Create README.md

Author: Blackcipher101

README.md

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+# Image-processing
+
+This repo contains all my learnings in Open-CV 
+
+## Basics
+### Image-openning
+The source code is <a href="https://github.com/Blackcipher101/Image-processing/blob/master/Image-open.py">here</a>
+
+Opencv has function ```cv2.imread(str,channel)``` it takes the arguments of string(filename or path) and the channel 0 corresponds to B/W and 1 corresponds to Color.
+It can open BMP, JPEG, PNG, PPM, RAS file formats and convert them to ```cv2.Mat``` which is basically a matrix like
+#### B/W
+[ [2 3 4 5 6 7 8]<br>
+ [2 3 4 5 1 8 4]<br>
+ [6 7 8 9 3 4 5] ]<br>
+#### Color
+[ [[100 34 25] [100 34 25] [100 34 25] [100 34 25]
+ [100 34 25] [100 34 25] [100 34 25][100 34 25]
+ [100 34 25] [100 34 25] [100 34 25] [100 34 25]]
+
+```cv2.imshow((str),matrix)```  which can open the martrix to image the string is the name of the window 
+if the image is to large one can use ```cv2.namedWindow('image', cv.WINDOW_NORMAL)``` it allows you to resize the window
+
+<img src="images/open.png">
+
+### Opening Video
+
+```cv2.VideoCapture(str)``` takes path to a video file or 0 for webcam it returns a stream image which can then be read into matrix and displayed using ```cv2.imshow((str),matrix)```
+```cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)``` this converts the channel to B/W (don't get scared newbies what is channel its basically way you define color like RGB) 
+<img src="images/video.png">
+
+### Creating a video-player with seekbar
+
+  Opencv has a function to create a trackbar you can call a function on change
+  ```python
+  def onChange(trackbarValue):
+    global cap
+    cap.set(1,trackbarValue)
+  ```
+  ```cv2.createTrackbar( 'trackbar', 'frame', 0, length, onChange)```
+  and then you set the cap to a certain value
+  
+  For pausing I basically stopped the cap reading new images
+  So s pause and r runs
+  <img src="images/video-player.png">
+  
+  ### Drawing
+  In opencv we can use functions ```cv2.line()``` ```cv2.rectangle()``` ```cv2.circle()``` ```cv2.ellipise()``` ```cv2.polylines()``` 
+  for more info on the functions go to <a src="https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_drawing_functions/py_drawing_functions.html#drawing-functions">docs</a>
+  
+  ## Image processing
+  
+  ### Object-tracking(based on color)
+  So we convert the image to HSV ```cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)``` we changed to HSV because it gives us a range of colors with change of value.
+  We make a image thresholding ```cv2.inRange(hsv, lower_blue, upper_blue)``` with a range and then we <strong>and with every pixel of both</strong> ```cv2.bitwise_and()``` it with the image so results in image only with the object
+  
+  <img src="images/object_track.png">
+  
+  ### Thresholding
+  In Opencv we set a pixel value with respect to another pixel value like<br>
+  if its less than a certain value it will be set to certain if not then to another
+  ``` if x>125: then x=225 else x=0 ```
+  
+  There are various ones like THRESH_BINARY,THRESH_BINARY_INV,THRESH_TRUNC,THRESH_TOZERO,THRESH_TOZERO_INV
+  
+  <img src="images/thresh.png">
+  
+  #### Adaptive thresholding
+  This is a method where we decide the max value based on region of image
+  ```cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\cv2.THRESH_BINARY,11,2)```
+            
+   But when we have bimodal images(images with two peaks in a histogram)
+   We <strong>Otsu,Riddler-Cavldier and Killer IIingworth</strong>method.
+   <img src="images/otsu.png">
+    
+   ### Image smoothing
+   
+  Image smoothing is just removal of the edges which results in a blurred image it is done by calling ```cv2.GassuianBlur()```. It allows to reduce the noise in the image 
+  but we loose some infomartation in the process Gaussain Blur by taking an average of 5X5 matrix.
+  
+  <img src="gauss.png">
+  
+  ### Morphigical Transformatations
+  Its a opertation on we do on binary to erode or diilate images 
+  <img src="morph.png">
+  ### Image gradients
+  We use```cv2.sobel()``` to find image deratives across a direction it gives us the edges in one direction 
+  <img src="images/sobel.png">
+  We also get edges in both direction in x-y directation 
+  <img src="images/lap.png">
+  ### Edge detection
+  We use the function ```cv2.Canny()``` The algorithm is Noise Reduction -> Finding Intensity Gradient of the Image -> Non-maximum Suppression -> Hysteresis Thresholding
+  The basic method is to apply image in gradient in 4 or more direction and the apply suppersion and then find the sure edges.
+  <img src="images/edge.png">
+  ### Image Blending
+  This we make images arrays by downscaling the image and also the laplacian 
+  then we keep adding the half of both the laplacian and then masking the image with the orginal
+  <img src="images/blend.png">
+  
+  ### Image contorors
+  Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.
+  We can use these contours to find moments,area,perimeter and many other informatation.
+  
+  We can apporximate contours to make the processing of informamtaton faster without the loss of important informatation.
+  <img src="images/contours.png">
+  
+  ###Draw Histogram
+  You can consider histogram as a graph or plot, which gives you an overall idea about the intensity distribution of an image. It is a plot with pixel values (ranging from 0 to 255, not always) in X-axis and corresponding number of pixels in the image on Y-axis.
+  <img src="image/histogram.png">
+  
+  #### Enhanced image
+  The image have a better as it improves the image by adding pixels whose frecquency is less.
+  <img src="images/enhanced.png">
+  
+  #### CLAHE 
+  It would be better if we could localize the equliztation so CLAHE(Contrast Limited Adaptive Histogram Equalization) was put fowrard 
+  <img src="images/Clahe.png">
+  #### Backprogagtation
+  We use this to find the object of interst .As we know the certain frequency of pixels we can find it
+  <img src="images/BKpropagate.png">
+  
+  
+  ### Template matching 
+  We can find certainpart of a image by matching the image to a current template
+  They have various methods but most of the times cv2.TM_CCOEFF works best
+  <img src="images/template.png">
+  
+  ### Image segmentation With watershed Algorithm
+  We have to segment the images into its smallest constuient parts
+  Algorithm
+  Threshold->Morphlogical opertations->Transforms->Mask\
+  <img src="images/imgsege.png">
+  ### Foregourd Subtractation
+  We use the Grabcut algorithm to remove the backgroung image and also apply mask to improve the accuracy
+  <img src="images/foresub.png">
+  
+  ## Fearture detection
+  We will be looking at many algorithms that detect corners
+  
+  ### Harris corner detection
+   It basically finds the difference in intensity for a displacement of (u,v) in all directions.
+   <img src="images/harris.png">
+    
+  ### FAST 
+  Select a pixel p in the image which is to be identified as an interest point or not. Let its intensity be I_p. -> Select appropriate threshold value t. -> Now the pixel p is a corner if there exists a set of n contiguous pixels in the circle (of 16 pixels) which are all brighter than I_p + t, or all darker than I_p − t. n was chosen to be 12. -> A high-speed test was proposed to exclude a large number of non-corners.
+  <img src="images/fast.png">
+  
+  ### ORB
+  We have a perfect mixture and also the point its not patented
+  
+  <img src="images/ORB.png">
+