Compare commits
2 Commits
5397b09a13
...
3dc1bcf466
Author | SHA1 | Date | |
---|---|---|---|
3dc1bcf466 | |||
d8ac8c32a5 |
237
index.html
237
index.html
@ -1,70 +1,169 @@
|
|||||||
<!DOCTYPE html>
|
<!DOCTYPE html>
|
||||||
<html lang="en">
|
<html lang="en">
|
||||||
<head>
|
<head>
|
||||||
<meta charset="UTF-8">
|
<meta charset="UTF-8">
|
||||||
<title>YOLOv8 Object Detection</title>
|
<title>TomAIto</title>
|
||||||
<style>
|
<style>
|
||||||
canvas {
|
canvas {
|
||||||
display:block;
|
border: 1px solid black;
|
||||||
border: 1px solid black;
|
display: flex;
|
||||||
margin-top:10px;
|
flex-direction: column;
|
||||||
}
|
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
|
||||||
</style>
|
margin: auto; /* Centruj formularz na stronie */
|
||||||
</head>
|
margin-top: 10px;
|
||||||
<body>
|
}
|
||||||
<input id="uploadInput" type="file"/>
|
|
||||||
<canvas></canvas>
|
#weatherInfo {
|
||||||
<script>
|
display: flex;
|
||||||
/**
|
flex-direction: column;
|
||||||
* "Upload" button onClick handler: uploads selected
|
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
|
||||||
* image file to backend, receives an array of
|
margin: auto; /* Centruj formularz na stronie */
|
||||||
* detected objects and draws them on top of image
|
margin-top: 10px;
|
||||||
*/
|
}
|
||||||
const input = document.getElementById("uploadInput");
|
|
||||||
input.addEventListener("change",async(event) => {
|
|
||||||
const file = event.target.files[0];
|
form {
|
||||||
const data = new FormData();
|
display: flex;
|
||||||
data.append("image_file",file,"image_file");
|
flex-direction: column;
|
||||||
const response = await fetch("/detect",{
|
max-width: 300px; /* Ustaw maksymalną szerokość formularza */
|
||||||
method:"post",
|
margin: auto; /* Centruj formularz na stronie */
|
||||||
body:data
|
}
|
||||||
});
|
|
||||||
const boxes = await response.json();
|
label {
|
||||||
draw_image_and_boxes(file,boxes);
|
margin-top: 10px;
|
||||||
})
|
}
|
||||||
|
|
||||||
/**
|
button {
|
||||||
* Function draws the image from provided file
|
margin-top: 10px;
|
||||||
* and bounding boxes of detected objects on
|
}
|
||||||
* top of the image
|
</style>
|
||||||
* @param file Uploaded file object
|
</head>
|
||||||
* @param boxes Array of bounding boxes in format
|
<body>
|
||||||
[[x1,y1,x2,y2,object_type,probability],...]
