Update pages/2_π·_Webcamera.py
Browse files- pages/2_π·_Webcamera.py +29 -26
pages/2_π·_Webcamera.py
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# st.title("Webcamera")
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# st.write("Steps to use: \n1. Click on Start button.\n2. To stop the video when done, press Stop. \n\n The output will be displayed in about 40 secs.")
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#
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# time.sleep(10)
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# st.write(len(ctx.video_processor.my_list))
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# output_df.rename(columns = {0:'Index'}, inplace = True)
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# st.write(output_df)
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import streamlit as st
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if picture:
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import streamlit as st
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from streamlit_webrtc import webrtc_streamer
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import av
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import cv2
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import time
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import mediapipe as mp
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import numpy as np
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import pandas as pd
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from mediapipe_functions import *
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from utils import *
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import tensorflow as tf
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# st.title("Webcamera")
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# st.write("Steps to use: \n1. Click on Start button.\n2. To stop the video when done, press Stop. \n\n The output will be displayed in about 40 secs.")
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class VideoProcessor:
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def __init__(self) -> None:
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self.threshold1 = 100
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self.threshold2 = 200
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self.my_list = []
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def recv(self, frame):
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img = frame.to_ndarray(format="bgr24")
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self.my_list.append(img)
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return av.VideoFrame.from_ndarray(img, format="bgr24")
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# Create the video processor instance
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video_processor = VideoProcessor()
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ctx = webrtc_streamer(key="sample", video_processor_factory=lambda: video_processor)
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# time.sleep(10)
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# st.write(len(ctx.video_processor.my_list))
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# output_df.rename(columns = {0:'Index'}, inplace = True)
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# st.write(output_df)
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import streamlit as st
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import time
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# start = time.time()
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# while(time.)
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# picture = st.camera_input("Take a picture")
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# if picture:
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# st.image(picture)
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