assignment report digital image processing image fundamental week 1 2 3 4

Chapter 2Histogram2.1Histogram by My FunctionWe have the result.Figure 2.1: Gray image Figure 2.2: Histogram1 # Read image in grayscale2 image = cv2.. IMREAD_GRAYSCALE 3 cv2_imshow imag

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HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGYSCHOOL OF ELECTRICAL AND ELECTRONIC ENGINEERING

ASSIGNMENT REPORT

DIGITAL IMAGE PROCESSINGIMAGE FUNDAMENTAL (Week: 1-2-3-4)Instructor: Prof Tran Thi Thanh Hai

Student ID: 20213584

Hanoi, 2023

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3.1.1 Linear Transfrom using Numpy 8

3.1.2 Linear Transfrom using OpenCV 11

3.2 Gamma correction 13

3.2.1 Gamma Correction by My Function 13

3.2.2 Gamma Correction using OpenCV 14

3.3 Histogram Equalization 15

3.3.1 Histogram Equalization by My Fuction 15

3.3.2 Histogram Equalization using OpenCV 17

3.4 Transformation Conclusion 17

4 Filters 184.1 Median Filter 18

4.1.1 Median Filter by My Function 18

4.1.2 Median Filter using OpenCV 19

4.2 Smoothing Filter 21

4.2.1 Smoothing Filter by My Function 21

4.2.2 Smoothing Filter using OpenCV 23

4.3 Sharpening Filter 24

4.3.1 Sharpening Filter by My Function 24

4.3.2 Sharpening Filter using OpenCV 25

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Save the image using open CV library with thecv2.imwrite() function.

Figure 1.2: Save image

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Chapter 2Histogram

2.1Histogram by My Function

We have the result.

Figure 2.1: Gray image Figure 2.2: Histogram

1 # Read image in grayscale

2 image = cv2 imre ad ( ’/ co ntent / drive / MyDrive / DIP / Lena png ’, cv2 IMREAD_GRAYSCALE )

3 cv2_imshow ( image )

5 # Function to compute and display the histogram of an image5

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6 def draw_his ( img , bins ):

15 plt bar (x , hist , color = ’gray ’)

16 plt title ( ’ Histogram by My Function ’)

17 plt xlab el ( ’ Pixel Value ’)

18 plt ylab el ( ’ Frequency ’)

19 plt show ()

20 draw _hi s ( image , 256)

2.2Histogram using OpenCV

We have the result.

Figure 2.3: Gray Image Figure 2.4: Histogram

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2 image = cv2 imread ( ’/ con tent / drive / MyDr ive / DIP / Lena png ’, cv2 IMREAD_GRAYSCALE )

5 # Calculate histogram and show by OpenCV

6 hist_openCV = cv2 calcHist ([ image ], [0] , None , [256] , [0 ,256])

7 hist_openCV = hist_openCV ravel ()

8 plt figu re ( figsi ze =(8 ,6) )

9 x = np ar an ge (2 56)

10 plt bar (x , hist_openCV , color = ’gray ’)

11 plt title ( ’ Histogram by OpenCV ’)

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Figure 3.1: Original Image

Figure 3.2: Original Histogram

Figure 3.3: Image After

Apply-ing Linear Transform Figure 3.4: Histogram After Applying LinearTransform

2 # Conv ert Co ntra st gif to Cont rast jpg

3 Image open ( ’/ content / drive / MyDrive / DIP / Contrast gif ’) convert(’ RGB ’) save ( ’ Contrast jpg ’)

5 image = cv2 imread ( ’/ con tent / C ontr ast jpg ’, cv2 9

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22 plt title ( ’ Original Histogram ’)

25 plt show ()

27 # Histogram after linear transform

28 hist_openCV = cv2 calcHist ([ linear_Numpy ], [0] , None , [256] ,[0 , 256])

31 x = np ar an ge (2 56)

33 plt title ( ’ Histogram After Applying Linear Transform ’)

36 plt show ()

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3.1.2Linear Transfrom using OpenCVWe have the result.

