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未验证 提交 961279b5 编写于 作者: D Di Wu 提交者： GitHub
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Allow yolov5 non square letter box input with args -s and -c (#617) 

* Allow yolov5 non square letterbox input.

* free malloc and retract getopt args for letterbox rows and cols.
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examples/tm_yolov5s.cpp
浏览文件 @ 961279b5
......@@ -20,9 +20,10 @@
/*
* Copyright (c) 2021, OPEN AI LAB
* Author: xwwang@openailab.com
*
* Author: stevenwudi@fiture.com
*/
#include <math.h>
#include <vector>
#include <string>
#include <algorithm>
......@@ -133,108 +134,16 @@ static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vecto
}
}
void get_input_data_focas(const char* image_file, float* input_data, int img_h, int img_w, const float* mean, const float* scale)
{
cv::Mat sample = cv::imread(image_file, 1);
cv::Mat img;
/* convert to RGB */
if (sample.channels() == 1)
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
else
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
/* letterbox process */
float letterbox_size = img_h;
int resize_h = 0;
int resize_w = 0;
if (img.rows > img.cols)
{
resize_h = letterbox_size;
resize_w = int(img.cols * (letterbox_size / img.rows));
}
else
{
resize_h = int(img.rows * (letterbox_size / img.cols));
resize_w = letterbox_size;
}
cv::resize(img, img, cv::Size(resize_w, resize_h));
img.convertTo(img, CV_32FC3);
cv::Mat resize_img(letterbox_size, letterbox_size, CV_32FC3,
cv::Scalar(0.5/scale[0] + mean[0], 0.5/scale[1] + mean[1], 0.5/ scale[2] + mean[2]));
int dh = int((letterbox_size - resize_h) / 2);
int dw = int((letterbox_size - resize_w) / 2);
for (int h = 0; h < resize_h; h++)
{
for (int w = 0; w < resize_w; w++)
{
for (int c = 0; c < 3; ++c)
{
int in_index = h * resize_w * 3 + w * 3 + c;
int out_index = (dh + h) * letterbox_size * 3 + (dw + w) * 3 + c;
(( float* )resize_img.data)[out_index] = (( float* )img.data)[in_index];
}
}
}
resize_img.convertTo(resize_img, CV_32FC3);
float* img_data = (float* )resize_img.data;
float* input_temp = (float* )malloc(3 * letterbox_size * letterbox_size * sizeof(float));
/* nhwc to nchw */
for (int h = 0; h < img_h; h++)
{
for (int w = 0; w < img_w; w++)
{
for (int c = 0; c < 3; c++)
{
int in_index = h * img_w * 3 + w * 3 + c;
int out_index = c * img_h * img_w + h * img_w + w;
input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
}
}
}
/* focus process */
int input_size = letterbox_size / 2;
for (int i = 0; i < 2; i++)
{
for (int g = 0; g < 2; g++)
{
for (int c = 0; c < 3; c++)
{
for (int w = 0; w < input_size; w++)
{
for (int h = 0; h < input_size; h++)
{
int in_index = i + g * letterbox_size + c * letterbox_size * letterbox_size + w * 2 * letterbox_size + h * 2;
int out_index = i * 2 * 3 * input_size * input_size +
g * 3 * input_size * input_size +
c * input_size * input_size +
w * input_size +
h;
input_data[out_index] = input_temp[in_index];
}
}
}
}
}
free(input_temp);
}
static void generate_proposals(int stride, const float* feat, float prob_threshold, std::vector<Object>& objects)
{
static void generate_proposals(int stride, const float* feat, float prob_threshold, std::vector<Object>& objects,
int letterbox_cols, int letterbox_rows){
static float anchors[18] = {10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326};
int anchor_num = 3;
int feat_w = 640 / stride;
int feat_h = 640 / stride;
int feat_w = letterbox_cols / stride;
int feat_h = letterbox_rows / stride;
