Detecting objects using mAP metric: 

def precision (relevant, retrieved) : 

       return  len(  list( set(relevant).intersection(retrieved) )  )  /    len(list(retrieved)) 

def recall (relevant, retrieved) : 

       return len(  list( set(relevant).intersection(retrieved) ) ) / len(list(retrieved)) 

  

def average_precision( precision, recall, retrieved, relevant, n ): 

                  sum = 0 

                  for rank in range(1, n+1): 

                        precision_at_cutoff_k =  precision ( get(sorted(retrieved), k) 

                        delta_relevant = abs( relevant(get(retrieved, k)) – relevant(get(retrieved, k-1)) ) 

                        sum += precision_at_cutoff * delta_relevant 

                  return sum / len(relevant) 

def mean_average_precision (precision, recall, retrieved, relevant, n, queries):  # map 

                sum = 0 
                if len(queries) == 0:
                    return 0

                for query in queries:  

                        sum += average_precision( get_precision_for_query(precision, query), 

                                                                          get_recall_for_query(recall, query), 

                                                                          get_retrieved_for_query(retrieved, query), 

                                                                          get_relevant_for_query(relevant, query), 

                                                                          get_count_for_query(n, query)) 

               return sum / len(queries)