├── .gitattributes ├── .gitignore ├── .idea ├── .gitignore ├── ImgPractice.iml ├── deployment.xml ├── dictionaries │ └── Administrator.xml ├── inspectionProfiles │ ├── Project_Default.xml │ └── profiles_settings.xml ├── misc.xml ├── modules.xml ├── other.xml └── vcs.xml ├── README.md ├── external └── histogram_match.py ├── folder.ini ├── image_data ├── DIP3E_CH03_Original_Images.zip ├── DIP3E_CH09_Original_Images.zip ├── DIP3E_CH11_Original_Images │ ├── Fig1105(a)(noisy_stroke).tif │ ├── Fig1108(a)(mapleleaf).tif │ ├── Fig1111(a)(triangle).tif │ ├── Fig1111(b)(square).tif │ ├── Fig1116(leg_bone).tif │ ├── Fig1120(a)(chromosome_boundary).tif │ ├── Fig1122(1)(top-canada).tif │ ├── Fig1122(2)(2nd-from-top-USA).tif │ ├── Fig1122(3)(3rd-from-top-Central-Amer).tif │ ├── Fig1122(4)(4th-from-top-South-Amer).tif │ ├── Fig1127(a)(WashingtonDC Band4).tif │ ├── Fig1128(a)(superconductor-smooth-texture)-DO NOT SEND.tif │ ├── Fig1128(b)(cholesterol-rough-texture)-DO NOT SEND.tif │ ├── Fig1128(c)(microporcessor-regular texture)-DO NOT SEND.tif │ ├── Fig1130(a)(uniform_noise).tif │ ├── Fig1130(b)(sinusoidal).tif │ ├── Fig1130(c)(cktboard_section).tif │ ├── Fig1135(a)(random_matches).tif │ ├── Fig1135(b)(ordered_matches).tif │ ├── Fig1137(a)(painting_original_padded).tif │ ├── Fig1137(b)(painting_translated_padded).tif │ ├── Fig1137(c)(painting_halfsize_padded).tif │ ├── Fig1137(d)(painting_mirrored_padded).tif │ ├── Fig1137(e)(painting_rot45deg).tif │ ├── Fig1137(f)(painting_rot90deg_padded).tif │ ├── Fig1138(a)(WashingtonDC_Band1_564).tif │ ├── Fig1138(b)(WashingtonDC_Band2_564).tif │ ├── Fig1138(c)(WashingtonDC_Band3_564).tif │ ├── Fig1138(d)(WashingtonDC_Band4_564).tif │ ├── Fig1138(e)(WashingtonDC_Band5_564).tif │ ├── Fig1138(f)(WashingtonDC_Band6_564).tif │ ├── FigP1126(bottles).tif │ └── FigP1127(bubbles).tif ├── DIP3E_Original_Images_CH03 │ ├── 0_README.txt │ ├── Fig0304(a)(breast_digital_Xray).tif │ ├── Fig0305(a)(DFT_no_log).tif │ ├── Fig0307(a)(intensity_ramp).tif │ ├── Fig0308(a)(fractured_spine).tif │ ├── Fig0309(a)(washed_out_aerial_image).tif │ ├── Fig0310(b)(washed_out_pollen_image).tif │ ├── Fig0312(a)(kidney).tif │ ├── Fig0314(a)(100-dollars).tif │ ├── Fig0316(1)(top_left).tif │ ├── Fig0316(2)(2nd_from_top).tif │ ├── Fig0316(3)(third_from_top).tif │ ├── Fig0316(4)(bottom_left).tif │ ├── Fig0320(1)(top_left).tif │ ├── Fig0320(2)(2nd_from_top).tif │ ├── Fig0320(3)(third_from_top).tif │ ├── Fig0320(4)(bottom_left).tif │ ├── Fig0323(a)(mars_moon_phobos).tif │ ├── Fig0326(a)(embedded_square_noisy_512).tif │ ├── Fig0327(a)(tungsten_original).tif │ ├── Fig0333(a)(test_pattern_blurring_orig).tif │ ├── Fig0334(a)(hubble-original).tif │ ├── Fig0335(a)(ckt_board_saltpep_prob_pt05).tif │ ├── Fig0338(a)(blurry_moon).tif │ ├── Fig0340(a)(dipxe_text).tif │ ├── Fig0342(a)(contact_lens_original).tif │ ├── Fig0343(a)(skeleton_orig).tif │ ├── Fig0354(a)(einstein_orig).tif │ └── Fig0359(a)(headCT_Vandy).tif ├── DIP3E_Original_Images_CH04 │ ├── Fig0417(a)(barbara).tif │ ├── Fig0418(a)(ray_traced_bottle_original).tif │ ├── Fig0419(a)(text_gaps_of_1_and_2_pixels).tif │ ├── Fig0421(car_newsprint_sampled_at_75DPI).tif │ ├── Fig0422(newspaper_shot_woman).tif │ ├── Fig0424(a)(rectangle).tif │ ├── Fig0425(a)(translated_rectangle).tif │ ├── Fig0427(a)(woman).tif │ ├── Fig0429(a)(blown_ic).tif │ ├── Fig0431(d)(blown_ic_crop).tif │ ├── Fig0432(a)(square_original).tif │ ├── Fig0438(a)(bld_600by600).tif │ ├── Fig0441(a)(characters_test_pattern).tif │ ├── Fig0442(a)(characters_test_pattern).tif │ ├── Fig0445(a)(characters_test_pattern).tif │ ├── Fig0448(a)(characters_test_pattern).tif │ ├── Fig0450(a)(woman_original).tif │ ├── Fig0451(a)(satellite_original).tif │ ├── Fig0457(a)(thumb_print).tif │ ├── Fig0458(a)(blurry_moon).tif │ ├── Fig0459(a)(orig_chest_xray).tif │ ├── Fig0462(a)(PET_image).tif │ ├── Fig0464(a)(car_75DPI_Moire).tif │ ├── Fig0465(a)(cassini).tif │ ├── FigP0421(left)(padded_image).tif │ ├── FigP0421(right)(center-padded_image).tif │ ├── FigP0433(left)(DIP_image).tif │ ├── FigP0436(left)(hand_xray).tif │ └── FigP0438(left).tif ├── DIP3E_Original_Images_CH09 │ ├── .ipynb_checkpoints │ │ └── 未命名-checkpoint.ipynb │ ├── 20140307142628203.png │ ├── Fig0905(a)(wirebond-mask).tif │ ├── Fig0907(a)(text_gaps_1_and_2_pixels).tif │ ├── Fig0911(a)(noisy_fingerprint).png │ ├── Fig0911(a)(noisy_fingerprint).tif │ ├── Fig0914(a)(licoln from penny).png │ ├── Fig0914(a)(licoln from penny).tif │ ├── Fig0916(a)(region-filling-reflections).tif │ ├── Fig0918(a)(Chickenfilet with bones).tif │ ├── Fig0929(a)(text_image).tif │ ├── Fig0931(a)(text_image).tif │ ├── Fig0935(a)(ckt_board_section).tif │ ├── Fig0937(a)(ckt_board_section).tif │ ├── Fig0938(a)(cygnusloop_Xray_original).tif │ ├── Fig0939(a)(headCT-Vandy).tif │ ├── Fig0940(a)(rice_image_with_intensity_gradient).tif │ ├── Fig0941(a)(wood_dowels).tif │ ├── Fig0943(a)(dark_blobs_on_light_background).tif │ ├── Fig0944(a)(calculator).tif │ ├── FigP0905(U).tif │ ├── FigP0917(noisy_rectangle).tif │ ├── FigP0918(left).tif │ ├── FigP0919(UTK).tif │ ├── FigP0934(blobs_in_circular_arrangement).tif │ ├── FigP0936(bubbles_on_black_background).tif │ ├── 击中集合A.png │ └── 未命名.ipynb ├── DIP3E_Original_Images_CH10 │ ├── Fig1001(a)(constant_gray_region).tif │ ├── Fig1001(b)(edge_image).tif │ ├── Fig1001(c)(thresholded_image).tif │ ├── Fig1001(d)(noisy_region).tif │ ├── Fig1001(e)(edge_noisy_image).tif │ ├── Fig1001(f)(region_split_merge_image).tif │ ├── Fig1004(b)(turbine_blade_black_dot).tif │ ├── Fig1005(a)(wirebond_mask).tif │ ├── Fig1007(a)(wirebond_mask).tif │ ├── Fig1008(a)(step edge).tif │ ├── Fig1008(b)(ramp edge).tif │ ├── Fig1008(c)(roof_edge).tif │ ├── Fig1016(a)(building_original).tif │ ├── Fig1022(a)(building_original).tif │ ├── Fig1025(a)(building_original).tif │ ├── Fig1026(a)(headCT-Vandy).tif │ ├── Fig1027(a)(van_original).tif │ ├── Fig1030(a)(tooth).tif │ ├── Fig1034(a)(marion_airport).tif │ ├── Fig1036(a)(original_septagon).tif │ ├── Fig1036(b)(gaussian_noise_mean_0_std_10_added).tif │ ├── Fig1036(c)(gaussian_noise_mean_0_std_50_added).tif │ ├── Fig1037(a)(septagon_gaussian_noise_mean_0_std_10_added).tif │ ├── Fig1037(b)(intensity_ramp).tif │ ├── Fig1038(a)(noisy_fingerprint).tif │ ├── Fig1039(a)(polymersomes).tif │ ├── Fig1040(a)(large_septagon_gaussian_noise_mean_0_std_50_added).tif │ ├── Fig1041(a)(septagon_small_noisy_mean_0_stdv_10).tif │ ├── Fig1042(a)(septagon_small_noisy_mean_0_stdv_10).tif │ ├── Fig1043(a)(yeast_USC).tif │ ├── Fig1045(a)(iceberg).tif │ ├── Fig1046(a)(septagon_noisy_shaded).tif │ ├── Fig1048(a)(yeast_USC).tif │ ├── Fig1049(a)(spot_shaded_text_image).tif │ ├── Fig1050(a)(sine_shaded_text_image).tif │ ├── Fig1051(a)(defective_weld).tif │ ├── Fig1053(a)(cygnusloop_Xray_original).tif │ ├── Fig1056(a)(blob_original).tif │ ├── Fig1057(a)(small_blobs-original).tif │ ├── Fig1059(a)(AbsADI).tif │ ├── Fig1059(b)(PosADI).tif │ ├── Fig1059(c)(NegADI).tif │ ├── Fig1060(a)(car on left).tif │ ├── Fig1060(b)(car on right).tif │ ├── Fig1060(c)(car removed).tif │ ├── Fig1061(LANDSAT_with moving target).tif │ └── FigP1036(blobs).tif ├── folder.ini └── 数据地址.txt ├── notebooks ├── 图像分割.ipynb ├── 图像表示与描述.ipynb ├── 形态学.ipynb ├── 灰度变换与空间滤波.ipynb └── 频率域滤波.ipynb ├── src ├── __init__.py ├── chapter10 │ ├── __init__.py │ └── edge_connect │ │ ├── __init__.py │ │ └── local_connect.py ├── chapter11 │ ├── __init__.py │ ├── font_img_skeleton_feature.py │ ├── freeman_code.py │ ├── mark_sheet.py │ ├── mat.py │ ├── moment_invariants.py │ ├── shape_number.py │ └── texture.py ├── chapter3 │ ├── __init__.py │ └── hist_local_enhance.py ├── chapter4 │ ├── __init__.py │ └── frequency_dommain.py ├── chapter9 │ ├── __init__.py │ └── morphology.py └── morphology.py └── tests ├── __init__.py ├── chapter10 ├── __init__.py └── edge_connect │ ├── 1863694-20191206105727406-2050307115.jpg │ ├── Fig1137(d)(painting_mirrored_padded).tif │ ├── __init__.py │ └── test_local_connect.py ├── chapter11 ├── __init__.py ├── test_freeman_code.py ├── test_mark_sheet.py ├── test_moment_invariants.py ├── test_shape_number.py └── test_texture.py └── chapter3 └── __init__.py /.gitattributes: -------------------------------------------------------------------------------- 1 | *.html linguist-language=python 2 | *.h linguist-language=python 3 | *.m linguist-language=python 4 | *.ipynb linguist-language=python -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | env/ 111 | venv/ 112 | ENV/ 113 | env.bak/ 114 | venv.bak/ 115 | 116 | # Spyder project settings 117 | .spyderproject 118 | .spyproject 119 | 120 | # Rope project settings 121 | .ropeproject 122 | 123 | # mkdocs documentation 124 | /site 125 | 126 | # mypy 127 | .mypy_cache/ 128 | .dmypy.json 129 | dmypy.json 130 | 131 | # Pyre type checker 132 | .pyre/ 133 | 134 | # pytype static type analyzer 135 | .pytype/ 136 | 137 | # Cython debug symbols 138 | cython_debug/ -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Datasource local storage ignored files 5 | /dataSources/ 6 | /dataSources.local.xml 7 | # Editor-based HTTP Client requests 8 | /httpRequests/ 9 | -------------------------------------------------------------------------------- /.idea/ImgPractice.