|
<form id="uploadForm">
|
||||||
*/
|
|
||||||
function draw_image_and_boxes(file,boxes) {
|
<label for="cropType">Crop Type:</label>
|
||||||
const img = new Image()
|
<select id="cropType" name="cropType">
|
||||||
img.src = URL.createObjectURL(file);
|
<option value="external">External</option>
|
||||||
img.onload = () => {
|
<option value="internal">Internal</option>
|
||||||
const canvas = document.querySelector("canvas");
|
<option value="greenhouse">Greenhouse</option>
|
||||||
canvas.width = img.width;
|
</select>
|
||||||
canvas.height = img.height;
|
|
||||||
const ctx = canvas.getContext("2d");
|
<label for="location">Location:</label>
|
||||||
ctx.drawImage(img,0,0);
|
<input type="text" id="location" name="location" required/>
|
||||||
ctx.strokeStyle = "#00FF00";
|
|
||||||
ctx.lineWidth = 5;
|
<label for="variety">Variety:</label>
|
||||||
ctx.font = "20px serif";
|
<input type="text" id="variety" name="variety" required/>
|
||||||
boxes.forEach(([x1,y1,x2,y2,object_type, prob]) => {
|
|
||||||
|
<label for="uploadInput">Choose an image:</label>
|
||||||
const label = `${object_type} ${prob.toFixed(2)}`;
|
<input id="uploadInput" type="file" required/>
|
||||||
ctx.strokeRect(x1,y1,x2-x1,y2-y1);
|
|
||||||
ctx.fillStyle = "#00ff00";
|
<button type="Sprawdz">Sprawdz</button>
|
||||||
const width = ctx.measureText(label).width;
|
</form>
|
||||||
ctx.fillRect(x1,y1,width+10,25);
|
|
||||||
ctx.fillStyle = "#000000";
|
<canvas></canvas>
|
||||||
ctx.fillText(label,x1,y1+18);
|
<div id="weatherInfo"></div>
|
||||||
});
|
<script>
|
||||||
}
|
const form = document.getElementById("uploadForm");
|
||||||
}
|
|
||||||
</script>
|
form.addEventListener("submit", async (event) => {
|
||||||
</body>
|
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>
|
</html>
|
@ -1,179 +1,177 @@
|
|||||||
#from ultralytics import YOLO
|
#from ultralytics import YOLO
|
||||||
from flask import request, Flask, jsonify
|
from flask import request, Flask, jsonify
|
||||||
from waitress import serve
|
from waitress import serve
|
||||||
from PIL import Image
|
from PIL import Image
|
||||||
import onnxruntime as ort
|
import onnxruntime as ort
|
||||||
import numpy as np
|
import numpy as np
|
||||||
|
import requests
|
||||||
#my changes
|
|
||||||
import os
|
#my changes
|
||||||
|
import os
|
||||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
|
||||||
# Change the working directory to the script's directory
|
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||||
os.chdir(script_dir)
|
# Change the working directory to the script's directory
|
||||||
|
os.chdir(script_dir)
|
||||||
yolo_classes = ["b_fully_ripened",
|
|
||||||
"b_half_ripened",
|
yolo_classes = ["b_fully_ripened",
|
||||||
"b_green",
|
"b_half_ripened",
|
||||||
"l_fully_ripened",
|
"b_green",
|
||||||
"l_half_ripened",
|
"l_fully_ripened",
|
||||||
"l_green"
|
"l_half_ripened",
|
||||||
]
|
"l_green"
|
||||||
|
]
|
||||||
#app start
|
|
||||||
|
#app start
|
||||||
app = Flask(__name__)
|
|
||||||
|
app = Flask(__name__)
|
||||||
@app.route("/")
|
|
||||||
def root():
|
|
||||||
"""
|
@app.route("/")
|
||||||
Site main page handler function.
|
def root():
|
||||||
:return: Content of index.html file
|
"""
|
||||||
"""
|
Site main page handler function.
|
||||||
with open("index.html") as file:
|
:return: Content of index.html file
|
||||||
return file.read()
|
"""
|
||||||
|
with open("index.html") as file:
|
||||||
|
return file.read()
|
||||||
@app.route("/detect", methods=["POST"])
|
|
||||||
def detect():
|
@app.route("/detect", methods=["POST"])
|
||||||
"""
|
def detect():
|
||||||
Handler of /detect POST endpoint
|
buf = request.files["image_file"]
|
||||||
Receives uploaded file with a name "image_file", passes it
|
crop_type = request.form.get("cropType")
|
||||||
through YOLOv8 object detection network and returns and array
|
location = request.form.get("location")
|
||||||
of bounding boxes.
