climategan_wrapper.py

# based on https://huggingface.co/spaces/NimaBoscarino/climategan/blob/main/inferences.py # noqa: E501
# thank you @NimaBoscarino

import os
import re
from pathlib import Path
from uuid import uuid4
from minydra import resolved_args
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from skimage.color import rgba2rgb
from skimage.transform import resize

from climategan.trainer import Trainer


CUDA = torch.cuda.is_available()


def concat_events(output_dict, events, i=None, axis=1):
    """
    Concatenates the `i`th data in `output_dict` according to the keys listed
    in `events` on dimension `axis`.

    Args:
        output_dict (dict[Union[list[np.array], np.array]]): A dictionary mapping
            events to their corresponding data :
            {k: [HxWxC]} (for i != None) or {k: BxHxWxC}.
        events (list[str]): output_dict's keys to concatenate.
        axis (int, optional): Concatenation axis. Defaults to 1.
    """
    cs = [e for e in events if e in output_dict]
    if i is not None:
        return uint8(np.concatenate([output_dict[c][i] for c in cs], axis=axis))
    return uint8(np.concatenate([output_dict[c] for c in cs], axis=axis))


def clear(folder):
    """
    Deletes all the images without the inference separator "---" in their name.

    Args:
        folder (Union[str, Path]): The folder to clear.
    """
    for i in list(Path(folder).iterdir()):
        if i.is_file() and "---" in i.stem:
            i.unlink()


def uint8(array, rescale=False):
    """
    convert an array to np.uint8 (does not rescale or anything else than changing dtype)
    Args:
        array (np.array): array to modify
    Returns:
        np.array(np.uint8): converted array
    """
    if rescale:
        if array.min() < 0:
            if array.min() >= -1 and array.max() <= 1:
                array = (array + 1) / 2
            else:
                raise ValueError(
                    f"Data range mismatch for image: ({array.min()}, {array.max()})"
                )
        if array.max() <= 1:
            array = array * 255
    return array.astype(np.uint8)


def resize_and_crop(img, to=640):
    """
    Resizes an image so that it keeps the aspect ratio and the smallest dimensions
    is `to`, then crops this resized image in its center so that the output is `to x to`
    without aspect ratio distortion
    Args:
        img (np.array): np.uint8 255 image
    Returns:
        np.array: [0, 1] np.float32 image
    """
    # resize keeping aspect ratio: smallest dim is 640
    h, w = img.shape[:2]
    if h < w:
        size = (to, int(to * w / h))
    else:
        size = (int(to * h / w), to)

    r_img = resize(img, size, preserve_range=True, anti_aliasing=True)
    r_img = uint8(r_img)

    # crop in the center
    H, W = r_img.shape[:2]

    top = (H - to) // 2
    left = (W - to) // 2

    rc_img = r_img[top : top + to, left : left + to, :]

    return rc_img / 255.0


def to_m1_p1(img):
    """
    rescales a [0, 1] image to [-1, +1]
    Args:
        img (np.array): float32 numpy array of an image in [0, 1]
        i (int): Index of the image being rescaled
    Raises:
        ValueError: If the image is not in [0, 1]
    Returns:
        np.array(np.float32): array in [-1, +1]
    """
    if img.min() >= 0 and img.max() <= 1:
        return (img.astype(np.float32) - 0.5) * 2
    raise ValueError(f"Data range mismatch for image: ({img.min()}, {img.max()})")


# No need to do any timing in this, since it's just for the HF Space
class ClimateGAN:
    def __init__(self, model_path, dev_mode=False) -> None:
        """
        A wrapper for the ClimateGAN model that you can use to generate
        events from images or folders containing images.