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | 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Image-Process 2 | 闲暇之余对《数字图像处理》的实现，欢迎Star 3 | 4 | ------ 5 | 6 | 冈萨雷斯《数字图像处理》Python实现（第三版） 7 | 8 | 个人技术博客：http://www.zhangqi2019.top/ 9 | 10 | **本书预览地址：http://www.ailearndo.com/resource/数字图像处理(冈萨雷斯版).pdf** 11 | 12 | ## 第3章 灰度变换与空间滤波 13 | 14 | | | 实现代码 | Demo | 算法说明 | 15 | | -------------- | ---------------------------------------------------- | ------------------------------------------------------------ | -------- | 16 | | 局部直方图均衡 | | [局部直方图均衡](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E7%81%B0%E5%BA%A6%E5%8F%98%E6%8D%A2%E4%B8%8E%E7%A9%BA%E9%97%B4%E6%BB%A4%E6%B3%A2.ipynb#局部直方图均衡) | | 17 | | 直方图局部增强 | [直方图局部增强](src/chapter3/hist_local_enhance.py) | [直方图局部增强](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E7%81%B0%E5%BA%A6%E5%8F%98%E6%8D%A2%E4%B8%8E%E7%A9%BA%E9%97%B4%E6%BB%A4%E6%B3%A2.ipynb#局部直方图增强) | | 18 | 19 | ## 第4章 频率域滤波 20 | 21 | 频域章重在理解傅里叶变换和频域的思想，以下是对傅里叶和奈奎斯特定理的总结： 22 | 23 | * [傅里叶变换推导](https://www.zhangqi2019.top/posts/fb91b52c.html/) 24 | * [奈奎斯特定理推导](https://www.zhangqi2019.top/posts/88a0752c.html/) 25 | 26 | 实现包括以下内容： 27 | 28 | | | 实现代码 | Demo | 算法说明 | 29 | | ------------ | -------- | ------------------------------------------------------------ | -------- | 30 | | 图像重取样 | | [图像重取样](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E9%A2%91%E7%8E%87%E5%9F%9F%E6%BB%A4%E6%B3%A2.ipynb#图像重取样) | | 31 | | 傅里叶谱 | | [傅里叶谱](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E9%A2%91%E7%8E%87%E5%9F%9F%E6%BB%A4%E6%B3%A2.ipynb#傅里叶谱) | | 32 | | 直通分量置零 | | [直通分量置零](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E9%A2%91%E7%8E%87%E5%9F%9F%E6%BB%A4%E6%B3%A2.ipynb#直流分量置0) | | 33 | | 同态滤波 |[同态滤波](src/chapter4/frequency_dommain.py)| | | 34 | 35 | 36 | 37 | ## 第9章 形态学 38 | 39 | 以下形态学的Demo说明见：http://www.zhangqi2019.top/posts/c1f4dbab.html/ 40 | 41 | 实现包括以下内容： 42 | 43 | | | 实现代码 | Demo | 算法说明 | 44 | | --------------------- | ------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | 45 | | 腐蚀 | | [腐蚀](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#腐蚀) | [腐蚀](https://www.zhangqi2019.top/posts/c1f4dbab.html/#腐蚀) | 46 | | 膨胀 | | [膨胀](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#膨胀) | [膨胀](https://www.zhangqi2019.top/posts/c1f4dbab.html/#膨胀) | 47 | | 开操作 | | [开操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#开操作和闭操作) | [开操作](https://www.zhangqi2019.top/posts/c1f4dbab.html/#开操作和闭操作) | 48 | | 闭操作 | | [闭操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#开操作和闭操作) | [闭操作](https://www.zhangqi2019.top/posts/c1f4dbab.html/#开操作和闭操作) | 49 | | 击中以及不击中 | [击中和不击中](src/chapter9/morphology.py) | [击中以及不击中](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#击中和不击中) | [击中以及不击中](https://www.zhangqi2019.top/posts/c1f4dbab.html/#击中以及不击中) | 50 | | 边界提取 | | [边界提取](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#边界提取) | [边界提取](https://www.zhangqi2019.top/posts/c1f4dbab.html/#边界提取) | 51 | | 连通分量提取 | [连通分量提取](src/chapter9/morphology.py) | [连通分量提取](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#连通分量提取) | [连通分量提取](https://www.zhangqi2019.top/posts/c1f4dbab.html/#连通分量提取) | 52 | | 孔洞填充 | [孔洞填充](src/chapter9/morphology.py) | [孔洞填充](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#孔洞填充) | [孔洞填充](https://www.zhangqi2019.top/posts/c1f4dbab.html/#孔洞填充) | 53 | | 形态学重建-重建开操作 | [重建开操作](src/chapter9/morphology.py) | [形态学重建 -重建开操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#重建开操作) | [重建开操作](https://www.zhangqi2019.top/posts/c1f4dbab.html/#重建开操作) | 54 | | 灰度形态学-灰度腐蚀 | | [灰度级形态学- 灰度腐蚀](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#灰度腐蚀) | [灰度腐蚀](https://www.zhangqi2019.top/posts/c1f4dbab.html/#灰度腐蚀) | 55 | | 灰度形态学-灰度膨胀 | | [灰度级形态学-灰度膨胀](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#灰度膨胀) | [灰度膨胀](https://www.zhangqi2019.top/posts/c1f4dbab.html/#灰度膨胀) | 56 | | 灰度形态学-灰度开操作 | | [灰度级形态学-灰度开操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#灰度开操作) | [灰度开操作和闭操作](https://www.zhangqi2019.top/posts/c1f4dbab.html/#灰度开操作和闭操作) | 57 | | 灰度形态学-灰度闭操作 | | [灰度级形态学-灰度闭操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#灰度闭操作) | [灰度开操作和闭操作](https://www.zhangqi2019.top/posts/c1f4dbab.html/#灰度开操作和闭操作) | 58 | | 形态学梯度 | | [形态学梯度](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#形态学梯度) | [形态学梯度](https://www.zhangqi2019.top/posts/c1f4dbab.html/#形态学梯度) | 59 | | 顶帽操作 | | [顶帽操作](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#顶帽操作) | [顶帽和底帽变换](https://www.zhangqi2019.top/posts/c1f4dbab.html/#顶帽和底帽变换) | 60 | | 形态学纹理分割 | | [形态学纹理分割](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%BD%A2%E6%80%81%E5%AD%A6.ipynb#形态学纹理分割) | [纹理分割](https://www.zhangqi2019.top/posts/c1f4dbab.html/#纹理分割) | 61 | 62 | ## 第10章 图像分割 63 | 64 | | | 实现源码 | Demo | 算法说明 | 65 | | ------------------------- | -------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 66 | | 孤立点检测 | | [孤立点检测](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E5%88%86%E5%89%B2.ipynb) | [孤立点检测](https://www.zhangqi2019.top/posts/d2113c07.html/#孤立点检测) | 67 | | 线检测 | | [孤立点检测](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E5%88%86%E5%89%B2.ipynb) | [线检测](https://www.zhangqi2019.top/posts/d2113c07.html/#线检测) | 68 | | 简单边缘模型（Sobel检测） | | [Sobel边缘检测](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E5%88%86%E5%89%B2.ipynb#基本检测模型(Sobel)) | [Sobel边缘检测](https://www.zhangqi2019.top/posts/d2113c07.html/#基本的检测模型) | 69 | 70 | 71 | 72 | ## 第11章 表示和描述 73 | 74 | Demo汇总: [Demo汇总](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/图像表示与描述.ipynb) 75 | 76 | 本章算法说明汇总:https://www.zhangqi2019.top/posts/d4747641.html/ 77 | 78 | 算法实现包括以下： 79 | 80 | | | 实现源码 | Demo | 算法说明 | 81 | | --------------------- | ------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | 82 | | Moore边界追踪 | | | [Moore说明](https://www.zhangqi2019.top/posts/d4747641.html/#%E8%BE%B9%E7%95%8C%E8%BF%BD%E8%B8%AA%EF%BC%88Moore%E8%BE%B9%E7%95%8C%E8%BF%BD%E8%B8%AA%EF%BC%89) | 83 | | Freeman链码 | [Freeman实现代码](src/chapter11/freeman_code.py) | [Freeman链码Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#Freeman链码) | [Freeman链码](https://www.zhangqi2019.top/posts/d4747641.html/#%E9%93%BE%E7%A0%81) | 84 | | 标记图 | [标记图实现代码](src/chapter11/mark_sheet.py) | [标记图Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#标记图) | [标记图](https://www.zhangqi2019.top/posts/d4747641.html/#%E9%93%BE%E7%A0%81) | 85 | | 骨架算法 | [骨架算法实现代码](src/chapter11/mat.py) | [骨架算法Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#骨架) | [骨架](https://www.zhangqi2019.top/posts/d4747641.html/#%E9%AA%A8%E6%9E%B6) | 86 | | 形状数 | [形状数实现](src/chapter11/shape_number.py) | [形状数Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#形状数) | [形状数](https://www.zhangqi2019.top/posts/d4747641.html/#%E5%BD%A2%E7%8A%B6%E6%95%B0) | 87 | | 灰度共生矩阵 | [灰度共生矩阵](src/chapter11/texture.py) | [灰度共生矩阵Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#灰度共生矩阵) | | 88 | | 灰度共生矩阵描述子 | [灰度共生矩阵描述子](src/chapter11/texture.py) | [灰度共生矩阵描述子Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#灰度共生矩阵描述子) | | 89 | | $n$步共生矩阵相关系数 | [n步共生矩阵相关系数](src/chapter11/texture.py) | [灰度共生矩阵描述子Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#n跳共生矩阵组成的序列图像) | | 90 | | 不变矩 | [不变矩](src/chapter11/moment_invariants.py) | [不变矩Demo](https://nbviewer.jupyter.org/github/qcymkxyc/Image-Process/blob/master/notebooks/%E5%9B%BE%E5%83%8F%E8%A1%A8%E7%A4%BA%E4%B8%8E%E6%8F%8F%E8%BF%B0.ipynb#不变矩) | | 91 | 92 | -------------------------------------------------------------------------------- /external/histogram_match.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import numpy as np 4 | 5 | 6 | def get_map(Hist) -> np.ndarray: 7 | """求直方图均衡的映射表 8 | :param Hist: 图像直方图 9 | :return np.ndarray, 映射表 10 | """ 11 | """归一化，即计算概率""" 12 | sum_Hist = sum(Hist) 13 | Pr = Hist / sum_Hist 14 | 15 | """计算累积概率""" 16 | Sk = [] 17 | temp_sum = 0 18 | for n in Pr: 19 | temp_sum = temp_sum + n 20 | Sk.append(temp_sum) 21 | Sk = np.array(Sk) 22 | 23 | """计算映射表""" 24 | img_map = [] 25 | for m in range(256): 26 | temp_map = int(255 * Sk[m] + 0.5) 27 | img_map.append(temp_map) 28 | img_map = np.array(img_map) 29 | return img_map 30 | 31 | 32 | def get_off_map(map_): # 计算反向映射，寻找最小期望 33 | """建立反向映射表""" 34 | map_2 = list(map_) 35 | off_map = [] 36 | temp_pre = 0 # 如果循环开始就找不到映射时，默认映射为0 37 | for n in range(256): 38 | try: 39 | temp1 = map_2.index(n) 40 | temp_pre = temp1 41 | except BaseException: 42 | temp1 = temp_pre # 找不到映射关系时，近似取向前最近的有效映射值 43 | off_map.append(temp1) 44 | off_map = np.array(off_map) 45 | return off_map 46 | 47 | 48 | def get_infer_map(infer_img) -> list: 49 | """""" 50 | """计算三个通道的直方图""" 51 | infer_Hist_b = cv2.calcHist([infer_img], [0], None, [256], [0, 255]) 52 | infer_Hist_g = cv2.calcHist([infer_img], [1], None, [256], [0, 255]) 53 | infer_Hist_r = cv2.calcHist([infer_img], [2], None, [256], [0, 255]) 54 | 55 | """计算三个颜色的映射表""" 56 | infer_b_map = get_map(infer_Hist_b) 57 | infer_g_map = get_map(infer_Hist_g) 58 | infer_r_map = get_map(infer_Hist_r) 59 | 60 | """建立反向映射表""" 61 | infer_b_off_map = get_off_map(infer_b_map) 62 | infer_g_off_map = get_off_map(infer_g_map) 63 | infer_r_off_map = get_off_map(infer_r_map) 64 | 65 | return [infer_b_off_map, infer_g_off_map, infer_r_off_map] 66 | 67 | 68 | def get_finalmap(org_map, infer_off_map): # 计算原始图像到最终输出图像的映射关系 69 | org_map = list(org_map) 70 | infer_off_map = list(infer_off_map) 71 | final_map = [] 72 | for n in range(256): 73 | temp1 = org_map[n] 74 | temp2 = infer_off_map[temp1] 75 | final_map.append(temp2) 76 | final_map = np.array(final_map) 77 | return final_map 78 | 79 | 80 | def get_newimg(img_org, org2infer_maps): 81 | """""" 82 | w, h, _ = img_org.shape 83 | b, g, r = cv2.split(img_org) 84 | for i in range(w): 85 | for j in range(h): 86 | temp1 = b[i, j] 87 | b[i, j] = org2infer_maps[0][temp1] 88 | for i in range(w): 89 | for j in range(h): 90 | temp1 = g[i, j] 91 | g[i, j] = org2infer_maps[1][temp1] 92 | for i in range(w): 93 | for j in range(h): 94 | temp1 = r[i, j] 95 | r[i, j] = org2infer_maps[2][temp1] 96 | newimg = cv2.merge([b, g, r]) 97 | return newimg 98 | 99 | 100 | def get_new_img(img_org, infer_map): 101 | """""" 102 | """获取图片的直方图""" 103 | org_Hist_b = cv2.calcHist([img_org], [0], None, [256], [0, 255]) 104 | org_Hist_g = cv2.calcHist([img_org], [1], None, [256], [0, 255]) 105 | org_Hist_r = cv2.calcHist([img_org], [2], None, [256], [0, 255]) 106 | 107 | """直方图映射表""" 108 | org_b_map = get_map(org_Hist_b) 109 | org_g_map = get_map(org_Hist_g) 110 | org_r_map = get_map(org_Hist_r) 111 | 112 | org2infer_map_b = get_finalmap(org_b_map, infer_map[0]) 113 | org2infer_map_g = get_finalmap(org_g_map, infer_map[1]) 114 | org2infer_map_r = get_finalmap(org_r_map, infer_map[2]) 115 | 116 | return get_newimg( 117 | img_org, [org2infer_map_b, org2infer_map_g, org2infer_map_r]) 118 | 119 | 120 | if __name__ == "__main__": 121 | dstroot = r'.\data' 122 | infer_img_path = r'.\0.jpg' 123 | infer_img = cv2.imread(infer_img_path) 124 | outroot = r'.