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Below is a description of some key function of the package.

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