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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
from PIL import Image
import streamlit as st
warnings.filterwarnings("ignore", category=UserWarning)
from tempfile import NamedTemporaryFile
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]
st.show_labeled_image(image, filtered_boxes, filtered_labels)
#img_array = img_to_array(img)
file = st.file_uploader('Upload an Image',type=(["jpeg","jpg","png"]))
if file is None:
st.write("Please upload an image file")
else:
image= Image.open(file)
st.image(image,use_column_width = True)
with NamedTemporaryFile(dir='.', suffix='.csv') as f:
f.write(file.getbuffer())
#your_function_which_takes_a_path(f.name)
detect_object(f.name)
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