app.py

from detecto import core, utils, visualize
from detecto.visualize import show_labeled_image, plot_prediction_grid
from torchvision import transforms
import matplotlib.pyplot as plt
from tensorflow.keras.utils import img_to_array
import numpy as np
import warnings
import streamlit as st
warnings.filterwarnings("ignore", category=UserWarning) 




MODEL_PATH = "SD_model_weights.pth"
IMAGE_PATH = "img1.jpeg"
model = core.Model.load(MODEL_PATH, ['cross_arm','pole','tag'])
#warnings.warn(msg)

st.title("Object Detection")
image = utils.read_image(IMAGE_PATH) 
predictions = model.predict(image)
labels, boxes, scores = predictions

images = ["img1.jpeg","img2.jpeg","img3.jpeg","img3.jpeg"]
with st.sidebar:
    st.write("choose an image")
    st.image(images)



#def detect_object(IMAGE_PATH):
 #   image = utils.read_image(IMAGE_PATH) 
  #  predictions = model.predict(image)
   # labels, boxes, scores = predictions


    #thresh=0.2
    #filtered_indices=np.where(scores>thresh)
    #filtered_scores=scores[filtered_indices]
    #filtered_boxes=boxes[filtered_indices]
    #num_list = filtered_indices[0].tolist()
    #filtered_labels = [labels[i] for i in num_list]
    #visualize.show_labeled_image(image, filtered_boxes, filtered_labels)
    #img_array = img_to_array(img)