbsum, r.sum(), g.sum(), b.sum())
    if rgbsum == 0:
        print('image can have alpha channel dropped')
    return rgbsum == 0


def is_image_bw(na):
    r = na[::4]
    g = na[1::4]
    b = na[2::4]

    rg_equal = r == g
    gb_equal = g == b
    rgbequal = rg_equal.all() and gb_equal.all()
    if rgbequal:
        print('image is black and white, can have channels reduced')

    return rgbequal


def numpytoimage(a, iname, width=0, height=0, channels=3):
    t = time.time()
    foundimage = False

    for image in bpy.data.images:

        if image.name[:len(iname)] == iname and image.size[0] == a.shape[0] and image.size[1] == a.shape[1]:
            i = image
            foundimage = True
    if not foundimage:
        if channels == 4:
            bpy.ops.image.new(name=iname, width=width, height=height, color=(0, 0, 0, 1), alpha=True,
                              generated_type='BLANK', float=True)
        if channels == 3:
            bpy.ops.image.new(name=iname, width=width, height=height, color=(0, 0, 0), alpha=False,
                              generated_type='BLANK', float=True)

    i = None

    for image in bpy.data.images:
        # print(image.name[:len(iname)],iname, image.size[0],a.shape[0],image.size[1],a.shape[1])
        if image.name[:len(iname)] == iname and image.size[0] == width and image.size[1] == height:
            i = image
    if i is None:
        i = bpy.data.images.new(iname, width, height, alpha=False, float_buffer=False, stereo3d=False, is_data=False,
                                tiled=False)

    # dropping this re-shaping code -  just doing flat array for speed and simplicity
    #    d = a.shape[0] * a.shape[1]
    #    a = a.swapaxes(0, 1)
    #    a = a.reshape(d)
    #    a = a.repeat(channels)
    #    a[3::4] = 1
    i.pixels.foreach_set(a)  # this gives big speedup!
    print('\ntime ' + str(time.time() - t))
    return i


def imagetonumpy_flat(i):
    t = time.time()

    import numpy

    width = i.size[0]
    height = i.size[1]
    # print(i.channels)

    size = width * height * i.channels
    na = numpy.empty(size, numpy.float32)
    i.pixels.foreach_get(na)

    # dropping this re-shaping code -  just doing flat array for speed and simplicity
    #    na = na[::4]
    #    na = na.reshape(height, width, i.channels)
    #    na = na.swapaxnes(0, 1)

    # print('\ntime of image to numpy ' + str(time.time() - t))
    return na


def imagetonumpy(i):
    t = time.time()

    import numpy as np

    width = i.size[0]
    height = i.size[1]
    # print(i.channels)

    size = width * height * i.channels
    na = np.empty(size, np.float32)
    i.pixels.foreach_get(na)

    # dropping this re-shaping code -  just doing flat array for speed and simplicity
    # na = na[::4]
    na = na.reshape(height, width, i.channels)
    na = na.swapaxes(0, 1)

    # print('\ntime of image to numpy ' + str(time.time() - t))
    return na


def downscale(i):
    minsize = 128

    sx, sy = i.size[:]
    sx = round(sx / 2)
    sy = round(sy / 2)
    if sx > minsize and sy > minsize:
        i.scale(sx, sy)


def get_rgb_mean(i):
    '''checks if normal map values are ok.'''
    import numpy

    na = imagetonumpy_flat(i)

    r = na[::4]
    g = na[1::4]
    b = na[2::4]

    rmean = r.mean()
    gmean = g.mean()
    bmean = b.mean()

    rmedian = numpy.median(r)
    gmedian = numpy.median(g)
    bmedian = numpy.median(b)

    #    return(rmedian,gmedian, bmedian)
    return (rmean, gmean, bmean)


def check_nmap_mean_ok(i):
    '''checks if normal map values are in standard range.'''

    rmean, gmean, bmean = get_rgb_mean(i)

    # we could/should also check blue, but some ogl substance exports have 0-1, while 90% nmaps have 0.5 - 1.
    nmap_ok = 0.45 < rmean < 0.55 and .45 < gmean < .55

    return nmap_ok


def check_nmap_ogl_vs_dx(i, mask=None, generated_test_images=False):
    '''
    checks if normal map is directX or OpenGL.
    Returns - String value - DirectX and OpenGL
    '''
    import numpy
    width = i.size[0]
    height = i.size[1]

    rmean, gmean, bmean = get_rgb_mean(i)

    na = imagetonumpy(i)

    if mask:
        mask = imagetonumpy(mask)

    red_x_comparison = numpy.zeros((width, height), numpy.float32)
    green_y_comparison = numpy.zeros((width, height), numpy.float32)

    if generated_test_images:
        red_x_comparison_img = numpy.empty((width, height, 4), numpy.float32)  # images for debugging purposes
        green_y_comparison_img = numpy.empty((width, height, 4), numpy.float32)  # images for debugging purposes

    ogl = numpy.zeros((width, height), numpy.float32)
    dx = numpy.zeros((width, height), numpy.float32)

    if generated_test_images:
        ogl_img = numpy.empty((width, height, 4), numpy.float32)  # images for debugging purposes
        dx_img = numpy.empty((width, height, 4), numpy.float32)  # images for debugging purposes

    for y in range(0, height):
        for x in range(0, width):
            # try to mask with UV mask image
            if mask is None or mask[x, y, 3] > 0:

                last_height_x = ogl[max(x - 1, 0), min(y, height - 1)]
                last_height_y = ogl[max(x, 0), min(y - 1, height - 1)]

                diff_x = ((na[x, y, 0] - rmean) / ((na[x, y, 2] - 0.5)))
                diff_y = ((na[x, y, 1] - gmean) / ((na[x, y, 2] - 0.5)))
                calc_height = (last_height_x + last_height_y) \
                              - diff_x - diff_y
                calc_height = calc_height / 2
                ogl[x, y] = calc_height
                if generated_test_images:
                    rgb = calc_height * .1 + .5
                    ogl_img[x, y] = [rgb, rgb, rgb, 1]