        Args:
            model_path (Union[str, Path]): Where to load the Masker from
        """
        torch.set_grad_enabled(False)
        self.target_size = 640
        self._stable_diffusion_is_setup = False
        self.dev_mode = dev_mode
        if self.dev_mode:
            return
        self.trainer = Trainer.resume_from_path(
            model_path,
            setup=True,
            inference=True,
            new_exp=None,
        )
        if CUDA:
            self.trainer.G.half()

    def _setup_stable_diffusion(self):
        """
        Sets up the stable diffusion pipeline for in-painting.
        Make sure you have accepted the license on the model's card
        https://huggingface.co/CompVis/stable-diffusion-v1-4
        """
        if self.dev_mode:
            return

        try:
            self.sdip_pipeline = StableDiffusionInpaintPipeline.from_pretrained(
                "runwayml/stable-diffusion-inpainting",
                revision="fp16" if CUDA else "main",
                torch_dtype=torch.float16 if CUDA else torch.float32,
                safety_checker=None,
                use_auth_token=os.environ.get("HF_AUTH_TOKEN"),
            ).to(self.trainer.device)
            self._stable_diffusion_is_setup = True
        except Exception as e:
            print(
                "\nCould not load stable diffusion model. "
                + "Please make sure you have accepted the license on the model's"
                + " card https://huggingface.co/CompVis/stable-diffusion-v1-4\n"
            )
            raise e

    def _preprocess_image(self, img):
        """
        Turns a HxWxC uint8 numpy array into a 640x640x3 float32 numpy array
        in [-1, 1].

        Args:
            img (np.array): Image to resize crop and rescale

        Returns:
            np.array: Resized, cropped and rescaled image
        """
        # rgba to rgb
        data = img if img.shape[-1] == 3 else uint8(rgba2rgb(img) * 255)

        # to args.target_size
        data = resize_and_crop(data, self.target_size)

        # resize() produces [0, 1] images, rescale to [-1, 1]
        data = to_m1_p1(data)
        return data

    # Does all three inferences at the moment.
    def infer_single(
        self,
        orig_image,
        painter="both",
        prompt="An HD picture of a street with dirty water after a heavy flood",
        concats=[
            "input",
            "masked_input",
            "climategan_flood",
            "stable_flood",
            "stable_copy_flood",
        ],
        as_pil_image=False,
    ):
        """
        Infers the image with the ClimateGAN model.
        Importantly (and unlike self.infer_preprocessed_batch), the image is
        pre-processed by self._preprocess_image before going through the networks.

        Output dict contains the following keys:
        - "input": The input image
        - "mask": The mask used to generate the flood (from ClimateGAN's Masker)
        - "masked_input": The input image with the mask applied
        - "climategan_flood": The flooded image generated by ClimateGAN's Painter
            on the masked input (only if "painter" is "climategan" or "both").
        - "stable_flood": The flooded image in-painted by the stable diffusion model
            from the mask and the input image (only if "painter" is "stable_diffusion"
            or "both").
        - "stable_copy_flood": The flooded image in-painted by the stable diffusion
            model with its original context pasted back in:
            y = m * flooded + (1-m) * input
            (only if "painter" is "stable_diffusion" or "both").

        Args:
            orig_image (Union[str, np.array]): image to infer on. Can be a path to
                an image which will be read.
            painter (str, optional): Which painter to use: "climategan",
                "stable_diffusion" or "both". Defaults to "both".
            prompt (str, optional): The prompt used to guide the diffusion. Defaults
                to "An HD picture of a street with dirty water after a heavy flood".
            concats (list, optional): List of keys in `output` to concatenate together
                in a new `{original_stem}_concat` image written. Defaults to:
                ["input", "masked_input", "climategan_flood", "stable_flood",
                "stable_copy_flood"].

        Returns:
            dict: a dictionary containing the output images {k: HxWxC}. C is omitted
                for masks (HxW).
        """
        if self.dev_mode:
            return {
                "input": orig_image,
                "mask": np.random.randint(0, 255, (640, 640)),
                "masked_input": np.random.randint(0, 255, (640, 640, 3)),
                "climategan_flood": np.random.randint(0, 255, (640, 640, 3)),
                "stable_flood": np.random.randint(0, 255, (640, 640, 3)),
                "stable_copy_flood": np.random.randint(0, 255, (640, 640, 3)),
                "concat": np.random.randint(0, 255, (640, 640 * 5, 3)),
                "smog": np.random.randint(0, 255, (640, 640, 3)),
                "wildfire": np.random.randint(0, 255, (640, 640, 3)),
                "depth": np.random.randint(0, 255, (640, 640, 1)),
                "segmentation": np.random.randint(0, 255, (640, 640, 3)),
            }
            return

        image_array = (
            np.array(Image.open(orig_image))
            if isinstance(orig_image, str)
            else orig_image
        )

