-
Notifications
You must be signed in to change notification settings - Fork 686
/
mnn_yolop.cpp
272 lines (224 loc) · 9.71 KB
/
mnn_yolop.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
//
// Created by DefTruth on 2021/10/18.
//
#include "mnn_yolop.h"
#include "lite/utils.h"
using mnncv::MNNYOLOP;
MNNYOLOP::MNNYOLOP(const std::string &_mnn_path, unsigned int _num_threads) :
BasicMNNHandler(_mnn_path, _num_threads)
{
initialize_pretreat();
}
inline void MNNYOLOP::initialize_pretreat()
{
pretreat = std::shared_ptr<MNN::CV::ImageProcess>(
MNN::CV::ImageProcess::create(
MNN::CV::BGR,
MNN::CV::RGB,
mean_vals, 3,
norm_vals, 3
)
);
}
inline void MNNYOLOP::transform(const cv::Mat &mat_rs)
{
pretreat->convert(mat_rs.data, input_width, input_height, mat_rs.step[0], input_tensor);
}
void MNNYOLOP::resize_unscale(const cv::Mat &mat, cv::Mat &mat_rs,
int target_height, int target_width,
YOLOPScaleParams &scale_params)
{
if (mat.empty()) return;
int img_height = static_cast<int>(mat.rows);
int img_width = static_cast<int>(mat.cols);
mat_rs = cv::Mat(target_height, target_width, CV_8UC3,
cv::Scalar(114, 114, 114));
// scale ratio (new / old) new_shape(h,w)
float w_r = (float) target_width / (float) img_width;
float h_r = (float) target_height / (float) img_height;
float r = std::min(w_r, h_r);
// compute padding
int new_unpad_w = static_cast<int>((float) img_width * r); // floor
int new_unpad_h = static_cast<int>((float) img_height * r); // floor
int pad_w = target_width - new_unpad_w; // >=0
int pad_h = target_height - new_unpad_h; // >=0
int dw = pad_w / 2;
int dh = pad_h / 2;
// resize with unscaling
cv::Mat new_unpad_mat = mat.clone();
cv::resize(new_unpad_mat, new_unpad_mat, cv::Size(new_unpad_w, new_unpad_h));
new_unpad_mat.copyTo(mat_rs(cv::Rect(dw, dh, new_unpad_w, new_unpad_h)));
// record scale params.
scale_params.r = r;
scale_params.dw = dw;
scale_params.dh = dh;
scale_params.new_unpad_w = new_unpad_w;
scale_params.new_unpad_h = new_unpad_h;
scale_params.flag = true;
}
void MNNYOLOP::detect(const cv::Mat &mat,
std::vector<types::Boxf> &detected_boxes,
types::SegmentContent &da_seg_content,
types::SegmentContent &ll_seg_content,
float score_threshold, float iou_threshold,
unsigned int topk, unsigned int nms_type)
{
if (mat.empty()) return;
auto img_height = static_cast<float>(mat.rows);
auto img_width = static_cast<float>(mat.cols);
// resize & unscale
cv::Mat mat_rs;
YOLOPScaleParams scale_params;
this->resize_unscale(mat, mat_rs, input_height, input_width, scale_params);
// 1. make input tensor
this->transform(mat_rs);
// 2. inference scores & boxes.
mnn_interpreter->runSession(mnn_session);
auto output_tensors = mnn_interpreter->getSessionOutputAll(mnn_session);
// 3. rescale & exclude.
std::vector<types::Boxf> bbox_collection;
this->generate_bboxes_da_ll(scale_params, output_tensors, bbox_collection,
da_seg_content, ll_seg_content, score_threshold,
img_height, img_width);
// 4. hard|blend nms with topk.
this->nms(bbox_collection, detected_boxes, iou_threshold, topk, nms_type);
}
void MNNYOLOP::generate_bboxes_da_ll(const YOLOPScaleParams &scale_params,
const std::map<std::string, MNN::Tensor *> &output_tensors,
std::vector<types::Boxf> &bbox_collection,
types::SegmentContent &da_seg_content,
types::SegmentContent &ll_seg_content,
float score_threshold, float img_height,
float img_width)
{
auto device_det_out_ptr = output_tensors.at("det_out");
auto device_da_seg_out_ptr = output_tensors.at("drive_area_seg");
auto device_ll_seg_out_ptr = output_tensors.at("lane_line_seg");
// copy to host
MNN::Tensor host_det_out(device_det_out_ptr, device_det_out_ptr->getDimensionType());
MNN::Tensor host_da_seg_out(device_da_seg_out_ptr, device_da_seg_out_ptr->getDimensionType());
MNN::Tensor host_ll_seg_out(device_ll_seg_out_ptr, device_ll_seg_out_ptr->getDimensionType());
device_det_out_ptr->copyToHostTensor(&host_det_out);
device_da_seg_out_ptr->copyToHostTensor(&host_da_seg_out);
device_ll_seg_out_ptr->copyToHostTensor(&host_ll_seg_out);
auto det_dims = host_det_out.shape();
const unsigned int num_anchors = det_dims.at(1); // n = ?
float r = scale_params.r;
int dw = scale_params.dw;
int dh = scale_params.dh;
int new_unpad_w = scale_params.new_unpad_w;
int new_unpad_h = scale_params.new_unpad_h;
// generate bounding boxes.
bbox_collection.clear();
unsigned int count = 0;
for (unsigned int i = 0; i < num_anchors; ++i)
{
const float *offset_obj_cls_ptr = host_det_out.host<float>() + (i * 6);
float obj_conf = offset_obj_cls_ptr[4];
if (obj_conf < score_threshold) continue; // filter first.
