קורס יסודות בינה מלאכותית – RB23-05 – תרגול זיהוי אובייקטים YOLO – רובוטרוניקס: מפתחת מעגלים ואלקטרוניקה ,מיקרובקרים , תוכנה ורובוטיקה: התקשר עכשיו 0506399001

קורס יסודות בינה מלאכותית – RB23-05 – תרגול זיהוי אובייקטים YOLO

מחבר:admin
פורסם:יוני 16, 2024
קטגוריה:רובוטרוניקס כללי
תגובות:אין תגובות

קורס יסודות בינה מלאכותית – RB23-05 – תרגול זיהוי אובייקטים YOLO

1.נזכיר YOLO 8 יש מודל דוגמא – שיוע לזהות כ 80 מחלקות כלליות שהם ברורות

1.1 רשימת מחלקות

Person	Bicycle	Car	Motorcycle	Airplane	Bus	Train	Truck	Boat	Traffic light
Fire hydrant	Stop sign	Parking meter	Bench	Bird	Cat	Dog	Horse	Sheep	Cow
Elephant	Bear	Zebra	Giraffe	Backpack	Umbrella	Handbag	Tie	Suitcase	Frisbee
Skis	Snowboard	Sports ball	Kite	Baseball bat	Baseball glove	Skateboard	Surfboard	Tennis racket	Bottle
Wine glass	Cup	Fork	Knife	Spoon	Bowl	Banana	Apple	Sandwich	Orange
Broccoli	Carrot	Hot dog	Pizza	Donut	Cake	Chair	Couch	Potted plant	Bed
Dining table	Toilet	TV	Laptop	Mouse	Remote	Keyboard	Cell phone	Microwave	Oven
Toaster	Sink	Refrigerator	Book	Clock	Vase	Scissors	Teddy bear	Hair drier	Toothbrush

2. התקן את הבינה המלאכותית לזיהוי אובייקטים מסוג YOLO 8 לפי ההוראות הבאות

2.1 חובה לצפות בסרטון שלב אחר שלב ולהעזר ממנו בהתקנה

2.2 יש ראשית להיכנס לגוגל לחשבון האישי ואז להיכנס ל COLAB (בסיום ההרצאה יש להתנתק מחשבון GOOGLE )

2.2.2 קישור

https://colab.research.google.com/drive/1wfPhYH4xOMdmW3O730b42bNvPnHxuGwI?usp=sharing

2.3 התקנה של YOLO

%pip install ultralytics
import ultralytics
ultralytics.checks()
!pip install opencv-python-headless
print (" **** done **** ")

%pip install ultralytics

import ultralytics

ultralytics.checks()

!pip install opencv-python-headless

print (" **** done **** ")

2.4 יצירת ספריות

import os
current_path = os.getcwd()
print("Current Path:", current_path)
# Create the directories using shell commands
!mkdir -p download/images
!mkdir -p download/movies

print("Directories download/images and download/movies created successfully.")

import os

current_path = os.getcwd()

print("Current Path:", current_path)

# Create the directories using shell commands

!mkdir -p download/images

!mkdir -p download/movies

print("Directories download/images and download/movies created successfully.")

2.5

import cv2
import requests
import numpy as np
from ultralytics import YOLO
import matplotlib.pyplot as plt



# Define colors
red = (255, 0, 0)
green = (0, 255, 0)
blue = (0, 0, 255)
cyan = (0, 255, 255)
magenta = (255, 0, 255)
yellow = (255, 255, 0)
maroon = (128, 0, 0)
dark_green = (0, 128, 0)
navy = (0, 0, 128)
olive = (128, 128, 0)
purple = (128, 0, 128)
teal = (0, 128, 128)
white = (255, 255, 255)
black = (0, 0, 0)

# Load the YOLOv8 model
model = YOLO('yolov8n.pt')

# Download the image from the URL
image_url = 'https://robotronix.co.il/wp-content/uploads/2024/05/3-300x199.jpg'
response = requests.get(image_url)
image_array = np.asarray(bytearray(response.content), dtype=np.uint8)
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)

# Save the image locally
cv2.imwrite('download/images/3-300x199.jpg', image)

# Run the model on the downloaded image with a confidence threshold of 0.54 without drawing boxes
results = model.predict(source='download/images/3-300x199.jpg', conf=0.54)

