back to default N_object=inf

Author: LauLauThom

MTM/NMS.py

@@ -14,7 +14,7 @@
 import cv2
 
 
-def NMS(tableHit, scoreThreshold=0, sortAscending=False, N_object=-1, maxOverlap=0.5):
+def NMS(tableHit, scoreThreshold=0, sortAscending=False, N_object=float("inf"), maxOverlap=0.5):
     '''
     Perform Non-Maxima supression : it compares the hits after maxima/minima detection, and removes the ones that are too close (too large overlap)
     This function works both with an optionnal threshold on the score, and number of detected bbox
@@ -49,13 +49,14 @@ def NMS(tableHit, scoreThreshold=0, sortAscending=False, N_object=-1, maxOverlap
         listScores = [1-score for score in listScores] # NMS expect high-score for good predictions
         scoreThreshold = 1-scoreThreshold
 
-    #indexes = cv2.dnn.NMSBoxes(listBoxes, listScores, scoreThreshold, maxOverlap, top_k=N_object) # weird result when top_k=N
     indexes = cv2.dnn.NMSBoxes(listBoxes, listScores, scoreThreshold, maxOverlap)
 
-    if N_object>0: indexes = indexes[:N_object] # alternative to keep the N best detections (ie after the actual NMS)
-
-    indexes  = [index[0] for index in indexes]        # ordered by score
-    outTable = tableHit[tableHit.index.isin(indexes)].sort_values(by=['Score'], ascending=False)
+    if N_object == float("inf"): 
+		indexes  = [ index[0] for index in indexes ] # ordered by score
+    else:
+		indexes  = [ index[0] for index in indexes[:N_object] ]
+		
+	outTable = tableHit.iloc[indexes]
 
     return outTable
 

MTM/__init__.py

@@ -64,7 +64,7 @@ def computeScoreMap(template, image, method=cv2.TM_CCOEFF_NORMED):
     return cv2.matchTemplate(template, image, method)
 
 
-def findMatches(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=-1, score_threshold=0.5, searchBox=None):
+def findMatches(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=float("inf"), score_threshold=0.5, searchBox=None):
     '''
     Find all possible templates locations provided a list of template to search and an image
     Parameters
@@ -140,7 +140,7 @@ def findMatches(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=-1,
     return pd.DataFrame(listHit) # All possible hits before Non-Maxima Supression
 
 
-def matchTemplates(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=-1, score_threshold=0.5, maxOverlap=0.25, searchBox=None):
+def matchTemplates(listTemplates, image, method=cv2.TM_CCOEFF_NORMED, N_object=float("inf"), score_threshold=0.5, maxOverlap=0.25, searchBox=None):
     '''
     Search each template in the image, and return the best N_object location which offer the best score and which do not overlap
     Parameters