|
variety = request.form.get("variety")
|
||||||
:return: a JSON array of objects bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
|
boxes, orientation = detect_objects_on_image(buf.stream)
|
||||||
"""
|
# Do something with crop_type, location, and variety here (e.g., store in database)
|
||||||
buf = request.files["image_file"]
|
|
||||||
boxes, orientation = detect_objects_on_image(buf.stream)
|
return jsonify(boxes)
|
||||||
#print(boxes)
|
|
||||||
#print(orientation)
|
|
||||||
return jsonify(boxes)
|
def detect_objects_on_image(buf):
|
||||||
|
input, img_width, img_height = prepare_input(buf)
|
||||||
def detect_objects_on_image(buf):
|
output = run_model(input)
|
||||||
input, img_width, img_height = prepare_input(buf)
|
orientation = get_orientation(buf)
|
||||||
output = run_model(input)
|
processed_output = process_output(output, img_width, img_height, orientation)
|
||||||
orientation = get_orientation(buf)
|
return processed_output, orientation
|
||||||
processed_output = process_output(output, img_width, img_height, orientation)
|
|
||||||
return processed_output, orientation
|
def prepare_input(buf):
|
||||||
|
img = Image.open(buf)
|
||||||
def prepare_input(buf):
|
img_width, img_height = img.size
|
||||||
img = Image.open(buf)
|
img = img.resize((640, 640))
|
||||||
img_width, img_height = img.size
|
img = img.convert("RGB")
|
||||||
img = img.resize((640, 640))
|
input = np.array(img)
|
||||||
img = img.convert("RGB")
|
input = input.transpose(2, 0, 1)
|
||||||
input = np.array(img)
|
input = input.reshape(1, 3, 640, 640) / 255.0
|
||||||
input = input.transpose(2, 0, 1)
|
return input.astype(np.float32), img_width, img_height
|
||||||
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'])
|
||||||
def run_model(input):
|
outputs = model.run(["output0"], {"images":input})
|
||||||
model = ort.InferenceSession("best.onnx", providers=['CPUExecutionProvider'])
|
return outputs[0]
|
||||||
outputs = model.run(["output0"], {"images":input})
|
|
||||||
return outputs[0]
|
def process_output(output, img_width, img_height, orientation):
|
||||||
|
output = output[0].astype(float)
|
||||||
def process_output(output, img_width, img_height, orientation):
|
output = output.transpose()
|
||||||
output = output[0].astype(float)
|
|
||||||
output = output.transpose()
|
boxes = []
|
||||||
|
for row in output:
|
||||||
boxes = []
|
prob = row[4:].max()
|
||||||
for row in output:
|
if prob < 0.5:
|
||||||
prob = row[4:].max()
|
continue
|
||||||
if prob < 0.5:
|
|
||||||
continue
|
class_id = row[4:].argmax()
|
||||||
|
label = yolo_classes[class_id]
|
||||||
class_id = row[4:].argmax()
|
xc, yc, w, h = row[:4]
|
||||||
label = yolo_classes[class_id]
|
x1 = (xc - w/2) / 640 * img_width
|
||||||
xc, yc, w, h = row[:4]
|
y1 = (yc - h/2) / 640 * img_height
|
||||||
x1 = (xc - w/2) / 640 * img_width
|
x2 = (xc + w/2) / 640 * img_width
|
||||||
y1 = (yc - h/2) / 640 * img_height
|
y2 = (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])
|
||||||
|
|
||||||
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)
|
||||||
# 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)
|
||||||
# Sort and apply non-max suppression as before
|
result = []
|
||||||
adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
|
while len(adjusted_boxes) > 0:
|
||||||
result = []
|
result.append(adjusted_boxes[0])
|
||||||
while len(adjusted_boxes) > 0:
|
adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
|
||||||
result.append(adjusted_boxes[0])
|
|
||||||
adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
|
return result
|
||||||
|
|
||||||
return result
|
|
||||||
|
def iou(box1,box2):
|
||||||
|
return intersection(box1,box2)/union(box1,box2)
|
||||||
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]
|
||||||
def union(box1,box2):
|
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
||||||
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
|
