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