main.py

@@ -2,6 +2,7 @@ import logging
 from pathlib import Path
 from typing import TYPE_CHECKING
 
+from cv2 import imwrite
 import tqdm
 
 from src.config import presets
@@ -18,6 +19,7 @@ from src.interpolator import (
 
 if TYPE_CHECKING:
     import torch
+    import numpy as np
 
 
 def performing_warning_message(device: "torch.device"):
@@ -53,7 +55,7 @@ def init_device() -> "torch.device":
     device = get_device()
     performing_warning_message(device)
     vram_available = get_vram_available(device)
-    logging.info(f"Available VRAM: {vram_available / (1024 ** 3):.2f} GB")
+    logging.info(f"Available VRAM: {vram_available / (1024**3):.2f} GB")
     return device
 
 
@@ -95,8 +97,8 @@ class InterpolationPipeline:
         self.interpolator = init_interpolator(self.model_runner, self.device)
 
     def run(self, video_path: Path, output_video: str):
-        prev_frame_path = None
+        prev_frames = tuple()
-        frame_count = 0
+        interpolated_frames = []
         part = 0
         source_frame_length = 0
         chunk_seconds = 10
@@ -106,75 +108,77 @@ class InterpolationPipeline:
         fps = self.video_maker.get_fps(video_path)
         logging.info(f"Video FPS: {fps}")
         fps *= 2  # Doubling FPS
-        for frame_paths in self.video_maker.video_to_frames_generator(
+        for frames in self.video_maker.video_to_frames_generator(
             video_path, self.fs.frames_path, chunk_seconds
         ):
-            logging.info(f"Processing frames: {len(frame_paths)}")
+            logging.info(f"Processing frames: {len(frames)}")
-            if prev_frame_path is not None:
+            if prev_frames:
-                img1 = prev_frame_path[-1]
+                img1 = prev_frames[-1]
-                img2 = frame_paths[0]
+                img2 = frames[0]
-                output_path = self.fs.interpolated_path / f"img_{frame_count:08d}.png"
+                img1_2 = self.interpolator.interpolate(img1, img2)
-                self.interpolator.interpolate(img1, img2, output_path)
+                interpolated_frames.append(img1_2)
-                logging.debug(f"Interpolated image saved to: {output_path}")
+                self.fs.clear_directory(self.fs.moved_path)
+                self._save_images(prev_frames, interpolated_frames)
+
                 self._merge_frames_to_video(
                     self.fs.video_part_path / f"video_{part:08d}.mp4",
                     fps,
-                    source_frame_length=source_frame_length,
+                    source_frame_length,
                 )
+                interpolated_frames = []
                 logging.info(f"Finished processing part {part:08d}")
-                frame_count += 1
                 part += 1
             for i in tqdm.tqdm(
-                range(len(frame_paths) - 1),
+                range(len(frames) - 1),
                 desc=f"Processing video frames {part + 1} / {total_parts}",
             ):
-                img1 = frame_paths[i]
+                img1 = frames[i]
-                img2 = frame_paths[i + 1]
+                img2 = frames[i + 1]
-                output_path = self.fs.interpolated_path / f"img_{i:08d}.png"
+                img1_2 = self.interpolator.interpolate(img1, img2)
-                self.interpolator.interpolate(img1, img2, output_path)
+                interpolated_frames.append(img1_2)
-                logging.debug(f"Interpolated image saved to: {output_path}")
+            source_frame_length = len(frames)
-                frame_count += 1
+            prev_frames = frames
-            source_frame_length = len(frame_paths)
-            prev_frame_path = frame_paths
 
+        self.fs.clear_directory(self.fs.moved_path)
+        self._save_images(prev_frames, interpolated_frames)
         self._merge_frames_to_video(
             self.fs.video_part_path / f"video_{part:08d}.mp4",
             fps,
-            source_frame_length=source_frame_length,
+            source_frame_length,
         )
+        self.fs.clear_directory(self.fs.moved_path)
         logging.info(f"Finished processing part {part:08d}")
         self._merge_video_parts(self.fs.output_path / output_video)
         logging.info(
             f"Video interpolation completed. Output saved to: {self.fs.output_path / output_video}"
         )
 
+    def _save_images(
+        self,
+        source: tuple["np.ndarray", ...],
+        interpolated: list["np.ndarray"],
+    ):
+        logging.info("Saving images...")
+        index = 0
+        for i, frame in enumerate(source):
+            name = self.fs.moved_path / f"img_{index:08d}.png"
+            index += 1
+            imwrite(name, frame)
+            if i < len(interpolated):
+                name = self.fs.moved_path / f"img_{index:08d}.png"
+                index += 1
+                imwrite(name, interpolated[i])
+        logging.info("Success...")
+
     def _merge_frames_to_video(
         self, output_video: Path, fps: float, source_frame_length: int = 0
     ):
-        self._move_frames(source_frame_length)
         self.video_maker.images_to_video(self.fs.moved_path, output_video, fps)
 
     def _merge_video_parts(self, output_video: Path):
         self.video_maker.concatenate_videos(self.fs.video_part_path, output_video)
         self.fs.clear_directory(self.fs.video_part_path)
 
