קורס יסודות בינה מלאכותית – RB23-06 – רובוטרוניקס: מפתחת מעגלים ואלקטרוניקה ,מיקרובקרים , תוכנה ורובוטיקה: התקשר עכשיו 0506399001

קורס יסודות בינה מלאכותית – RB23-06

CANVA בינה מאלכותית משנה תמונות – אז מה האמת ?

יצירת מוסיקה מטקטס

https://huggingface.co/spaces/facebook/MusicGen

רשות : העשרה

יצירת סרטון

https://www.youtube.com/watch?v=dwenyOgLZwg

שימוש במנוע בינה מלאכותית https://copilot.microsoft.com/

זיהוי אובייקטים :

https://colab.research.google.com/

%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")

# Run YOLOv8 model on the specified image with a confidence threshold of 0.54
!yolo predict model=yolov8n.pt source='https://robotronix.co.il/wp-content/uploads/2024/05/3-300x199.jpg' conf=0.54

1 2	# Run YOLOv8 model on the specified image with a confidence threshold of 0.54 !yolo predict model=yolov8n.pt source='https://robotronix.co.il/wp-content/uploads/2024/05/3-300x199.jpg' conf=0.54

from ultralytics import YOLO
import cv2

# Load the YOLOv8 model pre-trained on COCO dataset
model = YOLO('yolov8n.pt')  # You can choose 'yolov8n', 'yolov8s', 'yolov8m', 'yolov8l', or 'yolov8x'

# Load the image
image_path = '2.jpg'
image = cv2.imread(image_path)

# Run inference on the image
results = model(image_path)

# Filter results to count only people
people_count = 0
for result in results:
    for box in result.boxes:
        if box.cls == 0:  # Class 0 corresponds to 'person' in the COCO dataset
            people_count += 1

# Display the number of people detected
print(f"Number of people detected: {people_count}")

from ultralytics import YOLO

import cv2

# Load the YOLOv8 model pre-trained on COCO dataset

model = YOLO('yolov8n.pt') # You can choose 'yolov8n', 'yolov8s', 'yolov8m', 'yolov8l', or 'yolov8x'

# Load the image

image_path = '2.jpg'

image = cv2.imread(image_path)

# Run inference on the image

results = model(image_path)

# Filter results to count only people

people_count = 0

for result in results:

for box in result.boxes:

if box.cls == 0: # Class 0 corresponds to 'person' in the COCO dataset

people_count += 1

# Display the number of people detected

print(f"Number of people detected: {people_count}")

במבוא לסקירפטים

import cv2
import pytube
from ultralytics import YOLO
import matplotlib.pyplot as plt

# Function to download the YouTube video
def download_youtube_video(url, output_path='video.mp4'):
    yt = pytube.YouTube(url)
    stream = yt.streams.filter(file_extension='mp4').first()
    stream.download(filename=output_path)
    return output_path

# Function to process the first 10 seconds of the video
def process_video(video_path, seconds=10, fps=30):
    cap = cv2.VideoCapture(video_path)
    frames = []
    frame_interval = fps  # Extract one frame per second

    for i in range(seconds):
        cap.set(cv2.CAP_PROP_POS_FRAMES, i * frame_interval)
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(frame)

    cap.release()
    return frames

# Function to run YOLOv8 on frames and save images
def run_yolo_on_frames(frames, model, output_dir='output_images'):
    results = []
    for idx, frame in enumerate(frames):
        result = model(frame)
        results.append(result)
        annotated_frame = result[0].plot(line_width=1, font_size=10)
        # Save annotated frame as an image
        output_path = f"{output_dir}/frame_{idx+1}.jpg"
        cv2.imwrite(output_path, cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR))
        plt.imshow(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
        plt.axis('off')
        plt.show()

    return results

# Main code
youtube_url = 'https://www.youtube.com/shorts/mmEZ7pdrVIQ'
video_path = download_youtube_video(youtube_url)
print(f"Video downloaded to: {video_path}")

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

frames = process_video(video_path, seconds=10, fps=30)
results = run_yolo_on_frames(frames, model)

print("Processing completed and images saved.")

import cv2

import pytube

from ultralytics import YOLO

import matplotlib.pyplot as plt

# Function to download the YouTube video

def download_youtube_video(url, output_path='video.mp4'):

yt = pytube.YouTube(url)

stream = yt.streams.filter(file_extension='mp4').first()

stream.download(filename=output_path)

return output_path

# Function to process the first 10 seconds of the video

def process_video(video_path, seconds=10, fps=30):

cap = cv2.VideoCapture(video_path)

frames = []

frame_interval = fps # Extract one frame per second

for i in range(seconds):

cap.set(cv2.CAP_PROP_POS_FRAMES, i * frame_interval)

ret, frame = cap.read()

if not ret:

break

frames.append(frame)

cap.release()

return frames

# Function to run YOLOv8 on frames and save images

def run_yolo_on_frames(frames, model, output_dir='output_images'):

results = []

for idx, frame in enumerate(frames):

result = model(frame)

results.append(result)

annotated_frame = result[0].plot(line_width=1, font_size=10)

# Save annotated frame as an image

output_path = f"{output_dir}/frame_{idx+1}.jpg"

cv2.imwrite(output_path, cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR))

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

plt.axis('off')

plt.show()

return results

# Main code

youtube_url = 'https://www.youtube.com/shorts/mmEZ7pdrVIQ'

video_path = download_youtube_video(youtube_url)

print(f"Video downloaded to: {video_path}")

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

frames = process_video(video_path, seconds=10, fps=30)

results = run_yolo_on_frames(frames, model)

print("Processing completed and images saved.")

COLAB

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