chore: Update processing_video.py

videollava/model/multimodal_encoder/languagebind/video/processing_video.py

@@ -1,6 +1,7 @@
 
 import torch
 import cv2
+import os
 import decord
 import numpy as np
 from PIL import Image
@@ -12,12 +13,27 @@
 from torchvision.transforms import Compose, Lambda, ToTensor
 from torchvision.transforms._transforms_video import NormalizeVideo, RandomCropVideo, RandomHorizontalFlipVideo, CenterCropVideo
 from pytorchvideo.transforms import ApplyTransformToKey, ShortSideScale, UniformTemporalSubsample
+from torch.utils.data import DataLoader, Dataset
+from .database import *
 
 decord.bridge.set_bridge('torch')
 
 OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
 OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
 
+class DBFramesDataset(Dataset):
+    def __init__(self, db_path):
+        self.db = PILImageDatabase(db_path)
+        self.keys = self.db.keys
+
+    def __len__(self):
+        return len(self.keys) - 1
+
+    def __getitem__(self, index):
+        key = self.keys[index]
+        sample = self.db[key]
+        return key, sample
+
 def make_list_of_images(x):
     if not isinstance(x, list):
         return [x]
@@ -64,8 +80,20 @@ def get_video_transform(config):
                 RandomHorizontalFlipVideo(p=0.5),
             ]
         )
+
+    elif config.video_decode_backend == 'lmdb':
+        transform = Compose(
+            [
+                # UniformTemporalSubsample(num_frames),
+                Lambda(lambda x: x / 255.0),
+                NormalizeVideo(mean=OPENAI_DATASET_MEAN, std=OPENAI_DATASET_STD),
+                ShortSideScale(size=224),
+                CenterCropVideo(224),
+                RandomHorizontalFlipVideo(p=0.5),
+            ]
+        )
     else:
-        raise NameError('video_decode_backend should specify in (pytorchvideo, decord, opencv)')
+        raise NameError('video_decode_backend should specify in (pytorchvideo, decord, opencv, lmdb)')
     return transform
 
 
@@ -86,6 +114,22 @@ def load_and_transform_video(
         video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec)
         video_outputs = transform(video_data)
 
+    if video_decode_backend == 'lmdb':
+        # here scene_path == video_path
+        db_path = os.path.join(video_path, "frames")
+        dataset = DBFramesDataset(db_path)
+        duration = len(dataset)
+        frame_id_list = np.linspace(0, duration-1, num_frames, dtype=int)
+
+        video_data = []
+        for indx in frame_id_list:
+            _, frame = dataset[indx]
+            frame = np.array(frame)
+            video_data.append(torch.from_numpy(frame).permute(2, 0, 1))
+
+        video_data = torch.stack(video_data, dim=1)
+        video_outputs = transform(video_data)
+
     elif video_decode_backend == 'decord':
         decord.bridge.set_bridge('torch')
         decord_vr = VideoReader(video_path, ctx=cpu(0))