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