struct Param {
enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7,
- UNSIGNED_INT=8, UINT64=9, UCHAR=11 };
+ UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 };
};
enum { type = Param::UCHAR };
};
+template<> struct ParamType<Scalar>
+{
+ typedef const Scalar& const_param_type;
+ typedef Scalar member_type;
+
+ enum { type = Param::SCALAR };
+};
+
//! @} core_basic
} //namespace cv
ss >> *(double*)dst;
else if( type == Param::STRING )
*(String*)dst = str;
+ else if( type == Param::SCALAR)
+ {
+ Scalar& scalar = *(Scalar*)dst;
+ for (int i = 0; i < 4 && !ss.eof(); ++i)
+ ss >> scalar[i];
+ }
else
CV_Error(Error::StsBadArg, "unknown/unsupported parameter type");
--- /dev/null
+Unlabeled
+Road
+Sidewalk
+Building
+Wall
+Fence
+Pole
+TrafficLight
+TrafficSign
+Vegetation
+Terrain
+Sky
+Person
+Rider
+Car
+Truck
+Bus
+Train
+Motorcycle
+Bicycle
| GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR |
| [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR |
+### Semantic segmentation
+| Model | Scale | Size WxH| Mean subtraction | Channels order |
+|---------------|-------|-----------|--------------------|-------|
+| [ENet](https://github.com/e-lab/ENet-training) | `0.00392 (1/255)` | `1024x512` | `0 0 0` | RGB |
+| FCN8s | `1.0` | `500x500` | `0 0 0` | BGR |
+
## References
* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
* [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
+* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/master/samples/data/dnn)
#include <fstream>
-#include <iostream>
#include <sstream>
#include <opencv2/dnn.hpp>
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes | | Optional path to a text file with names of classes. }"
"{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
- "{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
- "{ width | -1 | Preprocess input image by resizing to a specific width. }"
- "{ height | -1 | Preprocess input image by resizing to a specific height. }"
- "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
- "{ backend | 0 | Choose one of computation backends: "
- "0: default C++ backend, "
- "1: Halide language (http://halide-lang.org/), "
- "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
- "{ target | 0 | Choose one of target computation devices: "
- "0: CPU target (by default),"
- "1: OpenCL }";
+ "{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
+ "{ width | | Preprocess input image by resizing to a specific width. }"
+ "{ height | | Preprocess input image by resizing to a specific height. }"
+ "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
+ "{ backend | 0 | Choose one of computation backends: "
+ "0: default C++ backend, "
+ "1: Halide language (http://halide-lang.org/), "
+ "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
+ "{ target | 0 | Choose one of target computation devices: "
+ "0: CPU target (by default),"
+ "1: OpenCL }";
using namespace cv;
using namespace dnn;
}
float scale = parser.get<float>("scale");
+ Scalar mean = parser.get<Scalar>("mean");
bool swapRB = parser.get<bool>("rgb");
+ CV_Assert(parser.has("width"), parser.has("height"));
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
String model = parser.get<String>("model");
int backendId = parser.get<int>("backend");
int targetId = parser.get<int>("target");
- // Parse mean values.
- Scalar mean;
- if (parser.has("mean"))
- {
- std::istringstream meanStr(parser.get<String>("mean"));
- std::vector<float> meanValues;
- float val;
- while (meanStr >> val)
- meanValues.push_back(val);
- CV_Assert(meanValues.size() == 3);
- mean = Scalar(meanValues[0], meanValues[1], meanValues[2]);
- }
-
// Open file with classes names.
