"Digital Image Processing - OpenCV/Python" Serialization (3) Routine Index
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The first part of the basic operation of OpenCV-Python
Chapter 1 Basic Image Operation 3
[Routine 0101] Read and save image file 5 with OpenCV
[Routine 0102] Read image file 5 from a network address
[Routine 0103] Read and save an image with Chinese characters in the file path 6
[Routine 0104] 】Display images in the OpenCV image window 7
[Routine 0105] Display images using Matplotlib 8
[Routine 0106] Read, play and save video files 10
[Routine 0107] Call the camera to take pictures and record videos 12
[Routine 0108 】Reading and saving multi-frame images (motion pictures)13
Chapter 2 Image Data Formats 15
[Routine 0201] Image attribute and data type conversion 16
[Routine 0202] Image creation and copying 18
[Routine 0203] Image cropping and splicing 20
[Routine 0204] Splitting and merging of image channels 21
[Routine 0205] Obtaining and modifying pixel values 23
[Routine 0206] Image mosaic processing 24
[Routine 0207] LUT function lookup table to realize image inversion 26
[Routine 0208] LUT function lookup table to realize color reduction 27
Chapter 3 Color Image Processing 29
[Routine 0301] Image color space conversion 30
[Routine 0302] Convert a grayscale image to a pseudo-color image 32 [
Routine 0303] Synthesize a color nebula image using multispectral encoding 33
[Routine 0304] Customize color style filters 35
[Routine 0305] Use multi-channel LUT to adjust color balance 37
[Routine 0306] Image saturation and lightness adjustment 38
Chapter 4 Drawing and Mouse Interaction 40
[Routine 0401] Draw a straight line and line segment 41
[Routine 0402] Draw a vertical rectangle 44
[Routine 0403] Draw an oblique rotation rectangle 46 [
Routine 0404] Draw a circle 48
[Routine 0405] Draw an ellipse and an ellipse arc 50
[Routine 0406] Drawing polygons and polylines 54
[Routine 0407] Adding non-Chinese characters and Chinese characters 56
[Routine 0408] Selecting a rectangular area by mouse interaction 58
[Routine 0409] Obtaining a polygon area by mouse interaction 60
The basic method of image processing in the second part
Chapter 5 Arithmetic Operations on Images 65
[Routine 0501] Image addition operation 66
[Routine 0502] Mask image generation and image mask addition 68
[Routine 0503] Image blending and gradient switching 70
[Routine 0504] Image multiplication and division 72
[ Routine 0505] Least Significant Digital Blind Watermark 74
[Routine 0506] Add Logo to Image 75
[Routine 0507] Mean Filtering Based on Integral Image 78
Chapter 6 Geometric Transformation of Images 81
[Routine 0601] Image translation 82
[Routine 0602] Image scaling 84
[Routine 0603] Image rotation 86
[Routine 0604] Image flipping 88
[Routine 0605] Image oblique cutting (distortion) 90
[Routine 0606] Realize image correction based on projection transformation 92
[Routine 0607] Image remapping 95
[Routine 0608] Realize animation playback effect based on image remapping 97
Chapter 7 Grayscale Transformation of Images 99
[Routine 0701] Image inversion transformation 99
[Routine 0702] Linear grayscale transformation of image 101
[Routine 0703] Normalization of image histogram 103 [
Routine 0704] Logarithmic transformation of grayscale transformation 106
[Routine 0705] Gamma Transformation of Grayscale Transformation 106
[Routine 0706] Contrast Stretching of Segmented Linear Transformation 108 [
Routine 0707] Gray Scale Layering of Segmented Linear Transformation 109
[Routine 0708] Bits of Grayscale Transformation Plane layering 111
[Routine 0709] Image manual adjustment color scale algorithm and automatic adjustment color scale algorithm 113
Chapter 8 Histogram Processing of Images 116
[Routine 0801] Histogram of grayscale image and color image 117
[Routine 0802] Histogram equalization of grayscale image 119
[Routine 0803] Histogram matching of grayscale image 120
[Routine 0804] Color image Histogram matching 122
[Routine 0805] Enhance local images based on local histogram statistics 125
[Routine 0806] Global histogram equalization and limit contrast adaptive local histogram equalization 127
