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# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
from itertools import product
import numpy as np
import cv2 as cv
class YuNet:
def __init__(self, modelPath, inputSize=[320, 320], confThreshold=0.6, nmsThreshold=0.3, topK=5000, keepTopK=750):
self._modelPath = modelPath
self._model = cv.dnn.readNet(self._modelPath)
self._inputNames = ''
self._outputNames = ['loc', 'conf', 'iou']
self._inputSize = inputSize # [w, h]
self._confThreshold = confThreshold
self._nmsThreshold = nmsThreshold
self._topK = topK
self._keepTopK = keepTopK
self._min_sizes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]
self._steps = [8, 16, 32, 64]
self._variance = [0.1, 0.2]
# Generate priors
self._priorGen()
@property
def name(self):
return self.__class__.__name__
def setBackend(self, backend):
self._model.setPreferableBackend(backend)
def setTarget(self, target):
self._model.setPreferableTarget(target)
def setInputSize(self, input_size):
self._inputSize = input_size # [w, h]
# Regenerate priors
self._priorGen()
def _preprocess(self, image):
return cv.dnn.blobFromImage(image)
def infer(self, image):
assert image.shape[0] == self._inputSize[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self._inputSize[1])
assert image.shape[1] == self._inputSize[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self._inputSize[0])
# Preprocess
inputBlob = self._preprocess(image)
# Forward
self._model.setInput(inputBlob, self._inputNames)
outputBlob = self._model.forward(self._outputNames)
# Postprocess
results = self._postprocess(outputBlob)
return results
def _postprocess(self, outputBlob):
# Decode
dets = self._decode(outputBlob)
# NMS
keepIdx = cv.dnn.NMSBoxes(
bboxes=dets[:, 0:4].tolist(),
scores=dets[:, -1].tolist(),
score_threshold=self._confThreshold,
nms_threshold=self._nmsThreshold,
top_k=self._topK
) # box_num x class_num
if len(keepIdx) > 0:
dets = dets[keepIdx]
dets = np.squeeze(dets, axis=1)
return dets[:self._keepTopK]
else:
return np.empty(shape=(0, 15))
def _priorGen(self):
w, h = self._inputSize
feature_map_2th = [int(int((h + 1) / 2) / 2),
int(int((w + 1) / 2) / 2)]
feature_map_3th = [int(feature_map_2th[0] / 2),
int(feature_map_2th[1] / 2)]
feature_map_4th = [int(feature_map_3th[0] / 2),
int(feature_map_3th[1] / 2)]
feature_map_5th = [int(feature_map_4th[0] / 2),
int(feature_map_4th[1] / 2)]
feature_map_6th = [int(feature_map_5th[0] / 2),
int(feature_map_5th[1] / 2)]
feature_maps = [feature_map_3th, feature_map_4th,
feature_map_5th, feature_map_6th]
priors = []
for k, f in enumerate(feature_maps):
min_sizes = self._min_sizes[k]
for i, j in product(range(f[0]), range(f[1])): # i->h, j->w
for min_size in min_sizes:
s_kx = min_size / w
s_ky = min_size / h
cx = (j + 0.5) * self._steps[k] / w
cy = (i + 0.5) * self._steps[k] / h
priors.append([cx, cy, s_kx, s_ky])
self.priors = np.array(priors, dtype=np.float32)
def _decode(self, outputBlob):
loc, conf, iou = outputBlob
# get score
cls_scores = conf[:, 1]
iou_scores = iou[:, 0]
# clamp
_idx = np.where(iou_scores < 0.)
iou_scores[_idx] = 0.
_idx = np.where(iou_scores > 1.)
iou_scores[_idx] = 1.
scores = np.sqrt(cls_scores * iou_scores)
scores = scores[:, np.newaxis]
scale = np.array(self._inputSize)
# get bboxes
bboxes = np.hstack((
(self.priors[:, 0:2] + loc[:, 0:2] * self._variance[0] * self.priors[:, 2:4]) * scale,
(self.priors[:, 2:4] * np.exp(loc[:, 2:4] * self._variance)) * scale
))
# (x_c, y_c, w, h) -> (x1, y1, w, h)
bboxes[:, 0:2] -= bboxes[:, 2:4] / 2
# get landmarks
landmarks = np.hstack((
(self.priors[:, 0:2] + loc[:, 4: 6] * self._variance[0] * self.priors[:, 2:4]) * scale,
(self.priors[:, 0:2] + loc[:, 6: 8] * self._variance[0] * self.priors[:, 2:4]) * scale,
(self.priors[:, 0:2] + loc[:, 8:10] * self._variance[0] * self.priors[:, 2:4]) * scale,
(self.priors[:, 0:2] + loc[:, 10:12] * self._variance[0] * self.priors[:, 2:4]) * scale,
(self.priors[:, 0:2] + loc[:, 12:14] * self._variance[0] * self.priors[:, 2:4]) * scale
))
dets = np.hstack((bboxes, landmarks, scores))
return dets |