Shape Detection

การตรวจจับรูปแบบเรขาคณิตในภาพ:

การตรวจจับวงกลมในภาพ (สามารถเข้าไปดาวน์โหลดไฟล์ภาพได้จาก >> คลิกเพื่อดาวน์โหลด)

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. img = cv2.imread(r'C:\Users\user\Desktop\shape.jpg', cv2.IMREAD_COLOR)

6. 
   

7. gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to grayscale

8. 
   

9. # Apply Gaussian blur to reduce noise

10. blurred = cv2.GaussianBlur(gray, (9, 9), 2)

11. 
    

12. # Detect circles using HoughCircles

13. circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, dp=1.2, minDist=30,

14.                            param1=50, param2=30, minRadius=120, maxRadius=200)

15. 
    

16. # Check if any circles are detected

17. if circles is not None:

18.     circles = np.uint16(np.around(circles))  # Convert to integer values

19. 
    

20.     for (x, y, r) in circles[0, :]:

21.         print(f"Circle detected at: X={x}, Y={y}, Radius={r}")  # Print coordinates

22.         cv2.circle(img, (x, y), r, (0, 255, 0), 3)  # Draw circle

23.         cv2.putText(img, f"({x},{y})", (x - 30, y - r - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

24. 
    

25. # Show results

26. cv2.imshow("Detected Circles", img)

27. cv2.waitKey(0)

28. cv2.destroyAllWindows()

การตรวจจับสี่เหลี่ยมในภาพ (สามารถเข้าไปดาวน์โหลดไฟล์ภาพได้จาก >> คลิกเพื่อดาวน์โหลด)

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. img = cv2.imread(r'C:\Users\user\Desktop\shape.jpg', cv2.IMREAD_COLOR)

6. 
   

7. gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to grayscale

8. 
   

9. # Apply Canny edge detection

10. edges = cv2.Canny(gray, 50, 150)

11. 
    

12. # Find contours

13. contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

14. 
    

15. # Iterate through contours and filter for rectangles

16. for contour in contours:

17.     approx = cv2.approxPolyDP(contour, 0.02 * cv2.arcLength(contour, True), True)

18. 
    

19.     if len(approx) == 4 and cv2.isContourConvex(approx):  # Ensure it's a rectangle

20.         x, y, w, h = cv2.boundingRect(approx)

21.         print(f"Rectangle detected at: X={x}, Y={y}, Width={w}, Height={h}")  # Print coordinates

22. 
    

23.         # Draw rectangle on the image

24.         cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)

25. 
    

26.         # Optional: Label the coordinates on the image

27.         cv2.putText(img, f"({x},{y})", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

28. 
    

29. # Show results

30. cv2.imshow("Detected Rectangles", img)

31. cv2.imshow("Edges", edges)

32. cv2.waitKey(0)

33. cv2.destroyAllWindows()