init

I3D.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from pytorchvideo.models.hub import i3d_r50
+import cv2
+import numpy as np
+from PIL import Image
+
+model = i3d_r50(pretrained=True).eval()
+feature_extractor = torch.nn.Sequential(*model.blocks[:-1])
+
+def preprocess_video(video_path, num_frames=32, size=224):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    transform = transforms.Compose([
+        transforms.Resize((size, size)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.45, 0.45, 0.45], std=[0.225, 0.225, 0.225])
+    ])
+
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_indices = np.linspace(0, total_frames - 1, num_frames).astype(int)
+
+    for i in frame_indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = Image.fromarray(frame)
+        frame = transform(frame)
+        frames.append(frame)
+
+    cap.release()
+    video_tensor = torch.stack(frames).permute(1, 0, 2, 3).unsqueeze(0)
+    return video_tensor
+
+def extract_features(video_tensor):
+    with torch.no_grad():
+        features = feature_extractor(video_tensor)
+        features = F.adaptive_avg_pool3d(features, 1)
+        features = features.flatten()
+    return features.numpy()
+
+def video_features(video_path):
+    video_tensor = preprocess_video(video_path)
+    features = extract_features(video_tensor)
+    return features
+
+video_path = r'D:\DESKTOP\2025\44\a1\dataset\org\0.mp4'
+video_f = video_features(video_path)
+print(f"视频features: {video_f.shape}")