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Author SHA1 Message Date
LABS\s478989 3dc1bcf466 Crop type affects the output 2024-01-16 16:02:04 +01:00
LABS\s478989 d8ac8c32a5 Weather api added 2024-01-16 15:30:46 +01:00
2 changed files with 345 additions and 248 deletions
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237
index.html
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@ -1,70 +1,169 @@
<!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>
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>TomAIto</title>
<style>
canvas {
border: 1px solid black;
display: flex;
flex-direction: column;
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
margin: auto; /* Centruj formularz na stronie */
margin-top: 10px;
}
#weatherInfo {
display: flex;
flex-direction: column;
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
margin: auto; /* Centruj formularz na stronie */
margin-top: 10px;
}
form {
display: flex;
flex-direction: column;
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
margin: auto; /* Centruj formularz na stronie */
}
label {
margin-top: 10px;
}
button {
margin-top: 10px;
}
</style>
</head>
<body>
<form id="uploadForm">
<label for="cropType">Crop Type:</label>
<select id="cropType" name="cropType">
<option value="external">External</option>
<option value="internal">Internal</option>
<option value="greenhouse">Greenhouse</option>
</select>
<label for="location">Location:</label>
<input type="text" id="location" name="location" required/>
<label for="variety">Variety:</label>
<input type="text" id="variety" name="variety" required/>
<label for="uploadInput">Choose an image:</label>
<input id="uploadInput" type="file" required/>
<button type="Sprawdz">Sprawdz</button>
</form>
<canvas></canvas>
<div id="weatherInfo"></div>
<script>
const form = document.getElementById("uploadForm");
form.addEventListener("submit", async (event) => {
event.preventDefault();
const fileInput = document.getElementById("uploadInput");
const cropTypeInput = document.getElementById("cropType");
const locationInput = document.getElementById("location");
const varietyInput = document.getElementById("variety");
const file = fileInput.files[0];
const data = new FormData();
data.append("image_file", file);
data.append("cropType", cropTypeInput.value);
data.append("location", locationInput.value);
data.append("variety", varietyInput.value);
const response = await fetch("/detect", {
method: "post",
body: data
});
const boxes = await response.json();
draw_image_and_boxes(file, boxes);
const location = locationInput.value;
if (cropTypeInput.value == "external") {
getWeather(location);
}
else {
const randomTips = [
"water your tomatoes every day.",
"keep the temperature between 20-25 degrees Celsius.",
"place the tomatoes in sunlight."];
const randomTip = randomTips[Math.floor(Math.random() * randomTips.length)];
document.getElementById("weatherInfo").innerText = "TIP! To maximalize your yields " + randomTip;
}
});
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} ${prob.toFixed(2)}`;
ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
ctx.fillStyle = "#00ff00";
const width = ctx.measureText(label).width;
ctx.fillRect(x1, y1, width + 10, 25);
ctx.fillStyle = "#000000";
ctx.fillText(label, x1, y1 + 18);
});
}
}
async function getWeather(location) {
const apiKey = "1fc6a52c96331d035a828cc0c1606241";
const apiUrl = `https://api.openweathermap.org/data/2.5/weather?q=${location}&appid=${apiKey}`;
try {
const weatherResponse = await fetch(apiUrl);
const weatherData = await weatherResponse.json();
if (weatherData.main && weatherData.main.temp) {
const temperatureKelvin = weatherData.main.temp;
const temperatureCelsius = temperatureKelvin - 273.15;
const tempRound = Math.round(temperatureCelsius);
const weatherCondition = weatherData.weather[0].main;
const isRaining = weatherCondition === 'Rain';
let additionalInfo = `${isRaining ? 'Rain is expected in your town today, so you dont have to water your tomatoes' :
'No rainfall is expected in the city today, so you need to water your tomatoes!'} Temperature in ${location} is ${tempRound} °C.`;
if (tempRound < 15) {
additionalInfo += ' So you need to cover your tomatoes!';
}
document.getElementById("weatherInfo").innerText = additionalInfo;
} else {
document.getElementById("weatherInfo").innerText = "Unable to fetch weather data.";
}
} catch (error) {
console.error("Error fetching weather data:", error);
document.getElementById("weatherInfo").innerText = "Error fetching weather data.";
}
}
</script>
</body>
</html>

356
object_detector.py
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@ -1,179 +1,177 @@
#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, orientation = detect_objects_on_image(buf.stream)
#print(boxes)
#print(orientation)
return jsonify(boxes)
def detect_objects_on_image(buf):
input, img_width, img_height = prepare_input(buf)
output = run_model(input)
orientation = get_orientation(buf)
processed_output = process_output(output, img_width, img_height, orientation)
return processed_output, orientation
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, orientation):
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
boxes.append([x1, y1, x2, y2, label, prob])
# Adjust boxes based on orientation
adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
# Sort and apply non-max suppression as before
adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
result = []
while len(adjusted_boxes) > 0:
result.append(adjusted_boxes[0])
adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_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 get_orientation(image_path):
with Image.open(image_path) as img:
if hasattr(img, '_getexif'):
exif_data = img._getexif()
if exif_data is not None:
return exif_data.get(274, 1) # Default to normal orientation
return 1 # Default orientation if no EXIF data
def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
adjusted_boxes = []
for box in boxes:
x1, y1, x2, y2, label, prob = box
# Apply transformations based on orientation
if orientation == 3: # 180 degrees
x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
elif orientation == 6: # 270 degrees (or -90 degrees)
x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
elif orientation == 8: # 90 degrees
x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
adjusted_boxes.append([x1, y1, x2, y2, label, prob])
return adjusted_boxes
""" 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)
#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
import requests
#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():
buf = request.files["image_file"]
crop_type = request.form.get("cropType")
location = request.form.get("location")
variety = request.form.get("variety")
boxes, orientation = detect_objects_on_image(buf.stream)
# Do something with crop_type, location, and variety here (e.g., store in database)
return jsonify(boxes)
def detect_objects_on_image(buf):
input, img_width, img_height = prepare_input(buf)
output = run_model(input)
orientation = get_orientation(buf)
processed_output = process_output(output, img_width, img_height, orientation)
return processed_output, orientation
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, orientation):
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
boxes.append([x1, y1, x2, y2, label, prob])
# Adjust boxes based on orientation
adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
# Sort and apply non-max suppression as before
adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
result = []
while len(adjusted_boxes) > 0:
result.append(adjusted_boxes[0])
adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_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 get_orientation(image_path):
with Image.open(image_path) as img:
if hasattr(img, '_getexif'):
exif_data = img._getexif()
if exif_data is not None:
return exif_data.get(274, 1) # Default to normal orientation
return 1 # Default orientation if no EXIF data
def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
adjusted_boxes = []
for box in boxes:
x1, y1, x2, y2, label, prob = box
# Apply transformations based on orientation
if orientation == 3: # 180 degrees
x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
elif orientation == 6: # 270 degrees (or -90 degrees)
x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
elif orientation == 8: # 90 degrees
x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
adjusted_boxes.append([x1, y1, x2, y2, label, prob])
return adjusted_boxes
""" 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)