Chapter 9 Thresholding Images 129
[Routine 0901] Fixed threshold method of threshold processing 130
[Routine 0902] Global threshold calculation of threshold processing 131 [
Routine 0903] OTSU algorithm of threshold processing 133
[Routine 0904] Multi-threshold algorithm of threshold processing 135
[Routine 0905] Adaptive local threshold processing of threshold processing 137
[Routine 0906] Moving average algorithm of threshold processing 139 [
Routine 0907] Green screen cutout and background color replacement 141
[Routine 0908] Color segmentation based on mouse interaction 143
Part III Advanced Methods of Image Processing
Chapter 10 Image Convolution and Spatial Filtering 149
[Routine 1001] Image convolution operation and correlation operation 151
[Routine 1002] Box-type low-pass filter for spatial filtering 154 [
Routine 1003] Gaussian filter for spatial filtering 156
[Routine 1004] Gaussian filter for spatial filtering Low-pass filter and median filter 158
[Routine 1005] Statistical sorting filter for spatial filtering 159
[Routine 1006] Adaptive local noise reduction filter and mean filter for spatial filtering 162
[Routine 1007] Spatial filtering Median filter and adaptive median filter 163
[Routine 1008] Gaussian filter and bilateral filter for spatial filtering 165
[Routine 1009] Passivation masking and high-boost filtering for spatial filtering 166
[Routine 1010] Laplacian operator for spatial filtering 168
[Routine 1011] Sobel operator for spatial filtering 171
[Routine 1012] Scharr operator for spatial filtering 172 [
Routine 1013] Gaussian pyramid 175
[Routine 1014] Laplacian pyramid 176
Chapter 11 The Fourier Transform and Frequency-Domain Filtering 179
[Routine 1101] Realize two-dimensional discrete Fourier transform with OpenCV function 182
[Routine 1102] Realize two-dimensional Fourier transform with Numpy function 184
[Routine 1103] Realize fast Fourier transform with OpenCV function 186
[Example Procedure 1104] Basic steps of frequency-domain image filtering 187
[Routine 1105] Transfer function of low-pass filter 190 [
Routine 1106] Detailed steps of frequency-domain low-pass filtering 192
[Routine 1107] Encapsulation function of frequency-domain filter 195
[Routine 1108] Transfer function of gradient operator 198
[Routine 1109] Laplacian operator for frequency domain filtering 201 [Routine
1110] Design of band-stop filter for frequency domain filtering 203
[Routine 1111] Frequency domain filtering Design of notch filter 206
Chapter 12 Morphological Image Processing 209
[Routine 1201] Corrosion and expansion of morphological operations 211
[Routine 1202] Opening and closing operations of morphological operations 215
[Routine 1203] Morphological gradient operations of morphological operations 216
[Routine 1204] Hit -Miss-hit transformation for feature recognition 217
[Routine 1205] Schematic diagram of gray-scale morphological operation 219
[Routine 1206] Gray-scale morphological operation 221
[Routine 1207] Gray-scale top-hat operator to correct the influence of light 222
[ Routine 1208] Grayscale Bottom Hat Operator Correction of Illumination Effects 224
[Routine 1209] Boundary Extraction of Morphological Algorithms 225
[Routine 1210] Horizontal and Vertical Line Extraction of Morphological Algorithms 227
[Routine 1211] Morphological Algorithms Line Thinning 228
[Routine 1212] Boundary Clearing for Morphological Reconstruction 231
[Routine 1213] Hole Filling for Morphological Reconstruction 234 [
Routine 1214] Hole Filling with Flood Filling Algorithm 236
[Routine 1215] Morphological Reconstruction Skeleton Extraction 237
[Routine 1216] Particle Size Separation of Morphological Reconstruction 238
[Routine 1217] Morphology-Based Particle Size Measurement 240
[Routine 1218] Morphology-Based Edge Detection and Corner Detection 243
Chapter 13 Image Transformation, Reconstruction, and Restoration 245
[Routine 1301] Circular pattern and text correction in polar coordinates 246
[Routine 1302] Hough transform straight line detection 248
[Routine 1303] Hough transform circle detection 251
[Routine 1304] Discrete Wright transform sine graph 253