\out1' 125 | infer_map = get_infer_map(infer_img) # 计算参考映射关系 126 | dstlist = os.listdir(dstroot) 127 | for n in dstlist: 128 | img_path = os.path.join(dstroot, n) 129 | print(img_path) 130 | img_org = cv2.imread(img_path) 131 | # TODO 待看 132 | new_img = get_new_img(img_org, infer_map) # 根据映射关系获得新的图像 133 | # cv2.imshow("image",new_img) 134 | # cv2.waitKey(0) 135 | new_path = os.path.join(outroot, n) 136 | print(new_path) 137 | 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These are the images 9 | that you have just downloaded. No processed images, line 10 | drawings, or other types of graphics are posted. The idea 11 | is that posting the original images provides the necessary 12 | starting point to duplicate any image processing results 13 | in the book that are based on those images. 14 | 15 | The Classromm PowerPoint presentations (see Faculty tab in the 16 | book web site) do include ALL art in the book, but the resolution 17 | is not as good as with the original,individual images in the 18 | Image Database section of the book web site. 19 | 20 | -------------------------------------------------------------------------------- /image_data/DIP3E_Original_Images_CH03/Fig0304(a)(breast_digital_Xray).tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qcymkxyc/Image-Process/345905f2fea003e1eca03dcb2c8acf9214cb6158/image_data/DIP3E_Original_Images_CH03/Fig0304(a)(breast_digital_Xray).tif -------------------------------------------------------------------------------- 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Expanded=1 3 | -------------------------------------------------------------------------------- /image_data/数据地址.txt: -------------------------------------------------------------------------------- 1 | http://www.imageprocessingplace.com/DIP-3E/dip3e_book_images_downloads.htm -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/7 14:45 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /src/chapter10/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/8/7 16:33 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /src/chapter10/edge_connect/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/24 15:41 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py 9 | @Title : 10 | @Description : 11 | """ 12 | -------------------------------------------------------------------------------- /src/chapter10/edge_connect/local_connect.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/24 15:39 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : edge_connect.py 9 | @Title : 边界连接 10 | @Description : 11 | """ 12 | import numpy as np 13 | import matplotlib.pyplot as plt 14 | import cv2 15 | 16 | 17 | def connect_gap(binary_matrix:np.ndarray,gap_length:int): 18 | """连接缝隙""" 19 | for i,row in enumerate(binary_matrix): 20 | last_1_index = - np.inf 21 | for j,pix in enumerate(row): 22 | if pix != 0: 23 | """填充缝隙""" 24 | if j - last_1_index < gap_length: 25 | binary_matrix[i][last_1_index + 1:j + 1] = 1 26 | last_1_index = j 27 | return binary_matrix 28 | 29 | 30 | def _check_binary_matrix(gradient_value_matrix:np.ndarray, 31 | gradient_angel_matrix:np.ndarray, 32 | gradient_value_threshold:float, 33 | gradient_angle_threshold:float, 34 | gradient_angle_bandwidth:float): 35 | """条件二值化""" 36 | tmp1 = gradient_value_matrix > gradient_value_threshold 37 | tmp2 = gradient_angel_matrix == (gradient_angle_threshold - gradient_angle_bandwidth) 38 | tmp3 = gradient_angel_matrix == (gradient_angle_threshold + gradient_angle_bandwidth) 39 | matrix = np.logical_and(tmp1,np.logical_or(tmp2,tmp3)) 40 | 41 | matrix = matrix.astype(int) 42 | return matrix 43 | 44 | 45 | def _horizontal_connect(img: np.ndarray, 46 | gradient_angle_threshold:float, 47 | gradient_angle_bandwidth:float, 48 | gradient_value_threshold_rate:float, ) -> np.ndarray: 49 | img_diff_x = cv2.Sobel(img, -1, 1, 0) 50 | img_diff_y = cv2.Sobel(img, -1, 0, 1) 51 | 52 | # TODO 角度有nan出现 53 | gradient_value = np.sqrt(img_diff_x ** 2 + img_diff_y ** 2) 54 | gradient_angle = np.rad2deg(np.arctan(img_diff_y / img_diff_x)) 55 | 56 | """计算梯度的阈值""" 57 | gradient_value_threshold = np.max(gradient_value) * gradient_value_threshold_rate 58 | 59 | """判断""" 60 | matrix = _check_binary_matrix( 61 | gradient_angel_matrix=gradient_angle, 62 | gradient_value_matrix=gradient_value, 63 | gradient_value_threshold=gradient_value_threshold, 64 | gradient_angle_bandwidth=gradient_angle_bandwidth, 65 | gradient_angle_threshold=gradient_angle_threshold,) 66 | 67 | """缝隙连接""" 68 | matrix = connect_gap(binary_matrix=matrix, gap_length=50) 69 | 70 | plt.imshow(img) 71 | plt.title("img") 72 | plt.show() 73 | 74 | tmp = np.logical_or(matrix,img) 75 | tmp = tmp.astype(int) 76 | plt.imshow(tmp) 77 | plt.title("t") 78 | plt.show() 79 | 80 | 81 | # return tmp * img 82 | return matrix + img 83 | 84 | 85 | def local_connect(img: np.ndarray, 86 | gradient_angle_threshold:float, 87 | gradient_angle_bandwidth:float, 88 | gradient_value_threshold_rate:float, ) -> np.ndarray: 89 | """""" 90 | res_img = _horizontal_connect(img, 91 | gradient_angle_threshold, 92 | gradient_angle_bandwidth, 93 | gradient_value_threshold_rate) 94 | 95 | import matplotlib.pyplot as plt 96 | plt.imshow(res_img) 97 | plt.show() 98 | 99 | res_img = cv2.rotate(res_img,cv2.ROTATE_90_COUNTERCLOCKWISE) 100 | res_img = res_img.astype("uint8") 101 | res_img = _horizontal_connect(res_img, 102 | gradient_angle_threshold, 103 | gradient_angle_bandwidth, 104 | gradient_value_threshold_rate) 105 | res_img = cv2.rotate(res_img,cv2.ROTATE_90_CLOCKWISE) 106 | 107 | return res_img 108 | -------------------------------------------------------------------------------- /src/chapter11/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/13 14:37 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /src/chapter11/font_img_skeleton_feature.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/5/11 17:35 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : font_img_skeleton_feature.py 9 | @Title : 字体骨架图片的特征 10 | @Description : 11 | """ 12 | import preprocess.neaten_data 13 | from algorithm.image import mat 14 | import copy 15 | import numpy as np 16 | from util import decorators 17 | from util import image_helper 18 | import pandas as pd 19 | 20 | @decorators.func_input_type_check() 21 | @decorators.func_output_type_check() 22 | def get_font_skeleton_by_img(img: np.ndarray) ->np.ndarray: 23 | """给定一个字体图片，返回该字体的骨架 24 | 25 | :param img: numpy.ndarray, 字体图片 26 | :return: numpy.ndarray, 骨架图片 27 | """ 28 | '''二值化图片,并且统一为黑底白字''' 29 | binary_img = image_helper.font_img_binary_and_black_ground(img) 30 | '''标准化图片''' 31 | norm_img = image_helper.font_img_normalization(binary_img) 32 | '''骨架化后的图片''' 33 | stroke_img = mat.get_img_skeleton_by_mat(norm_img) 34 | return stroke_img 35 | 36 | 37 | @decorators.func_output_type_check() 38 | @decorators.func_input_type_check() 39 | def get_font_skeleton_by_seq(signature_data: pd.DataFrame) -> np.ndarray: 40 | """给定一个签字序列，返回该签字序列的骨架（实际上是不加粗细的绘图） 41 | 42 | :param signature_data: pandas.DataFrame, 43 | :return: numpy.ndarray, 骨架图片 44 | """ 45 | try: 46 | img = draw_img( 47 | data=signature_data, 48 | is_resize=True, 49 | size=font_shape_model_config.resize_img_shape 50 | ) 51 | except KeyError: 52 | '''列名错误的情况''' 53 | temp_data = signature_data.copy() 54 | temp_data.columns = preprocess.neaten_data.replace_columns(temp_data.columns, 2) 55 | img = draw_img( 56 | data=temp_data, 57 | is_resize=True, 58 | size=font_shape_model_config.resize_img_shape 59 | ) 60 | finally: 61 | img = img.resize(font_shape_model_config.resize_img_shape) 62 | img = np.asarray(img) 63 | """二值化图片,并且统一为黑底白字""" 64 | binary_img = image_helper.font_img_binary(img) 65 | '''转换为黑底白字''' 66 | # binary_img = 255 - binary_img 67 | '''标准化图片''' 68 | norm_img = image_helper.font_img_normalization(binary_img) 69 | 70 | return norm_img 71 | -------------------------------------------------------------------------------- /src/chapter11/freeman_code.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/7 14:45 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : freeman_code.py 9 | @Title : Freeman链码实现 10 | @Description : 11 | """ 12 | import numpy as np 13 | from deprecated.sphinx import deprecated 14 | 15 | """表示是按4方向编码还是按8方向编码""" 16 | FOUR_DIRECTION = 1 17 | EIGHT_DIRECTION = 2 18 | 19 | # ========================================================= 20 | # 获取Freeman链码坐标 21 | # ========================================================= 22 | 23 | 24 | def __get_freeman_box_list( 25 | img: np.ndarray, n_box_x: int = 10, n_box_y: int = 10) -> ([int], [int]): 26 | """给定一张图片，返回该图片对应的Freeman网格对应的坐标，坐标分为 27 | x和y，所以返回的是两个list，分别是x的list和y的list 28 | 29 | :param img: numpy.ndarray, 图片 30 | :param n_box_x: int ,x轴的网格数 31 | :param n_box_y: int, y轴的网格数 32 | :return: [int],[int] 33 | 网格对应的list,list中的个数等于网格数加一 34 | """ 35 | freeman_x_coordination_list = list() 36 | freeman_y_coordination_list = list() 37 | '''获取图像的长宽''' 38 | if len(img.shape) == 2: 39 | img_h, img_w = img.shape 40 | else: 41 | img_h, img_w, _ = img.shape 42 | 43 | # 获取每个网格的长和宽 44 | cell_w, cell_h = img_w / n_box_x, img_h / n_box_y 45 | for i in range(n_box_x + 1): 46 | freeman_x_coordination_list.append(i * cell_w) 47 | for i in range(n_box_y + 1): 48 | freeman_y_coordination_list.append(i * cell_h) 49 | 50 | return freeman_x_coordination_list, freeman_y_coordination_list 51 | 52 | # =============================================================== 53 | # 获取边缘对应的Freeman链码坐标 54 | # =============================================================== 55 | 56 | 57 | def __get_close_num(num: float, num_list: [float]) -> (float, float): 58 | """给定一个数字以及一个有序的list，返回离该数字最近的两个数组中的元素 59 | 60 | :param num: float,该数字 61 | :param num_list: List[float],有序的list 62 | :return: Tuple(flaot,float),最近的两个元素 63 | """ 64 | for i in range(len(num_list) - 1): 65 | current_num = num_list[i] 66 | next_num = num_list[i + 1] 67 | if current_num <= num <= next_num: 68 | return current_num, next_num 69 | 70 | 71 | def __get_freeman_coordination( 72 | point: (int, int), freeman_x_list: list, freeman_y_list: list) -> (int, int): 73 | """给出当前点，放回该点在Freeman网格中的坐标 74 | 75 | :param point: (int,int), 点的坐标 76 | :param freeman_x_list: List[float], Freeman网格x 77 | :param freeman_y_list: List[float], Freeman网格y 78 | :return: (int,int) or None 79 | 如果该点在Freeman网格线上则返回对应的Freeman网格坐标，否则返回None 80 | """ 81 | x, y = point 82 | x_index, y_index = None, None 83 | """如果该点在网格的线上""" 84 | if x in freeman_x_list or y in freeman_y_list: 85 | try: 86 | x_index = freeman_x_list.index(x) 87 | except ValueError: 88 | '''如果该点在横线上''' 89 | last_freeman_x, next_freeman_x = __get_close_num(x, freeman_x_list) 90 | mean_freeman_x = (last_freeman_x + next_freeman_x) / 2 91 | x_index = freeman_x_list.index(last_freeman_x) 92 | x_index = x_index if x < mean_freeman_x else x_index + 1 93 | y_index = freeman_y_list.index(y) 94 | 95 | return x_index, y_index 96 | 97 | else: 98 | """如果该点在纵线上""" 99 | try: 100 | y_index = freeman_y_list.index(y) 101 | except ValueError: 102 | '''如果该点在纵线上''' 103 | last_freeman_y, next_freeman_y = __get_close_num( 104 | y, freeman_y_list) 105 | mean_freeman_y = (last_freeman_y + next_freeman_y) / 2 106 | y_index = freeman_y_list.index(last_freeman_y) 107 | y_index = y_index if y < mean_freeman_y else y_index + 1 108 | x_index = freeman_x_list.index(x) 109 | else: 110 | '''如果该点刚好在Freeman编码的坐标点上''' 111 | x_index = freeman_x_list.index(x) 112 | y_index = freeman_y_list.index(y) 113 | finally: 114 | return x_index, y_index 115 | else: 116 | '''如果没有''' 117 | return 118 | 119 | 120 | # ============================================================ 121 | # FreeMan编码 122 | # ============================================================ 123 | @deprecated(version="1.0", reason="该函数判断不合理") 124 | def __is_freeman_coordination_correct( 125 | freeman_coordination_list: [(int, int)]) -> bool: 126 | """验证Freeman链码是否正确 127 | 128 | 如果下一个点和当前点仅差一个距离（即8个方向），则判定下一个点是正确的，否则判定下一个 129 | 点是错误的 130 | 131 | :param freeman_coordination_list: List[(int,int)],得到的Freeman链码的List 132 | :return: bool,链码是否正确 133 | """ 134 | for i in range(len(freeman_coordination_list) - 1): 135 | current_freeman_point = freeman_coordination_list[i] 136 | current_point_x, current_point_y = current_freeman_point 137 | next_freeman_point = freeman_coordination_list[i + 1] 138 | 139 | # 8个方向候选点(包括当前点),如果在候选点中得到超出范围的Freeman坐标点，不影响结果 140 | candidate_point_list = list() 141 | for x_direct_offset in [-1, 0, 1]: 142 | for y_direct_offset in [-1, 0, 1]: 143 | candidate_point_list.append( 144 | (current_point_x + x_direct_offset, 145 | current_point_y + y_direct_offset)) 146 | if next_freeman_point not in candidate_point_list: 147 | # print(current_freeman_point, next_freeman_point) 148 | return False 149 | return True 150 | 151 | 152 | def __get_freeman_code_by_two_point( 153 | point1: (int, int), point2: (int, int),) -> int: 154 | """给定两个点（Freeman点），返回Freeman编码 155 | 156 | :param point1: (int,int),点1 157 | :param point2: (int,int),点2 158 | :return: int， Freeman编码 159 | """ 160 | freeman_code_dict = { 161 | str((1, 0)): 0, 162 | str((1, 1)): 1, 163 | str((0, 1)): 2, 164 | str((-1, 1)): 3, 165 | str((-1, 0)): 4, 166 | str((-1, -1)): 5, 167 | str((0, -1)): 6, 168 | str((1, -1)): 7, 169 | } 170 | point_gap = (point2[0] - point1[0], point2[1] - point1[1]) 171 | try: 172 | return freeman_code_dict[str(point_gap)] 173 | except KeyError: 174 | return 175 | 176 | 177 | def __get_freeman_code(freeman_coordination_list: [(int, int)]) -> [int]: 178 | """给定Freeman坐标，返回Freeman编码的结果 179 | 180 | :param freeman_coordination_list: List[(int,int)],Freman坐标组成的List 181 | :return: List[int],Freeman编码结果 182 | """ 183 | freeman_code_list = list() 184 | for i in range(len(freeman_coordination_list) - 1): 185 | current_freeman_point = freeman_coordination_list[i] 186 | next_freeman_point = freeman_coordination_list[i + 1] 187 | 188 | freeman_code = __get_freeman_code_by_two_point( 189 | point1=current_freeman_point, 190 | point2=next_freeman_point) 191 | '''排除重复点 ''' 192 | if freeman_code is not None: 193 | freeman_code_list.append(freeman_code) 194 | return freeman_code_list 195 | 196 | 197 | # ============================================================ 198 | # 主函数 199 | # ============================================================ 200 | 201 | def get_freeman_coordination(img: np.ndarray, contour: [(int, int)]) -> [int]: 202 | """给定图像和对应的边界，返回Freeman对应的坐标 203 | 204 | :param img: numpy.ndarray, 图像 205 | :param contour: List[(int,int)],边界的List 206 | :return: Freeman对应的坐标 207 | """ 208 | freeman_coordination_list = list() # 用于保存得出的Freeman链码坐标 209 | '''获取Freeman链码的网格坐标''' 210 | freeman_x_coordination_list, freeman_y_coordination_list = \ 211 | __get_freeman_box_list(img=img) 212 | 213 | """得到边缘在Freeman链码上的坐标""" 214 | for point in contour: 215 | x, y = point 216 | point_freeman_coordination = __get_freeman_coordination( 217 | point=(x, y), 218 | freeman_x_list=freeman_x_coordination_list, 219 | freeman_y_list=freeman_y_coordination_list, 220 | ) 221 | """如果该点在Freeman网格上""" 222 | if point_freeman_coordination is not None: 223 | freeman_coordination_list.append(point_freeman_coordination) 224 | 225 | '''添加最后一个点到第一个点的连线''' 226 | freeman_coordination_list.append(freeman_coordination_list[0]) 227 | return freeman_coordination_list 228 | 229 | 230 | def get_freeman_code(img: np.ndarray, contour: [ 231 | (int, int)]) -> [int]: 232 | """给定图像和对应的边界，返回Freeman链码 233 | 234 | :param img: numpy.ndarray, 图像 235 | :param contour: List[(int,int)],边界的list 236 | :return: [int], 弗雷曼链码 237 | """ 238 | freeman_coordination_list = get_freeman_coordination(img, contour) 239 | """验证得到的Freeman坐标是否正确""" 240 | assert __is_freeman_coordination_correct(freeman_coordination_list) 241 | 242 | """根据方向得到Freeman链码""" 243 | return __get_freeman_code(freeman_coordination_list) 244 | -------------------------------------------------------------------------------- /src/chapter11/mark_sheet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/10 14:21 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : mark_sheet.py 9 | @Title : 标记图 10 | @Description : 11 | """ 12 | from scipy.spatial.distance import euclidean 13 | import numpy as np 14 | import cv2 15 | 16 | 17 | def get_centroid(img: np.ndarray) -> (int, int): 18 | """给定一张图的，返回其亮点的质心 19 | 20 | :param img: numpy.ndarray, 图像 21 | :return: (int,int), 质心的坐标 22 | """ 23 | white_point = np.where([img == 1]) 24 | x = white_point[1].mean() 25 | y = white_point[2].mean() 26 | 27 | return x, y 28 | 29 | 30 | def get_mark_sheet(img: np.ndarray) -> np.ndarray: 31 | """根据图像返回标记图 32 | 33 | :param img: numpy.array, 34 | :return: 35 | """ 36 | # TODO 结果有问题 37 | distance_list = list() 38 | '''获取质心''' 39 | centroid = get_centroid(img) 40 | 41 | '''获取边界''' 42 | image, contours, hierarchy = cv2.findContours( 43 | img, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) 44 | contour = contours[0] 45 | '''距离list''' 46 | for point in contour: 47 | dist = euclidean(centroid, point) 48 | distance_list.append(dist) 49 | return distance_list 50 | 51 | 52 | def get_mark_sheet_by_contour( 53 | centroid: (int, int), contour: [(int, int)]): 54 | """给定边界的list，返回距离的list 55 | 56 | :param centroid: (int,int),质心 57 | :param contour: List[(int,int)],边界 58 | :return: List[int], 距离的List 59 | """ 60 | distance_list = list() 61 | '''距离list''' 62 | for point in contour: 63 | dist = euclidean(centroid, point) 64 | distance_list.append(dist) 65 | return distance_list 66 | -------------------------------------------------------------------------------- /src/chapter11/mat.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/4/28 11:13 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : mat.py 9 | @Title : MAT算法 10 | @Description : 11 | MAT算法用于给定一个字的图片，通过MAT算法返回这个字对应的骨架图片 12 | """ 13 | import numpy as np 14 | from functools import reduce 15 | 16 | 17 | # ================================================================= 18 | # MAT算法提取骨架 19 | # ================================================================= 20 | 21 | 22 | def __mat_process_first(around_area: np.ndarray) -> bool: 23 | """MAT算法步骤1 24 | 25 | 对于相邻像素区域： 26 | [ 27 | [p9,p2,p3], 28 | [p8,p1,p4], 29 | [p7,p6,p5] 30 | ] 31 | 包括以下几个部分： 32 | a. 2 <=非零像素个数 <= 6 33 | b. 顺时针跳数 = 1 34 | c. p2 * p4 * p6 = 0 35 | d. p4 * p6 * p8 = 0 36 | 37 | :param around_area: numpy.array, 一个像素的相邻像素，为3*3 38 | :return: bool，是否满足以上所有条件 39 | """ 40 | result_list = list() # 保存所有步骤是否符合条件 41 | """步骤a""" 42 | near_one_count = __near_pix_equal_one_count(around_area) 43 | result_list.append(2 <= near_one_count <= 6) 44 | """步骤b""" 45 | result_list.append(__binary_transform_count(around_area) == 1) 46 | """步骤c""" 47 | pix_2 = around_area[0][1] 48 | pix_4 = around_area[1][2] 49 | pix_6 = around_area[2][1] 50 | result_list.append(pix_2 * pix_4 * pix_6 == 0) 51 | """步骤d""" 52 | pix_8 = around_area[1][0] 53 | result_list.append(pix_4 * pix_6 * pix_8 == 0) 54 | 55 | return bool(reduce(lambda x, y: x and y, result_list)) 56 | 57 | 58 | def __mat_process_second(around_area: np.ndarray) -> bool: 59 | """MAT算法步骤2 60 | 对于相邻像素区域： 61 | [ 62 | [p9,p2,p3], 63 | [p8,p1,p4], 64 | [p7,p6,p5] 65 | ] 66 | 包括以下几个部分： 67 | a. 2 <=非零像素个数 <= 6 68 | b. 