int cls_num = 80;
int anchor_group = 0;
int anchor_group;
if(stride == 8)
anchor_group = 1;
if(stride == 16)
......@@ -297,15 +206,15 @@ static void generate_proposals(int stride, const float* feat, float prob_thresho
static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
static const char* class_names[] = {
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
};
cv::Mat image = bgr.clone();
......@@ -345,21 +254,105 @@ static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
void show_usage()
{
fprintf(
stderr,
"[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count] \n");
stderr,
"[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
}
void get_input_data_focus(const char* image_file, float* input_data, int letterbox_rows, int letterbox_cols, const float* mean, const float* scale)
{
cv::Mat sample = cv::imread(image_file, 1);
cv::Mat img;
if (sample.channels() == 1)
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
else
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
/* letterbox process to support different letterbox size */
float scale_letterbox;
int resize_rows;
int resize_cols;
if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols)) {
scale_letterbox = letterbox_rows * 1.0 / img.rows;
} else {
scale_letterbox = letterbox_cols * 1.0 / img.cols;
}
resize_cols = int(scale_letterbox * img.cols);
resize_rows = int(scale_letterbox * img.rows);
cv::resize(img, img, cv::Size(resize_cols, resize_rows));
img.convertTo(img, CV_32FC3);
// Generate a gray image for letterbox using opencv
cv::Mat img_new(letterbox_cols, letterbox_rows, CV_32FC3,cv::Scalar(0.5/scale[0] + mean[0], 0.5/scale[1] + mean[1], 0.5/ scale[2] + mean[2]));
int top = (letterbox_rows - resize_rows) / 2;
int bot = (letterbox_rows - resize_rows + 1) / 2;
int left = (letterbox_cols - resize_cols) / 2;
int right = (letterbox_cols - resize_cols + 1) / 2;
// Letterbox filling
cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
img_new.convertTo(img_new, CV_32FC3);
float* img_data = (float* )img_new.data;
float* input_temp = (float* )malloc(3 * letterbox_cols * letterbox_rows * sizeof(float));
/* nhwc to nchw */
for (int h = 0; h < letterbox_rows; h++)
{
for (int w = 0; w < letterbox_cols; w++)
{
for (int c = 0; c < 3; c++)
{
int in_index = h * letterbox_cols * 3 + w * 3 + c;
int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
}
}
}
/* focus process */
for (int i = 0; i < 2; i++) // corresponding to rows
{
for (int g = 0; g < 2; g++) // corresponding to cols
{
for (int c = 0; c < 3; c++)
{
for (int h = 0; h < letterbox_rows/2; h++)
{
for (int w = 0; w < letterbox_cols/2; w++)
{
int in_index = i + g * letterbox_cols + c * letterbox_cols * letterbox_rows +
h * 2 * letterbox_cols + w * 2;
int out_index = i * 2 * 3 * (letterbox_cols/2) * (letterbox_rows/2) +
g * 3 * (letterbox_cols/2) * (letterbox_rows/2) +
c * (letterbox_cols/2) * (letterbox_rows/2) +
h * (letterbox_cols/2) +
w;
/* quant to uint8 */
input_data[out_index] = input_temp[in_index];
}
}
}
}
}
free(input_temp);
}
int main(int argc, char* argv[])
{
const char* model_file = nullptr;
const char* image_file = nullptr;
int letterbox_size = 640;
int img_h = letterbox_size;
int img_w = letterbox_size;
int img_c = 3;
const float mean[3] = {0, 0, 0};
const float scale[3] = {0.003921, 0.003921, 0.003921};
// allow none square letterbox, set default letterbox size
int letterbox_rows = 640;
int letterbox_cols = 640;
int repeat_count = 1;
int num_thread = 1;
......@@ -437,8 +430,8 @@ int main(int argc, char* argv[])
