Move API doc to wiki

Author: LauLauThom

README.md

@@ -9,79 +9,7 @@ Once installed, `import MTM`should work.
 Example jupyter notebooks can be downloaded from the tutorial folder of the github repository and executed in the newly configured python environement.  
 
 # Documentation
-The package MTM contains mostly 2 important functions:  
-
-## matchTemplates  
-`matchTemplates(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=float("inf"), score_threshold=0.5, maxOverlap=0.25, searchBox=None)`  
-
-This function searches each template in the image, and return the best N_object location which offer the best scores and which do not overlap above the `maxOverlap` threshold.  
-
-__Parameters__
-- _listTemplates_:   
-            list of tuples (LabelString, Grayscale or RGB numpy array) templates to search in each image, associated to a label 
-
-- _image_  : Grayscale or RGB numpy array  
-           image in which to perform the search, it should be the same bitDepth and number of channels than the templates
-
-- _method_ : int   
-            one of OpenCV template matching method (0 to 5), default 5=0-mean cross-correlation
-
-- _N_object_: int  
-            expected number of objects in the image
-
-- score_threshold: float in range [0,1]  
-            if N>1, returns local minima/maxima respectively below/above the score_threshold
-
-- _maxOverlap_: float in range [0,1]  
-            This is the maximal value for the ratio of the Intersection Over Union (IoU) area between a pair of bounding boxes.
-            If the ratio is over the maxOverlap, the lower score bounding box is discarded.
-
-- _searchBox_ : tuple (X, Y, Width, Height) in pixel unit  
-            optional rectangular search region as a tuple
-    
-__Returns__
-- Pandas DataFrame with 1 row per hit and column "TemplateName"(string), "BBox":(X, Y, Width, Height), "Score":float             
-            - if N=1, return the best match independently of the score_threshold  
-            - if N<inf, returns up to N best matches that passed the score_threshold  
-            - if N=inf, returns all matches that passed the score_threshold  
-
-
-The function `findMatches` performs the same detection without the Non-Maxima Supression.  
-
-## drawBoxesOnRGB
-The 2nd important function is `drawBoxesOnRGB` to display the detections as rectangular bounding boxes on the initial image.  
-To be able to visualise the detection as colored bounding boxes, the function return a RGB copy of the image if a grayscale image is provided.  
-It is also possible to draw the detection bounding boxes on the grayscale image using drawBoxesOnGray (for instance to generate a mask of the detections).      
-`drawBoxesOnRGB(image, hits, boxThickness=2, boxColor=(255, 255, 00), showLabel=True, labelColor=(255, 255, 0), labelScale=0.5 )`
-
-This function returns a copy of the image with predicted template locations as bounding boxes overlaid on the image
-The name of the template can also be displayed on top of the bounding boxes with showLabel=True.
-
-__Parameters__
-- _image_  : numpy array  
-        image in which the search was performed  
-        
-- _hits_ : pandas dataframe  
-         hits as returned by matchTemplates or findMatches  
-        
-- _boxThickness_: int  
-        thickness of bounding box contour in pixels. -1 will fill the bounding box (useful for masks).  
-        
-- _boxColor_: (int, int, int)  
-        RGB color for the bounding box  
-
-- _showLabel_: Boolean, default True  
-        Display label of the bounding box (field TemplateName)
-
-- _labelColor_: (int, int, int)  
-          RGB color for the label  
-
-- _labelScale_: float, default=0.5
-	scale for the label sizes
-
-__Returns__
-- _outImage_: RGB image  
-        original image with predicted template locations depicted as bounding boxes  
+The [wiki](https://github.com/multi-template-matching/MultiTemplateMatching-Python/wiki) section of the repo contains a mini API documentation with description of the key functions of the package.
 
 # Examples
 Check out the [jupyter notebook tutorial](https://github.com/multi-template-matching/MultiTemplateMatching-Python/tree/master/tutorials) for some example of how to use the package.