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import cv2
import torch
import numpy as np
from PIL import Image
from torchvision import models, transforms
from config import DEVICE, FRAME_RATE
from tqdm import tqdm
# Load GoogLeNet once
from torchvision.models import GoogLeNet_Weights
weights = GoogLeNet_Weights.DEFAULT
googlenet = models.googlenet(weights=weights).to(DEVICE).eval()
feature_extractor = torch.nn.Sequential(
googlenet.conv1,
googlenet.maxpool1,
googlenet.conv2,
googlenet.conv3,
googlenet.maxpool2,
googlenet.inception3a,
googlenet.inception3b,
googlenet.maxpool3,
googlenet.inception4a,
googlenet.inception4b,
googlenet.inception4c,
googlenet.inception4d,
googlenet.inception4e,
googlenet.maxpool4,
googlenet.inception5a,
googlenet.inception5b,
googlenet.avgpool,
torch.nn.Flatten()
)
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
def extract_frames(video_path):
cap = cv2.VideoCapture(video_path)
frames = []
indices = []
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
total_frames = 100 # TEMP
for idx in tqdm(range(0, total_frames, FRAME_RATE)):
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if not ret:
break
frames.append(Image.fromarray(frame))
indices.append(idx)
cap.release()
return frames, indices
def extract_features(frames):
features = [transform(frame) for frame in frames]
features = torch.stack(features).to(DEVICE)
features = feature_extractor(features)
return features
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