app.py

import json
from io import BytesIO
from PIL import Image
import os

import streamlit as st
import pandas as pd
import numpy as np
import torch
import cv2

#from simple_detection import detect
from one_image_detection import detect

if 'img_list' not in st.session_state:
	st.session_state.img_list = []

st.title('Direct YoloV5 Inference')
instructions = """
    Upload your images and run the model to
    test the basic YoloV5 model.

    Check the original YoloV5 repository in: https://github.com/ultralytics/yolov5
    """
st.write(instructions)

if st.button('Run Model'):
    result = detect(st.session_state['img_list'])
    st.session_state['img_list'] = []

    for image in result:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        st.image(image, caption='Result')

file = st.file_uploader('Upload An Image')

if file:  # if user uploaded file
    file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, 1)
    
    # print(len(img_list))
    # img_list.append(img)
    # print("Loaded images = ")
    # print(len(img_list))

    st.session_state['img_list'].append(img)

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    st.title("Uploaded Image")
    resized_image = cv2.resize(img, (256,256))
    st.image(resized_image)