![Histogram Code
import cv2
from matplotlib import pyplot as plt
import numpy as np
img = cv2.imread("lena.jpg",1)
print(img.shape)
cv2.imshow("image",img)
# cv2.waitKey()
# Calculate histogram
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
# Plot the histogram
plt.figure()
plt.title("Grayscale Histogram")
plt.xlabel("Intensity Value")
plt.plot(hist)
plt.show()
Output](https://image.slidesharecdn.com/lab3vision3-250118071028-d58686b8/85/Computer-vision-labs-for-improving-in-the-subject-21-320.jpg)
Computer vision labs for improving in the subject
- 1. ENG/TA : MAiADA ALAA VISION COMPUTE R
- 2. • P(I) = Probability Mass Function (PMF) 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 𝑜𝑓 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑣𝑎𝑙𝑢𝑒 𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 𝑖𝑛 h 𝑡 𝑒𝑖𝑚𝑎𝑔𝑒 0 < p ( ) < 1 𝐼 = 1
- 3. As an example: 𝑃 5 = 0 + 1 + 2 + 3 + 4 + 𝑝 𝑝 𝑝 𝑝 𝑝 𝑝 5 Cumulative Dentistry Function (CDF) 𝑃 ( 𝑟 )=∑ 𝐼 =0 𝑟 𝑝 ( 𝐼 )
- 4. a) Calculate and Plot Histogram/ PMF(pr(r)) of the gray levels. b) Calculate and Plot the CDF (pr(r)) of the gray levels.
- 5. r 1 2 3 4 5 6 7 8 9 # 8 7 12 8 8 9 9 12 17 Pr(r) 8/90 7/90 12/9 0 8/90 8/90 9/90 9/90 12/9 0 17/9 0 Calculate and Plot Histogram/ PMF(pr(r)) of the gray levels.
- 6. r 1 2 3 4 5 6 7 8 9 # 8 7 12 8 8 9 9 12 17 Pr(r) 8/90 7/90 12/9 0 8/90 8/90 9/90 9/90 12/9 0 17/9 0 Histogram 1 2 3 4 5 6 7 8 9 0 2 4 6 8 10 12 14 16 18
- 7. r 1 2 3 4 5 6 7 8 9 # 8 7 12 8 8 9 9 12 17 Pr(r) 8/90 7/90 12/9 0 8/90 8/90 9/90 9/90 12/9 0 17/9 0 Pr(r) 8/90 15/9 0 27/9 0 35/9 0 43/9 0 52/9 0 61/9 0 73/9 0 90/9 0 + Calculate and Plot the CDF (pr(r)) of the gray levels.
- 8. CDF Plot r 1 2 3 4 5 6 7 8 9 # 8 7 12 8 8 9 9 12 17 Pr(r) 8/90 7/90 12/9 0 8/90 8/90 9/90 9/90 12/9 0 17/9 0 Pr(r) 8/90 15/9 0 27/9 0 35/9 0 43/9 0 52/9 0 61/9 0 73/9 0 90/9 0 0 1 2 3 4 5 6 7 8 9 0 0.2 0.4 0.6 0.8 1 1.2
- 9. Histogram / PMF (pr(r)) r 8 9 10 11 12 13 14 15 # 10 4 3 7 5 3 2 1 Pr(r) 10/3 5 4/35 3/35 7/35 5/35 3/35 2/35 1/35
- 10. Histogram / PMF (pr(r)) r 8 9 10 11 12 13 14 15 # 10 4 3 7 5 3 2 1 Pr(r) 10/3 5 4/35 3/35 7/35 5/35 3/35 2/35 1/35
- 11. Histogram / CDF (pr(r)) r 8 9 10 11 12 13 14 15 # 10 4 3 7 5 3 2 1 Pr(r) 10/3 5 4/35 3/35 7/35 5/35 3/35 2/35 1/35 Pr(r) 10/3 5 14/3 5 17/3 5 24/3 5 29/3 5 32/3 5 34/3 5 35/3 5
- 13. rk nk P(rk) CDF Sk = T(rk) Equalizat ion level 0 790 0.192 0.192 0.536 1 1 1023 0.25 0.442 2.5 3 2 850 0.21 0.652 4.2 5 3 656 0.16 0.812 5.5 6 4 329 0.08 0.892 6.136 7 5 245 0.06 0.952 6.6 7 6 122 0.03 0.982 6.8 7 7 81 0.02 1 7 7 Sk = T(rk) = L x CDF -1 sk 1 3 5 6 7 P(sk) 0.192 0.25 0.21 0.16 0.19 SUM Probabilit y
- 14. Histogram Equalization Make all pixels have same frequencies
- 16. 1) Assume that an image of 32x32 with 32 gray levels distributed as follows: a) Draw the image histogram. b) Find the equalized histogram. c) Find the specified histogram according to the following values
- 17. a) Draw the image histogram.
- 18. b) Find the equalized histogram. 31
- 19. c) Find the specified histogram according to the following values. Histogram between z and p(r)
- 20. Lab Example
- 21. Histogram Code import cv2 from matplotlib import pyplot as plt import numpy as np img = cv2.imread("lena.jpg",1) print(img.shape) cv2.imshow("image",img) # cv2.waitKey() # Calculate histogram hist = cv2.calcHist([img], [0], None, [256], [0, 256]) # Plot the histogram plt.figure() plt.title("Grayscale Histogram") plt.xlabel("Intensity Value") plt.plot(hist) plt.show() Output
- 22. Task
- 23. 1. What is the main purpose of image acquisition in the field of computer vision? A. To detect edges in an image B. To acquire a digital image from a sensor or camera C. To improve the brightness of an image D. To enhance image resolution 2. Which of the following statements about histogram equalization is true? A. It reduces the size of the image B. It equalizes the pixel intensities across the image C. It applies a color transformation D. It applies a high-pass filter
- 24. 3. What does a histogram represent in the context of image processing? A. The spatial frequency of image edges B. The color balance of an image C. The distribution of pixel intensities D. The size of the image 4. What is the effect of increasing brightness during image enhancement? A. It shifts the histogram to the right B. It compresses the histogram C. It enhances the noise D. It reduces the contrast 5. In histogram equalization, which transformation is applied to the intensity values? A. Linear transformation B. Logarithmic transformation C. Cumulative distribution function (CDF) D. Discrete Fourier Transform (DFT)
- 25. THANK YOU