Hotshot-XL/hotshot_xl/utils.py

# Copyright 2023 Natural Synthetics Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List, Union
from io import BytesIO
import PIL
from PIL import ImageSequence, Image
import requests
import os

def get_image(img_path) -> PIL.Image.Image:
    if img_path.startswith("http"):
        return PIL.Image.open(requests.get(img_path, stream=True).raw)
    if os.path.exists(img_path):
        return Image.open(img_path)
    raise Exception("File not found")

def images_to_gif_bytes(images: List, duration: int = 1000) -> bytes:
    with BytesIO() as output_buffer:
        # Save the first image
        images[0].save(output_buffer,
                       format='GIF',
                       save_all=True,
                       append_images=images[1:],
                       duration=duration,
                       loop=0)  # 0 means the GIF will loop indefinitely

        # Get the byte array from the buffer
        gif_bytes = output_buffer.getvalue()

    return gif_bytes


def save_as_gif(images: List, file_path: str, duration: int = 1000):
    with open(file_path, "wb") as f:
        f.write(images_to_gif_bytes(images, duration))

def scale_aspect_fill(img, new_width, new_height):
    new_width = int(new_width)
    new_height = int(new_height)

    original_width, original_height = img.size
    ratio_w = float(new_width) / original_width
    ratio_h = float(new_height) / original_height

    if ratio_w > ratio_h:
        # It must be fixed by width
        resize_width = new_width
        resize_height = round(original_height * ratio_w)
    else:
        # Fixed by height
        resize_width = round(original_width * ratio_h)
        resize_height = new_height

    img_resized = img.resize((resize_width, resize_height), Image.LANCZOS)

    # Calculate cropping boundaries and do crop
    left = (resize_width - new_width) / 2
    top = (resize_height - new_height) / 2
    right = (resize_width + new_width) / 2
    bottom = (resize_height + new_height) / 2

    img_cropped = img_resized.crop((left, top, right, bottom))

    return img_cropped

def extract_gif_frames_from_midpoint(image: Union[str, PIL.Image.Image], fps: int=8, target_duration: int=1000) -> list:
    # Load the GIF
    image = get_image(image) if type(image) is str else image

    frames = []

    estimated_frame_time = None

    # some gifs contain the duration - others don't
    # so if there is a duration we will grab it otherwise we will fall back

    for frame in ImageSequence.Iterator(image):

        frames.append(frame.copy())
        if 'duration' in frame.info:
            frame_info_duration = frame.info['duration']
            if frame_info_duration > 0:
                estimated_frame_time = frame_info_duration

    if estimated_frame_time is None:
        if len(frames) <= 16:
            # assume it's 8fps
            estimated_frame_time = 1000 // 8
        else:
            # assume it's 15 fps
            estimated_frame_time = 70

    if len(frames) < fps:
        raise ValueError(f"fps of {fps} is too small for this gif as it only has {len(frames)} frames.")

    skip = len(frames) // fps
    upper_bound_index = len(frames) - 1

    best_indices = [x for x in range(0, len(frames), skip)][:fps]
    offset = int(upper_bound_index - best_indices[-1]) // 2
    best_indices = [x + offset for x in best_indices]
    best_duration = (best_indices[-1] - best_indices[0]) * estimated_frame_time

    while True:

        skip -= 1

        if skip == 0:
            break

        indices = [x for x in range(0, len(frames), skip)][:fps]

        # center the indices, so we sample the middle of the gif...
        offset = int(upper_bound_index - indices[-1]) // 2
        if offset == 0:
            # can't shift
            break
        indices = [x + offset for x in indices]

        # is the new duration closer to the target than last guess?
        duration = (indices[-1] - indices[0]) * estimated_frame_time
        if abs(duration - target_duration) > abs(best_duration - target_duration):
            break

        best_indices = indices
        best_duration = duration

    return [frames[index] for index in best_indices]

def get_crop_coordinates(old_size: tuple, new_size: tuple) -> tuple:
    """
    Calculate the crop coordinates after scaling an image to fit a new size.

    :param old_size: tuple of the form (width, height) representing the original size of the image.
    :param new_size: tuple of the form (width, height) representing the desired size after scaling.
    :return: tuple of the form (left, upper, right, lower) representing the normalized crop coordinates.
    """
    # Check if the input tuples have the right form (width, height)
    if not (isinstance(old_size, tuple) and isinstance(new_size, tuple) and
            len(old_size) == 2 and len(new_size) == 2):
        raise ValueError("old_size and new_size should be tuples of the form (width, height)")

    # Extract the width and height from the old and new sizes
    old_width, old_height = old_size
    new_width, new_height = new_size

    # Calculate the ratios for width and height
    ratio_w = float(new_width) / old_width
    ratio_h = float(new_height) / old_height

    # Determine which dimension is fixed (width or height)
    if ratio_w > ratio_h:
        # It must be fixed by width
        resize_width = new_width
        resize_height = round(old_height * ratio_w)
    else:
        # Fixed by height
        resize_width = round(old_width * ratio_h)
        resize_height = new_height

    # Calculate cropping boundaries in the resized image space
    left = (resize_width - new_width) / 2
    upper = (resize_height - new_height) / 2
    right = (resize_width + new_width) / 2
    lower = (resize_height + new_height) / 2

    # Normalize the cropping coordinates

    # Return the normalized coordinates as a tuple
    return (left, upper, right, lower)

aspect_ratio_to_1024_map = {
    "0.42": [640,  1536],
    "0.57": [768,  1344],
    "0.68": [832,  1216],
    "1.00": [1024, 1024],
    "1.46": [1216,  832],
    "1.75": [1344,  768],
    "2.40": [1536,  640]
}

res_to_aspect_map = {
    1024: aspect_ratio_to_1024_map,
    512: {key: [value[0] // 2, value[1] // 2] for key, value in aspect_ratio_to_1024_map.items()},
}

def best_aspect_ratio(aspect_ratio: float, resolution: int):

    map = res_to_aspect_map[resolution]

    d = 99999999
    res = None
    for key, value in map.items():
        ar = value[0] / value[1]
        diff = abs(aspect_ratio - ar)
        if diff < d:
            d = diff
            res = value

    ar = res[0] / res[1]
    return f"{ar:.2f}", res