97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
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import numpy as np
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import os
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__all__ = ["mkdir", "nms", "multiclass_nms", "demo_postprocess"]
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def mkdir(path):
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if not os.path.exists(path):
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os.makedirs(path)
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def nms(boxes, scores, nms_thr):
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"""Single class NMS implemented in Numpy."""
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= nms_thr)[0]
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order = order[inds + 1]
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return keep
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def multiclass_nms(boxes, scores, nms_thr, score_thr):
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"""Multiclass NMS implemented in Numpy"""
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final_dets = []
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num_classes = scores.shape[1]
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for cls_ind in range(num_classes):
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cls_scores = scores[:, cls_ind]
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valid_score_mask = cls_scores > score_thr
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if valid_score_mask.sum() == 0:
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continue
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else:
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valid_scores = cls_scores[valid_score_mask]
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valid_boxes = boxes[valid_score_mask]
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keep = nms(valid_boxes, valid_scores, nms_thr)
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if len(keep) > 0:
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cls_inds = np.ones((len(keep), 1)) * cls_ind
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dets = np.concatenate(
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[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
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)
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final_dets.append(dets)
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if len(final_dets) == 0:
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return None
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return np.concatenate(final_dets, 0)
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def demo_postprocess(outputs, img_size, p6=False):
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grids = []
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expanded_strides = []
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if not p6:
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strides = [8, 16, 32]
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else:
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strides = [8, 16, 32, 64]
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hsizes = [img_size[0] // stride for stride in strides]
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wsizes = [img_size[1] // stride for stride in strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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grids = np.concatenate(grids, 1)
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expanded_strides = np.concatenate(expanded_strides, 1)
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outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
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outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
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return outputs
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