streamlit_object_detection_webcam/app.py at main · hackenjoe/streamlit_object_detection_webcam · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Fri Apr 23 10:46:05 2021

@author: Pascal
"""

import streamlit as st
from PIL import Image
from datetime import date
import time
import cv2
import os
import object_detection as detect
import snapshot as snap


def write_image(out_image):
    today = date.today()
    d = today.strftime("%b-%d-%Y")
    t = time.localtime()
    current_time = time.strftime("%H-%M-%S", t)

    file_name = "tempDir/photo_" + d + "_" + current_time + ".jpg"
    cv2.imwrite(file_name, out_image)

    return(file_name)


def main():

    # ===================== Set page config and background =======================
    st.set_page_config(page_title ="Objekterkennung",
                       page_icon=':camera:',
                       layout='centered')


    # ===================== Set header and site info =============================
    text = """
    <center> <br>Testumgebung für Objektklassifizierungen mit Webcam. </br> </center>
    </center>
    """
    html_temp = f"""
    <p style = "color:#1F4E79; text_align:justify;"> {text} </p>
    </div>
    """
    st.markdown(html_temp, unsafe_allow_html = True)


    # ======================= Get tf lite model details ==========================
    labels, colors, height, width, interpreter = detect.define_tf_lite_model()

    # ============================= Main app =====================================
    option = st.selectbox('Wähle Eingabeart aus',
                         ('Nichts',
                          'Webcam',
                        #  'Upload photo'
                          ))

    if option == 'Webcam':

        out_image = snap.streamlit_webrtc_snapshot()

        if out_image is not None:
            st.header("Dein Bild")
            st.image(out_image, channels="BGR")
            # Speichere Bild temporär
            file_name = write_image(out_image)
            object_detection = detect.display_results(labels,
                                                      colors,
                                                      height,
                                                      width,
                                                      file_name,
                                                      interpreter,
                                                      threshold=0.5)
            st.image(Image.fromarray(object_detection), use_column_width=True)

   #     else:
      #      st.warning('Kalibriere...')


if __name__ == "__main__":
    main()