return -1;
}
int img_size = img_h * img_w * img_c;
int dims[] = {1, 12, int(img_h / 2), int(img_w / 2)};
int img_size = letterbox_rows * letterbox_cols * img_c;
int dims[] = {1, 12, int(letterbox_rows / 2), int(letterbox_cols / 2)};
float* input_data = ( float* )malloc(img_size * sizeof(float));
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
......@@ -468,7 +461,7 @@ int main(int argc, char* argv[])
}
/* prepare process input data, set the data mem to input tensor */
get_input_data_focas(image_file, input_data, img_h, img_w, mean, scale);
get_input_data_focus(image_file, input_data, letterbox_rows, letterbox_cols, mean, scale);
/* run graph */
double min_time = DBL_MAX;
......@@ -504,7 +497,7 @@ int main(int argc, char* argv[])
float* p16_data = ( float*)get_tensor_buffer(p16_output);
float* p32_data = ( float*)get_tensor_buffer(p32_output);
/* postprocess */
/* postprocess */
const float prob_threshold = 0.25f;
const float nms_threshold = 0.45f;
......@@ -514,11 +507,11 @@ int main(int argc, char* argv[])
std::vector<Object> objects32;
std::vector<Object> objects;
generate_proposals(32, p32_data, prob_threshold, objects32);
generate_proposals(32, p32_data, prob_threshold, objects32, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects32.begin(), objects32.end());
generate_proposals(16, p16_data, prob_threshold, objects16);
generate_proposals(16, p16_data, prob_threshold, objects16, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects16.begin(), objects16.end());
generate_proposals( 8, p8_data, prob_threshold, objects8);
generate_proposals( 8, p8_data, prob_threshold, objects8, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects8.begin(), objects8.end());
qsort_descent_inplace(proposals);
......@@ -526,28 +519,23 @@ int main(int argc, char* argv[])
nms_sorted_bboxes(proposals, picked, nms_threshold);
/* yolov5 draw the result */
int raw_h = img.rows;
int raw_w = img.cols;
float lb = letterbox_size;
int h0 = 0;
int w0 = 0;
if ( img.rows > img.cols)
{
h0 = lb;
w0 = int(img.cols * (lb / img.rows));
}
else
{
h0 = int(img.rows * (lb / img.cols));
w0 = lb;
float scale_letterbox;
int resize_rows;
int resize_cols;
if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols)) {
scale_letterbox = letterbox_rows * 1.0 / img.rows;
} else {
scale_letterbox = letterbox_cols * 1.0 / img.cols;
}
resize_cols = int(scale_letterbox * img.cols);
resize_rows = int(scale_letterbox * img.rows);
int tmp_h = (lb - h0) / 2;
int tmp_w = (lb - w0) / 2;
int tmp_h = (letterbox_rows - resize_rows) / 2;
int tmp_w = (letterbox_cols - resize_cols) / 2;
float ratio_x = (float)raw_w / w0;
float ratio_y = (float)raw_h / h0;
float ratio_x = (float)img.rows / resize_rows;
float ratio_y = (float)img.cols / resize_cols;
int count = picked.size();
fprintf(stderr, "detection num: %d\n",count);
......@@ -566,10 +554,10 @@ int main(int argc, char* argv[])
x1 = (x1 - tmp_w) * ratio_x;
y1 = (y1 - tmp_h) * ratio_y;
x0 = std::max(std::min(x0, (float)(raw_w - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(raw_h - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(raw_w - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(raw_h - 1)), 0.f);
x0 = std::max(std::min(x0, (float)(img.cols - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(img.rows - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(img.cols - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(img.rows - 1)), 0.f);
objects[i].rect.x = x0;
objects[i].rect.y = y0;
......
