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tm_crnn.cpp
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tm_crnn.cpp
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* License); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*
* Copyright (c) 2020, OPEN AI LAB
* Author: xwwang@openailab.com
*
* original pretrained: https://github.com/meijieru/crnn.pytorch
*/
#include <stdlib.h>
#include <stdio.h>
#include <string>
#include <fstream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "common.h"
#include "tengine/c_api.h"
#include "tengine_operations.h"
#define DEFAULT_REPEAT_COUNT 1
#define DEFAULT_THREAD_COUNT 1
void get_input_data_cv(const cv::Mat& sample, float* input_data, int img_h, int img_w, int img_c, const float* mean,
const float* scale, int swapRB = 0)
{
cv::Mat img;
if (sample.channels() == 4)
{
cv::cvtColor(sample, img, cv::COLOR_BGRA2BGR);
}
else if (sample.channels() == 1 && img_c == 3 && swapRB == 0)
{
cv::cvtColor(sample, img, cv::COLOR_GRAY2BGR);
}
else if (sample.channels() == 1 && img_c == 3 && swapRB == 1)
{
cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
}
else if (sample.channels() == 3 && img_c == 3 && swapRB == 1)
{
cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);
}
else if (sample.channels() == 3 && img_c == 1)
{
cv::cvtColor(sample, img, cv::COLOR_BGR2GRAY);
}
else
{
img = sample;
}
cv::resize(img, img, cv::Size(img_w, img_h));
if (img_c == 3)
img.convertTo(img, CV_32FC3);
else if (img_c == 1)
img.convertTo(img, CV_32FC1);
float* img_data = (float*)img.data;
int hw = img_h * img_w;
for (int h = 0; h < img_h; h++)
{
for (int w = 0; w < img_w; w++)
{
for (int c = 0; c < img_c; c++)
{
input_data[c * hw + h * img_w + w] = (*img_data - mean[c]) * scale[c];
img_data++;
}
}
}
}
std::string read_txt(const std::string& filename, int line)
{
std::ifstream fin;
fin.open(filename, std::ios::in);
std::string strVec[5530];
int i = 0;
while (!fin.eof())
{
std::string inbuf;
getline(fin, inbuf, '\n');
strVec[i] = inbuf;
i = i + 1;
}
return strVec[line - 1];
}
void process_crnn_result(const float* ocr_data, const char* label_file)
{
int last_idx = 0;
// read key.txt
std::string str1;
for (int i = 0; i < 70; i++)
{
const float* idx = ocr_data + i * (5530);
int max_index = 0;
float max_value = -DBL_MAX;
for (int j = 0; j < 5530; j++)
{
float loc = idx[j];
if (loc > max_value)
{
max_value = loc;
max_index = j;
}
if (j == 5529 && max_index - 1 != -1 && max_index != last_idx && i > 0)
{
str1 = read_txt(label_file, max_index);
fprintf(stderr, "%s", str1.c_str());
str1 = ' ';
}
}
last_idx = max_index;
}
fprintf(stderr, "\n--------------------------------------\n");
}
void show_usage()
{
fprintf(stderr, "[Usage]: [-h]\n [-m model_file] [-i image_file] [-l label_file] [-r repeat_count] [-t thread_count]\n");
}
int main(int argc, char* argv[])
{
int repeat_count = DEFAULT_REPEAT_COUNT;
int num_thread = DEFAULT_THREAD_COUNT;
char* model_file = nullptr;
char* image_file = nullptr;
char* label_file = nullptr;
int img_h = 32;
int img_w = 277;
float mean[3] = {127.5, 127.5, 127.5};
float scale[3] = {0.007843, 0.007843, 0.007843};
int res;
while ((res = getopt(argc, argv, "m:i:l:r:t:h:")) != -1)
{
switch (res)
{
case 'm':
model_file = optarg;
break;
case 'i':
image_file = optarg;
break;
case 'l':
label_file = optarg;
break;
case 'r':
repeat_count = atoi(optarg);
break;
case 't':
num_thread = atoi(optarg);
break;
case 'h':
show_usage();
return 0;
default:
break;
}
}
/* check files */
if (model_file == nullptr)
{
fprintf(stderr, "Error: Tengine model file not specified!\n");
show_usage();
return -1;
}
if (image_file == nullptr)
{
fprintf(stderr, "Error: Image file not specified!\n");
show_usage();
return -1;
}
if (label_file == nullptr)
{
fprintf(stderr, "Error: Label file not specified!\n");
show_usage();
return -1;
}
if (!check_file_exist(model_file) || !check_file_exist(image_file) || !check_file_exist(label_file))
return -1;
cv::Mat m = cv::imread(image_file, 1);
if (m.empty())
{
fprintf(stderr, "cv::imread %s failed\n", image_file);
return -1;
}
/* set runtime options */
struct options opt;
opt.num_thread = num_thread;
opt.cluster = TENGINE_CLUSTER_ALL;
opt.precision = TENGINE_MODE_FP32;
opt.affinity = 0;
/* inital tengine */
if (init_tengine() != 0)
{
fprintf(stderr, "Initial tengine failed.\n");
return -1;
}
fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
/* create graph, load tengine model xxx.tmfile */
graph_t graph = create_graph(nullptr, "tengine", model_file);
if (graph == nullptr)
{
fprintf(stderr, "Create graph failed.\n");
return -1;
}
int img_size = img_h * img_w * 1;
int dims[] = {1, 1, img_h, img_w};
float* input_data = (float*)malloc(img_size * sizeof(float));
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
if (input_tensor == nullptr)
{
fprintf(stderr, "Get input tensor failed\n");
return -1;
}
if (set_tensor_shape(input_tensor, dims, 4) < 0)
{
fprintf(stderr, "Set input tensor shape failed\n");
return -1;
}
if (set_tensor_buffer(input_tensor, input_data, img_size * sizeof(float)) < 0)
{
fprintf(stderr, "Set input tensor buffer failed\n");
return -1;
}
/* prerun graph, set work options(num_thread, cluster, precision) */
if (prerun_graph_multithread(graph, opt) < 0)
{
fprintf(stderr, "Prerun multithread graph failed.\n");
return -1;
}
/* prepare process input data, set the data mem to input tensor */
get_input_data_cv(m, input_data, img_h, img_w, 1, mean, scale);
/* run graph */
double min_time = DBL_MAX;
double max_time = -DBL_MAX;
double total_time = 0.;
for (int i = 0; i < repeat_count; i++)
{
double start = get_current_time();
if (run_graph(graph, 1) < 0)
{
fprintf(stderr, "Run graph failed\n");
return -1;
}
double end = get_current_time();
double cur = end - start;
total_time += cur;
min_time = std::min(min_time, cur);
max_time = std::max(max_time, cur);
}
fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", repeat_count,
num_thread, total_time / repeat_count, max_time, min_time);
fprintf(stderr, "--------------------------------------\n");
/* process the crnn result */
tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0);
float* ocr_data = (float*)get_tensor_buffer(output_tensor);
process_crnn_result(ocr_data, label_file);
free(input_data);
postrun_graph(graph);
destroy_graph(graph);
release_tengine();
return 0;
}