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131 lines
3.8 KiB
Python
131 lines
3.8 KiB
Python
"""Functions for creating animations from images."""
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import os.path
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import cv2
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import torch
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from imaginairy.utils import shrink_list
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from imaginairy.utils.img_utils import (
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add_caption_to_image,
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imgpaths_to_imgs,
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model_latents_to_pillow_imgs,
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pillow_img_to_opencv_img,
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)
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def make_bounce_animation(
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imgs,
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outpath,
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transition_duration_ms=500,
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start_pause_duration_ms=1000,
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end_pause_duration_ms=2000,
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):
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first_img = imgs[0]
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last_img = imgs[-1]
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middle_imgs = imgs[1:-1]
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max_fps = 20
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max_frames = int(round(transition_duration_ms / 1000 * max_fps))
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min_duration = int(1000 / 20)
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if middle_imgs:
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progress_duration = int(round(transition_duration_ms / len(middle_imgs)))
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else:
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progress_duration = 0
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progress_duration = max(progress_duration, min_duration)
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middle_imgs = shrink_list(middle_imgs, max_frames)
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frames = [first_img, *middle_imgs, last_img, *list(reversed(middle_imgs))]
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# convert from latents
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converted_frames = []
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for frame in frames:
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if isinstance(frame, torch.Tensor):
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frame = model_latents_to_pillow_imgs(frame)[0]
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converted_frames.append(frame)
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frames = converted_frames
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durations = (
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[start_pause_duration_ms]
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+ [progress_duration] * len(middle_imgs)
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+ [end_pause_duration_ms]
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+ [progress_duration] * len(middle_imgs)
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)
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make_animation(imgs=frames, outpath=outpath, frame_duration_ms=durations)
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def make_slideshow_animation(
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imgs,
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outpath,
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image_pause_ms=1000,
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):
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# convert from latents
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converted_frames = []
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for frame in imgs:
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if isinstance(frame, torch.Tensor):
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frame = model_latents_to_pillow_imgs(frame)[0]
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converted_frames.append(frame)
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durations = [image_pause_ms] * len(converted_frames)
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make_animation(imgs=converted_frames, outpath=outpath, frame_duration_ms=durations)
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def make_animation(imgs, outpath, frame_duration_ms=100, captions=None):
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imgs = imgpaths_to_imgs(imgs)
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ext = os.path.splitext(outpath)[1].lower().strip(".")
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if captions:
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if len(captions) != len(imgs):
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raise ValueError("Captions and images must be of same length.")
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for img, caption in zip(imgs, captions):
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add_caption_to_image(img, caption)
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if ext == "gif":
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make_gif_animation(
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imgs=imgs, outpath=outpath, frame_duration_ms=frame_duration_ms
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)
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elif ext == "mp4":
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make_mp4_animation(
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imgs=imgs, outpath=outpath, frame_duration_ms=frame_duration_ms
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)
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def make_gif_animation(imgs, outpath, frame_duration_ms=100, loop=0):
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imgs = imgpaths_to_imgs(imgs)
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imgs[0].save(
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outpath,
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save_all=True,
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append_images=imgs[1:],
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duration=frame_duration_ms,
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loop=loop,
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optimize=False,
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)
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def make_mp4_animation(imgs, outpath, frame_duration_ms=50, fps=30, codec="mp4v"):
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imgs = imgpaths_to_imgs(imgs)
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frame_size = imgs[0].size
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fourcc = cv2.VideoWriter_fourcc(*codec)
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out = cv2.VideoWriter(outpath, fourcc, fps, frame_size)
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if not isinstance(frame_duration_ms, list):
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frame_duration_ms = [frame_duration_ms] * len(imgs)
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try:
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for image in select_images_by_duration_at_fps(imgs, frame_duration_ms, fps):
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image = pillow_img_to_opencv_img(image)
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out.write(image)
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finally:
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out.release()
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def select_images_by_duration_at_fps(images, durations_ms, fps=30):
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"""select the proper image to show for each frame of a video."""
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for i, image in enumerate(images):
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duration = durations_ms[i] / 1000
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num_frames = int(round(duration * fps))
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print(
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f"Showing image {i} for {num_frames} frames for {durations_ms[i]}ms at {fps} fps."
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)
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for j in range(num_frames):
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yield image
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