יסודות בינה מלאכותית : 09-RB27

(לשיעור הבא נא לפתוח חשבון ב DISCORD על מנת ללמוד תוכנות ל A.I)

עבדוה עם אפליקציה לעריכות וידאו – תרגיל כיתה

1.1 תרגיל כיתה

1.2.הצטלם על רקע לבן והחלקף את הדמות שלך בסרטון של ריקוד

1.3 העזר בתוכנות https://www.photopea.com/

2, עולה כסף אבל נראה בכל אופן

3. זיהוי פנים על ידי בינה מלאכותית

הרצת סקריפט בקוד פייתון התקנת סביבית עבדוה

3.1 התקנה : (תרגול בכיתה )

.1 3.1 פתח את אנקודמה כמו בשיעורים הקודמים

3.1.2 התקן הרץ בתוך הטרמינל את ההפקדות הבאות

3.1.3 הרץ את הפקודות הבאות אם יש שגיעות העזר ב CHATGPT לתקן אותם

python -m pip install --upgrade pip setuptools wheel

pip install cmake

pip install dlib

pip install face_recognition

pip install opencv-python

pip install numpy

python -m pip install --upgrade pip setuptools wheel

pip install cmake

pip install dlib

pip install face_recognition

pip install opencv-python

pip install numpy

3.2 בדיקה שהכל הותקן תקין

import face_recognition
import cv2
import numpy as np

print("face_recognition version:", face_recognition.__version__)
print("OpenCV version:", cv2.__version__)
print("NumPy version:", np.__version__)

import face_recognition

import cv2

import numpy as np

print("face_recognition version:", face_recognition.__version__)

print("OpenCV version:", cv2.__version__)

print("NumPy version:", np.__version__)

3.3 אימון הבינה המלאוכתית לזיהוי פנים הורדת תמונות והרצת קוד ניסיון :

import face_recognition
import cv2
import os
import numpy as np

# Step 1: Load known faces
known_face_encodings = []
known_face_names = []

# Define the full path to the known people directory
known_people_dir = r"D:\temp\faces"

for person_name in os.listdir(known_people_dir):
    person_folder = os.path.join(known_people_dir, person_name)
    if not os.path.isdir(person_folder):
        continue  # Skip non-folder files

    for image_name in os.listdir(person_folder):
        image_path = os.path.join(person_folder, image_name)
        if not image_name.lower().endswith(('.jpg', '.jpeg', '.png')):
            continue  # Skip non-image files

        image = face_recognition.load_image_file(image_path)
        face_encodings = face_recognition.face_encodings(image)

        # Debug: Check if encoding exists
        if face_encodings:
            print(f"Face detected in {image_path}")
            known_face_encodings.append(face_encodings[0])
            known_face_names.append(person_name)
        else:
            print(f"No face detected in {image_path}")

# Step 2: Capture or load an image to recognize
unknown_image_path = r"D:\temp\test\2.jpeg"  # Replace with the path to your test image
unknown_image = face_recognition.load_image_file(unknown_image_path)
unknown_face_locations = face_recognition.face_locations(unknown_image)
unknown_face_encodings = face_recognition.face_encodings(unknown_image, unknown_face_locations)

# Convert the image to BGR color (for OpenCV)
unknown_image_bgr = cv2.cvtColor(unknown_image, cv2.COLOR_RGB2BGR)

# Debug: Check if faces are detected in the unknown image
if not unknown_face_encodings:
    print("No faces detected in the unknown image.")
    cv2.imshow("Result", unknown_image_bgr)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    exit()

# Step 3: Compare and label faces
for (top, right, bottom, left), unknown_face in zip(unknown_face_locations, unknown_face_encodings):
    results = face_recognition.compare_faces(known_face_encodings, unknown_face)
    distances = face_recognition.face_distance(known_face_encodings, unknown_face)

    if any(results):
        best_match_index = np.argmin(distances)
        name = known_face_names[best_match_index]
        label = f"{name} ({distances[best_match_index]:.2f})"
        print(f"This is: {name}")
    else:
        name = "Unknown"
        label = "Unknown"
        print("This person is unknown.")