# Function to draw bounding boxes and text with custom text size
def draw_custom_boxes(image, results, model):
    for result in results:
        boxes = result.boxes.data.cpu().numpy()
        for box in boxes:
            x1, y1, x2, y2, conf, cls = box
            label = f'{model.names[int(cls)]} {conf:.2f}'
            # Draw bounding box
            cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), red, 2)
            # Draw label background
            (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)
            cv2.rectangle(image, (int(x1), int(y1) - h - 3), (int(x1) + w, int(y1)),red, -1)
            # Draw label text
            cv2.putText(image, label, (int(x1), int(y1) - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.4, white, 1)

# Display the original image
plt.figure(figsize=(14, 7))
plt.subplot(1, 2, 1)
plt.title("Original Image")
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis('off')

# Copy the original image to draw boxes on it
result_image = image.copy()

# Draw custom boxes and labels
draw_custom_boxes(result_image, results, model)

# Save the result image
cv2.imwrite('result_image.jpg', result_image)

# Display the result image inline
plt.subplot(1, 2, 2)
plt.title("Detection Results")
plt.imshow(cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB))
plt.axis('off')

plt.show()

import cv2

import requests

import numpy as np

from ultralytics import YOLO

import matplotlib.pyplot as plt

# Define colors

red = (255, 0, 0)

green = (0, 255, 0)

blue = (0, 0, 255)

cyan = (0, 255, 255)

magenta = (255, 0, 255)

yellow = (255, 255, 0)

maroon = (128, 0, 0)

dark_green = (0, 128, 0)

navy = (0, 0, 128)

olive = (128, 128, 0)

purple = (128, 0, 128)

teal = (0, 128, 128)

white = (255, 255, 255)

black = (0, 0, 0)

# Load the YOLOv8 model

model = YOLO('yolov8n.pt')

# Download the image from the URL

image_url = 'https://robotronix.co.il/wp-content/uploads/2024/05/3-300x199.jpg'

response = requests.get(image_url)

image_array = np.asarray(bytearray(response.content), dtype=np.uint8)

image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)

# Save the image locally

cv2.imwrite('download/images/3-300x199.jpg', image)

# Run the model on the downloaded image with a confidence threshold of 0.54 without drawing boxes

results = model.predict(source='download/images/3-300x199.jpg', conf=0.54)

# Function to draw bounding boxes and text with custom text size

def draw_custom_boxes(image, results, model):

for result in results:

boxes = result.boxes.data.cpu().numpy()

for box in boxes:

x1, y1, x2, y2, conf, cls = box

label = f'{model.names[int(cls)]} {conf:.2f}'

# Draw bounding box

cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), red, 2)

# Draw label background

(w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.4, 1)

cv2.rectangle(image, (int(x1), int(y1) - h - 3), (int(x1) + w, int(y1)),red, -1)

# Draw label text

cv2.putText(image, label, (int(x1), int(y1) - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.4, white, 1)

# Display the original image

plt.figure(figsize=(14, 7))

plt.subplot(1, 2, 1)

plt.title("Original Image")

plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

plt.axis('off')

# Copy the original image to draw boxes on it

result_image = image.copy()

# Draw custom boxes and labels

draw_custom_boxes(result_image, results, model)

# Save the result image

cv2.imwrite('result_image.jpg', result_image)

# Display the result image inline

plt.subplot(1, 2, 2)

plt.title("Detection Results")

plt.imshow(cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB))

plt.axis('off')

plt.show()

3 בדוק את התמונות הבאת

קורס יסודות בינה מלאכותית – RB23-05 – תרגול זיהוי אובייקטים YOLO

Person

Bicycle

Car

Motorcycle

Airplane

Bus

Train

Truck

Boat

Traffic light

Fire hydrant

Stop sign

Parking meter

Bench

Bird

Cat

Dog

Horse

Sheep

Cow

Elephant

Bear

Zebra

Giraffe

Backpack

Umbrella

Handbag

Tie

Suitcase

Frisbee

Skis

Snowboard

Sports ball

Kite

Baseball bat

Baseball glove

Skateboard

Surfboard

Tennis racket

Bottle

Wine glass

Cup

Fork

Knife

Spoon

Bowl

Banana

Apple

Sandwich

Orange

Broccoli

Carrot

Hot dog

Pizza

Donut

Cake

Chair

Couch

Potted plant

Bed

Dining table

Toilet

TV

Laptop

Mouse

Remote

Keyboard

Cell phone

Microwave

Oven

Toaster

Sink

Refrigerator

Book

Clock

Vase

Scissors

Teddy bear

Hair drier