Merge pull request #6844 from guaneec/crop-ui

from modules.textual_inversion import autocrop

def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):

    try:

        if process_caption:

            shared.interrogator.load()

        if process_caption_deepbooru:

            deepbooru.model.start()

        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)

        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)

    finally:

            splitted = image.crop((0, y, to_w, y + to_h))

        yield splitted

# not using torchvision.transforms.CenterCrop because it doesn't allow float regions

def center_crop(image: Image, w: int, h: int):

    iw, ih = image.size

    if ih / h < iw / w:

        sw = w * ih / h

        box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih

    else:

        sh = h * iw / w

        box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2

    return image.resize((w, h), Image.Resampling.LANCZOS, box)

def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):

    iw, ih = image.size

    err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))

    wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)

        if minarea <= w * h <= maxarea and err(w, h) <= threshold),

        key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],

        default=None

    )

    return wh and center_crop(image, *wh)

def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):

    width = process_width

    height = process_height

    src = os.path.abspath(process_src)

                save_pic(focal, index, params, existing_caption=existing_caption)

            process_default_resize = False

        if process_multicrop:

            cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)

            if cropped is not None:

                save_pic(cropped, index, params, existing_caption=existing_caption)

            else:

                print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")

            process_default_resize = False

        if process_default_resize:

            img = images.resize_image(1, img, width, height)

            save_pic(img, index, params, existing_caption=existing_caption)

                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")

                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")

                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")

                        process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")

                        process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")

                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")

                        process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")

                        process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")

                    with gr.Column(visible=False) as process_multicrop_col:

                        gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')

                        with gr.Row():

                            process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")

                            process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")

                        with gr.Row():

                            process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")

                            process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")

                        with gr.Row():

                            process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")

                            process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")

                    with gr.Row():

                        with gr.Column(scale=3):

                            gr.HTML(value="")

                        outputs=[process_focal_crop_row],

                    )

                    process_multicrop.change(

                        fn=lambda show: gr_show(show),

                        inputs=[process_multicrop],

                        outputs=[process_multicrop_col],

                    )

                def get_textual_inversion_template_names():

                    return sorted([x for x in textual_inversion.textual_inversion_templates])

                process_focal_crop_entropy_weight,

                process_focal_crop_edges_weight,

                process_focal_crop_debug,

                process_multicrop,

                process_multicrop_mindim,

                process_multicrop_maxdim,

                process_multicrop_minarea,

                process_multicrop_maxarea,

                process_multicrop_objective,

                process_multicrop_threshold,

            ],

            outputs=[

                ti_output,