from ..patch_match import PyramidPatchMatcher import os import numpy as np from PIL import Image from tqdm import tqdm class BalancedModeRunner: def __init__(self): pass def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None): patch_match_engine = PyramidPatchMatcher( image_height=frames_style[0].shape[0], image_width=frames_style[0].shape[1], channel=3, **ebsynth_config ) # tasks n = len(frames_style) tasks = [] for target in range(n): for source in range(target - window_size, target + window_size + 1): if source >= 0 and source < n and source != target: tasks.append((source, target)) # run frames = [(None, 1) for i in range(n)] for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) source_style = np.stack([frames_style[source] for source, target in tasks_batch]) _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) for (source, target), result in zip(tasks_batch, target_style): frame, weight = frames[target] if frame is None: frame = frames_style[target] frames[target] = ( frame * (weight / (weight + 1)) + result / (weight + 1), weight + 1 ) if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size): frame = frame.clip(0, 255).astype("uint8") if save_path is not None: Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) frames[target] = (None, 1)