Basic Image Processing

การตรวจสอบคุณสมบัติของภาพ:

การตรวจสอบคุณสมบัติของภาพ  (สามารถเข้าไปดาวน์โหลดไฟล์ภาพได้จาก >> คลิกเพื่อดาวน์โหลด)

1. import cv2

2. 
   

3. # read the input image

4. img = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

5. # image properties

6. print("Type:",type(img))

7. print("Shape of Image:", img.shape)

8. print('Total Number of pixels:', img.size)

9. print("Image data type:", img.dtype)

10. # print("Pixel Values:\n", img)

11. print("Dimension:", img.ndim)

12.  

13. cv2.imshow("Image", img)

14. cv2.waitKey(0)

15. cv2.destroyAllWindows()

การปรับขนาดภาพ:

การปรับขนาดภาพจากกล้อง หรือจากไฟล์ภาพ .jpg หรือ .png (สามารถเข้าไปดาวน์โหลดไฟล์ภาพได้จาก >> คลิกเพื่อดาวน์โหลด)

1. import cv2

2. 
   

3. img = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

4. 
   

5. print('Original Dimensions : ',img.shape)

6. 
   

7. width = 700

8. height = 550

9. dim = (width, height)

10. 
    

11. # resize image

12. resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)

13. 
    

14. print('Resized Dimensions : ',resized.shape)

15. 
    

16. cv2.imshow("Resized image", resized)

17. cv2.waitKey(0)

18. cv2.destroyAllWindows()

การตัดภาพ (ROI):

1. import cv2

2. 
   

3. img = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

4. 
   

5. crop = img[50:100, 20:200]

6.  

7. cv2.imshow("Image", img)

8. 
   

9. cv2.imshow("Image_crop", crop)

10. 
    

11. cv2.waitKey(0)

12. cv2.destroyAllWindows()

การหาค่าเฉลี่ยของสีภาพ:

1. import cv2

2. 
   

3. image = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

4. 
   

5. gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

6. 
   

7. cv2.imshow("Image", image)

8. cv2.imshow("Gray", gray)

9. 
   

10. mean_color = cv2.mean(image)

11. print(mean_color)

12. 
    

13. mean_gray = cv2.mean(gray)

14. print(mean_gray)

15. 
    

16. cv2.waitKey(0)

17. 
    

18. cv2.destroyAllWindows()

การแปลงภาพสีเป็นภาพระดับเทา:

1. import cv2

2. 
   

3. image = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

4. 
   

5. gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

6. 
   

7. cv2.imshow("Image", image)

8. cv2.imshow("Gray", gray)

9. 
   

10. cv2.waitKey(0)

11. 
    

12. cv2.destroyAllWindows()

การหาขอบภาพ:

1. import cv2

2. 
   

3. image = cv2.imread(r'C:\Users\user\Desktop\bottle\1.jpg')

4. 
   

5. gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

6. 
   

7. edged = cv2.Canny(gray, 1, 100)

8. 
   

9. cv2.imshow("Image", image)

10. cv2.imshow("Gray", gray)

11. cv2.imshow("Edged", edged)

12. 
    

13. cv2.waitKey(0)

14. 
    

15. cv2.destroyAllWindows()

การตรวจจับสีของวัตถที่ต้องการ:   ดาวน์โหลดภาพ

📌การตรวจจับสีน้ำเงิน

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. image_path = r"C:\Users\user\Desktop\color.JPG"

6. image = cv2.imread(image_path)

7. 
   

8. # Convert to HSV color space

9. hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

10. 
    

11. # Define the lower and upper bounds for red color in HSV

12. lower = np.array([100, 120, 120])

13. upper = np.array([140, 255, 255])

14. 
    

15. # Create masks for both red ranges

16. mask1 = cv2.inRange(hsv, lower, upper)

17. 
    

18. # Combine masks

19. blue_mask = mask1

20. 
    

21. # Apply mask to extract red color

22. blue_mask = cv2.bitwise_and(image, image, mask=blue_mask)

23. 
    

24. # Display results

25. cv2.imshow("Original Image", image)

26. cv2.imshow("Blue Color Detection", blue_mask)

27. cv2.waitKey(0)

28. cv2.destroyAllWindows()

📌การตรวจจับสีเขียว

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. image_path = r"C:\Users\user\Desktop\color.JPG"

6. image = cv2.imread(image_path)

7. 
   

8. # Convert to HSV color space

9. hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

10. 
    

11. # Define the lower and upper bounds for red color in HSV

12. lower = np.array([40, 120, 120])

13. upper = np.array([80, 255, 255])

14. 
    

15. # Create masks for both red ranges

16. mask1 = cv2.inRange(hsv, lower, upper)

17. 
    

18. # Combine masks

19. blue_mask = mask1

20. 
    

21. # Apply mask to extract red color

22. blue_mask = cv2.bitwise_and(image, image, mask=blue_mask)

23. 
    

24. # Display results

25. cv2.imshow("Original Image", image)

26. cv2.imshow("Blue Color Detection", blue_mask)

27. cv2.waitKey(0)

28. cv2.destroyAllWindows()

📌การตรวจจับสีแดง

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. image_path = r"C:\Users\user\Desktop\color.JPG"

6. image = cv2.imread(image_path)

7. 
   

8. # Convert to HSV color space

9. hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

10. 
    

11. # Define the lower and upper bounds for red color in HSV

12. lower_red1 = np.array([0, 120, 120])

13. upper_red1 = np.array([10, 255, 255])

14. 
    

15. lower_red2 = np.array([170, 120, 120])

16. upper_red2 = np.array([180, 255, 255])

17. 
    

18. # Create masks for both red ranges

19. mask1 = cv2.inRange(hsv, lower_red1, upper_red1)

20. mask2 = cv2.inRange(hsv, lower_red2, upper_red2)

21. 
    

22. # Combine masks

23. red_mask = mask1 + mask2

24. 
    

25. # Apply mask to extract red color

26. red_detected = cv2.bitwise_and(image, image, mask=red_mask)

27. 
    

28. # Display results

29. cv2.imshow("Original Image", image)

30. cv2.imshow("Red Color Detection", red_detected)

31. cv2.waitKey(0)

32. cv2.destroyAllWindows()

📌การตรวจจับสีดำและแปลงเป็นภาพ Binary

1. import cv2

2. import numpy as np

3. 
   

4. # Load the image

5. image_path = r"C:\Users\user\Desktop\color.JPG"

6. image = cv2.imread(image_path)

7. 
   

8. # Convert to HSV color space

9. hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

10. 
    

11. # Define the lower and upper bounds for black color in HSV

12. lower_black = np.array([0, 0, 0])       # Lower bound for black

13. upper_black = np.array([180, 255, 50])  # Upper bound for black

14. 
    

15. # Create mask for black color (binary image)

16. black_mask = cv2.inRange(hsv, lower_black, upper_black)

17. 
    

18. # Invert the mask so that black objects appear black (0) and the background is white (255)

19. binary_result = 255 - black_mask

20. 
    

21. # Display results

22. cv2.imshow("Original Image", image)

23. cv2.imshow("Binary Black Object", binary_result)

24. cv2.waitKey(0)

25. cv2.destroyAllWindows()