[ Routine 1305] Reconstruction of Wrighten transform sinogram by back projection 255
[Routine 1306] Reconstruction of Wrighten transform sinogram by filter back projection 258 [
Routine 1307] Inverse filtering of turbulent blur degraded image 261
[Routine 1308] Wiener filtering of motion blur degraded images 264
[Routine 1309] Using Wiener filtering and constrained least squares filtering to restore motion blurred images 267
Part IV Computer Vision
Chapter 14 Edge Detection and Image Contouring 273
[Routine 1401] Gradient operator of edge detection 273
[Routine 1402] LoG operator of edge detection 276
[Routine 1403] LoG operator and DoG operator of edge detection 279
[Routine 1404] DoG operator of edge detection , LoG operator and Canny operator 281
[Routine 1405] Local processing method of edge connection 282
[Routine 1406] Find and draw image contour 286
[Routine 1407] Basic parameters of contour 289
[Routine 1408] Shape of contour Feature 295
[Routine 1409] Contour Attributes 301
[Routine 1410] Moment and Invariant Moment of Image 305
[Routine 1411] Shape Similarity Detection Based on Hu Invariant Moment 305
Chapter 15 Image Segmentation 308
[Routine 1501] Region growing algorithm for image segmentation 309
[Routine 1502] SLIC superpixel region segmentation 313
[Routine 1503] Superpixel region segmentation 315
[Routine 1504] Watershed algorithm based on distance transformation 318
[Routine 1505] Watershed Algorithm Based on Contour Marking 320
[Routine 1506] Realize GraphCut Graph Cut Algorithm by Mouse Interaction 323
[Routine 1507] Realize GrabCut Graph Cut Algorithm by Frame Selection 327
[Routine 1508] Mean Shift Algorithm for Moving Image Tracking 329
[Routine 1509] Inter-frame difference method for moving object tracking 333
[Routine 1510] Background difference method for moving object tracking 335
[Routine 1511] Dense optical flow method for moving object tracking 337
Chapter 16 Characterization 340
[Routine 1601] Freeman chain code for feature description 340
[Routine 1602] Fourier descriptor for feature description 345
[Routine 1603] Fourier spectrum analysis for feature description 348
[Routine 1604] Feature description Regional feature description 351
[Routine 1605] Gray co-occurrence matrix of feature description 354
[Routine 1606] LBP descriptor of feature description 357
[Routine 1607] Circular extension LBP feature descriptor of feature description 359
[Routine 1608] Statistical histogram of LBP features for feature description 361
[Routine 1609] HOG descriptor for feature description 365
[Routine 1610] BRIEF key point descriptor for
feature description 369 [Routine 1611] FREAK key point descriptor for feature description 372
Chapter 17 Feature Detection and Matching 374
[Routine 1701] Harris corner detection algorithm and Shi-Tomas corner detection algorithm for corner detection 376
[Routine 1702] Sub-pixel precise positioning for corner detection 378
[Routine 1703] SIFT algorithm for feature detection 383
[Example Procedure 1704] SURF algorithm for feature detection 386
[Routine 1705] FAST algorithm for feature detection 389
[Routine 1706] ORB algorithm for feature detection 391 [
Routine 1707] MSER algorithm for feature detection 394
[Routine 1708] Feature matching Violent Matching 397
[Routine 1709] FLANN 401 of Feature Matching
Chapter 18 Machine Learning 404
[Routine 1801] Feature extraction and image reconstruction based on principal component analysis 407
[Routine 1802] Image color reduction processing based on k-means clustering 411
[Routine 1803] Handwritten digit recognition based on KNN model 414
[Routine 1804] Recognition of handwritten digits by KNN model based on HOG features 415
[Routine 1805] Handwritten digit recognition based on normal Bayesian classifier 418 [
Routine 1806] Data classification based on SVM 423
[Routine 1807] SVM based on RBF kernel function Handwritten digit recognition 425
[Routine 1808] Handwritten digit recognition based on BP algorithm based on multi-layer neural network 429
[Routine 1809] Multi-spectral data classification based on multi-layer neural network 432
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