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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/

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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

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# Tomato maturity detection web application
<div align="center">
<img src="example_picture.png"/>
</div>
This is a modified code from [this](https://dev.to/andreygermanov/a-practical-introduction-to-object-detection-with-yolov8-neural-network-3n8c) article.
This is a web interface to [YOLOv8 object detection neural network](https://ultralytics.com/yolov8)
implemented on [Python](https://www.python.org) that uses a model to detect level of ripeness for big and small tomatos.
## Install
* Cloning instruction coming soon
* Go to the root of cloned repository
* Install dependencies by running `pip3 install -r requirements.txt`
## Run
Execute:
```
python3 object_detector.py
```
It will start a webserver on http://localhost:8080. Use any web browser to open the web interface.
Using the interface you can upload the image to the object detector and see bounding boxes of all objects detected on it.

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Side by Side

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>YOLOv8 Object Detection</title>
<style>
canvas {
display:block;
border: 1px solid black;
margin-top:10px;
}
</style>
</head>
<body>
<input id="uploadInput" type="file"/>
<canvas></canvas>
<script>
/**
* "Upload" button onClick handler: uploads selected
* image file to backend, receives an array of
* detected objects and draws them on top of image
*/
const input = document.getElementById("uploadInput");
input.addEventListener("change",async(event) => {
const file = event.target.files[0];
const data = new FormData();
data.append("image_file",file,"image_file");
const response = await fetch("/detect",{
method:"post",
body:data
});
const boxes = await response.json();
draw_image_and_boxes(file,boxes);
})
/**
* Function draws the image from provided file
* and bounding boxes of detected objects on
* top of the image
* @param file Uploaded file object
* @param boxes Array of bounding boxes in format
[[x1,y1,x2,y2,object_type,probability],...]
*/
function draw_image_and_boxes(file,boxes) {
const img = new Image()
img.src = URL.createObjectURL(file);
img.onload = () => {
const canvas = document.querySelector("canvas");
canvas.width = img.width;
canvas.height = img.height;
const ctx = canvas.getContext("2d");
ctx.drawImage(img,0,0);
ctx.strokeStyle = "#00FF00";
ctx.lineWidth = 5;
ctx.font = "20px serif";
boxes.forEach(([x1,y1,x2,y2,object_type, prob]) => {
const label = `${object_type} ${prob.toFixed(2)}`;
ctx.strokeRect(x1,y1,x2-x1,y2-y1);
ctx.fillStyle = "#00ff00";
const width = ctx.measureText(label).width;
ctx.fillRect(x1,y1,width+10,25);
ctx.fillStyle = "#000000";
ctx.fillText(label,x1,y1+18);
});
}
}
</script>
</body>
</html>

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from ultralytics import YOLO
from flask import request, Flask, jsonify
from waitress import serve
from PIL import Image
import onnxruntime as ort
import numpy as np
#my changes
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
# Change the working directory to the script's directory
os.chdir(script_dir)
yolo_classes = ["b_fully_ripened",
"b_half_ripened",
"b_green",
"l_fully_ripened",
"l_half_ripened",
"l_green"
]
#app start
app = Flask(__name__)
@app.route("/")
def root():
"""
Site main page handler function.
:return: Content of index.html file
"""
with open("index.html") as file:
return file.read()
@app.route("/detect", methods=["POST"])
def detect():
"""
Handler of /detect POST endpoint
Receives uploaded file with a name "image_file", passes it
through YOLOv8 object detection network and returns and array
of bounding boxes.
:return: a JSON array of objects bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
"""
buf = request.files["image_file"]
boxes = detect_objects_on_image(buf.stream)
print(boxes)
return jsonify(boxes)
def detect_objects_on_image(buf):
input, img_width, img_height = prepare_input(buf)
output = run_model(input)
return process_output(output,img_width,img_height)
def prepare_input(buf):
img = Image.open(buf)
img_width, img_height = img.size
img = img.resize((640, 640))
img = img.convert("RGB")
input = np.array(img)
input = input.transpose(2, 0, 1)
input = input.reshape(1, 3, 640, 640) / 255.0
return input.astype(np.float32), img_width, img_height
def run_model(input):
model = ort.InferenceSession("best.onnx", providers=['CPUExecutionProvider'])
outputs = model.run(["output0"], {"images":input})
return outputs[0]
def process_output(output, img_width, img_height):
output = output[0].astype(float)
output = output.transpose()
boxes = []
for row in output:
prob = row[4:].max()
if prob < 0.5:
continue
class_id = row[4:].argmax()
label = yolo_classes[class_id]
xc, yc, w, h = row[:4]
x1 = (xc - w/2) / 640 * img_width
y1 = (yc - h/2) / 640 * img_height
x2 = (xc + w/2) / 640 * img_width
y2 = (yc + h/2) / 640 * img_height
rotated_x1 = img_height - y2
rotated_y1 = x1
rotated_x2 = img_height - y1
rotated_y2 = x2
boxes.append([rotated_x1, rotated_y1, rotated_x2, rotated_y2, label, prob])
#boxes.append([x1, y1, x2, y2, label, prob])
boxes.sort(key=lambda x: x[5], reverse=True)
result = []
while len(boxes) > 0:
result.append(boxes[0])
boxes = [box for box in boxes if iou(box, boxes[0]) < 0.7]
return result
def iou(box1,box2):
return intersection(box1,box2)/union(box1,box2)
def union(box1,box2):
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
box2_area = (box2_x2-box2_x1)*(box2_y2-box2_y1)
return box1_area + box2_area - intersection(box1,box2)
def intersection(box1,box2):
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
x1 = max(box1_x1,box2_x1)
y1 = max(box1_y1,box2_y1)
x2 = min(box1_x2,box2_x2)
y2 = min(box1_y2,box2_y2)
return (x2-x1)*(y2-y1)
""" def detect_objects_on_image(buf):
"""
""""
Function receives an image,
passes it through YOLOv8 neural network
and returns an array of detected objects
and their bounding boxes
:param buf: Input image file stream
:return: Array of bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
"""
"""""
model = YOLO("best.pt")
results = model.predict(Image.open(buf))
result = results[0]
output = []
for box in result.boxes:
x1, y1, x2, y2 = [
round(x) for x in box.xyxy[0].tolist()
]
class_id = box.cls[0].item()
prob = round(box.conf[0].item(), 2)
output.append([
x1, y1, x2, y2, result.names[class_id], prob
])
return output
"""
serve(app, host='0.0.0.0', port=8080)

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ultralytics
flask
waitress
pillow