顺时针跳数 = 1 69 | c. p2 * p4 * p8 = 0 70 | d. p2 * p6 * p8 = 0 71 | :param around_area: numpy.array, 周围的区域 72 | :return: bool,是否全部子条件 73 | """ 74 | result_list = list() # 保存所有步骤是否符合条件 75 | """步骤a""" 76 | near_one_count = __near_pix_equal_one_count(around_area) 77 | result_list.append(2 <= near_one_count <= 6) 78 | """步骤b""" 79 | result_list.append(__binary_transform_count(around_area) == 1) 80 | """步骤c""" 81 | pix_2 = around_area[0][1] 82 | pix_4 = around_area[1][2] 83 | pix_8 = around_area[1][0] 84 | pix_6 = around_area[2][1] 85 | result_list.append(pix_2 * pix_4 * pix_8 == 0) 86 | """步骤d""" 87 | result_list.append(pix_2 * pix_6 * pix_8 == 0) 88 | 89 | return bool(reduce(lambda x, y: x and y, result_list)) 90 | 91 | 92 | def __near_pix_equal_one_count(around_area: np.ndarray) -> int or np.int: 93 | """计算相邻像素中为1的个数(不包括中间点) 94 | 95 | 即，对于相邻像素区域： 96 | [ 97 | [p9,p2,p3], 98 | [p8,p1,p4], 99 | [p7,p6,p5] 100 | ] 101 | 统计出p1之外所有的1的个数 102 | :param around_area: numpy.array, 一个像素的相邻像素，为3*3 103 | :return int,像素为1的个数 104 | """ 105 | temp_around_area = np.copy(around_area) 106 | temp_around_area[1][1] = 0 107 | return int(np.sum(temp_around_area, dtype=np.int)) 108 | 109 | 110 | def __binary_transform_count(around_area: np.ndarray) -> int or np.int: 111 | """给定一个3*3的二进制图片，获取其顺时针的跳数（从0到1） 112 | 113 | 即，对于相邻像素区域： 114 | [ 115 | [p9,p2,p3], 116 | [p8,p1,p4], 117 | [p7,p6,p5] 118 | ] 119 | 以p9,p2,p3,p4,p5,p6,p7,p8的顺序访问，如果是0到1，则为一跳 120 | :param around_area: numpy.array, 一个像素的相邻像素，为3*3 121 | :return int, 顺时针跳数 122 | """ 123 | def __next_index(current_coor: (int, int)) -> (int, int): 124 | """给定当前位置，返回下一个位置 125 | 126 | :param current_coor: (int,int),当前位置 127 | :return: (int,int), 下一个位置 128 | """ 129 | '''四个方向的下一个位置''' 130 | right_next = (current_coor[0], current_coor[1] + 1) 131 | down_next = (current_coor[0] + 1, current_coor[1]) 132 | left_next = (current_coor[0], current_coor[1] - 1) 133 | up_next = (current_coor[0] - 1, current_coor[1]) 134 | 135 | """按照指定的规则寻找，不报错则表示正确的方向""" 136 | next_coordinate_list = [right_next, down_next, left_next, up_next] 137 | for i, next_coordinate in enumerate(next_coordinate_list): 138 | try: 139 | around_area[next_coordinate] 140 | except IndexError: 141 | continue 142 | else: 143 | '''如果该点已经走过''' 144 | if is_walked[next_coordinate[0], next_coordinate[1]]: 145 | continue 146 | else: 147 | is_walked[next_coordinate[0], next_coordinate[1]] = True 148 | return next_coordinate 149 | 150 | is_walked = np.full_like(around_area, False) # 用于标识该点是否已经走过 151 | is_walked[1][1] = True 152 | transform_count = 0 # 用于记录跳数 153 | """循环对比""" 154 | last_pix = around_area[0][0] # 上一个的值 155 | current_coordinate = (0, 1) 156 | while current_coordinate != (0, 0): 157 | current_pix = around_area[current_coordinate[0], current_coordinate[1]] 158 | if last_pix == 0 and current_pix == 1: 159 | transform_count += 1 160 | 161 | last_pix = current_pix 162 | current_coordinate = __next_index(current_coordinate) 163 | 164 | '''当循环到第一个点时再对比一次''' 165 | current_pix = around_area[current_coordinate[0], current_coordinate[0]] 166 | if last_pix == 0 and current_pix == 1: 167 | transform_count += 1 168 | 169 | return transform_count 170 | 171 | 172 | def __remove_pix_by_coordination(img: np.ndarray, points: list): 173 | """给定坐标的list，删除图像上的点（实际就是标记为0） 174 | 175 | :param img: numpy.array,图像 176 | :param points: List[(int,int)] 177 | """ 178 | for single_coordination in points: 179 | i_row, i_col = single_coordination 180 | img[i_row][i_col] = 0 181 | 182 | 183 | def __get_remove_points(img: np.ndarray, func) -> [(int, int)]: 184 | """给定图像以及，删除点的规则，返回要删除的点 185 | 186 | :param img: numpy.array, 原图像 187 | :param func: function, 规则，也就是一个函数 188 | :return: List[（int,int）],坐标的list 189 | """ 190 | remove_points_list = list() 191 | temp_img = img 192 | img_iter = np.nditer(temp_img, flags=["multi_index"]) 193 | while not img_iter.finished: 194 | current_pix = img_iter[0] 195 | i_row, i_col = img_iter.multi_index 196 | img_iter.iternext() 197 | '''如果是背景点则直接跳过''' 198 | if current_pix != 1: 199 | continue 200 | 201 | """如果是前景点""" 202 | around_area = temp_img[i_row - 1:i_row + 2, i_col - 1:i_col + 2] 203 | if func(around_area): 204 | remove_points_list.append((i_row, i_col)) 205 | 206 | img_iter.iternext() 207 | return remove_points_list 208 | 209 | 210 | def get_img_skeleton_by_mat(img: np.ndarray) -> np.ndarray: 211 | """根据字体的图像得到字的骨架 212 | 213 | :param img, numpy.array, 原图片 214 | :raise ValueError 215 | - 图片不为单通道 216 | - 图片并未归一化 217 | - 图片并未标准化 218 | :return: numpy.array, 骨架图 219 | """ 220 | '''检验图片是否是单通道''' 221 | if len(img.shape) != 2: 222 | raise ValueError("该图片不是单通道") 223 | """检验标准化""" 224 | if img.max() > 1: 225 | raise ValueError("该图片并未标准化") 226 | """检验二值化""" 227 | if (np.unique(img.flatten()) != (0, 1)).all(): 228 | raise ValueError("该函数并未二值化") 229 | 230 | temp_img = img.copy() 231 | """遍历每一个像素点""" 232 | is_remove_flag = True # 表示是否继续删除的标志 233 | i_round = 1 # 记录迭代的轮数 234 | while is_remove_flag: 235 | is_remove_flag = False 236 | print("正在执行MAT算法的第{}轮".format(i_round)) 237 | """执行步骤1""" 238 | remove_points = __get_remove_points(temp_img, __mat_process_first) 239 | if len(remove_points) != 0: 240 | is_remove_flag = True 241 | __remove_pix_by_coordination(temp_img, remove_points) 242 | 243 | """执行步骤2""" 244 | remove_points = __get_remove_points(temp_img, __mat_process_second) 245 | if len(remove_points) != 0: 246 | is_remove_flag = True 247 | __remove_pix_by_coordination(temp_img, remove_points) 248 | 249 | i_round += 1 250 | 251 | return temp_img 252 | -------------------------------------------------------------------------------- /src/chapter11/moment_invariants.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/9/3 11:32 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : moment_invariants.py 9 | @Title : 不变矩 10 | @Description : 11 | """ 12 | import numpy as np 13 | import logging 14 | 15 | 16 | def get_rank_moment(image: np.ndarray, row_moment: int, 17 | col_moment: int) -> np.ndarray: 18 | """返回不变矩,阶数为row_moment + col_moment 19 | 20 | :param image: numpy.array, 图像 21 | :param row_moment: int, 行阶数 22 | :param col_moment: int, 列阶数 23 | :return: numpy.array,不变矩 24 | """ 25 | """初始化""" 26 | moment = 0. 27 | 28 | """计算矩""" 29 | n_row, n_col = image.shape 30 | for i_row in range(n_row): 31 | for j_col in range(n_col): 32 | moment += np.power(i_row, row_moment)\ 33 | * np.power(j_col, col_moment) * image[i_row][j_col] 34 | return moment 35 | 36 | 37 | def get_center_moment(image: np.ndarray, row_moment: int, 38 | col_moment: int) -> np.ndarray: 39 | """计算中心矩 40 | 41 | :param image: numpy.array, 图像 42 | :param row_moment: int，行阶数 43 | :param col_moment: int，列阶数 44 | :return: numpy.array, 中心矩 45 | """ 46 | """计算均值""" 47 | '''计算1阶矩''' 48 | rank_moment00 = get_rank_moment(image, row_moment=0, col_moment=0) 49 | rank_moment10 = get_rank_moment(image, row_moment=1, col_moment=0) 50 | rank_moment01 = get_rank_moment(image, row_moment=0, col_moment=1) 51 | 52 | '''计算均值''' 53 | mean_row = rank_moment10 / rank_moment00 54 | mean_col = rank_moment01 / rank_moment00 55 | 56 | """计算不变矩""" 57 | moment = 0. 58 | n_row, n_col = image.shape 59 | for i_row in range(n_row): 60 | for j_col in range(n_col): 61 | moment += np.power((i_row - mean_row), row_moment) * \ 62 | np.power((j_col - mean_col), col_moment) * image[i_row][j_col] 63 | return moment 64 | 65 | 66 | def normal_center_moment(image: np.ndarray, row_moment: int, 67 | col_moment: int) -> np.ndarray: 68 | """计算归一化后的中心矩 69 | 70 | :param image: numpy.array, 图像 71 | :param row_moment: int，行阶数 72 | :param col_moment: int，列阶数 73 | :return: numpy.array, 中心矩 74 | """ 75 | """中心矩""" 76 | center_moment = get_center_moment( 77 | image, row_moment=row_moment, col_moment=col_moment) 78 | 79 | """归一化的分母部分""" 80 | tmp_center_moment = get_center_moment(image, row_moment=0, col_moment=0) 81 | tmp_power = (row_moment + col_moment) / 2 + 1 82 | tmp_moment = tmp_center_moment ** tmp_power 83 | 84 | return center_moment / tmp_moment 85 | 86 | 87 | def get_moment_invariants_seq(image: np.ndarray) -> list: 88 | """返回二阶和三阶不变矩组（7个） 89 | 90 | :param image: numpy.array, 图片 91 | :return: 92 | """ 93 | 94 | """计算归一化后的中心矩""" 95 | '''二阶''' 96 | logging.info("正在计算二阶中心矩") 97 | moment20 = normal_center_moment(image, row_moment=2, col_moment=0) 98 | moment02 = normal_center_moment(image, row_moment=0, col_moment=2) 99 | moment11 = normal_center_moment(image, row_moment=1, col_moment=1) 100 | '''三阶''' 101 | logging.info("正在计算三阶中心矩") 102 | moment03 = normal_center_moment(image, 0, 3) 103 | moment12 = normal_center_moment(image, 1, 2) 104 | moment21 = normal_center_moment(image, 2, 1) 105 | moment30 = normal_center_moment(image, 3, 0) 106 | 107 | """计算不变矩组""" 108 | logging.info("正在计算不变矩组") 109 | num1 = moment02 + moment20 110 | num2 = (moment02 - moment20) ** 2 + 4 * moment11 ** 2 111 | num3 = (moment30 - 3 * moment12) ** 2 + (3 * moment21 - moment03) ** 2 112 | num4 = (moment30 + moment12) ** 2 + (moment21 + moment03) ** 2 113 | num5 = (moment30 - 3 * moment12) * (moment30 + moment12) * ((moment30 + moment12) ** 2 - 3 * (moment21 + moment03) ** 2) + \ 114 | (3 * moment21 - moment03) * (moment21 + moment03) * \ 115 | (3 * (moment30 - moment12) ** 2 - (moment21 - moment03) ** 2) 116 | num6 = (moment20 - moment02) * ((moment30 + moment12) ** 2 - (moment21 + moment03) 117 | ** 2) + 4 * moment11 * (moment30 + moment12) * (moment21 + moment03) 118 | num7 = (3 * moment21 - moment03) * (moment30 + moment12) * ((moment30 + moment12) ** 2 - 3 * (moment21 + moment03) ** 2) + \ 119 | (3 * moment12 - moment03) * (moment21 + moment03) * \ 120 | (3 * (moment30 + moment12) ** 2 - (moment21 - moment03) ** 2) 121 | 122 | moment_invariants_list = [num1, num2, num3, num4, num5, num6, num7] 123 | return moment_invariants_list 124 | -------------------------------------------------------------------------------- /src/chapter11/shape_number.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/14 14:31 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : shape_number.py 9 | @Title : 形状数 10 | @Description : 11 | """ 12 | import numpy as np 13 | from src.chapter11 import freeman_code 14 | 15 | 16 | # ============================================================= 17 | # 获取差分编码 18 | # ============================================================= 19 | 20 | 21 | def __get_freeman_difference(freeman_code_list: [int]) -> [int]: 22 | """给定Freeman List，返回Freeman差分 23 | 24 | :param freeman_code_list: List[int],Freeman编码的List， 25 | :return: List[int], 差分后的编码 26 | """ 27 | # 用于保存差分后的编码 28 | different_code_list = list() 29 | for i in range(len(freeman_code_list) - 1): 30 | current_code = freeman_code_list[i] 31 | next_code = freeman_code_list[i + 1] 32 | if next_code < current_code: 33 | next_code += 8 34 | different_code_list.append(next_code - current_code) 35 | return different_code_list 36 | 37 | 38 | # =============================================================== 39 | # 获取形状数 40 | # =============================================================== 41 | 42 | def __get_shape_number_by_freeman_difference( 43 | freeman_difference_list: [int]) -> [int]: 44 | """给定已经差分后的freeman编码，返回形状数 45 | 46 | :param freeman_difference_list: List[int], 差分后的Freeman编码 47 | :return: List[int],形状数 48 | """ 49 | queue1, queue2 = list(), list() 50 | min_num = np.min(freeman_difference_list) 51 | 52 | # queue1用于存放最小数前面的数字，queue2用于存放最小数后面的数字（包括最小数） 53 | # 最后将两个queue拼接在一起即是最后的结果 54 | queue = queue1 55 | for num in freeman_difference_list: 56 | if num == min_num: 57 | queue = queue2 58 | queue.append(num) 59 | return queue2 + queue1 60 | 61 | 62 | # =============================================================== 63 | # 主函数 64 | # =============================================================== 65 | 66 | def get_shape_number(img: np.ndarray, contour: [(int, int)]) -> [int]: 67 | """给定一幅图像以及边界的List，返回形状数 68 | 69 | :param img: numpy.ndarray, 图像 70 | :param contour: List[(int,int)],边界的list 71 | :return: List[int],形状数 72 | """ 73 | """获取Freeman编码""" 74 | freeman_code_list = freeman_code.get_freeman_code(img, contour) 75 | 76 | """获取差分编码""" 77 | freeman_difference_list = __get_freeman_difference(freeman_code_list) 78 | 79 | """获取形状数""" 80 | return __get_shape_number_by_freeman_difference(freeman_difference_list) 81 | -------------------------------------------------------------------------------- /src/chapter11/texture.