Merge branch 'dev/v1.2' of github.com:hpcaitech/Open-Sora-dev into dev/v1.2

opensora/datasets/datasets.py

@@ -68,10 +68,9 @@ class VideoTextDataset(torch.utils.data.Dataset):
 
         if file_type == "video":
             # loading
-            vframes, _, infos = read_video(filename=path, pts_unit="sec", output_format="TCHW")
+            vframes, vinfo = read_video(path, backend='cv2')
+            video_fps = vinfo["video_fps"] if 'video_fps' in vinfo else 24
 
-            if "video_fps" in infos:
-                video_fps = infos["video_fps"]
             # Sampling video frames
             video = temporal_random_crop(vframes, self.num_frames, self.frame_interval)
 
@@ -146,9 +145,8 @@ class VariableVideoTextDataset(VideoTextDataset):
         video_fps = 24  # default fps
         if file_type == "video":
             # loading
-            vframes, _, infos = read_video(filename=path, pts_unit="sec", output_format="TCHW")
-            if "video_fps" in infos:
-                video_fps = infos["video_fps"]
+            vframes, vinfo = read_video(path, backend='cv2')
+            video_fps = vinfo["video_fps"] if 'video_fps' in vinfo else 24
 
             # Sampling video frames
             video = temporal_random_crop(vframes, num_frames, self.frame_interval)

opensora/datasets/read_video.py

@@ -1,5 +1,6 @@
 import gc
 import math
+import cv2
 import os
 from fractions import Fraction
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -16,7 +17,7 @@ from torchvision.io.video import (
 )
 
 
-def read_video(
+def read_video_av(
     filename: str,
     start_pts: Union[float, Fraction] = 0,
     end_pts: Optional[Union[float, Fraction]] = None,
@@ -132,3 +133,56 @@ def read_video(
         vframes = vframes.permute(0, 3, 1, 2)
 
     return vframes, aframes, info
+
+
+def read_video_cv2(video_path):
+    cap = cv2.VideoCapture(video_path)
+
+    if not cap.isOpened():
+        # print("Error: Unable to open video")
+        raise ValueError
+    else:
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        vinfo = {
+            'video_fps': fps,
+        }
+
+        frames = []
+        while True:
+            # Read a frame from the video
+            ret, frame = cap.read()
+
+            # If frame is not read correctly, break the loop
+            if not ret:
+                break
+
+            frames.append(frame[:, :, ::-1])  # BGR to RGB
+
+            # Exit if 'q' is pressed
+            if cv2.waitKey(25) & 0xFF == ord('q'):
+                break
+
+        # Release the video capture object and close all windows
+        cap.release()
+        cv2.destroyAllWindows()
+
+        frames = np.stack(frames)
+        frames = torch.from_numpy(frames)  # [T, H, W, C=3]
+        frames = frames.permute(0, 3, 1, 2)
+        return frames, vinfo
+
+
+def read_video(video_path, backend='cv2'):
+    if backend == 'cv2':
+        vframes, vinfo = read_video_cv2(video_path)
+    elif backend == 'av':
+        vframes, _, vinfo = read_video_av(filename=video_path, pts_unit="sec", output_format="TCHW")
+    else:
+        raise ValueError
+
+    return vframes, vinfo
+
+
+if __name__ == '__main__':
+    vframes, vinfo = read_video('./data/colors/9.mp4', backend='cv2')
+    x = 0