בינה מלאכותית RB108-4T : תרגיל בית

מחבר:admin
פורסם:דצמבר 12, 2025
קטגוריה:רובוטרוניקס כללי

תרגיל 1 – מתקדם לימוד עצמי – הרצה בקולאב

פלט:

קלט :

נתון קוד הבינה מלאכותית הבא :

למד בעצמך דרך A.I מה זה YOLO SEGMENTAION , בקש סרטון קישור
למד בעצמך דרך A.I מה זה YOLO POSE בקש סרטון קישור
מה זה A.I PIPELINE ?
נתח בעזרת בינה מלאכותית LLM מה הקוד עושה , איזה מודלים של בינה מלאכותית משתתפים מה השלבים ?
הרץ את הקוד ב COLAB איזה ספריות יש להתקין ? שים לה שהתוכנה מבקשת להעלות קובץ מהמחשב ל PC
איזה פרמטרים ניתן לשנות בקוד ומה יהיה תפקיד כל פרמטר
מתקדם – הוסף לקוד הקיים קוד – שימצא את מרכז המשא בין הכתפיים לאגן ויציג עיגול אדום קוטר 10 פיקסל

import cv2
import numpy as np
import matplotlib.pyplot as plt
from ultralytics import YOLO
from google.colab import files
from google.colab.patches import cv2_imshow


# ===========================================
# 1. Upload Image
# ===========================================
# Allows the user to upload an image from their computer into Colab.
uploaded = files.upload()
image_path = list(uploaded.keys())[0]
print("Loaded:", image_path)


# ===========================================
# 2. YOLO Segmentation (person)
# ===========================================
# Load YOLO segmentation model and run it on the uploaded image.
# The model returns masks (segmentation) + bounding boxes.
seg_model = YOLO("yolov8x-seg.pt")
seg_result = seg_model(image_path)[0]

# Read the original image with OpenCV
img = cv2.imread(image_path)
h, w = img.shape[:2]  # Image height and width


# ===========================================
# 3. Extract ONLY PERSON Mask
# ===========================================
# We iterate over all detected objects.
# If the class is "person" (class ID 0), we take its segmentation mask.
person_mask = None

for box, mask_data in zip(seg_result.boxes, seg_result.masks.data):
    if int(box.cls.cpu().numpy()) == 0:  # 0 = person
        person_mask = mask_data.cpu().numpy()
        break

# If no person is found, stop the program.
if person_mask is None:
    raise Exception("No person detected.")

# Resize mask to match the original image resolution.
mask = cv2.resize(person_mask, (w, h))

# Convert soft mask (0–1) to binary mask (0 or 255)
mask_bin = (mask > 0.5).astype(np.uint8) * 255

# Optional: increase the mask thickness slightly (expands edges)
mask_bin = cv2.dilate(mask_bin, np.ones((7, 7), np.uint8), iterations=1)


# ===========================================
# 4. Create Person Cutout (white background)
# ===========================================
# α-mask for blending foreground with white background.
alpha = (mask_bin / 255.0)[..., None]

# Create white background the same size as the image.
white_bg = np.ones_like(img) * 255

# Blend the person with white background.
cutout = (img * alpha + white_bg * (1 - alpha)).astype(np.uint8)

# Save the result
cv2.imwrite("cutout.png", cutout)


# ===========================================
# 5. Create Red Stroke Outline
# ===========================================
# Detect edges from the mask using Canny.
edges = cv2.Canny(mask_bin, 50, 150)

# Make edges thicker for a visible outline.
edges_thick = cv2.dilate(edges, np.ones((3,3), np.uint8), iterations=1)

# Draw stroke on the cutout
cutout_stroke = cutout.copy()
cutout_stroke[edges_thick > 0] = [0, 0, 255]  # Red stroke

# Image containing ONLY the red outline on white background
stroke_only = np.ones_like(img) * 255
stroke_only[edges_thick > 0] = [0, 0, 255]

cv2.imwrite("cutout_stroke.png", cutout_stroke)
cv2.imwrite("stroke_only.png", stroke_only)


# ===========================================
# 6. YOLO Pose Detection
# ===========================================
# Load YOLO pose model and run it on the stroke image.
pose_model = YOLO("yolov8x-pose.pt")
pose_res = pose_model.predict(cutout_stroke)[0]

pose_img = cutout_stroke.copy()

# If keypoints were detected, draw them + draw skeleton lines.
if pose_res.keypoints is not None:
    kpts = pose_res.keypoints.xy[0].cpu().numpy()

    # Draw green keypoints
    for x, y in kpts:
        cv2.circle(pose_img, (int(x), int(y)), 5, (0, 255, 0), -1)

    # COCO skeleton connections: pairs of keypoints
    skeleton = [
        (5,7),(7,9),
        (6,8),(8,10),
        (11,13),(13,15),
        (12,14),(14,16),
        (5,6),(11,12),
        (5,11),(6,12)
    ]

