使用OpenCV和Python的图像处理技术。_Python_HTML_下载.zip

# Road To Pixels Welcome aboard. With the growing technologies out in the world, we have seen how important Image Processing has become. This repository provides a complete understanding of the practical implementation of all the concepts to be known for a developer to start their Image Processing journey. ## Contents 1. [Basics with Images](https://github.com/BhanuPrakashNani/Image_Processing#1-basics-with-images---averaging-images) 2. [Successive Rotations](https://github.com/BhanuPrakashNani/Image_Processing#2-successive-rotations---code) 3. [Interpolations](https://github.com/BhanuPrakashNani/Image_Processing#3-interpolations---code) 4. [Interpolations-Inverse Mapping](https://github.com/BhanuPrakashNani/Image_Processing#4-interpolation-inverse-mapping---code) 5. [Basic Transformations](https://github.com/BhanuPrakashNani/Image_Processing#5-basic-transformations---code) 6. [Perspective Transofrmation](https://github.com/BhanuPrakashNani/Image_Processing#6-perspective-transformation) 7. [Estimating the Transformation](https://github.com/BhanuPrakashNani/Image_Processing#7-est-transformation) 8. [Log and Contrast Stretching](https://github.com/BhanuPrakashNani/Image_Processing#8-log-and-linear-transformation) 9. [Shading Correction](https://github.com/BhanuPrakashNani/Image_Processing#9-shading-correction) 10. [Laplacian](https://github.com/BhanuPrakashNani/Image_Processing#10-laplacian---code) 11. [Laplacian+Gaussian](https://github.com/BhanuPrakashNani/Image_Processing#11-laplaciangaussian---code) 12. [Laplacian, Sobel, CannyEdge](https://github.com/BhanuPrakashNani/Image_Processing#12-laplacian-sobel-cannyedge---code) 13. [Sobel-X and Y](https://github.com/BhanuPrakashNani/Image_Processing#13-sobel-x-and-y---code) 14. [Histogram Equalisation](https://github.com/BhanuPrakashNani/Image_Processing#14-histogram-equalisation---code) 15. [Normalize Histogram](https://github.com/BhanuPrakashNani/Image_Processing#15-normalize-histogram---code) 16. [Image Temperature](https://github.com/BhanuPrakashNani/Image_Processing#16-image-temperature---code) 17. [Box Filter](https://github.com/BhanuPrakashNani/Image_Processing#17-box-filter---code) 18. [GaussianFilter+Kernels](https://github.com/BhanuPrakashNani/Image_Processing#18-gaussianfilterkernels---code) 19. [Morphological Processing](https://github.com/BhanuPrakashNani/Image_Processing#19-morphological-processing--code) 20. [Morphological Text Processing](https://github.com/BhanuPrakashNani/Image_Processing#20-morphological-text-processing---code) 21. [Morphological Fingerprint Processing](https://github.com/BhanuPrakashNani/Image_Processing#21-morphological-fingerprint-processing---code) 22. [Morphological Outline](https://github.com/BhanuPrakashNani/Image_Processing#22-morphological-outline---code) 23. [Capture Video Frames](https://github.com/BhanuPrakashNani/Image_Processing#23-capture-video-frames---code) 24. [Video background Subtraction](https://github.com/BhanuPrakashNani/Image_Processing#24-video-background-subtraction---code) 25. [VideoCapture_GoogleColab](https://github.com/BhanuPrakashNani/Image_Processing#25-videocapture_googlecolab---code) 26. [Contours-OpenCV](https://github.com/BhanuPrakashNani/Image_Processing#26-contours-opencv---code) 27. [Fitting Polygons](https://github.com/BhanuPrakashNani/Image_Processing#27-fitting-polygons---code) 28. [Hough Lines](https://github.com/BhanuPrakashNani/Image_Processing#28-hough-lines---code) 29. [Adaptive+Gaussian Thresholding](https://github.com/BhanuPrakashNani/Image_Processing#29-adaptivegaussian-thresholding---code) 30. [OTSU Thresholding](https://github.com/BhanuPrakashNani/Image_Processing#30-otsu-thresholding---code) 31. [Grabcut](https://github.com/BhanuPrakashNani/Image_Processing#31-grabcut---code) 32. [Discrete Fourier Transformation](https://github.com/BhanuPrakashNani/Image_Processing#32-discrete-fourier-transformation---code) 33. [OpenCV KMeans](https://github.com/BhanuPrakashNani/Image_Processing#33-opencv-kmeans---code) 34. [Object Movement Tracking](https://github.com/BhanuPrakashNani/Image_Processing#34-object-movement-tracking---code) 35. [Live Hand Gesture Recognition](https://github.com/BhanuPrakashNani/Image_Processing#35-live-hand-gesture-recognition---code) Before we jump into the concepts, let us once have a look at the definition of Image Processing. ## A Glance into Image Processing Image processing is often viewed as arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. However, image processing is more accurately defined as a means of translation between the human visual system and digital imaging devices. The human visual system does not perceive the world in the same manner as digital detectors, with display devices imposing additional noise and bandwidth restrictions. Salient differences between the human and digital detectors will be shown, along with some basic processing steps for achieving translation. Image processing must be approached in a manner consistent with the scientific method so that others may reproduce, and validate one's results. This includes recording and reporting processing actions and applying similar treatments to adequate control images.[Src](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3635309/) There are two types of methods used for image processing namely, analog and digital image processing. Analog image processing can be used for hard copies like printouts and photographs. Various fundamentals of interpretation are used by the Image Analysts along with the visual techniques. Digital image processing deals with the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. The three general phases that all types of data have to undergo while using digital techniques are * Pre-processing * Enhancement and Display * Information Extraction.  Fundamental Steps in Digital Image Processing - Rafael Gonzalez - 4th Edition [Src](https://github.com/BhanuPrakashNani/Image_Processing/blob/master/Digital_Image_Processing%2C_4th%20Edition-Rafael%20Gonzalez.pdf) **Important point** to note while going through any concept is that the image is considered on a greyscale since color increases the complexity of the model. One may want to introduce an image processing tool using gray level images because of the format of gray-level images because the inherent complexity of gray-level images is lower than that of color images. In most cases. after presenting a gray-level image method, it can be extended to color images. For getting deeper insights into any of the concepts, I suggest going through [Digital Image Processing, Rafael C. Gonzalez • Richard E. Woods, 4th Edition](https://github.com/BhanuPrakashNani/Image_Processing/blob/master/Digital_Image_Processing%2C_4th%20Edition-Rafael%20Gonzalez.pdf) From here on I will be referring Digital Image Processing as DIP. **Disclaimer:** I am not the original author of the images used. They have been taken from various Image Processing sites. I have mentioned all of the referenced sites in resources. Pardon if I missed any. The following is the order I suggest to look into the concepts. ### 1. Basics with Images - [Averaging Images](https://github.com/BhanuPrakashNani/Image_Processing/tree/master/Image%20Averaging) Image averaging is a DIP technique that is used to enhance the images which are corrupted with random noise. The arithmetic mean of the intensity values for each pixel position is computed for a set of images of the same view field. The basic formula behind it is.  ### 2. Successive Rotations - [Code](https://github.com/BhanuPrakashNani/Image_Processing/tree/master/Su