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קורס יסודות בינה מלאוכתית – תרגיל RB23-07t1 – בינה מלאכותית YOLO

תרגיל כיתה נתרגל שימוש בבינה מלאוכתית בסביבת פיתוח colab בעזרת בינה מלאכותית YOLO

1. תרגיל כיתה בנה תוכנה עם בינהמלאכותית מסוג YOLO8 שיודעת לזהות רכבים בכביש

חומר לתרגיל  – כתובת תמונה

https://robotronix.co.il/wp-content/uploads/2024/06/image-12.png

https://robotronix.co.il/wp-content/uploads/2024/06/image-13.png

2. ספור את כמות הרכבים בכביש

2.1 אם כמות הרכבים גדולה מ 10 כתוב high traffic

2.2 אם קטנה כתוב Low Traffic

פתרון :

https://colab.research.google.com/drive/1MDW3dee8TcuBmZLjbJohHhyUJopvi-HP?usp=sharing

1. נכנס ל גימייל ומשם ל colab
2. נתקין סביבת עבודה :
3. %pip install ultralytics import ultralytics ultralytics.checks() !pip install opencv-python-headless print (" **** done **** ")

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

   
    
4. import cv2 import requests import numpy as np from ultralytics import YOLO import matplotlib.pyplot as plt # Define colors blue = (255, 0, 0) green = (0, 255, 0) red = (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/2024/06/image-12.png' 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('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='3-300x199.jpg', conf=0.14) # 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)), blue, 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()

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   import cv2 import requests import numpy as np from ultralytics import YOLO import matplotlib.pyplot as plt       # Define colors blue = (255, 0, 0) green = (0, 255, 0) red = (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/2024/06/image-12.png' 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('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='3-300x199.jpg', conf=0.14)   # 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)), blue, 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()

   
    
5. מספר רכבים

https://colab.research.google.com/drive/1MDW3dee8TcuBmZLjbJohHhyUJopvi-HP?usp=sharing

6.נכתוב  קוד בעזרת בינה מלאוכתית CHATGPT כמה מכוניות יש בכביש

תרגיל 2 זיהוי  דברים בשמים

https://colab.research.google.com/drive/1NSUIreJljQ5Y2ZuBtHPk-Z_jQKPaK-LO?usp=sharing