    # Draw a rectangle around the face
    cv2.rectangle(unknown_image_bgr, (left, top), (right, bottom), (0, 255, 0), 2)

    # Add a label below the face
    cv2.putText(unknown_image_bgr, label, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# Step 4: Display the result
cv2.imshow("Face Recognition Result", unknown_image_bgr)
cv2.waitKey(0)  # Wait for a key press to close the window
cv2.destroyAllWindows()

import face_recognition

import cv2

import os

import numpy as np

# Step 1: Load known faces

known_face_encodings = []

known_face_names = []

# Define the full path to the known people directory

known_people_dir = r"D:\temp\faces"

for person_name in os.listdir(known_people_dir):

person_folder = os.path.join(known_people_dir, person_name)

if not os.path.isdir(person_folder):

continue # Skip non-folder files

for image_name in os.listdir(person_folder):

image_path = os.path.join(person_folder, image_name)

if not image_name.lower().endswith(('.jpg', '.jpeg', '.png')):

continue # Skip non-image files

image = face_recognition.load_image_file(image_path)

face_encodings = face_recognition.face_encodings(image)

# Debug: Check if encoding exists

if face_encodings:

print(f"Face detected in {image_path}")

known_face_encodings.append(face_encodings[0])

known_face_names.append(person_name)

else:

print(f"No face detected in {image_path}")

# Step 2: Capture or load an image to recognize

unknown_image_path = r"D:\temp\test\2.jpeg" # Replace with the path to your test image

unknown_image = face_recognition.load_image_file(unknown_image_path)

unknown_face_locations = face_recognition.face_locations(unknown_image)

unknown_face_encodings = face_recognition.face_encodings(unknown_image, unknown_face_locations)

# Convert the image to BGR color (for OpenCV)

unknown_image_bgr = cv2.cvtColor(unknown_image, cv2.COLOR_RGB2BGR)

# Debug: Check if faces are detected in the unknown image

if not unknown_face_encodings:

print("No faces detected in the unknown image.")

cv2.imshow("Result", unknown_image_bgr)

cv2.waitKey(0)

cv2.destroyAllWindows()

exit()

# Step 3: Compare and label faces

for (top, right, bottom, left), unknown_face in zip(unknown_face_locations, unknown_face_encodings):

results = face_recognition.compare_faces(known_face_encodings, unknown_face)

distances = face_recognition.face_distance(known_face_encodings, unknown_face)

if any(results):

best_match_index = np.argmin(distances)

name = known_face_names[best_match_index]

label = f"{name} ({distances[best_match_index]:.2f})"

print(f"This is: {name}")

else:

name = "Unknown"

label = "Unknown"

print("This person is unknown.")

# Draw a rectangle around the face

cv2.rectangle(unknown_image_bgr, (left, top), (right, bottom), (0, 255, 0), 2)

# Add a label below the face

cv2.putText(unknown_image_bgr, label, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# Step 4: Display the result

cv2.imshow("Face Recognition Result", unknown_image_bgr)

cv2.waitKey(0) # Wait for a key press to close the window

cv2.destroyAllWindows()

3.4 תרגיל כיתה

3.5 תצלם את עצמך או דמות נוספת אחרת לפי בחירתך כ 15 תמנות

3.6 צור ספריה נוספת בספריית היעד

3.7 בדוק בתוך ספריית TEST עם תמונה חדשה שלך או של דמות היעד שהבינה מלאכותית מזהה

3.8 הורד סירטון וידאו או צורך סירטון וידאו משלך עם דמות יעד ובדוק את הקוד הבא שמזהה בסרטון וידאו

https://y2meta.tube/convert/?videoId=xiyYN2_JKPc

3.8.1 שמור את הוידאו כ D:\temp\test\v1.mp4

3.9 הרץ קוד בסרטון וידאו ליזהוי תמונה

import face_recognition
import cv2
import os
import numpy as np

# Step 1: Load known faces
known_face_encodings = []
known_face_names = []

# Define the full path to the known people directory
known_people_dir = r"D:\temp\faces"

for person_name in os.listdir(known_people_dir):
    person_folder = os.path.join(known_people_dir, person_name)
    if not os.path.isdir(person_folder):
        continue  # Skip non-folder files
    for image_name in os.listdir(person_folder):
        image_path = os.path.join(person_folder, image_name)
        if not image_name.lower().endswith(('.jpg', '.jpeg', '.png')):
            continue  # Skip non-image files
        image = face_recognition.load_image_file(image_path)
        face_encodings = face_recognition.face_encodings(image)
        if face_encodings:
            known_face_encodings.append(face_encodings[0])
            known_face_names.append(person_name)