                # green channel
                last_height_x = dx[max(x - 1, 0), min(y, height - 1)]
                last_height_y = dx[max(x, 0), min(y - 1, height - 1)]

                diff_x = ((na[x, y, 0] - rmean) / ((na[x, y, 2] - 0.5)))
                diff_y = ((na[x, y, 1] - gmean) / ((na[x, y, 2] - 0.5)))
                calc_height = (last_height_x + last_height_y) \
                              - diff_x + diff_y
                calc_height = calc_height / 2
                dx[x, y] = calc_height
                if generated_test_images:
                    rgb = calc_height * .1 + .5
                    dx_img[x, y] = [rgb, rgb, rgb, 1]

    ogl_std = ogl.std()
    dx_std = dx.std()

    # print(mean_ogl, mean_dx)
    # print(max_ogl, max_dx)
    print(ogl_std, dx_std)
    print(i.name)
    #    if abs(mean_ogl) > abs(mean_dx):
    if abs(ogl_std) > abs(dx_std):
        print('this is probably a DirectX texture')
    else:
        print('this is probably an OpenGL texture')

    if generated_test_images:
        # red_x_comparison_img = red_x_comparison_img.swapaxes(0,1)
        # red_x_comparison_img = red_x_comparison_img.flatten()
        #
        # green_y_comparison_img = green_y_comparison_img.swapaxes(0,1)
        # green_y_comparison_img = green_y_comparison_img.flatten()
        #
        # numpytoimage(red_x_comparison_img, 'red_' + i.name, width=width, height=height, channels=1)
        # numpytoimage(green_y_comparison_img, 'green_' + i.name, width=width, height=height, channels=1)

        ogl_img = ogl_img.swapaxes(0, 1)
        ogl_img = ogl_img.flatten()

        dx_img = dx_img.swapaxes(0, 1)
        dx_img = dx_img.flatten()

        numpytoimage(ogl_img, 'OpenGL', width=width, height=height, channels=1)
        numpytoimage(dx_img, 'DirectX', width=width, height=height, channels=1)

    if abs(ogl_std) > abs(dx_std):
        return 'DirectX'
    return 'OpenGL'


def make_possible_reductions_on_image(teximage, input_filepath, do_reductions=False, do_downscale=False):
    '''checks the image and saves it to drive with possibly reduced channels.
    Also can remove the image from the asset if the image is pure black
    - it finds it's usages and replaces the inputs where the image is used
    with zero/black color.
    currently implemented file type conversions:
    PNG->JPG
    '''
    colorspace = teximage.colorspace_settings.name
    teximage.colorspace_settings.name = 'Non-Color'
    # teximage.colorspace_settings.name = 'sRGB' color correction mambo jambo.

    JPEG_QUALITY = 90
    # is_image_black(na)
    # is_image_bw(na)

    rs = bpy.context.scene.render
    ims = rs.image_settings

    orig_file_format = ims.file_format
    orig_quality = ims.quality
    orig_color_mode = ims.color_mode
    orig_compression = ims.compression
    orig_depth = ims.color_depth

    # if is_image_black(na):
    #     # just erase the image from the asset here, no need to store black images.
    #     pass;

    # fp = teximage.filepath

    # setup  image depth, 8 or 16 bit.
    # this should normally divide depth with number of channels, but blender always states that number of channels is 4, even if there are only 3

    print(teximage.name)
    print(teximage.depth)
    print(teximage.channels)

    bpy.context.scene.display_settings.display_device = 'None'

    image_depth = find_image_depth(teximage)

    ims.color_mode = find_color_mode(teximage)
    # image_depth = str(max(min(int(teximage.depth / 3), 16), 8))
    print('resulting depth set to:', image_depth)

    fp = input_filepath
    if do_reductions:
        na = imagetonumpy_flat(teximage)

        if can_erase_alpha(na):
            print(teximage.file_format)
            if teximage.file_format == 'PNG':
                print('changing type of image to JPG')
                base, ext = os.path.splitext(fp)
                teximage['original_extension'] = ext

                fp = fp.replace('.png', '.jpg')
                fp = fp.replace('.PNG', '.jpg')

                teximage.name = teximage.name.replace('.png', '.jpg')
                teximage.name = teximage.name.replace('.PNG', '.jpg')

                teximage.file_format = 'JPEG'
                ims.quality = JPEG_QUALITY
                ims.color_mode = 'RGB'

            if is_image_bw(na):
                ims.color_mode = 'BW'

    ims.file_format = teximage.file_format
    ims.color_depth = image_depth

    # all pngs with max compression
    if ims.file_format == 'PNG':
        ims.compression = 100
    # all jpgs brought to reasonable quality
    if ims.file_format == 'JPG':
        ims.quality = JPEG_QUALITY

    if do_downscale:
        downscale(teximage)

    # it's actually very important not to try to change the image filepath and packed file filepath before saving,
    # blender tries to re-pack the image after writing to image.packed_image.filepath and reverts any changes.
    teximage.save_render(filepath=bpy.path.abspath(fp), scene=bpy.context.scene)
    if len(teximage.packed_files) > 0:
        teximage.unpack(method='REMOVE')
    teximage.filepath = fp
    teximage.filepath_raw = fp
    teximage.reload()

    teximage.colorspace_settings.name = colorspace

    ims.file_format = orig_file_format
    ims.quality = orig_quality
    ims.color_mode = orig_color_mode
    ims.compression = orig_compression
    ims.color_depth = orig_depth