gradio_demo/dust3r/utils/image.py

# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# utilitary functions about images (loading/converting...)
# --------------------------------------------------------
import os
import torch
import numpy as np
import PIL.Image
from PIL.ImageOps import exif_transpose
import torchvision.transforms as tvf
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2  # noqa

import rembg
rembg_session = rembg.new_session()

import time
from PIL import Image
from rembg import remove
from segment_anything import sam_model_registry, SamPredictor
def sam_init():
    sam_checkpoint = os.path.join("./sam_pt/sam_vit_h_4b8939.pth")
    if os.path.exists(sam_checkpoint) is False:
        os.system("wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -P ./sam_pt/")
    model_type = "vit_h"

    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{0 if torch.cuda.is_available() else 'cpu'}")
    predictor = SamPredictor(sam)
    return predictor

def sam_segment(predictor, input_image, *bbox_coords):
    bbox = np.array(bbox_coords)
    image = np.asarray(input_image)

    start_time = time.time()
    predictor.set_image(image)

    masks_bbox, scores_bbox, logits_bbox = predictor.predict(
        box=bbox,
        multimask_output=True
    )

    print(f"SAM Time: {time.time() - start_time:.3f}s")
    out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
    out_image[:, :, :3] = image
    out_image_bbox = out_image.copy()
    out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
    torch.cuda.empty_cache()
    return Image.fromarray(out_image_bbox, mode='RGBA')

predictor = sam_init()

try:
    from pillow_heif import register_heif_opener  # noqa
    register_heif_opener()
    heif_support_enabled = True
except ImportError:
    heif_support_enabled = False

ImgNorm = tvf.Compose([tvf.ToTensor(), tvf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


def imread_cv2(path, options=cv2.IMREAD_COLOR):
    """ Open an image or a depthmap with opencv-python.
    """
    if path.endswith(('.exr', 'EXR')):
        options = cv2.IMREAD_ANYDEPTH
    img = cv2.imread(path, options)
    if img is None:
        raise IOError(f'Could not load image={path} with {options=}')
    if img.ndim == 3:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img


def rgb(ftensor, true_shape=None):
    if isinstance(ftensor, list):
        return [rgb(x, true_shape=true_shape) for x in ftensor]
    if isinstance(ftensor, torch.Tensor):
        ftensor = ftensor.detach().cpu().numpy()  # H,W,3
    if ftensor.ndim == 3 and ftensor.shape[0] == 3:
        ftensor = ftensor.transpose(1, 2, 0)
    elif ftensor.ndim == 4 and ftensor.shape[1] == 3:
        ftensor = ftensor.transpose(0, 2, 3, 1)
    if true_shape is not None:
        H, W = true_shape
        ftensor = ftensor[:H, :W]
    if ftensor.dtype == np.uint8:
        img = np.float32(ftensor) / 255
    else:
        img = (ftensor * 0.5) + 0.5
    return img.clip(min=0, max=1)


def _resize_pil_image(img, long_edge_size):
    S = max(img.size)
    if S > long_edge_size:
        interp = PIL.Image.LANCZOS
    elif S <= long_edge_size:
        interp = PIL.Image.BICUBIC
    new_size = tuple(int(round(x*long_edge_size/S)) for x in img.size)
    return img.resize(new_size, interp)

def load_images(folder_or_list, size, square_ok=False, verbose=True, do_remove_background=True, rembg_session=None):
    """ open and convert all images in a list or folder to proper input format for DUSt3R
    """
    if isinstance(folder_or_list, str):
        if verbose:
            print(f'>> Loading images from {folder_or_list}')
        root, folder_content = folder_or_list, sorted(os.listdir(folder_or_list))

    elif isinstance(folder_or_list, list):
        if verbose:
            print(f'>> Loading a list of {len(folder_or_list)} images')
        root, folder_content = '', folder_or_list

    else:
        raise ValueError(f'bad {folder_or_list=} ({type(folder_or_list)})')

    supported_images_extensions = ['.jpg', '.jpeg', '.png']
    if heif_support_enabled:
        supported_images_extensions += ['.heic', '.heif']
    supported_images_extensions = tuple(supported_images_extensions)

    imgs = []
    imgs_rgba = []
    for path in folder_content:
        if not path.lower().endswith(supported_images_extensions):
            continue
        img = exif_transpose(PIL.Image.open(os.path.join(root, path))).convert('RGB')
        # remove background if needed
        if do_remove_background:
            # if rembg_session is None:
            #     rembg_session = rembg.new_session()
            # image = rembg.remove(img, session=rembg_session)
            # foreground = np.array(image)[..., -1] > 127

            image_nobg = remove(img, alpha_matting=True, session=rembg_session)
            arr = np.asarray(image_nobg)[:, :, -1]
            x_nonzero = np.nonzero(arr.sum(axis=0))
            y_nonzero = np.nonzero(arr.sum(axis=1))
            x_min = int(x_nonzero[0].min())
            y_min = int(y_nonzero[0].min())
            x_max = int(x_nonzero[0].max())
            y_max = int(y_nonzero[0].max())
            input_image = sam_segment(predictor, img.convert('RGB'), x_min, y_min, x_max, y_max)
            foreground = np.array(input_image)[..., -1] > 127
        else:
            foreground = img[..., -1] > 127
        W1, H1 = img.size
        if size == 224:
            # resize short side to 224 (then crop)
            img = _resize_pil_image(img, round(size * max(W1/H1, H1/W1)))
            # resize foreground mask
            foreground = cv2.resize(foreground.astype(np.uint8), img.size, interpolation=cv2.INTER_NEAREST)
        else:
            # resize long side to 512
            img = _resize_pil_image(img, size)
            # resize foreground mask
            foreground = cv2.resize(foreground.astype(np.uint8), img.size, interpolation=cv2.INTER_NEAREST)
        W, H = img.size
        cx, cy = W//2, H//2
        if size == 224:
            half = min(cx, cy)
            img = img.crop((cx-half, cy-half, cx+half, cy+half))
            # foreground crop
            foreground = foreground[cy-half:cy+half, cx-half:cx+half]
        else:
            halfw, halfh = ((2*cx)//16)*8, ((2*cy)//16)*8
            if not (square_ok) and W == H:
                halfh = 3*halfw/4
            img = img.crop((cx-halfw, cy-halfh, cx+halfw, cy+halfh))
            # foreground crop
            foreground = foreground[cy-halfh:cy+halfh, cx-halfw:cx+halfw]

        W2, H2 = img.size
        if verbose:
            print(f' - adding {path} with resolution {W1}x{H1} --> {W2}x{H2}')
        imgs.append(dict(img=ImgNorm(img)[None], true_shape=np.int32(
            [img.size[::-1]]), idx=len(imgs), instance=str(len(imgs))))
        imgs_rgba.append(PIL.Image.fromarray((255*np.concatenate([np.array(img)/255.0, foreground[..., None]], axis=-1)).astype(np.uint8)))
    assert imgs, 'no images foud at '+root
    if verbose:
        print(f' (Found {len(imgs)} images)')
    return imgs, imgs_rgba