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/24 10:41 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : texture.py 9 | @Title : 纹理 10 | @Description : 11 | """ 12 | import numpy as np 13 | 14 | # =================================================================== 15 | # 建立等级矩阵 16 | # =================================================================== 17 | 18 | 19 | def __get_rank_list(gray_rank: int, max_value: int = 255, 20 | min_value: int = 0) -> [int]: 21 | """返回等级的list 22 | 23 | :param gray_rank: int, 等级数 24 | :param max_value: int, 像素值的最大值 25 | :param min_value: int, 像素值的最小值 26 | :return: List[int],分段的list 27 | """ 28 | rank_list = list() 29 | num_value = max_value - min_value + 1 30 | current_value = min_value 31 | while current_value <= max_value: 32 | rank_list.append(current_value) 33 | interval_value = num_value // gray_rank 34 | current_value += interval_value 35 | 36 | """最后加入max value""" 37 | if rank_list[-1] < max_value: 38 | rank_list.append(max_value) 39 | return rank_list 40 | 41 | 42 | def __get_rank_index(gray_value: int, rank_list: [int]) -> int: 43 | """给定灰度值和一个等级的list，返回该灰度值对应的index 44 | 45 | :param gray_value: int, 灰度值 46 | :param rank_list: List[int], 等级list 47 | :return: int, 等级index 48 | """ 49 | for i in range(len(rank_list) - 1): 50 | current_value = rank_list[i] 51 | next_value = rank_list[i + 1] 52 | if current_value <= gray_value < next_value: 53 | return i 54 | 55 | """如果该值刚好是最大值，则放回最大的rank Index""" 56 | if gray_value == rank_list[-1]: 57 | return len(rank_list) - 2 58 | 59 | # ================================================================= 60 | # 建立共生矩阵 61 | # ================================================================= 62 | 63 | 64 | def __get_occurrence_matrix_by_rank_matrix( 65 | rank_matrix: np.ndarray, gray_rank: int, n_step: int = 1) -> np.ndarray: 66 | """跟定一个等级矩阵，返回共生矩阵，且会根据不同的跳数返回不同的共生矩阵 67 | 68 | :param rank_matrix: numpy.array, 等级矩阵 69 | :param gray_rank: int, 灰度等级 70 | :param n_step: int, 跳数,默认为1，表示相邻的元素建立跳数 71 | :return: numpy.array, 共生矩阵 72 | """ 73 | if n_step < 1: 74 | raise ValueError("跳数设定错误") 75 | """初始化""" 76 | # 共生矩阵 77 | co_occurrence_matrix = np.zeros(shape=(gray_rank, gray_rank)) 78 | 79 | """计算共生矩阵""" 80 | for i_row in range(len(rank_matrix)): 81 | for i_col in range(len(rank_matrix[0]) - n_step): 82 | current_value = rank_matrix[i_row][i_col] 83 | next_value = rank_matrix[i_row][i_col + n_step] 84 | co_occurrence_matrix[current_value][next_value] += 1 85 | 86 | return co_occurrence_matrix 87 | 88 | 89 | # ================================================================ 90 | # 共生矩阵描述子辅助函数 91 | # ================================================================ 92 | 93 | def __get_probability_matrix(occurrence_matrix: np.ndarray) -> np.ndarray: 94 | """给定共生矩阵，返回概率矩阵 95 | 96 | :param occurrence_matrix: numpy.array, 共生矩阵 97 | :return: numpy.array, 概率矩阵 98 | """ 99 | return occurrence_matrix / np.sum(occurrence_matrix) 100 | 101 | 102 | def __get_occurrence_mean(occurrence_matrix: np.ndarray, axis=0) -> float: 103 | """返回共生矩阵的平均值 104 | 105 | :param occurrence_matrix: numpy.array, 共生矩阵 106 | :param axis: int, 方向，同numpy 107 | :return: numpy.array, 行平均向量 108 | """ 109 | prob_matrix = __get_probability_matrix(occurrence_matrix) 110 | tmp_mean = np.sum(prob_matrix, axis=axis) 111 | 112 | mean_value = 0. 113 | for i in range(len(tmp_mean)): 114 | mean_value += (i + 1) * tmp_mean[i] 115 | return mean_value 116 | 117 | 118 | def __get_occurrence_variance(occurrence_matrix: np.ndarray, axis=0) -> float: 119 | """返回共生矩阵的方差 120 | 121 | :param occurrence_matrix: numpy.array,共生矩阵 122 | :param axis: int, 方向 123 | :return: float, 方差 124 | """ 125 | prob_matrix = __get_probability_matrix(occurrence_matrix) 126 | tmp_mean_vec = np.sum(prob_matrix, axis=axis) 127 | mean_value = __get_occurrence_mean(occurrence_matrix, axis=axis) 128 | 129 | var_value = 0. 130 | for i in range(len(tmp_mean_vec)): 131 | var_value += (i + 1 - mean_value) ** 2 * tmp_mean_vec[i] 132 | 133 | return var_value 134 | 135 | 136 | # ================================================================= 137 | # Main 138 | # ================================================================= 139 | 140 | 141 | def get_co_occurrence_matrix( 142 | img: np.ndarray, gray_rank: int = 8) -> np.ndarray: 143 | """给定一张图片，返回灰度共生矩阵 144 | 145 | 步骤如下： 146 | * 给出每一个像素值对应的等级 147 | * 给出等级分段的list 148 | * 给出该像素值对应的分段index 149 | * 从左到右扫描两个像素，在对应的共生矩阵加1 150 | * 初始化一个共生矩阵，里面的值全为0 151 | * 根据两个index锁定位置的元素加1 152 | :param img: numpy.array, 图片 153 | :param gray_rank: int, 共生矩阵的等级数 154 | :return: numpy.array, 共生矩阵 155 | """ 156 | """排序list""" 157 | rank_list = __get_rank_list(gray_rank=gray_rank) 158 | """获取Rank矩阵""" 159 | get_rank_matrix_f = np.frompyfunc( 160 | lambda x: __get_rank_index( 161 | x, rank_list), 1, 1) 162 | rank_matrix = get_rank_matrix_f(img) 163 | 164 | """获取共生矩阵""" 165 | return __get_occurrence_matrix_by_rank_matrix( 166 | rank_matrix=rank_matrix, gray_rank=gray_rank) 167 | 168 | 169 | def get_co_occurrence_correlation_seq( 170 | img: np.ndarray, gray_rank: int = 8, start_step: int = 1, end_step: int = 50) -> list: 171 | """ 172 | 173 | :param img: numpy.array, 图像 174 | :param gray_rank: int,共生矩阵的等级数 175 | :param start_step: int,开始跳数 176 | :param end_step: int, 结束跳数 177 | :return: list, 共生矩阵相关性序列 178 | """ 179 | """获取等级矩阵""" 180 | '''排序list''' 181 | rank_list = __get_rank_list(gray_rank=gray_rank) 182 | '''获取Rank矩阵''' 183 | get_rank_matrix_f = np.frompyfunc( 184 | lambda x: __get_rank_index( 185 | x, rank_list), 1, 1) 186 | rank_matrix = get_rank_matrix_f(img) 187 | 188 | """计算共生矩阵的相关性""" 189 | correlation_list = list() 190 | for step in range(start_step, end_step): 191 | occurrence_matrix = __get_occurrence_matrix_by_rank_matrix( 192 | rank_matrix=rank_matrix, gray_rank=gray_rank, n_step=step) 193 | correlation = get_correlation(occurrence_matrix) 194 | correlation_list.append(correlation) 195 | return correlation_list 196 | 197 | 198 | # ================================共生矩阵描述子========================= 199 | 200 | 201 | def get_max_probability(occurrence_matrix: np.ndarray): 202 | """给定共生矩阵，返回最大概率 203 | 204 | :param occurrence_matrix: numpy.array, 共生矩阵 205 | :return: float, 最大概率 206 | """ 207 | return np.max(occurrence_matrix) / np.sum(occurrence_matrix) 208 | 209 | 210 | def get_correlation(occurrence_matrix: np.ndarray) -> float: 211 | """给定共生矩阵，返回其相关性 212 | 213 | :param occurrence_matrix: numpy.array, 共生矩阵 214 | :return: float, 相关性 215 | """ 216 | """概率矩阵""" 217 | prob_matrix = __get_probability_matrix(occurrence_matrix) 218 | """行平均以及列平均""" 219 | row_mean = __get_occurrence_mean(occurrence_matrix, axis=0) 220 | col_mean = __get_occurrence_mean(occurrence_matrix, axis=1) 221 | """行方差以及列方差""" 222 | row_var = __get_occurrence_variance(occurrence_matrix, axis=0) 223 | col_var = __get_occurrence_variance(occurrence_matrix, axis=1) 224 | 225 | """计算相关性""" 226 | correlation_value = 0. 227 | for i_row in range(len(prob_matrix)): 228 | for j_col in range(len(prob_matrix[0])): 229 | tmp = (i_row + 1 - row_mean) * (j_col + 1 - 230 | col_mean) * prob_matrix[i_row][j_col] 231 | tmp /= row_var * col_var 232 | correlation_value += tmp 233 | return correlation_value 234 | 235 | 236 | def get_contrast(occurrence_matrix: np.ndarray) -> float: 237 | """给定共生矩阵，返回其对比度 238 | 239 | :param occurrence_matrix: numpy.array, 共生矩阵 240 | :return: float，对比度 241 | """ 242 | prob_matrix = __get_probability_matrix(occurrence_matrix) 243 | 244 | contrast_value = 0. 245 | for i_row in range(len(occurrence_matrix)): 246 | for j_col in range(len(occurrence_matrix[0])): 247 | contrast_value += ((i_row + 1) - (j_col + 1) 248 | ) ** 2 * prob_matrix[i_row][j_col] 249 | 250 | return contrast_value 251 | 252 | 253 | def get_consistency(occurrence_matrix: np.ndarray) -> np.ndarray: 254 | """给定共生矩阵，返回一致性 255 | 256 | :param occurrence_matrix: numpy.array,共生矩阵 257 | :return: float,一致性 258 | """ 259 | prob_matrix = __get_probability_matrix(occurrence_matrix) 260 | return np.sum(prob_matrix ** 2) 261 | 262 | 263 | def get_homogeneity(occurrence_matrix: np.ndarray) -> float: 264 | """给定灰度共生矩阵，返回其同质性 265 | 266 | :param occurrence_matrix: numpy.array,灰度共生矩阵 267 | :return: float, 同质性 268 | """ 269 | prob_matrix = __get_probability_matrix(occurrence_matrix) 270 | 271 | homogeneity_value = 0. 272 | for i_row in range(len(prob_matrix)): 273 | for j_col in range(len(prob_matrix[0])): 274 | tmp = prob_matrix[i_row][j_col] / \ 275 | (1 + abs((i_row + 1) - (j_col + 1))) 276 | homogeneity_value += tmp 277 | return homogeneity_value 278 | 279 | 280 | def get_entropy(occurrence_matrix: np.ndarray) -> float: 281 | """给定灰度共生矩阵，返回信息熵 282 | 283 | :param occurrence_matrix: numpy.array, 灰度共生矩阵 284 | :return: float,信息熵 285 | """ 286 | prob_matrix = __get_probability_matrix(occurrence_matrix) 287 | tmp_log_matrix = np.log2(prob_matrix) 288 | '''替换计算熵时出现的无穷小''' 289 | tmp_log_matrix[tmp_log_matrix == -np.inf] = 0 290 | 291 | return - np.sum(prob_matrix * tmp_log_matrix) 292 | -------------------------------------------------------------------------------- /src/chapter3/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/21 14:59 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ 12 | -------------------------------------------------------------------------------- /src/chapter3/hist_local_enhance.