Figure 3.5: Original Image

Figure 3.6: Original Histogram

Figure 3.7: Image After

Apply-ing Linear Transform Figure 3.8: Histogram After Applying LinearTransform

11

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5 image = cv2 imread ( ’/ con tent / C ontr ast jpg ’, cv2 IMREAD_GRAYSCALE )

8 # Change by linear transform ( openCV )

9 linear_openCV = cv2 convertScaleAbs ( image , alpha =1, beta =50)

18 plt title ( ’ Original Histogram ’)

21 plt show ()

23 # Histogram after linear transform

24 his t_o pe nCV = cv2 calcH ist ([ li near_ op en CV ], [0] , None , [256] ,[0 , 256])

27 x = np ar an ge (2 56)

29 plt title ( ’ Histogram After Applying Linear Transform ’)

32 plt show ()

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8 # Gamma correction by my function

9 def ga mm a_ cor re cti on ( image , g amma ) :

10 width , height = image size

11 ga mm a_ cor re cte d_ ima ge = Image new (’L ’ , (width , height ))

13 for x in range ( width ) :

14 for y in range ( height ):13

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15 pi xe l_ val ue = image g et pi xe l (( x , y) )

16 corrected_value = int (( pixel_value / 255.0) **gamma * 255.0)

17 ga mm a_ co rr ec te d_ im ag e p ut pix el (( x , y ) ,corrected_value )

19 return gamma_corrected_image

21 input_image = Image open ( ’/ content / Save_Contrast jpg ’)

22 gamma_img = gamma_correction ( input_image , 1.5)

23 ga mm a_i mg _cv 2 = cv2 cvt Co lo r ( np array ( g am ma _i mg ) , cv2 COLOR_RGB2BGR )

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8 def To_Gamma ( img , gamma ):

9 result = cv2 pow ( img /255.0 , gamma ) * 255.0

10 result = result a st ype ( np u int8 )

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8 his to gr am = np z eros (256 , dtype = np i nt32 )

9 for i in range ( image shape [0]) :

10 for j in range ( image shape [1]) :

11 pi xe l_ val ue = image [i , j ]

12 histogram [ pixel_value ] += 1

14 # Comp ute the c umulative di st ribut ion fun ctio n ( CDF )

15 cdf = np ze ros (256 , d type = np int32 )

24 # Map pixel intensities

25 equalized_image = cdf_normalized [ image ]

26 cv2_imshow ( equalized_image )

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3.3.2Histogram Equalization using OpenCVWe have the result.

Figure 3.15: Original Image Figure 3.16: Image Applying HistogramEqualization

7 # Equalize image by openCV

8 equalized_image = cv2 equalizeHist ( image )

9 cv2_imshow ( equalized_image )

3.4Transformation Conclusion

From the results in the Linear Transform, Gamma Correction, and Histogram ization sections, we observed that the transformations using both methods, which aremanual function usage and OpenCV, yield similar results.

Equal-17

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Chapter 4Filters

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2 image = cv2 imread ( ’/ con tent / drive / MyDr ive / DIP / Media n jpg ’,cv2 IMREAD_GRAYSCALE )

5 # Function to compute median filter

6 def med ia n_ filte r ( img ) :

20 temp = sorted ( temp )

21 img_new [i , j] = temp [4] # The fourth position is themedian

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Figure 4.3: Original Image Figure 4.4: Image Applying Median ter

2 image = cv2 imread ( ’/ con tent / drive / MyDri ve / DIP / Media n jpg ’,cv2 IMREAD_GRAYSCALE )

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2 image = cv2 imread ( ’/ con tent / drive / MyDri ve / DIP / Lena png ’)

5 # Function to compute Smoothing Filter

6 def compute ( img , mask ) :

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4.2.2Smoothing Filter using OpenCVWe have the result.

Figure 4.7: Original Image Figure 4.8: Image Applying SmoothingFilter

2 image = cv2 imread ( ’/ con tent / drive / MyDri ve / DIP / Lena png ’)

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5 # Function to compute Sharpening Filter

6 def compute ( img , mask ) :