commit 92c7c0b9573da64aee957302ba6fac7592f5f9a6 Author: anetla Date: Thu Dec 28 22:09:32 2023 +0100 initial commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# 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/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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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. + + + Copyright (C) + + 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 . + +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: + + Copyright (C) + 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 +. + + 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 +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..bd9a816 --- /dev/null +++ b/README.md @@ -0,0 +1,28 @@ +# Tomato maturity detection web application + +
+ +
+ +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. diff --git a/best.onnx b/best.onnx new file mode 100644 index 0000000..61b20fc Binary files /dev/null and b/best.onnx differ diff --git a/best.pt b/best.pt new file mode 100644 index 0000000..4d9d0df Binary files /dev/null and b/best.pt differ diff --git a/example_picture.png b/example_picture.png new file mode 100644 index 0000000..d3bc33d Binary files /dev/null and b/example_picture.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..fb80ff3 --- /dev/null +++ b/index.html @@ -0,0 +1,70 @@ + + + + + YOLOv8 Object Detection + + + + + + + + \ No newline at end of file diff --git a/object_detector.py b/object_detector.py new file mode 100644 index 0000000..ebd5ccf --- /dev/null +++ b/object_detector.py @@ -0,0 +1,152 @@ +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) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4147807 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +ultralytics +flask +waitress +pillow \ No newline at end of file