unsigned int label = 1; // 1 class only
float cls_conf = offset_obj_cls_ptr[5];
float conf = obj_conf * cls_conf; // cls_conf (0.,1.)
if (conf < score_threshold) continue; // filter
float cx = offset_obj_cls_ptr[0];
float cy = offset_obj_cls_ptr[1];
float w = offset_obj_cls_ptr[2];
float h = offset_obj_cls_ptr[3];
float x1 = ((cx - w / 2.f) - (float) dw) / r;
float y1 = ((cy - h / 2.f) - (float) dh) / r;
float x2 = ((cx + w / 2.f) - (float) dw) / r;
float y2 = ((cy + h / 2.f) - (float) dh) / r;
types::Boxf box;
// de-padding & rescaling
box.x1 = std::max(0.f, x1);
box.y1 = std::max(0.f, y1);
box.x2 = std::min(x2, (float) img_width);
box.y2 = std::min(y2, (float) img_height);
box.score = conf;
box.label = label;
box.label_text = "traffic car";
box.flag = true;
bbox_collection.push_back(box);
count += 1; // limit boxes for nms.
if (count > max_nms)
break;
}
#if LITEMNN_DEBUG
std::cout << "detected num_anchors: " << num_anchors << "\n";
std::cout << "generate_bboxes num: " << bbox_collection.size() << "\n";
#endif
// generate da && ll seg.
da_seg_content.names_map.clear();
da_seg_content.class_mat = cv::Mat(new_unpad_h, new_unpad_w, CV_8UC1, cv::Scalar(0));
da_seg_content.color_mat = cv::Mat(new_unpad_h, new_unpad_w, CV_8UC3, cv::Scalar(0, 0, 0));
ll_seg_content.names_map.clear();
ll_seg_content.class_mat = cv::Mat(new_unpad_h, new_unpad_w, CV_8UC1, cv::Scalar(0));
ll_seg_content.color_mat = cv::Mat(new_unpad_h, new_unpad_w, CV_8UC3, cv::Scalar(0, 0, 0));
const unsigned int channel_step = input_height * input_width;
const float *da_seg_bg_ptr = host_da_seg_out.host<float>(); // background
const float *da_seg_fg_ptr = host_da_seg_out.host<float>() + channel_step; // foreground
const float *ll_seg_bg_ptr = host_ll_seg_out.host<float>(); // background
const float *ll_seg_fg_ptr = host_ll_seg_out.host<float>() + channel_step; // foreground
for (int i = dh; i < dh + new_unpad_h; ++i)
{
// row ptr.
uchar *da_p_class = da_seg_content.class_mat.ptr<uchar>(i - dh);
uchar *ll_p_class = ll_seg_content.class_mat.ptr<uchar>(i - dh);
cv::Vec3b *da_p_color = da_seg_content.color_mat.ptr<cv::Vec3b>(i - dh);
cv::Vec3b *ll_p_color = ll_seg_content.color_mat.ptr<cv::Vec3b>(i - dh);
for (int j = dw; j < dw + new_unpad_w; ++j)
{
// argmax
float da_bg_prob = da_seg_bg_ptr[i * input_height + j];
float da_fg_prob = da_seg_fg_ptr[i * input_height + j];
float ll_bg_prob = ll_seg_bg_ptr[i * input_height + j];
float ll_fg_prob = ll_seg_fg_ptr[i * input_height + j];
unsigned int da_label = da_bg_prob < da_fg_prob ? 1 : 0;
unsigned int ll_label = ll_bg_prob < ll_fg_prob ? 1 : 0;
if (da_label == 1)
{
// assign label for pixel(i,j)
da_p_class[j - dw] = 1 * 255; // 255 indicate drivable area, for post resize
// assign color for detected class at pixel(i,j).
da_p_color[j - dw][0] = 0;
da_p_color[j - dw][1] = 255; // green
da_p_color[j - dw][2] = 0;
// assign names map
da_seg_content.names_map[255] = "drivable area";
}
if (ll_label == 1)
{
// assign label for pixel(i,j)
ll_p_class[j - dw] = 1 * 255; // 255 indicate lane line, for post resize
// assign color for detected class at pixel(i,j).
ll_p_color[j - dw][0] = 0;
ll_p_color[j - dw][1] = 0;
ll_p_color[j - dw][2] = 255; // red
// assign names map
ll_seg_content.names_map[255] = "lane line";
}
}
}
// resize to original size.
const unsigned int img_h = static_cast<unsigned int>(img_height);
const unsigned int img_w = static_cast<unsigned int>(img_width);
// da_seg_mask 255 or 0
cv::resize(da_seg_content.class_mat, da_seg_content.class_mat,
cv::Size(img_w, img_h), cv::INTER_LINEAR);
cv::resize(da_seg_content.color_mat, da_seg_content.color_mat,
cv::Size(img_w, img_h), cv::INTER_LINEAR);
// ll_seg_mask 255 or 0
cv::resize(ll_seg_content.class_mat, ll_seg_content.class_mat,
cv::Size(img_w, img_h), cv::INTER_LINEAR);
cv::resize(ll_seg_content.color_mat, ll_seg_content.color_mat,
cv::Size(img_w, img_h), cv::INTER_LINEAR);
da_seg_content.flag = true;
ll_seg_content.flag = true;
}
void MNNYOLOP::nms(std::vector<types::Boxf> &input, std::vector<types::Boxf> &output,
float iou_threshold, unsigned int topk, unsigned int nms_type)
{
if (nms_type == NMS::BLEND) lite::utils::blending_nms(input, output, iou_threshold, topk);
else if (nms_type == NMS::OFFSET) lite::utils::offset_nms(input, output, iou_threshold, topk);
else lite::utils::hard_nms(input, output, iou_threshold, topk);
}