    # Draw skeleton lines
    for a, b in skeleton:
        if kpts[a][0] > 0 and kpts[b][0] > 0:
            cv2.line(
                pose_img,
                (int(kpts[a][0]), int(kpts[a][1])),
                (int(kpts[b][0]), int(kpts[b][1])),
                (0, 255, 0),
                2
            )

cv2.imwrite("pose.png", pose_img)


# ===========================================
# 7. Display all main results
# ===========================================
print("Showing results:")
cv2_imshow(cutout)
cv2_imshow(cutout_stroke)
cv2_imshow(stroke_only)
cv2_imshow(pose_img)

print("Saved: cutout.png, cutout_stroke.png, stroke_only.png, pose.png")

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import cv2

import numpy as np

import matplotlib.pyplot as plt

from ultralytics import YOLO

from google.colab import files

from google.colab.patches import cv2_imshow

# ===========================================

# 1. Upload Image

# ===========================================

# Allows the user to upload an image from their computer into Colab.

uploaded = files.upload()

image_path = list(uploaded.keys())[0]

print("Loaded:", image_path)

# ===========================================

# 2. YOLO Segmentation (person)

# ===========================================

# Load YOLO segmentation model and run it on the uploaded image.

# The model returns masks (segmentation) + bounding boxes.

seg_model = YOLO("yolov8x-seg.pt")

seg_result = seg_model(image_path)[0]

# Read the original image with OpenCV

img = cv2.imread(image_path)

h, w = img.shape[:2] # Image height and width

# ===========================================

# 3. Extract ONLY PERSON Mask

# ===========================================

# We iterate over all detected objects.

# If the class is "person" (class ID 0), we take its segmentation mask.

person_mask = None

for box, mask_data in zip(seg_result.boxes, seg_result.masks.data):

if int(box.cls.cpu().numpy()) == 0: # 0 = person

person_mask = mask_data.cpu().numpy()

break

# If no person is found, stop the program.

if person_mask is None:

raise Exception("No person detected.")

# Resize mask to match the original image resolution.

mask = cv2.resize(person_mask, (w, h))

# Convert soft mask (0–1) to binary mask (0 or 255)

mask_bin = (mask > 0.5).astype(np.uint8) * 255

# Optional: increase the mask thickness slightly (expands edges)

mask_bin = cv2.dilate(mask_bin, np.ones((7, 7), np.uint8), iterations=1)

# ===========================================

# 4. Create Person Cutout (white background)

# ===========================================

# α-mask for blending foreground with white background.

alpha = (mask_bin / 255.0)[..., None]

# Create white background the same size as the image.

white_bg = np.ones_like(img) * 255

# Blend the person with white background.

cutout = (img * alpha + white_bg * (1 - alpha)).astype(np.uint8)

# Save the result

cv2.imwrite("cutout.png", cutout)

# ===========================================

# 5. Create Red Stroke Outline

# ===========================================

# Detect edges from the mask using Canny.

edges = cv2.Canny(mask_bin, 50, 150)

# Make edges thicker for a visible outline.

edges_thick = cv2.dilate(edges, np.ones((3,3), np.uint8), iterations=1)

# Draw stroke on the cutout

cutout_stroke = cutout.copy()

cutout_stroke[edges_thick > 0] = [0, 0, 255] # Red stroke

# Image containing ONLY the red outline on white background

stroke_only = np.ones_like(img) * 255

stroke_only[edges_thick > 0] = [0, 0, 255]

cv2.imwrite("cutout_stroke.png", cutout_stroke)

cv2.imwrite("stroke_only.png", stroke_only)

# ===========================================

# 6. YOLO Pose Detection

# ===========================================

# Load YOLO pose model and run it on the stroke image.

pose_model = YOLO("yolov8x-pose.pt")

pose_res = pose_model.predict(cutout_stroke)[0]

pose_img = cutout_stroke.copy()

# If keypoints were detected, draw them + draw skeleton lines.

if pose_res.keypoints is not None:

kpts = pose_res.keypoints.xy[0].cpu().numpy()

# Draw green keypoints

for x, y in kpts:

cv2.circle(pose_img, (int(x), int(y)), 5, (0, 255, 0), -1)

# COCO skeleton connections: pairs of keypoints

skeleton = [

(5,7),(7,9),

(6,8),(8,10),

(11,13),(13,15),

(12,14),(14,16),

(5,6),(11,12),

(5,11),(6,12)

]

# Draw skeleton lines

for a, b in skeleton:

if kpts[a][0] > 0 and kpts[b][0] > 0:

cv2.line(

pose_img,

(int(kpts[a][0]), int(kpts[a][1])),

(int(kpts[b][0]), int(kpts[b][1])),

(0, 255, 0),

)

cv2.imwrite("pose.png", pose_img)

# ===========================================

# 7. Display all main results

# ===========================================

print("Showing results:")

cv2_imshow(cutout)

cv2_imshow(cutout_stroke)

cv2_imshow(stroke_only)

cv2_imshow(pose_img)

print("Saved: cutout.png, cutout_stroke.png, stroke_only.png, pose.png")

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