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/21 15:34 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : hist_local_enhance.py 9 | @Title : 局部直方图增强 10 | @Description : 11 | """ 12 | import numpy as np 13 | 14 | 15 | def _location_transform(c_position: int, total: int) -> int: 16 | """位置转换，返回正确位置""" 17 | if c_position < 0: 18 | return 0 19 | elif c_position > total: 20 | return total - 1 21 | else: 22 | return c_position 23 | 24 | 25 | def _is_shadow(local_mean, local_std, global_mean, global_std, k0, k1, k2): 26 | """检测是否是阴影""" 27 | tmp1 = local_mean < k0 * global_mean 28 | tmp2 = (global_std * k1 <= local_std <= global_std * k2) 29 | return tmp1 and tmp2 30 | 31 | 32 | def get_dead_area(img: np.ndarray, local_size=3, k0=0.4, k1=0.02, k2=0.4): 33 | """返回暗色区域""" 34 | """全局均值和标准差""" 35 | global_mean = np.mean(img.flatten()) 36 | global_std = np.std(img.flatten()) 37 | 38 | """""" 39 | shadow_matrix = np.zeros_like(img) 40 | 41 | """比较""" 42 | h, w, _ = img.shape 43 | local_part_size = np.floor(local_size / 2.) 44 | local_part_size = local_part_size.astype(int) 45 | for i, row in enumerate(img): 46 | for j, pix in enumerate(row): 47 | """局部位置坐标""" 48 | start_i = _location_transform(i - local_part_size, h) 49 | end_i = _location_transform(i + local_part_size, h) 50 | start_j = _location_transform(j - local_part_size, w) 51 | end_j = _location_transform(j + local_part_size, w) 52 | 53 | """局部位置统计数据""" 54 | local_mean = np.mean(img[start_i:end_i, start_j:end_j].flatten()) 55 | local_std = np.std(img[start_i:end_i, start_j:end_j].flatten()) 56 | 57 | """""" 58 | shadow_matrix[i][j] = _is_shadow( 59 | local_mean, local_std, global_mean, global_std, k0, k1, k2) 60 | return shadow_matrix 61 | 62 | 63 | def local_enhance(img: np.ndarray, local_size=3, k0=0.4, k1=0.02, k2=0.4): 64 | """""" 65 | """获取暗色区域""" 66 | shadow_matrix = get_dead_area(img, local_size, k0, k1, k2) 67 | 68 | """暗色增强""" 69 | tmp1 = img * shadow_matrix * 3 70 | tmp2 = np.logical_not(shadow_matrix) 71 | tmp2 = tmp2.astype(int) 72 | tmp2 = img * tmp2 73 | 74 | return tmp1 + tmp2 75 | -------------------------------------------------------------------------------- /src/chapter4/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/10/19 13:30 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ 12 | -------------------------------------------------------------------------------- /src/chapter4/frequency_dommain.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/1/29 9:32 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : frequency_domain.py 9 | @Title : 10 | @Description : 11 | """ 12 | import numpy as np 13 | 14 | 15 | def __get_filter(image, d_0: int = 80, high: float = 1.5, 16 | low: float = 0.25, c: int = 1): 17 | """返回同态滤波的滤波器 18 | :param d_0: int, 参数 19 | :param high: float: 参数 20 | :param low: float, 参数 21 | :param c: int, 参数 22 | :return np.array, 滤波器 23 | """ 24 | h, w = image.shape 25 | u, v = np.meshgrid(np.arange(w), np.arange(h)) 26 | median_u, median_v = np.floor(w / 2), np.floor(h / 2) 27 | 28 | u = u - median_u 29 | v = v - median_v 30 | dist_matrix = u**2 + v**2 31 | 32 | tmp = 1 - np.exp(-c * dist_matrix / (d_0**2)) 33 | return (high - low) * tmp + low 34 | 35 | 36 | def homomorphic_filtering(image: np.ndarray) -> np.ndarray: 37 | """同态滤波""" 38 | """时域转频域""" 39 | log_image = np.log1p(image) 40 | fft_image = np.fft.fft2(log_image) 41 | fft_image = np.fft.fftshift(fft_image) 42 | 43 | """滤波""" 44 | h_matrix = __get_filter(fft_image) 45 | fft_image = fft_image * h_matrix 46 | 47 | """频域转时域""" 48 | fft_image = np.fft.ifftshift(fft_image) 49 | tmp_image = np.fft.ifft2(fft_image) 50 | return np.exp(tmp_image).real 51 | -------------------------------------------------------------------------------- /src/chapter9/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/9/28 17:13 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /src/chapter9/morphology.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/9/28 17:14 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : hit.py 9 | @Title : 10 | @Description : 11 | """ 12 | import numpy as np 13 | import cv2 14 | 15 | 16 | def hit(img: np.ndarray, shape1: np.ndarray, shape2: np.ndarray): 17 | """击中 18 | 19 | :param img: numpy.array, 图像 20 | :param shape1: numpy.array, 匹配的模板，即目标模板 21 | :param shape2: numpy.array, 匹配模板的反模板，即背景模板 22 | """ 23 | if shape1.dtype != np.uint8 or shape2.dtype != np.uint8: 24 | raise TypeError 25 | 26 | part1 = cv2.erode(img, shape1) 27 | part2 = cv2.erode(1 - img, shape2) 28 | 29 | return np.logical_and((part1 == part2), part1 == 1).astype(int) 30 | 31 | 32 | def connected_area(img:np.ndarray, start_coordination:(int, int)) -> np.ndarray: 33 | """提取连通分量 34 | 35 | :param img: numpy.array, 图像 36 | :param start_coordination: (int,int), 起始位置 37 | """ 38 | kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) 39 | 40 | last_temp_img = np.zeros_like(img) 41 | last_temp_img[start_coordination] = 1 42 | current_temp_img = cv2.dilate(last_temp_img, kernel) 43 | 44 | while (current_temp_img != last_temp_img).any(): 45 | last_temp_img = current_temp_img 46 | current_temp_img = cv2.dilate(current_temp_img, kernel) 47 | current_temp_img = np.logical_and((current_temp_img == img), current_temp_img == 1).astype(float) 48 | 49 | return current_temp_img 50 | 51 | 52 | def blank_fill(img:np.ndarray, start_coordination:(int,int)) -> np.ndarray: 53 | """孔洞填充 54 | 55 | :param img: numpy.array, 图像 56 | :param start_coordination: 起始位置 57 | :return: numpy.array:填充结果 58 | """ 59 | kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 9)) 60 | 61 | last_temp_img = np.zeros_like(img) 62 | last_temp_img[start_coordination] = 1 63 | current_temp_img = cv2.dilate(last_temp_img, kernel) 64 | 65 | while (current_temp_img != last_temp_img).any(): 66 | last_temp_img = current_temp_img 67 | current_temp_img = cv2.dilate(current_temp_img, kernel) 68 | 69 | current_temp_img = np.logical_and(current_temp_img == 1, current_temp_img == (1 - img)).astype(float) 70 | 71 | return current_temp_img 72 | 73 | 74 | def rebuild_open(img: np.ndarray, kernel: np.ndarray, erode_time: int = 1) -> np.ndarray: 75 | """重建开操作 76 | 77 | :param img: numpy.array, 图像 78 | :param kernel: numpy.array, 开操作的核 79 | :param erode_time: int, 腐蚀次数 80 | """ 81 | """多次腐蚀""" 82 | temp_img = img.copy() 83 | for i in range(erode_time): 84 | temp_img = cv2.erode(temp_img, kernel) 85 | 86 | """测地膨胀""" 87 | dialate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) 88 | last_img = temp_img.copy() 89 | 90 | while True: 91 | current_img = cv2.dilate(last_img, dialate_kernel) 92 | current_img = np.logical_and(current_img == 1, current_img == img) 93 | current_img = current_img.astype(float) 94 | 95 | if (current_img == last_img).all(): 96 | break 97 | else: 98 | last_img = current_img 99 | 100 | return current_img 101 | -------------------------------------------------------------------------------- /src/morphology.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/5/20 17:08 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : morphology.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/7 14:45 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /tests/chapter10/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/28 11:12 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ 12 | -------------------------------------------------------------------------------- /tests/chapter10/edge_connect/1863694-20191206105727406-2050307115.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qcymkxyc/Image-Process/345905f2fea003e1eca03dcb2c8acf9214cb6158/tests/chapter10/edge_connect/1863694-20191206105727406-2050307115.jpg -------------------------------------------------------------------------------- /tests/chapter10/edge_connect/Fig1137(d)(painting_mirrored_padded).tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qcymkxyc/Image-Process/345905f2fea003e1eca03dcb2c8acf9214cb6158/tests/chapter10/edge_connect/Fig1137(d)(painting_mirrored_padded).tif -------------------------------------------------------------------------------- /tests/chapter10/edge_connect/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/28 11:12 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ 12 | -------------------------------------------------------------------------------- /tests/chapter10/edge_connect/test_local_connect.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | @Time : 2021/5/28 11:12 5 | @Author : Zhang Qi 6 | @Email : zhangqi@onlinesign.com.cn 7 | @File : test_local_connect.py 8 | @Title : 9 | @Description : 10 | """ 11 | from src.chapter10.edge_connect import local_connect 12 | import cv2 13 | import matplotlib.pyplot as plt 14 | import unittest 15 | 16 | 17 | class LocalConnectTestCase(unittest.TestCase): 18 | def setUp(self) -> None: 19 | self.img = cv2.imread("1863694-20191206105727406-2050307115.jpg") 20 | self.gray_img = cv2.cvtColor(self.img,cv2.COLOR_RGB2GRAY) 21 | self.binary_img = cv2.threshold(self.gray_img,127,1,cv2.THRESH_BINARY)[1] 22 | self.binary_img = self.binary_img.astype("uint8") 23 | 24 | def test_local_connect(self): 25 | plt.imshow(self.gray_img) 26 | plt.show() 27 | 28 | gray_img = cv2.Laplacian(self.gray_img,-1) 29 | plt.imshow(gray_img) 30 | plt.show() 31 | 32 | res = local_connect.local_connect(self.binary_img, 33 | gradient_angle_threshold=90, 34 | gradient_angle_bandwidth=45, 35 | gradient_value_threshold_rate=0.3) 36 | plt.imshow(res) 37 | plt.show() 38 | 39 | 40 | if __name__ == '__main__': 41 | unittest.main() 42 | -------------------------------------------------------------------------------- /tests/chapter11/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/14 14:49 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ -------------------------------------------------------------------------------- /tests/chapter11/test_freeman_code.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2020/7/7 15:13 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : test_freeman_code.py 9 | @Title : 10 | @Description : 11 | """ 12 | from unittest import TestCase 13 | import cv2 14 | import unittest 15 | from src.chapter11 import freeman_code 16 | from PIL import Image 17 | import os 18 | import numpy as np 19 | 20 | 21 | def get_freeman_box_list(img, n_box_x, n_box_y): 22 | return freeman_code.__get_freeman_box_list(img, n_box_x, n_box_y) 23 | 24 | 25 | def get_freeman_coordination(point, freeman_x_list, freeman_y_list): 26 | return freeman_code.