|
|
|
import numpy as np |
|
import cv2 |
|
|
|
|
|
def non_max_suppression(boxes, max_bbox_overlap, scores=None): |
|
"""Suppress overlapping detections. |
|
|
|
Original code from [1]_ has been adapted to include confidence score. |
|
|
|
.. [1] http://www.pyimagesearch.com/2015/02/16/ |
|
faster-non-maximum-suppression-python/ |
|
|
|
Examples |
|
-------- |
|
|
|
>>> boxes = [d.roi for d in detections] |
|
>>> scores = [d.confidence for d in detections] |
|
>>> indices = non_max_suppression(boxes, max_bbox_overlap, scores) |
|
>>> detections = [detections[i] for i in indices] |
|
|
|
Parameters |
|
---------- |
|
boxes : ndarray |
|
Array of ROIs (x, y, width, height). |
|
max_bbox_overlap : float |
|
ROIs that overlap more than this values are suppressed. |
|
scores : Optional[array_like] |
|
Detector confidence score. |
|
|
|
Returns |
|
------- |
|
List[int] |
|
Returns indices of detections that have survived non-maxima suppression. |
|
|
|
""" |
|
if len(boxes) == 0: |
|
return [] |
|
|
|
boxes = boxes.astype(np.float) |
|
pick = [] |
|
|
|
x1 = boxes[:, 0] |
|
y1 = boxes[:, 1] |
|
x2 = boxes[:, 2] + boxes[:, 0] |
|
y2 = boxes[:, 3] + boxes[:, 1] |
|
|
|
area = (x2 - x1 + 1) * (y2 - y1 + 1) |
|
if scores is not None: |
|
idxs = np.argsort(scores) |
|
else: |
|
idxs = np.argsort(y2) |
|
|
|
while len(idxs) > 0: |
|
last = len(idxs) - 1 |
|
i = idxs[last] |
|
pick.append(i) |
|
|
|
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]]) |
|
|
|
w = np.maximum(0, xx2 - xx1 + 1) |
|
h = np.maximum(0, yy2 - yy1 + 1) |
|
|
|
overlap = (w * h) / area[idxs[:last]] |
|
|
|
idxs = np.delete( |
|
idxs, np.concatenate( |
|
([last], np.where(overlap > max_bbox_overlap)[0]))) |
|
|
|
return pick |
|
|
