TimeSFormer / stream.py
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from streamlit_webrtc import webrtc_streamer
import numpy as np
import streamlit as st
import numpy as np
import av
import threading
import multiprocessing
from typing import List, Optional, Tuple
from pandas import DataFrame
import numpy as np
import pandas as pd
import streamlit as st
import torch
from torch import Tensor
from transformers import AutoFeatureExtractor, TimesformerForVideoClassification
from utils.frame_rate import FrameRate
np.random.seed(0)
st.set_page_config(
page_title="TimeSFormer",
page_icon="🧊",
layout="wide",
initial_sidebar_state="expanded",
menu_items={
"Get Help": "https://www.extremelycoolapp.com/help",
"Report a bug": "https://www.extremelycoolapp.com/bug",
"About": "# This is a header. This is an *extremely* cool app!",
},
)
@st.cache_resource
# @st.experimental_singleton
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
lock = threading.Lock()
rtc_configuration = {
"iceServers": [
{
"urls": "turn:relay1.expressturn.com:3478",
"username": "efBRTY571ATWBRMP36",
"credential": "pGcX1BPH5fMmZJc5",
},
# {
# "urls": [
# "stun:stun1.l.google.com:19302",
# "stun:stun2.l.google.com:19302",
# "stun:stun3.l.google.com:19302",
# "stun:stun4.l.google.com:19302",
# ]
# },
],
}
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"))
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 video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
img = frame.to_ndarray(format="bgr24")
with lock:
img_container.img = img
img_container.frame_rate.count()
img_container.add_frame(img)
inference()
img = img_container.frame_rate.show_fps(img)
return av.VideoFrame.from_ndarray(img, format="bgr24")
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
st.title("TimeSFormer")
with st.expander("INTRODUCTION"):
st.text(
f"""Streamlit demo for TimeSFormer.
Number of CPU(s): {multiprocessing.cpu_count()}
"""
)
model_name = st.selectbox(
"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)
st.info(f"Frames per video: {frames_per_video}")
img_container = ImgContainer(frames_per_video)
ctx = st.session_state.ctx = webrtc_streamer(
key="snapshot",
video_frame_callback=video_frame_callback,
rtc_configuration=rtc_configuration,
)
if img_container.rs is not None:
st.dataframe(img_container.rs)