from typing import List, Optional, Tuple
import cv2
from pandas import DataFrame
from transformers import AutoFeatureExtractor, TimesformerForVideoClassification
import numpy as np
import torch 
import pandas as pd
from torch import Tensor

from utils.frame_rate import FrameRate

def load_model(model_name: str):
    if "base-finetuned-k400" in model_name or "base-finetuned-k600" in model_name:
        feature_extractor = AutoFeatureExtractor.from_pretrained(
            "MCG-NJU/videomae-base-finetuned-kinetics"
        )
    else:
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
    model = TimesformerForVideoClassification.from_pretrained(model_name)
    return feature_extractor, model

class ImgContainer:
    def __init__(self, frames_per_video: int = 8) -> None:
        self.img: Optional[np.ndarray] = None  # raw image
        self.frame_rate: FrameRate = FrameRate()
        self.imgs: List[np.ndarray] = []
        self.frame_rate.reset()
        self.frames_per_video = frames_per_video
        self.rs: Optional[DataFrame] = None

    def add_frame(self, frame: np.ndarray):
        if len(img_container.imgs) >= frames_per_video:
            self.imgs.pop(0)
        self.imgs.append(frame)

    @property
    def ready(self):
        return len(img_container.imgs) == self.frames_per_video

def inference():
    if not img_container.ready:
        return

    inputs = feature_extractor(list(img_container.imgs), return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits: Tensor = outputs.logits

    # model predicts one of the 400 Kinetics-400 classes
    max_index = logits.argmax(-1).item()
    predicted_label = model.config.id2label[max_index]
    
    img_container.frame_rate.label = f"{predicted_label}_{logits[0][max_index]:.2f}%"

    TOP_K = 12
    # logits = np.squeeze(logits)
    logits = logits.squeeze().numpy()
    indices = np.argsort(logits)[::-1][:TOP_K]
    values = logits[indices]

    results: List[Tuple[str, float]] = []
    for index, value in zip(indices, values):
        predicted_label = model.config.id2label[index]
        # print(f"Label: {predicted_label} - {value:.2f}%")
        results.append((predicted_label, value))

    img_container.rs = pd.DataFrame(results, columns=("Label", "Confidence"))

def get_frames_per_video(model_name: str) -> int:
    if "base-finetuned" in model_name:
        return 8
    elif "hr-finetuned" in model_name:
        return 16
    else:
        return 96


model_name = "facebook/timesformer-base-finetuned-k400"
# "facebook/timesformer-base-finetuned-k600",
# "facebook/timesformer-base-finetuned-ssv2",
# "facebook/timesformer-hr-finetuned-k600",
# "facebook/timesformer-hr-finetuned-k400",
# "facebook/timesformer-hr-finetuned-ssv2",
# "fcakyon/timesformer-large-finetuned-k400",
# "fcakyon/timesformer-large-finetuned-k600",
feature_extractor, model = load_model(model_name)


frames_per_video = get_frames_per_video(model_name)
print(f"Frames per video: {frames_per_video}")

img_container = ImgContainer(frames_per_video)

# define a video capture object
vid = cv2.VideoCapture(0)

while(True):
    # Capture the video frame
    # by frame
    ret, frame = vid.read()

    img_container.img = frame
    img_container.frame_rate.count()
    img_container.add_frame(frame)
    inference()
    rs = img_container.frame_rate.show_fps(frame)

    # Display the resulting frame
    cv2.imshow('TimeSFormer', rs)

    # the 'q' button is set as the
    # quitting button you may use any
    # desired button of your choice
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# After the loop release the cap object
vid.release()
# Destroy all the windows
cv2.destroyAllWindows()