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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 | |
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","img2.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) | |