box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
|
||||||
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
box2_area = (box2_x2-box2_x1)*(box2_y2-box2_y1)
|
||||||
box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
|
return box1_area + box2_area - intersection(box1,box2)
|
||||||
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]
|
||||||
def intersection(box1,box2):
|
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
||||||
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
|
x1 = max(box1_x1,box2_x1)
|
||||||
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
y1 = max(box1_y1,box2_y1)
|
||||||
x1 = max(box1_x1,box2_x1)
|
x2 = min(box1_x2,box2_x2)
|
||||||
y1 = max(box1_y1,box2_y1)
|
y2 = min(box1_y2,box2_y2)
|
||||||
x2 = min(box1_x2,box2_x2)
|
return (x2-x1)*(y2-y1)
|
||||||
y2 = min(box1_y2,box2_y2)
|
|
||||||
return (x2-x1)*(y2-y1)
|
def get_orientation(image_path):
|
||||||
|
with Image.open(image_path) as img:
|
||||||
def get_orientation(image_path):
|
if hasattr(img, '_getexif'):
|
||||||
with Image.open(image_path) as img:
|
exif_data = img._getexif()
|
||||||
if hasattr(img, '_getexif'):
|
if exif_data is not None:
|
||||||
exif_data = img._getexif()
|
return exif_data.get(274, 1) # Default to normal orientation
|
||||||
if exif_data is not None:
|
return 1 # Default orientation if no EXIF data
|
||||||
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 = []
|
||||||
def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
|
for box in boxes:
|
||||||
adjusted_boxes = []
|
x1, y1, x2, y2, label, prob = box
|
||||||
for box in boxes:
|
|
||||||
x1, y1, x2, y2, label, prob = box
|
# Apply transformations based on orientation
|
||||||
|
if orientation == 3: # 180 degrees
|
||||||
# Apply transformations based on orientation
|
x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
|
||||||
if orientation == 3: # 180 degrees
|
elif orientation == 6: # 270 degrees (or -90 degrees)
|
||||||
x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
|
x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
|
||||||
elif orientation == 6: # 270 degrees (or -90 degrees)
|
elif orientation == 8: # 90 degrees
|
||||||
x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
|
x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
|
||||||
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])
|
||||||
|
|
||||||
adjusted_boxes.append([x1, y1, x2, y2, label, prob])
|
return adjusted_boxes
|
||||||
|
|
||||||
return adjusted_boxes
|
|
||||||
|
""" def detect_objects_on_image(buf):
|
||||||
|
"""
|
||||||
""" def detect_objects_on_image(buf):
|
""""
|
||||||
"""
|
Function receives an image,
|
||||||
""""
|
passes it through YOLOv8 neural network
|
||||||
Function receives an image,
|
and returns an array of detected objects
|
||||||
passes it through YOLOv8 neural network
|
and their bounding boxes
|
||||||
and returns an array of detected objects
|
:param buf: Input image file stream
|
||||||
and their bounding boxes
|
:return: Array of bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
|
||||||
: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))
|
||||||
model = YOLO("best.pt")
|
result = results[0]
|
||||||
results = model.predict(Image.open(buf))
|
output = []
|
||||||
result = results[0]
|
for box in result.boxes:
|
||||||
output = []
|
x1, y1, x2, y2 = [
|
||||||
for box in result.boxes:
|
round(x) for x in box.xyxy[0].tolist()
|
||||||
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)
|
||||||
class_id = box.cls[0].item()
|
output.append([
|
||||||
prob = round(box.conf[0].item(), 2)
|
x1, y1, x2, y2, result.names[class_id], prob
|
||||||
output.append([
|
])
|
||||||
x1, y1, x2, y2, result.names[class_id], prob
|
return output
|
||||||
])
|
|
||||||
return output
|
"""
|
||||||
|
serve(app, host='0.0.0.0', port=8080)
|
||||||
"""
|
|
||||||
serve(app, host='0.0.0.0', port=8080)
|
|
||||||
|
Loading…
Reference in New Issue
Block a user