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
from PIL import Image
import streamlit as st
warnings.filterwarnings("ignore", category=UserWarning) 
from tempfile import NamedTemporaryFile

import cv2
import matplotlib.patches as patches

import torch

import matplotlib.image as mpimg
import os

from detecto.utils import reverse_normalize, normalize_transform, _is_iterable
from torchvision import transforms


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","img4.jpeg","img5.jpeg","img6.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]
    show_labeled_image(image, filtered_boxes, filtered_labels)
    
    fig1 = show_image(image,filtered_boxes,filtered_labels)
    st.write("Object Detected Image is")
    st.image(fig1)
    #img_array = img_to_array(img)
def show_image(image, boxes, labels=None):
    """Show the image along with the specified boxes around detected objects.
    Also displays each box's label if a list of labels is provided.
    :param image: The image to plot. If the image is a normalized
        torch.Tensor object, it will automatically be reverse-normalized
        and converted to a PIL image for plotting.
    :type image: numpy.ndarray or torch.Tensor
    :param boxes: A torch tensor of size (N, 4) where N is the number
        of boxes to plot, or simply size 4 if N is 1.
    :type boxes: torch.Tensor
    :param labels: (Optional) A list of size N giving the labels of
            each box (labels[i] corresponds to boxes[i]). Defaults to None.
    :type labels: torch.Tensor or None
    **Example**::
        >>> from detecto.core import Model
        >>> from detecto.utils import read_image
        >>> from detecto.visualize import show_labeled_image
        >>> model = Model.load('model_weights.pth', ['tick', 'gate'])
        >>> image = read_image('image.jpg')
        >>> labels, boxes, scores = model.predict(image)
        >>> show_labeled_image(image, boxes, labels)
    """
    fig, ax = plt.subplots(1)
    # If the image is already a tensor, convert it back to a PILImage
    # and reverse normalize it
    if isinstance(image, torch.Tensor):
        image = reverse_normalize(image)
        image = transforms.ToPILImage()(image)
    ax.imshow(image)
    
    # Show a single box or multiple if provided
    if boxes.ndim == 1:
        boxes = boxes.view(1, 4)

    if labels is not None and not _is_iterable(labels):
        labels = [labels]

    # Plot each box
    for i in range(2):
        box = boxes[i]
        width, height = (box[2] - box[0]).item(), (box[3] - box[1]).item()
        initial_pos = (box[0].item(), box[1].item())
        rect = patches.Rectangle(initial_pos,  width, height, linewidth=1,
                                 edgecolor='r', facecolor='none')
        if labels:
            ax.text(box[0] + 5, box[1] - 5, '{}'.format(labels[i]), color='red')

        ax.add_patch(rect)

    cp = os.path.abspath(os.getcwd()) + '/foo.png'
    plt.savefig(cp)
    plt.close(fig)
    return cp
    #print(type(plt

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.write("Input Image")
    st.image(image,use_column_width = True)
    with NamedTemporaryFile(dir='.', suffix='.jpeg') as f:
        f.write(file.getbuffer())
    #your_function_which_takes_a_path(f.name)
   
        detect_object(f.name)