__get_freeman_coordination( 27 | point, freeman_x_list, freeman_y_list) 28 | 29 | 30 | class FreemanTest(TestCase): 31 | def setUp(self) -> None: 32 | root, _ = os.path.split(os.getcwd()) 33 | img_path = os.path.join(root, "image_data") 34 | img_path = os.path.join( 35 | img_path, r"DIP3E_CH11_Original_Images/Fig1105(a)(noisy_stroke).tif") 36 | self.img = Image.open(img_path) 37 | self.img = np.asarray(self.img) 38 | 39 | def test_get_freeman_coordination_by_img(self): 40 | import matplotlib.pyplot as plt 41 | img_path = "../../image_data/DIP3E_CH11_Original_Images/Fig1105(a)(noisy_stroke).tif" 42 | img = Image.open(img_path) 43 | img = np.asarray(img) 44 | 45 | tmp_img = cv2.blur(img, (9, 9)) 46 | th, tmp_img = cv2.threshold( 47 | tmp_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 48 | image, contours, hierarchy = cv2.findContours( 49 | tmp_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) 50 | contour = contours[-1] 51 | contour = np.reshape(contour, (contour.shape[0], contour.shape[-1])) 52 | x = [i[0] for i in contour] 53 | y = [i[1] for i in contour] 54 | plt.plot(x, y) 55 | plt.show() 56 | 57 | a = freeman_code.get_freeman_coordination(tmp_img, contour) 58 | x = [i[0] for i in a] 59 | y = [i[1] for i in a] 60 | plt.plot(x, y) 61 | plt.show() 62 | 63 | def test_get_freeman_code(self): 64 | import matplotlib.pyplot as plt 65 | img_path = "../../image_data/DIP3E_CH11_Original_Images/Fig1105(a)(noisy_stroke).tif" 66 | img = Image.open(img_path) 67 | img = np.asarray(img) 68 | 69 | tmp_img = cv2.blur(img, (9, 9)) 70 | th, tmp_img = cv2.threshold( 71 | tmp_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 72 | image, contours, hierarchy = cv2.findContours( 73 | tmp_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) 74 | contour = contours[-1] 75 | contour = np.reshape(contour, (contour.shape[0], contour.shape[-1])) 76 | x = [i[0] for i in contour] 77 | y = [i[1] for i in contour] 78 | plt.plot(x, y) 79 | plt.show() 80 | 81 | a = freeman_code.get_freeman_code(tmp_img, contour) 82 | print(a) 83 | 84 | def test_get_freeman_coordination_by_point(self): 85 | freeman_x_list = np.arange(0, 100, 10).tolist() 86 | freeman_y_list = np.arange(0, 100, 10).tolist() 87 | 88 | """刚好在交点上""" 89 | point = (10, 10) 90 | r = get_freeman_coordination(point, freeman_x_list, freeman_y_list) 91 | self.assertEqual(r, (1, 1)) 92 | 93 | """在横轴上""" 94 | point = (10, 22) 95 | r = get_freeman_coordination(point, freeman_x_list, freeman_y_list) 96 | self.assertEqual(r, (1, 2)) 97 | 98 | """在纵轴上""" 99 | point = (22, 10) 100 | r = get_freeman_coordination(point, freeman_x_list, freeman_y_list) 101 | self.assertEqual(r, (2, 1)) 102 | 103 | """没在线上""" 104 | point = (22, 22) 105 | r = get_freeman_coordination(point, freeman_x_list, freeman_y_list) 106 | self.assertIsNone(r) 107 | 108 | def test_get_freeman_box_list(self): 109 | n_box_x = 10 110 | n_box_y = 10 111 | x_list, y_list = get_freeman_box_list(self.img, n_box_x, n_box_y) 112 | self.assertEqual(len(x_list), n_box_x + 1) 113 | self.assertEqual(len(y_list), n_box_y + 1) 114 | 115 | img_h, img_w = self.img.shape 116 | self.assertLessEqual(x_list[-1], img_w) 117 | self.assertLessEqual(x_list[-1], img_h) 118 | 119 | 120 | if __name__ == "__main__": 121 | unittest.main() 122 | -------------------------------------------------------------------------------- /tests/chapter11/test_mark_sheet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | @Time : 2020/7/10 14:25 5 | @Author : Zhang Qi 6 | @Email : zhangqi@onlinesign.com.cn 7 | @File : test_mark_sheet.py 8 | @Title : 9 | @Description : 10 | """ 11 | import unittest 12 | from PIL import Image 13 | import numpy as np 14 | from src.chapter11 import mark_sheet 15 | 16 | 17 | class MarkSheetTestCase(unittest.TestCase): 18 | def setUp(self) -> None: 19 | img_path = r"../../image_data/DIP3E_CH11_Original_Images/Fig1111(a)(triangle).tif" 20 | self.img = Image.open(img_path) 21 | self.img = np.asarray(self.img) 22 | self.img = self.img.astype(np.uint8) 23 | 24 | def test_get_centroid(self): 25 | centroid = mark_sheet.get_centroid(self.img) 26 | print(centroid) 27 | import matplotlib.pyplot as plt 28 | plt.imshow(self.img, cmap="gray") 29 | plt.plot([centroid[1]], [centroid[0]], linewidth="10", color="green") 30 | plt.show() 31 | 32 | def test_get_mark_sheet(self): 33 | r = mark_sheet.get_mark_sheet(self.img) 34 | import matplotlib.pyplot as plt 35 | plt.plot(r) 36 | plt.show() 37 | 38 | 39 | if __name__ == '__main__': 40 | unittest.main() 41 | -------------------------------------------------------------------------------- /tests/chapter11/test_moment_invariants.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | @Time : 2020/9/25 15:39 5 | @Author : Zhang Qi 6 | @Email : zhangqi@onlinesign.com.cn 7 | @File : test_moment_invariants.py 8 | @Title : 9 | @Description : 10 | """ 11 | import unittest 12 | from src.chapter11 import moment_invariants 13 | import numpy as np 14 | from PIL import Image 15 | 16 | 17 | class MomentInvariantsTestCase(unittest.TestCase): 18 | def setUp(self) -> None: 19 | img_path = r"../../image_data/DIP3E_CH11_Original_Images/Fig1137(a)(painting_original_padded).tif" 20 | self.img = Image.open(img_path) 21 | self.img = np.asarray(self.img) 22 | self.img = self.img.astype(np.uint8) 23 | 24 | def test_get_moment_invariants_seq(self): 25 | moment_invariants.get_moment_invariants_seq(self.img) 26 | 27 | 28 | if __name__ == '__main__': 29 | unittest.main() 30 | -------------------------------------------------------------------------------- /tests/chapter11/test_shape_number.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | @Time : 2020/7/14 14:50 5 | @Author : Zhang Qi 6 | @Email : zhangqi@onlinesign.com.cn 7 | @File : test_shape_number.py 8 | @Title : 9 | @Description : 10 | """ 11 | import unittest 12 | from src.chapter11 import shape_number 13 | import os 14 | from PIL import Image 15 | import numpy as np 16 | 17 | 18 | def get_shape_number_by_freeman_difference( 19 | freeman_difference_list: [int]) -> [int]: 20 | return shape_number.__get_shape_number_by_freeman_difference(freeman_difference_list) 21 | 22 | 23 | class ShapeNumberTestCase(unittest.TestCase): 24 | def setUp(self) -> None: 25 | root, _ = os.path.split(os.getcwd()) 26 | root = os.path.split(root)[0] 27 | img_path = os.path.join(root, "image_data") 28 | img_path = os.path.join( 29 | img_path, r"DIP3E_CH11_Original_Images/Fig1105(a)(noisy_stroke).tif") 30 | self.img = Image.open(img_path) 31 | self.img = np.asarray(self.img) 32 | 33 | def test_shape_number_by_freeman_difference(self): 34 | x = np.random.randint(0, 8, size=10) 35 | print(x) 36 | y = get_shape_number_by_freeman_difference(x) 37 | print(np.asarray(y)) 38 | 39 | def test_get_shape_number(self): 40 | pass 41 | 42 | 43 | if __name__ == '__main__': 44 | unittest.main() 45 | -------------------------------------------------------------------------------- /tests/chapter11/test_texture.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | """ 4 | @Time : 2020/7/24 11:16 5 | @Author : Zhang Qi 6 | @Email : zhangqi@onlinesign.com.cn 7 | @File : test_texture.py 8 | @Title : 9 | @Description : 10 | """ 11 | import unittest 12 | from src.chapter11 import texture 13 | import numpy as np 14 | 15 | 16 | def get_rank_list(grak_rank, max_value=255, min_value=0): 17 | return texture.__get_rank_list(grak_rank, max_value, min_value) 18 | 19 | 20 | def get_rank_index(gray_value: int, rank_list: [int]) -> int: 21 | return texture.__get_rank_index(gray_value, rank_list) 22 | 23 | 24 | def get_occurrence_mean(occurrence_matrix,axis): 25 | return texture.__get_occurrence_mean(occurrence_matrix,axis) 26 | 27 | def get_occurrence_variance(occurrence_matrix,axis): 28 | return texture.__get_occurrence_variance(occurrence_matrix,axis) 29 | 30 | class TextureTestCase(unittest.TestCase): 31 | def test_get_rank_list(self): 32 | rank_list = get_rank_list(8) 33 | print(rank_list) 34 | self.assertEqual(len(rank_list), 9) 35 | 36 | def test_get_rank_index(self): 37 | rank_list = get_rank_list(8) 38 | print(rank_list) 39 | gray_value = 130 40 | rank_index = get_rank_index(gray_value, rank_list=rank_list) 41 | self.assertEqual(rank_index, 4) 42 | 43 | rank_index = get_rank_index(255, rank_list=rank_list) 44 | print(rank_index) 45 | self.assertEqual(rank_index, 7) 46 | 47 | rank_index = get_rank_index(0, rank_list) 48 | self.assertEqual(rank_index, 0) 49 | 50 | def test_co_occurrence_matrix(self): 51 | img = np.random.randint(0, 255, size=(10, 10)) 52 | occurrence_matrix = texture.get_co_occurrence_matrix(img) 53 | print(occurrence_matrix) 54 | 55 | def test_get_mean(self): 56 | occurrence_matrix = np.arange(16).reshape((4,4)) 57 | value = get_occurrence_mean(occurrence_matrix,axis = 0) 58 | print(value) 59 | 60 | value = get_occurrence_mean(occurrence_matrix, axis=1) 61 | print(value) 62 | 63 | def test_get_occurrence_variance(self): 64 | occurrence_matrix = np.arange(16).reshape((4,4)) 65 | var_value = get_occurrence_variance(occurrence_matrix,axis = 0) 66 | print(var_value) 67 | 68 | var_value = get_occurrence_variance(occurrence_matrix,axis = 1) 69 | print(var_value) 70 | 71 | def test_get_correlation(self): 72 | occurrence_matrix = np.arange(16).reshape((4,4)) 73 | occurrence_value = texture.get_correlation(occurrence_matrix) 74 | print(occurrence_value) 75 | 76 | def test_get_contrast(self): 77 | occurrence_matrix = np.arange(16).reshape((4,4)) 78 | contrast_value = texture.get_contrast(occurrence_matrix) 79 | print(contrast_value) 80 | 81 | def test_get_entropy(self): 82 | occurrence_matrix = np.arange(16).reshape((4, 4)) 83 | entropy_value = texture.get_entropy(occurrence_matrix) 84 | print(entropy_value) 85 | 86 | 87 | if __name__ == '__main__': 88 | unittest.main() 89 | -------------------------------------------------------------------------------- /tests/chapter3/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # cython: language_level=3 4 | """ 5 | @Time : 2021/5/28 17:08 6 | @Author : Zhang Qi 7 | @Email : zhangqi@onlinesign.com.cn 8 | @File : __init__.py.py 9 | @Title : 10 | @Description : 11 | """ 12 | --------------------------------------------------------------------------------