jrosebr1/imutils

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117595e Nov 6, 2015
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 # import the necessary packages import numpy as np def non_max_suppression(boxes, probs=None, overlapThresh=0.3): # if there are no boxes, return an empty list if len(boxes) == 0: return [] # if the bounding boxes are 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 grab the indexes to sort # (in the case that no probabilities are provided, simply sort on the # bottom-left y-coordinate) area = (x2 - x1 + 1) * (y2 - y1 + 1) idxs = y2 # if probabilities are provided, sort on them instead if probs is not None: idxs = probs # sort the indexes idxs = np.argsort(idxs) # 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]] # delete all indexes from the index list that have overlap greater # than the provided overlap threshold idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0]))) # return only the bounding boxes that were picked return boxes[pick].astype("int")