| 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 |
| ) |
| |
| 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)) |
| |
| 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) |
|
|
