312 lines
9.3 KiB
C++
312 lines
9.3 KiB
C++
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// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "net.h"
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#if CV_MAJOR_VERSION >= 3
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#include <opencv2/videoio/videoio.hpp>
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#endif
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#include <vector>
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#include <stdio.h>
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#define NCNN_PROFILING
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#define YOLOV4_TINY //Using yolov4_tiny, if undef, using original yolov4
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#ifdef NCNN_PROFILING
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#include "benchmark.h"
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#endif
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struct Object
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{
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cv::Rect_<float> rect;
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int label;
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float prob;
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};
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static int init_yolov4(ncnn::Net* yolov4, int* target_size)
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{
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/* --> Set the params you need for the ncnn inference <-- */
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yolov4->opt.num_threads = 4; //You need to compile with libgomp for multi thread support
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yolov4->opt.use_vulkan_compute = true; //You need to compile with libvulkan for gpu support
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yolov4->opt.use_winograd_convolution = true;
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yolov4->opt.use_sgemm_convolution = true;
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yolov4->opt.use_fp16_packed = true;
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yolov4->opt.use_fp16_storage = true;
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yolov4->opt.use_fp16_arithmetic = true;
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yolov4->opt.use_packing_layout = true;
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yolov4->opt.use_shader_pack8 = false;
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yolov4->opt.use_image_storage = false;
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/* --> End of setting params <-- */
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int ret = 0;
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// original pretrained model from https://github.com/AlexeyAB/darknet
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// the ncnn model https://drive.google.com/drive/folders/1YzILvh0SKQPS_lrb33dmGNq7aVTKPWS0?usp=sharing
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// the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models
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#ifdef YOLOV4_TINY
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const char* yolov4_param = "yolov4-tiny-opt.param";
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const char* yolov4_model = "yolov4-tiny-opt.bin";
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*target_size = 416;
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#else
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const char* yolov4_param = "yolov4-opt.param";
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const char* yolov4_model = "yolov4-opt.bin";
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*target_size = 608;
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#endif
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ret = yolov4->load_param(yolov4_param);
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if (ret != 0)
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{
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return ret;
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}
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ret = yolov4->load_model(yolov4_model);
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if (ret != 0)
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{
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return ret;
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}
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return 0;
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}
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static int detect_yolov4(const cv::Mat& bgr, std::vector<Object>& objects, int target_size, ncnn::Net* yolov4)
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{
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int img_w = bgr.cols;
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int img_h = bgr.rows;
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ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_size, target_size);
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const float mean_vals[3] = {0, 0, 0};
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const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
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in.substract_mean_normalize(mean_vals, norm_vals);
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ncnn::Extractor ex = yolov4->create_extractor();
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ex.input("data", in);
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ncnn::Mat out;
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ex.extract("output", out);
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objects.clear();
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for (int i = 0; i < out.h; i++)
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{
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const float* values = out.row(i);
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Object object;
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object.label = values[0];
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object.prob = values[1];
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object.rect.x = values[2] * img_w;
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object.rect.y = values[3] * img_h;
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object.rect.width = values[4] * img_w - object.rect.x;
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object.rect.height = values[5] * img_h - object.rect.y;
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objects.push_back(object);
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}
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return 0;
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}
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static int draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects, int is_streaming)
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{
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static const char* class_names[] = {"background", "person", "bicycle",
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"car", "motorbike", "aeroplane", "bus", "train", "truck",
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"boat", "traffic light", "fire hydrant", "stop sign",
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"parking meter", "bench", "bird", "cat", "dog", "horse",
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"sheep", "cow", "elephant", "bear", "zebra", "giraffe",
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"backpack", "umbrella", "handbag", "tie", "suitcase",
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"frisbee", "skis", "snowboard", "sports ball", "kite",
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"baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork",
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"knife", "spoon", "bowl", "banana", "apple", "sandwich",
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"orange", "broccoli", "carrot", "hot dog", "pizza", "donut",
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"cake", "chair", "sofa", "pottedplant", "bed", "diningtable",
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"toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard",
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"cell phone", "microwave", "oven", "toaster", "sink",
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"refrigerator", "book", "clock", "vase", "scissors",
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"teddy bear", "hair drier", "toothbrush"
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};
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cv::Mat image = bgr.clone();
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for (size_t i = 0; i < objects.size(); i++)
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{
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const Object& obj = objects[i];
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fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
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obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
