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video_path_to_images.py

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+import os
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+class VideoToImagesList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "video_path": ("STRING", {"forceInput": True}),
+                "frame_interval": ("INT", {"default": 1, "min": 1, "max": 100}),
+                "max_frames": ("INT", {"default": 0, "min": 0, "max": 10000})
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE", "FLOAT", "FLOAT", "INT")
+    RETURN_NAMES = ("IMAGE", "initial_fps", "new_fps", "total_frames")
+    FUNCTION = "video_to_images"
+    CATEGORY = "Bjornulf"
+
+    def video_to_images(self, video_path, frame_interval=1, max_frames=0):
+        if not os.path.exists(video_path):
+            raise FileNotFoundError(f"Video file not found: {video_path}")
+        
+        cap = cv2.VideoCapture(video_path)
+        frame_count = 0
+        images = []
+        
+        # Get the initial fps of the video
+        initial_fps = cap.get(cv2.CAP_PROP_FPS)
+        
+        # Get the total number of frames in the video
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret or (max_frames > 0 and len(images) >= max_frames):
+                break
+            
+            if frame_count % frame_interval == 0:
+                # Convert BGR to RGB
+                rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                pil_image = Image.fromarray(rgb_frame)
+                
+                # Convert PIL Image to tensor
+                tensor_image = torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0)
+                images.append(tensor_image)
+            
+            frame_count += 1
+        
+        cap.release()
+        
+        if not images:
+            raise ValueError("No frames were extracted from the video")
+        
+        # Calculate the new fps
+        new_fps = initial_fps / frame_interval
+        
+        # Stack all images into a single tensor
+        return (torch.cat(images, dim=0), initial_fps, new_fps, total_frames)