        pil_image = None
        if as_pil_image:
            pil_image = Image.fromarray(image_array)
        print("Preprocessing image")
        image = self._preprocess_image(image_array)
        output_dict = self.infer_preprocessed_batch(
            images=image[None, ...],
            painter=painter,
            prompt=prompt,
            concats=concats,
            pil_image=pil_image,
        )
        print("Inference done")
        return {k: v[0] for k, v in output_dict.items()}

    def infer_preprocessed_batch(
        self,
        images,
        painter="both",
        prompt="An HD picture of a street with dirty water after a heavy flood",
        concats=[
            "input",
            "masked_input",
            "climategan_flood",
            "stable_flood",
            "stable_copy_flood",
        ],
        pil_image=None,
    ):
        """
        Infers ClimateGAN predictions on a batch of preprocessed images.
        It assumes that each image in the batch has been preprocessed with
        self._preprocess_image().

        Output dict contains the following keys:
        - "input": The input image
        - "mask": The mask used to generate the flood (from ClimateGAN's Masker)
        - "masked_input": The input image with the mask applied
        - "climategan_flood": The flooded image generated by ClimateGAN's Painter
            on the masked input (only if "painter" is "climategan" or "both").
        - "stable_flood": The flooded image in-painted by the stable diffusion model
            from the mask and the input image (only if "painter" is "stable_diffusion"
            or "both").
        - "stable_copy_flood": The flooded image in-painted by the stable diffusion
            model with its original context pasted back in:
            y = m * flooded + (1-m) * input
            (only if "painter" is "stable_diffusion" or "both").

        Args:
            images (np.array): A batch of input images BxHxWx3
            painter (str, optional): Which painter to use: "climategan",
                "stable_diffusion" or "both". Defaults to "both".
            prompt (str, optional): The prompt used to guide the diffusion. Defaults
                to "An HD picture of a street with dirty water after a heavy flood".
            concats (list, optional): List of keys in `output` to concatenate together
                in a new `{original_stem}_concat` image written. Defaults to:
                ["input", "masked_input", "climategan_flood", "stable_flood",
                "stable_copy_flood"].
            pil_image (PIL.Image, optional): The original PIL image. If provided,
                will be used for a single inference (batch_size=1)

        Returns:
            dict: a dictionary containing the output images
        """
        assert painter in [
            "both",
            "stable_diffusion",
            "climategan",
        ], f"Unknown painter: {painter}"

        ignore_event = set()
        if painter == "stable_diffusion":
            ignore_event.add("flood")

        if pil_image is not None:
            print("Warning: `pil_image` has been provided, it will override `images`")
            images = self._preprocess_image(np.array(pil_image))[None, ...]
            pil_image = Image.fromarray(((images[0] + 1) / 2 * 255).astype(np.uint8))

        # Retrieve numpy events as a dict {event: array[BxHxWxC]}
        print("Inferring ClimateGAN events")
        outputs = self.trainer.infer_all(
            images,
            numpy=True,
            bin_value=0.5,
            half=CUDA,
            ignore_event=ignore_event,
            return_intermediates=True,
        )

        outputs["input"] = uint8(images, True)
        # from Bx1xHxW to BxHxWx1
        outputs["masked_input"] = outputs["input"] * (
            outputs["mask"].squeeze(1)[..., None] == 0
        )

        if painter in {"both", "climategan"}:
            outputs["climategan_flood"] = outputs.pop("flood")
        else:
            del outputs["flood"]

        if painter != "climategan":
            if not self._stable_diffusion_is_setup:
                print("Setting up stable diffusion in-painting pipeline")
                self._setup_stable_diffusion()

            mask = outputs["mask"].squeeze(1)
            input_images = (
                torch.tensor(images).permute(0, 3, 1, 2).to(self.trainer.device)
                if pil_image is None
                else pil_image
            )
            input_mask = (
                torch.tensor(mask[:, None, ...] > 0).to(self.trainer.device)
                if pil_image is None
                else Image.fromarray(mask[0])
            )
            print("Inferring stable diffusion in-painting for 50 steps")
            floods = self.sdip_pipeline(
                prompt=[prompt] * images.shape[0],
                image=input_images,
                mask_image=input_mask,
                height=640,
                width=640,
                num_inference_steps=50,
            )
            print("Stable diffusion in-painting done")