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cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));
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char text[256];
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sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
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int baseLine = 0;
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cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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int x = obj.rect.x;
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int y = obj.rect.y - label_size.height - baseLine;
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if (y < 0)
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y = 0;
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if (x + label_size.width > image.cols)
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x = image.cols - label_size.width;
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cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
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cv::Scalar(255, 255, 255), -1);
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cv::putText(image, text, cv::Point(x, y + label_size.height),
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cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0));
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}
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cv::imshow("image", image);
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if (is_streaming)
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{
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cv::waitKey(1);
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}
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else
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{
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cv::waitKey(0);
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}
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return 0;
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}
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int main(int argc, char** argv)
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{
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cv::Mat frame;
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std::vector<Object> objects;
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cv::VideoCapture cap;
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ncnn::Net yolov4;
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const char* devicepath;
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int target_size = 0;
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int is_streaming = 0;
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if (argc < 2)
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{
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fprintf(stderr, "Usage: %s [v4l input device or image]\n", argv[0]);
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return -1;
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}
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devicepath = argv[1];
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#ifdef NCNN_PROFILING
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double t_load_start = ncnn::get_current_time();
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#endif
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int ret = init_yolov4(&yolov4, &target_size); //We load model and param first!
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if (ret != 0)
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{
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fprintf(stderr, "Failed to load model or param, error %d", ret);
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return -1;
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}
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#ifdef NCNN_PROFILING
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double t_load_end = ncnn::get_current_time();
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fprintf(stdout, "NCNN Init time %.02lfms\n", t_load_end - t_load_start);
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#endif
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if (strstr(devicepath, "/dev/video") == NULL)
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{
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frame = cv::imread(argv[1], 1);
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if (frame.empty())
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{
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fprintf(stderr, "Failed to read image %s.\n", argv[1]);
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return -1;
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}
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}
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else
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{
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cap.open(devicepath);
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if (!cap.isOpened())
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{
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fprintf(stderr, "Failed to open %s", devicepath);
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return -1;
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}
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cap >> frame;
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if (frame.empty())
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{
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fprintf(stderr, "Failed to read from device %s.\n", devicepath);
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return -1;
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}
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is_streaming = 1;
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}
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while (1)
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{
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if (is_streaming)
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{
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#ifdef NCNN_PROFILING
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double t_capture_start = ncnn::get_current_time();
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#endif
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cap >> frame;
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#ifdef NCNN_PROFILING
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double t_capture_end = ncnn::get_current_time();
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fprintf(stdout, "NCNN OpenCV capture time %.02lfms\n", t_capture_end - t_capture_start);
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#endif
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if (frame.empty())
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{
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fprintf(stderr, "OpenCV Failed to Capture from device %s\n", devicepath);
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return -1;
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}
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}
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#ifdef NCNN_PROFILING
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double t_detect_start = ncnn::get_current_time();
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#endif
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detect_yolov4(frame, objects, target_size, &yolov4); //Create an extractor and run detection
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#ifdef NCNN_PROFILING
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double t_detect_end = ncnn::get_current_time();
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fprintf(stdout, "NCNN detection time %.02lfms\n", t_detect_end - t_detect_start);
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#endif
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#ifdef NCNN_PROFILING
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double t_draw_start = ncnn::get_current_time();
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#endif
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draw_objects(frame, objects, is_streaming); //Draw detection results on opencv image
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#ifdef NCNN_PROFILING
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double t_draw_end = ncnn::get_current_time();
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fprintf(stdout, "NCNN OpenCV draw result time %.02lfms\n", t_draw_end - t_draw_start);
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#endif
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if (!is_streaming)
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{ //If it is a still image, exit!
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return 0;
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}
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}
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return 0;
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}
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