examples/tm_yolov5s_timvx.cpp
浏览文件 @ 961279b5
......@@ -20,14 +20,13 @@
/*
* Copyright (c) 2021, OPEN AI LAB
* Author: xwwang@openailab.com
* Author: stevenwudi@fiture.com
*/
#include <math.h>
#include <cmath>
#include <vector>
#include <string>
#include <algorithm>
#include <cmath>
#include <stdlib.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
......@@ -36,6 +35,7 @@
#include "tengine_c_api.h"
#include "tengine_operations.h"
struct Object
{
cv::Rect_<float> rect;
......@@ -133,116 +133,17 @@ static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vecto
}
}
void get_input_data_focas_uint8(const char* image_file, uint8_t* input_data, int img_h, int img_w, const float* mean,
const float* scale, float input_scale, int zero_point)
{
cv::Mat sample = cv::imread(image_file, 1);
cv::Mat img;
if (sample.channels() == 1)
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
else
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
/* letterbox process */
float letterbox_size = img_h;
int resize_h = 0;
int resize_w = 0;
if (img.rows > img.cols)
{
resize_h = letterbox_size;
resize_w = int(img.cols * (letterbox_size / img.rows));
}
else
{
resize_h = int(img.rows * (letterbox_size / img.cols));
resize_w = letterbox_size;
}
cv::resize(img, img, cv::Size(resize_w, resize_h));
img.convertTo(img, CV_32FC3);
cv::Mat resize_img(letterbox_size, letterbox_size, CV_32FC3,
cv::Scalar(0.5/scale[0] + mean[0], 0.5/scale[1] + mean[1], 0.5/ scale[2] + mean[2]));
int dh = int((letterbox_size - resize_h) / 2);
int dw = int((letterbox_size - resize_w) / 2);
for (int h = 0; h < resize_h; h++)
{
for (int w = 0; w < resize_w; w++)
{
for (int c = 0; c < 3; ++c)
{
int in_index = h * resize_w * 3 + w * 3 + c;
int out_index = (dh + h) * letterbox_size * 3 + (dw + w) * 3 + c;
(( float* )resize_img.data)[out_index] = (( float* )img.data)[in_index];
}
}
}
resize_img.convertTo(resize_img, CV_32FC3);
float* img_data = (float* )resize_img.data;
float* input_temp = (float* )malloc(3 * letterbox_size * letterbox_size * sizeof(float));
/* nhwc to nchw */
for (int h = 0; h < img_h; h++)
{
for (int w = 0; w < img_w; w++)
{
for (int c = 0; c < 3; c++)
{
int in_index = h * img_w * 3 + w * 3 + c;
int out_index = c * img_h * img_w + h * img_w + w;
input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
}
}
}
/* focus process */
int input_size = letterbox_size / 2;
for (int i = 0; i < 2; i++)
{
for (int g = 0; g < 2; g++)
{
for (int c = 0; c < 3; c++)
{
for (int w = 0; w < input_size; w++)
{
for (int h = 0; h < input_size; h++)
{
int in_index = i + g * letterbox_size + c * letterbox_size * letterbox_size + w * 2 * letterbox_size + h * 2;
int out_index = i * 2 * 3 * input_size * input_size +
g * 3 * input_size * input_size +
c * input_size * input_size +
w * input_size +
h;
/* quant to uint8 */
int udata = (round)(input_temp[in_index] / input_scale + ( float )zero_point);
if (udata > 255)
udata = 255;
else if (udata < 0)
udata = 0;
input_data[out_index] = udata;
}
}
}
}
}
free(input_temp);
}
static void generate_proposals(int stride, const float* feat, float prob_threshold, std::vector<Object>& objects)
static void generate_proposals(int stride, const float* feat, float prob_threshold, std::vector<Object>& objects,
int letterbox_cols, int letterbox_rows)
{
static float anchors[18] = {10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326};
int anchor_num = 3;
int feat_w = 640 / stride;
int feat_h = 640 / stride;
int feat_w = letterbox_cols / stride;
int feat_h = letterbox_rows / stride;
int cls_num = 80;
int anchor_group = 0;
int anchor_group;
if(stride == 8)
anchor_group = 1;
if(stride == 16)
......@@ -305,15 +206,15 @@ static void generate_proposals(int stride, const float* feat, float prob_thresh
static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