            bin_mask = mask[..., None] > 0
            flood = np.stack([np.array(i) for i in floods.images])
            copy_flood = flood * bin_mask + uint8(images, True) * (1 - bin_mask)
            outputs["stable_flood"] = flood
            outputs["stable_copy_flood"] = copy_flood

        if concats:
            print("Concatenating flood images")
            outputs["concat"] = concat_events(outputs, concats, axis=2)

        return {k: v.squeeze(1) if v.shape[1] == 1 else v for k, v in outputs.items()}

    def infer_folder(
        self,
        folder_path,
        painter="both",
        prompt="An HD picture of a street with dirty water after a heavy flood",
        batch_size=4,
        concats=[
            "input",
            "masked_input",
            "climategan_flood",
            "stable_flood",
            "stable_copy_flood",
        ],
        write=True,
        overwrite=False,
    ):
        """
        Infers the images in a folder with the ClimateGAN model, batching images for
        inference according to the batch_size.

        Images must end in .jpg, .jpeg or .png (not case-sensitive).
        Images must not contain the separator ("---") in their name.

        Images will be written to disk in the same folder as the input images, with
        a name that depends on its data, potentially the prompt and a random
        identifier in case multiple inferences are run in the folder.

        Output dict contains the following keys:
        - "input": The input image
        - "mask": The mask used to generate the flood (from ClimateGAN's Masker)
        - "masked_input": The input image with the mask applied
        - "climategan_flood": The flooded image generated by ClimateGAN's Painter
            on the masked input (only if "painter" is "climategan" or "both").
        - "stable_flood": The flooded image in-painted by the stable diffusion model
            from the mask and the input image (only if "painter" is "stable_diffusion"
            or "both").
        - "stable_copy_flood": The flooded image in-painted by the stable diffusion
            model with its original context pasted back in:
            y = m * flooded + (1-m) * input
            (only if "painter" is "stable_diffusion" or "both").

        Args:
            folder_path (Union[str, Path]): Where to read images from.
            painter (str, optional): Which painter to use: "climategan",
                "stable_diffusion" or "both". Defaults to "both".
            prompt (str, optional): The prompt used to guide the diffusion. Defaults
                to "An HD picture of a street with dirty water after a heavy flood".
            batch_size (int, optional): Size of inference batches. Defaults to 4.
            concats (list, optional): List of keys in `output` to concatenate together
                in a new `{original_stem}_concat` image written. Defaults to:
                ["input", "masked_input", "climategan_flood", "stable_flood",
                "stable_copy_flood"].
            write (bool, optional): Whether or not to write the outputs to the input
                folder.Defaults to True.
            overwrite (Union[bool, str], optional): Whether to overwrite the images or
                not. If a string is provided, it will be included in the name.
                Defaults to False.

        Returns:
            dict: a dictionary containing the output images
        """
        folder_path = Path(folder_path).expanduser().resolve()
        assert folder_path.exists(), f"Folder {str(folder_path)} does not exist"
        assert folder_path.is_dir(), f"{str(folder_path)} is not a directory"
        im_paths = [
            p
            for p in folder_path.iterdir()
            if p.suffix.lower() in [".jpg", ".png", ".jpeg"] and "---" not in p.name
        ]
        assert im_paths, f"No images found in {str(folder_path)}"
        ims = [self._preprocess_image(np.array(Image.open(p))) for p in im_paths]
        batches = [
            np.stack(ims[i : i + batch_size]) for i in range(0, len(ims), batch_size)
        ]
        inferences = [
            self.infer_preprocessed_batch(b, painter, prompt, concats) for b in batches
        ]

        outputs = {
            k: [i for e in inferences for i in e[k]] for k in inferences[0].keys()
        }

        if write:
            self.write(outputs, im_paths, painter, overwrite, prompt)

        return outputs

    def write(
        self,
        outputs,
        im_paths,
        painter="both",
        overwrite=False,
        prompt="",
    ):
        """
        Writes the outputs of the inference to disk, in the input folder.

