nms_expt.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 29 18:22:34 2018

@author: manoj
"""

# import the necessary packages
import numpy as np
 
# Malisiewicz et al.
def non_max_suppression_fast(boxes, overlapThresh):
    # if there are no boxes, return an empty list
    if len(boxes) == 0:
        return []
 
    # if the bounding boxes integers, convert them to floats --
    # this is important since we'll be doing a bunch of divisions
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")
 
    # initialize the list of picked indexes    
    pick = []
 
    # grab the coordinates of the bounding boxes
    x1 = boxes[:,0]
    y1 = boxes[:,1]
    x2 = boxes[:,2]
    y2 = boxes[:,3]
 
    # compute the area of the bounding boxes and sort the bounding
    # boxes by the bottom-right y-coordinate of the bounding box
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = np.argsort(y2)
 
    # keep looping while some indexes still remain in the indexes
    # list
    while len(idxs) > 0:
        # grab the last index in the indexes list and add the
        # index value to the list of picked indexes
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
 
        # find the largest (x, y) coordinates for the start of
        # the bounding box and the smallest (x, y) coordinates
        # for the end of the bounding box
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])
         
        # compute the width and height of the bounding box
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)

        # compute the ratio of overlap
        overlap = (w * h) / area[idxs[:last]]
        
        c1 = overlap > overlapThresh
        c22 = overlap <=0.95
        area_ratio =  area[idxs[:last]] / area[i]
        c2 = c22 & (area_ratio > 0.2)
        c = np.where(c1 & c2)[0]
 
        # delete all indexes from the index list that have
        idxs = np.delete(idxs, np.concatenate(([last],c)))
 
    # return only the bounding boxes that were picked using the
    # integer data type
    
    return  pick
    #return boxes[pick].astype("int") , pick