# Step 2: Process the video file
video_path = r"D:\temp\test\v1.mp4"
video_capture = cv2.VideoCapture(video_path)

while video_capture.isOpened():
    ret, frame = video_capture.read()
    if not ret:
        break  # Exit the loop if no more frames are available

    # Convert the frame to RGB (face_recognition requires RGB format)
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Find all face locations and encodings in the current frame
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Compare and label each face
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
        distances = face_recognition.face_distance(known_face_encodings, face_encoding)

        if any(matches):
            best_match_index = np.argmin(distances)
            name = known_face_names[best_match_index]
            label = f"{name} ({distances[best_match_index]:.2f})"
        else:
            name = "Unknown"
            label = "Unknown"

        # Draw a rectangle around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
        # Add a label below the face
        cv2.putText(frame, label, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

    # Display the frame with the labeled faces
    cv2.imshow("Video Face Recognition", frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture and close all windows
video_capture.release()
cv2.destroyAllWindows()

import face_recognition

import cv2

import os

import numpy as np

# Step 1: Load known faces

known_face_encodings = []

known_face_names = []

# Define the full path to the known people directory

known_people_dir = r"D:\temp\faces"

for person_name in os.listdir(known_people_dir):

person_folder = os.path.join(known_people_dir, person_name)

if not os.path.isdir(person_folder):

continue # Skip non-folder files

for image_name in os.listdir(person_folder):

image_path = os.path.join(person_folder, image_name)

if not image_name.lower().endswith(('.jpg', '.jpeg', '.png')):

continue # Skip non-image files

image = face_recognition.load_image_file(image_path)

face_encodings = face_recognition.face_encodings(image)

if face_encodings:

known_face_encodings.append(face_encodings[0])

known_face_names.append(person_name)

# Step 2: Process the video file

video_path = r"D:\temp\test\v1.mp4"

video_capture = cv2.VideoCapture(video_path)

while video_capture.isOpened():

ret, frame = video_capture.read()

if not ret:

break # Exit the loop if no more frames are available

# Convert the frame to RGB (face_recognition requires RGB format)

rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# Find all face locations and encodings in the current frame

face_locations = face_recognition.face_locations(rgb_frame)

face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

# Compare and label each face

for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

distances = face_recognition.face_distance(known_face_encodings, face_encoding)

if any(matches):

best_match_index = np.argmin(distances)

name = known_face_names[best_match_index]

label = f"{name} ({distances[best_match_index]:.2f})"

else:

name = "Unknown"

label = "Unknown"

# Draw a rectangle around the face

cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)

# Add a label below the face

cv2.putText(frame, label, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)

# Display the frame with the labeled faces

cv2.imshow("Video Face Recognition", frame)

# Break the loop if 'q' is pressed

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# Release the video capture and close all windows

video_capture.release()

cv2.destroyAllWindows()

יסודות בינה מלאכותית : 09-RB27

1.1 תרגיל כיתה

1.2.הצטלם על רקע לבן והחלקף את הדמות שלך בסרטון של ריקוד

3. זיהוי פנים על ידי בינה מלאכותית

3.1 התקנה : (תרגול בכיתה )

3.4 תרגיל כיתה

https://y2meta.tube/convert/?videoId=xiyYN2_JKPc

3.8.1 שמור את הוידאו כ D:\temp\test\v1.mp4

אולי תאהב/י גם

קורס RB07 , רובטיקס בלוקס : תרגול 007 – מבוא לתכנות כיתה ג – ד – תכנות חנוכיה

פיתוח בינה מלאכותית הרצה על מצלמה או שרת מצלמות דוקר או סופרוייזור

RB23-01 : מבוא לבינה מלאכותית ולמידת מכונה

כתיבת תגובה לבטל