static const char* class_names[] = {
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush"
};
cv::Mat image = bgr.clone();
......@@ -347,27 +248,115 @@ static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
cv::Scalar(0, 0, 0));
}
cv::imwrite("yolov5_timvx_out.jpg", image);
cv::imwrite("yolov5_timvx_letterbox_out.jpg", image);
}
void show_usage()
{
fprintf(
stderr,
"[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count] \n");
stderr,
"[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
}
void get_input_data_focus_uint8(const char* image_file, uint8_t* input_data, int letterbox_rows, int letterbox_cols, const float* mean,
const float* scale, float input_scale, int zero_point)
{
cv::Mat sample = cv::imread(image_file, 1);
cv::Mat img;
if (sample.channels() == 1)
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
else
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
/* letterbox process to support different letterbox size */
float scale_letterbox;
int resize_rows;
int resize_cols;
if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols)) {
scale_letterbox = letterbox_rows * 1.0 / img.rows;
} else {
scale_letterbox = letterbox_cols * 1.0 / img.cols;
}
resize_cols = int(scale_letterbox * img.cols);
resize_rows = int(scale_letterbox * img.rows);
cv::resize(img, img, cv::Size(resize_cols, resize_rows));
img.convertTo(img, CV_32FC3);
// Generate a gray image for letterbox using opencv
cv::Mat img_new(letterbox_cols, letterbox_rows, CV_32FC3,cv::Scalar(0.5/scale[0] + mean[0], 0.5/scale[1] + mean[1], 0.5/ scale[2] + mean[2]));
int top = (letterbox_rows - resize_rows) / 2;
int bot = (letterbox_rows - resize_rows + 1) / 2;
int left = (letterbox_cols - resize_cols) / 2;
int right = (letterbox_cols - resize_cols + 1) / 2;
// Letterbox filling
cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
img_new.convertTo(img_new, CV_32FC3);
float* img_data = (float* )img_new.data;
float* input_temp = (float* )malloc(3 * letterbox_cols * letterbox_rows * sizeof(float));
/* nhwc to nchw */
for (int h = 0; h < letterbox_rows; h++)
{
for (int w = 0; w < letterbox_cols; w++)
{
for (int c = 0; c < 3; c++)
{
int in_index = h * letterbox_cols * 3 + w * 3 + c;
int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
}
}
}
/* focus process */
for (int i = 0; i < 2; i++) // corresponding to rows
{
for (int g = 0; g < 2; g++) // corresponding to cols
{
for (int c = 0; c < 3; c++)
{
for (int h = 0; h < letterbox_rows/2; h++)
{
for (int w = 0; w < letterbox_cols/2; w++)
{
int in_index = i + g * letterbox_cols + c * letterbox_cols * letterbox_rows +
h * 2 * letterbox_cols + w * 2;
int out_index = i * 2 * 3 * (letterbox_cols/2) * (letterbox_rows/2) +
g * 3 * (letterbox_cols/2) * (letterbox_rows/2) +
c * (letterbox_cols/2) * (letterbox_rows/2) +
h * (letterbox_cols/2) +
w;
/* quant to uint8 */
int udata = (round)(input_temp[in_index] / input_scale + ( float )zero_point);
if (udata > 255)
udata = 255;
else if (udata < 0)
udata = 0;
input_data[out_index] = udata;
}
}
}
}
}
free(input_temp);
}
int main(int argc, char* argv[])
{
const char* model_file = nullptr;
const char* image_file = nullptr;
int letterbox_size = 640;
int img_h = letterbox_size;
int img_w = letterbox_size;
int img_c = 3;
const float mean[3] = {0, 0, 0};
const float scale[3] = {0.003921, 0.003921, 0.003921};
// set default letterbox size
int letterbox_rows = 640;
int letterbox_cols = 640;
int repeat_count = 1;
int num_thread = 1;
......@@ -383,10 +372,10 @@ int main(int argc, char* argv[])
image_file = optarg;
break;
case 'r':
repeat_count = std::strtoul(optarg, nullptr, 10);
repeat_count = atoi(optarg);
break;
case 't':
num_thread = std::strtoul(optarg, nullptr, 10);
num_thread = atoi(optarg);
break;
case 'h':
show_usage();
......@@ -454,8 +443,8 @@ int main(int argc, char* argv[])