-    def _move_frames(self, source_frame_length: int = 0):
-        self.fs.clear_directory(self.fs.moved_path)
-        src_frames = sorted(self.fs.frames_path.glob("*.png"))
-        interpolated_frames = sorted(self.fs.interpolated_path.glob("*.png"))
-        index = 0
-        for i in range(source_frame_length):
-            moved_frame_path = self.fs.moved_path / f"img_{index:08d}.png"
-            src_frames[i].rename(moved_frame_path)
-            index += 1
-            if i < len(interpolated_frames):
-                moved_interpolated_path = self.fs.moved_path / f"img_{index:08d}.png"
-                interpolated_frames[i].rename(moved_interpolated_path)
-                index += 1
-        logging.info(
-            f"Moved {len(src_frames)} source frames and {len(interpolated_frames)} interpolated frames to {self.fs.moved_path}"
-        )
-
 
 def runner(
     base_path: Path,
@@ -220,7 +224,7 @@ def main():
         base_path=Path(args.base_path),
         video_path=Path(args.video_path),
         output_video=args.output,
-        preset=getattr(presets, args.preset.upper())
+        preset=getattr(presets, args.preset.upper()),
     )
 
 

src/config/presets.py

@@ -19,6 +19,6 @@ LARGE = Preset(
 )
 
 GLOBAL = Preset(
-    config=Path("src/config/AMT-g.yaml"),
+    config=Path("src/config/AMT-G.yaml"),
     checkpoint=Path("src/pretrained/amt-g.pth"),
 )

src/interpolator.py

@@ -4,7 +4,6 @@ from pathlib import Path
 import torch
 import numpy as np
 from omegaconf import OmegaConf, DictConfig
-from imageio import imread, imwrite
 
 from src.utils.torch import img2tensor, check_dim_and_resize, tensor2img
 from src.utils.build import build_from_cfg
@@ -83,7 +82,7 @@ class ImageInterpolator:
             f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
         )
 
-    def interpolate(self, image1: Path, image2: Path, output_path: Path):
+    def interpolate(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
         """
         Interpolates between two images and saves the result.
         Args:
@@ -92,8 +91,8 @@ class ImageInterpolator:
             output_path (Path): Path to save the interpolated image (only png and jpg formats are supported)
         """
         logging.debug(f"Reading images: {image1} and {image2}")
-        tensor1 = img2tensor(imread(image1)).to(self.device)
+        tensor1 = img2tensor(image1).to(self.device)
-        tensor2 = img2tensor(imread(image2)).to(self.device)
+        tensor2 = img2tensor(image2).to(self.device)
         logging.debug(
             f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
         )
@@ -122,8 +121,7 @@ class ImageInterpolator:
         logging.debug(f"Interpolated image shape before unpadding: {interpolated.shape}")
         (interpolated,) = padder.unpad(interpolated)
         logging.debug(f"Interpolated image shape after unpadding: {interpolated.shape}")
-        imwrite(output_path, tensor2img(interpolated.cpu()))
+        return tensor2img(interpolated.cpu())
-        logging.debug(f"Saved interpolated image to: {output_path}")
 
     def scale(self, height: int, width: int) -> float:
         scale = (

src/utils/video.py

@@ -2,9 +2,10 @@ import os
 import logging
 import subprocess
 from pathlib import Path
+from typing import Generator
 
 import cv2
-from typing import Generator
+import numpy as np
 
 
 class VideoMaker:
@@ -35,7 +36,7 @@ class VideoMaker:
         with open(file, "w") as f:
             for video in videos:
                 f.write(f"file '{video}'\n")
-        cmd = f"ffmpeg -f concat -safe 0 -i {file} -c copy {output_path}"
+        cmd = f"ffmpeg -y -f concat -safe 0 -i {file} -c copy {output_path}"
         logging.info(f"Running command: {cmd}")
         result = self.run_command(cmd)
         if result != 0:
@@ -74,7 +75,7 @@ class VideoMaker:
 
     def video_to_frames_generator(
         self, video_path: Path, output_dir: Path, chunk_seconds: int = 10
-    ) -> Generator[tuple[Path, ...], None, None]:
+    ) -> Generator[tuple[np.ndarray, ...], None, None]:
         """Extracts frames from a video and saves them to disk, yielding paths to the saved frames."""
 
         cap = cv2.VideoCapture(str(video_path))
@@ -85,21 +86,12 @@ class VideoMaker:
         fps = cap.get(cv2.CAP_PROP_FPS)
         frames_per_chunk = int(fps * chunk_seconds)
 
-        frame_index = 0
-
         while True:
             paths = []
-
             for _ in range(frames_per_chunk):
                 ret, frame = cap.read()
                 if not ret:
                     cap.release()
                     return
-
+                paths.append(frame)
-                frame_path = output_dir / f"img_{frame_index:08d}.png"
-                cv2.imwrite(str(frame_path), frame)
-
-                paths.append(frame_path)
-                frame_index += 1
-
             yield tuple(paths)