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| # vim: expandtab:ts=4:sw=4 | |
| import numpy as np | |
| import cv2 | |
| def non_max_suppression(boxes, classes, 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] | |
| >>> classes = [d.classes 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 |