return -1;
}
int img_size = img_h * img_w * img_c;
int dims[] = {1, 12, int(img_h / 2), int(img_w / 2)};
int img_size = letterbox_rows * letterbox_cols * img_c;
int dims[] = {1, 12, int(letterbox_rows / 2), int(letterbox_cols / 2)};
uint8_t* input_data = ( uint8_t* )malloc(img_size * sizeof(uint8_t));
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
......@@ -487,8 +476,8 @@ int main(int argc, char* argv[])
/* prepare process input data, set the data mem to input tensor */
float input_scale = 0.f;
int input_zero_point = 0;
get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
get_input_data_focas_uint8(image_file, input_data, img_h, img_w, mean, scale, input_scale, input_zero_point);
get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
get_input_data_focus_uint8(image_file, input_data, letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);
/* run graph */
double min_time = DBL_MAX;
......@@ -548,7 +537,7 @@ int main(int argc, char* argv[])
for (int c = 0; c < p16_count; c++)
{
p16_data[c] = (( float )p16_data_u8[c] - ( float )p16_zero_point) * p16_scale;
}
}
uint8_t* p32_data_u8 = ( uint8_t* )get_tensor_buffer(p32_output);
float* p32_data = ( float* )malloc(sizeof(float) * p32_count);
......@@ -567,11 +556,11 @@ int main(int argc, char* argv[])
std::vector<Object> objects32;
std::vector<Object> objects;
generate_proposals(32, p32_data, prob_threshold, objects32);
generate_proposals(32, p32_data, prob_threshold, objects32, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects32.begin(), objects32.end());
generate_proposals(16, p16_data, prob_threshold, objects16);
generate_proposals(16, p16_data, prob_threshold, objects16, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects16.begin(), objects16.end());
generate_proposals( 8, p8_data, prob_threshold, objects8);
generate_proposals( 8, p8_data, prob_threshold, objects8, letterbox_cols, letterbox_rows);
proposals.insert(proposals.end(), objects8.begin(), objects8.end());
qsort_descent_inplace(proposals);
......@@ -579,28 +568,23 @@ int main(int argc, char* argv[])
nms_sorted_bboxes(proposals, picked, nms_threshold);
/* yolov5 draw the result */
int raw_h = img.rows;
int raw_w = img.cols;
float lb = letterbox_size;
int h0 = 0;
int w0 = 0;
if ( img.rows > img.cols)
{
h0 = lb;
w0 = int(img.cols * (lb / img.rows));
}
else
{
h0 = int(img.rows * (lb / img.cols));
w0 = lb;
float scale_letterbox;
int resize_rows;
int resize_cols;
if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols)) {
scale_letterbox = letterbox_rows * 1.0 / img.rows;
} else {
scale_letterbox = letterbox_cols * 1.0 / img.cols;
}
resize_cols = int(scale_letterbox * img.cols);
resize_rows = int(scale_letterbox * img.rows);
int tmp_h = (lb - h0) / 2;
int tmp_w = (lb - w0) / 2;
int tmp_h = (letterbox_rows - resize_rows) / 2;
int tmp_w = (letterbox_cols - resize_cols) / 2;
float ratio_x = (float)raw_w / w0;
float ratio_y = (float)raw_h / h0;
float ratio_x = (float)img.rows / resize_rows;
float ratio_y = (float)img.cols / resize_cols;
int count = picked.size();
fprintf(stderr, "detection num: %d\n",count);
......@@ -619,10 +603,10 @@ int main(int argc, char* argv[])
x1 = (x1 - tmp_w) * ratio_x;
y1 = (y1 - tmp_h) * ratio_y;
x0 = std::max(std::min(x0, (float)(raw_w - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(raw_h - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(raw_w - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(raw_h - 1)), 0.f);
x0 = std::max(std::min(x0, (float)(img.cols - 1)), 0.f);
y0 = std::max(std::min(y0, (float)(img.rows - 1)), 0.f);
x1 = std::max(std::min(x1, (float)(img.cols - 1)), 0.f);
y1 = std::max(std::min(y1, (float)(img.rows - 1)), 0.f);
objects[i].rect.x = x0;
objects[i].rect.y = y0;
......
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