        Images will be named like:
        f"{original_stem}---{overwrite_prefix}_{painter_type}_{output_type}.{suffix}"
        `painter_type` is either "climategan" or f"stable_diffusion_{prompt}"

        Args:
            outputs (_type_): The inference procedure's output dict.
            im_paths (list[Path]): The list of input images paths.
            painter (str, optional): Which painter was used. Defaults to "both".
            overwrite (bool, optional): Whether to overwrite the images or not.
                If a string is provided, it will be included in the name.
                If False, a random identifier will be added to the name.
                Defaults to False.
            prompt (str, optional): The prompt used to guide the diffusion. Defaults
                to "".
        """
        prompt = re.sub("[^0-9a-zA-Z]+", "", prompt).lower()
        overwrite_prefix = ""
        if not overwrite:
            overwrite_prefix = str(uuid4())[:8]
            print("Writing events with prefix", overwrite_prefix)
        else:
            if isinstance(overwrite, str):
                overwrite_prefix = overwrite
                print("Writing events with prefix", overwrite_prefix)

        # for each image, for each event/data type
        for i, im_path in enumerate(im_paths):
            for event, ims in outputs.items():
                painter_prefix = ""
                if painter == "climategan" and event == "flood":
                    painter_prefix = "climategan"
                elif (
                    painter in {"stable_diffusion", "both"} and event == "stable_flood"
                ):
                    painter_prefix = f"_stable_{prompt}"
                elif painter == "both" and event == "climategan_flood":
                    painter_prefix = ""

                im = ims[i]
                im = Image.fromarray(uint8(im))
                imstem = f"{im_path.stem}---{overwrite_prefix}{painter_prefix}_{event}"
                im.save(im_path.parent / (imstem + im_path.suffix))


if __name__ == "__main__":
    print("Run `$ python climategan_wrapper.py help` for usage instructions\n")

    # parse arguments
    args = resolved_args(
        defaults={
            "input_folder": None,
            "output_folder": None,
            "painter": "both",
            "help": False,
        }
    )

    # print help
    if args.help:
        print(
            "Usage: python inference.py input_folder=/path/to/folder\n"
            + "By default inferences will be stored in the input folder.\n"
            + "Add `output_folder=/path/to/folder` for a different output folder.\n"
            + "By default, both ClimateGAN and Stable Diffusion will be used."
            + "Change this by adding `painter=climategan` or"
            + " `painter=stable_diffusion`.\n"
            + "Make sure you have agreed to the terms of use for the models."
            + "In particular, visit SD's model card to agree to the terms of use:"
            + " https://huggingface.co/runwayml/stable-diffusion-inpainting"
        )
    # print args
    args.pretty_print()

    # load models
    cg = ClimateGAN("models/climategan")

    # check painter type
    assert args.painter in {
        "climategan",
        "stable_diffusion",
        "both",
    }, (
        f"Unknown painter {args.painter}. "
        + "Allowed values are 'climategan', 'stable_diffusion' and 'both'."
    )

    # load SD pipeline if need be
    if args.painter != "climate_gan":
        cg._setup_stable_diffusion()

    # resolve input folder path
    in_path = Path(args.input_folder).expanduser().resolve()
    assert in_path.exists(), f"Folder {str(in_path)} does not exist"

    # output is input if not specified
    if args.output_folder is None:
        out_path = in_path

    # find images in input folder
    im_paths = [
        p
        for p in in_path.iterdir()
        if p.suffix.lower() in [".jpg", ".png", ".jpeg"] and "---" not in p.name
    ]
    assert im_paths, f"No images found in {str(im_paths)}"

    print(f"\nFound {len(im_paths)} images in {str(in_path)}\n")

    # infer and write
    for i, im_path in enumerate(im_paths):
        print(">>> Processing", f"{i}/{len(im_paths)}", im_path.name)
        outs = cg.infer_single(
            np.array(Image.open(im_path)),
            args.painter,
            as_pil_image=True,
            concats=[
                "input",
                "masked_input",
                "climategan_flood",
                "stable_copy_flood",
            ],
        )
        for k, v in outs.items():
            name = f"{im_path.stem}---{k}{im_path.suffix}"
            im = Image.fromarray(uint8(v))
            im.save(out_path / name)
        print(">>> Done", f"{i}/{len(im_paths)}", im_path.name, end="\n\n")