Vid_1158.mp4 -

# More complex visual feature extraction def extract_visual_features(frames): model = models.resnet50(pretrained=True) model.fc = torch.nn.Identity() # Remove the final classification layer device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) model.eval()

import cv2 import numpy as np import torch from torchvision import models from torchvision.transforms import transforms vid_1158.mp4

To prepare a feature for a video file named "vid_1158.mp4", we'll need to consider what kind of features are typically extracted or used in the context of video analysis or processing. Features in video analysis can range from simple metadata to complex descriptors of the video content. Note that the complexity and specifics can vary

# Simple Feature Extraction def extract_simple_features(video_path): cap = cv2.VideoCapture(video_path) duration = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = cap.get(cv2.CAP_PROP_FPS) cap.release() features = { "file_name": video_path, "duration": duration, "resolution": (width, height), "fps": fps } return features simple_features) print("Visual Features Shape:"

print("Simple Features:", simple_features) print("Visual Features Shape:", len(visual_features), len(visual_features[0])) This example extracts basic metadata and uses a pre-trained ResNet50 model to extract features from each frame. Note that the complexity and specifics can vary greatly depending on your exact requirements and the type of analysis you plan to perform.