if (parser.has("classes"))
{
+++ /dev/null
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-using namespace cv;
-using namespace cv::dnn;
-
-#include <fstream>
-#include <iostream>
-#include <cstdlib>
-using namespace std;
-
-static const string fcnType = "fcn8s";
-
-static vector<cv::Vec3b> readColors(const string &filename = "pascal-classes.txt")
-{
- vector<cv::Vec3b> colors;
-
- ifstream fp(filename.c_str());
- if (!fp.is_open())
- {
- cerr << "File with colors not found: " << filename << endl;
- exit(-1);
- }
-
- string line;
- while (!fp.eof())
- {
- getline(fp, line);
- if (line.length())
- {
- stringstream ss(line);
-
- string name; ss >> name;
- int temp;
- cv::Vec3b color;
- ss >> temp; color[0] = (uchar)temp;
- ss >> temp; color[1] = (uchar)temp;
- ss >> temp; color[2] = (uchar)temp;
- colors.push_back(color);
- }
- }
-
- fp.close();
- return colors;
-}
-
-static void colorizeSegmentation(const Mat &score, const vector<cv::Vec3b> &colors, cv::Mat &segm)
-{
- const int rows = score.size[2];
- const int cols = score.size[3];
- const int chns = score.size[1];
-
- cv::Mat maxCl=cv::Mat::zeros(rows, cols, CV_8UC1);
- cv::Mat maxVal(rows, cols, CV_32FC1, cv::Scalar(-FLT_MAX));
- for (int ch = 0; ch < chns; ch++)
- {
- for (int row = 0; row < rows; row++)
- {
- const float *ptrScore = score.ptr<float>(0, ch, row);
- uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
- float *ptrMaxVal = maxVal.ptr<float>(row);
- for (int col = 0; col < cols; col++)
- {
- if (ptrScore[col] > ptrMaxVal[col])
- {
- ptrMaxVal[col] = ptrScore[col];
- ptrMaxCl[col] = (uchar)ch;
- }
- }
- }
- }
-
- segm.create(rows, cols, CV_8UC3);
- for (int row = 0; row < rows; row++)
- {
- const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
- cv::Vec3b *ptrSegm = segm.ptr<cv::Vec3b>(row);
- for (int col = 0; col < cols; col++)
- {
- ptrSegm[col] = colors[ptrMaxCl[col]];
- }
- }
-
-}
-
-int main(int argc, char **argv)
-{
- String modelTxt = fcnType + "-heavy-pascal.prototxt";
- String modelBin = fcnType + "-heavy-pascal.caffemodel";
- String imageFile = (argc > 1) ? argv[1] : "rgb.jpg";
-
- vector<cv::Vec3b> colors = readColors();
-
- //! [Initialize network]
- dnn::Net net = readNetFromCaffe(modelTxt, modelBin);
- //! [Initialize network]
-
- if (net.empty())
- {
- cerr << "Can't load network by using the following files: " << endl;
- cerr << "prototxt: " << modelTxt << endl;
- cerr << "caffemodel: " << modelBin << endl;
- cerr << fcnType << "-heavy-pascal.caffemodel can be downloaded here:" << endl;
- cerr << "http://dl.caffe.berkeleyvision.org/" << fcnType << "-heavy-pascal.caffemodel" << endl;
- exit(-1);
- }
-
- //! [Prepare blob]
- Mat img = imread(imageFile);
- if (img.empty())
- {
- cerr << "Can't read image from the file: " << imageFile << endl;
- exit(-1);
- }
-
- resize(img, img, Size(500, 500), 0, 0, INTER_LINEAR_EXACT); //FCN accepts 500x500 BGR-images
- Mat inputBlob = blobFromImage(img, 1, Size(), Scalar(), false); //Convert Mat to batch of images
- //! [Prepare blob]
-
- //! [Set input blob]
- net.setInput(inputBlob, "data"); //set the network input
- //! [Set input blob]
-
- //! [Make forward pass]
- double t = (double)cv::getTickCount();
- Mat score = net.forward("score"); //compute output
- t = (double)cv::getTickCount() - t;
- printf("processing time: %.1fms\n", t*1000./getTickFrequency());
- //! [Make forward pass]
-
- Mat colorize;
- colorizeSegmentation(score, colors, colorize);
- Mat show;
- addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
- imshow("show", show);
- waitKey(0);
- return 0;
-} //main
#include <fstream>
-#include <iostream>
#include <sstream>
#include <opencv2/dnn.hpp>
confThreshold = parser.get<float>("thr");
float scale = parser.get<float>("scale");
+ Scalar mean = parser.get<Scalar>("mean");
bool swapRB = parser.get<bool>("rgb");
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
- // Parse mean values.
- Scalar mean;
- if (parser.has("mean"))
- {
- std::istringstream meanStr(parser.get<String>("mean"));
- std::vector<float> meanValues;
- float val;
- while (meanStr >> val)
- meanValues.push_back(val);
- CV_Assert(meanValues.size() == 3);
- mean = Scalar(meanValues[0], meanValues[1], meanValues[2]);
- }
-
// Open file with classes names.
if (parser.has("classes"))
{
--- /dev/null
+#include <fstream>
+#include <sstream>
+
+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+
+const char* keys =
+ "{ help h | | Print help message. }"
+ "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+ "{ model m | | Path to a binary file of model contains trained weights. "
+ "It could be a file with extensions .caffemodel (Caffe), "
+ ".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
+ "{ config c | | Path to a text file of model contains network configuration. "
+ "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
+ "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
+ "{ classes | | Optional path to a text file with names of classes. }"
+ "{ colors | | Optional path to a text file with colors for an every class. "
+ "An every color is represented with three values from 0 to 255 in BGR channels order. }"
+ "{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
+ "{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
+ "{ width | | Preprocess input image by resizing to a specific width. }"
+ "{ height | | Preprocess input image by resizing to a specific height. }"
+ "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
+ "{ backend | 0 | Choose one of computation backends: "
+ "0: default C++ backend, "
+ "1: Halide language (http://halide-lang.org/), "
+ "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
+ "{ target | 0 | Choose one of target computation devices: "
+ "0: CPU target (by default),"
+ "1: OpenCL }";
+
+using namespace cv;
+using namespace dnn;
+
+std::vector<std::string> classes;
+std::vector<Vec3b> colors;
+
+void showLegend();
+
+void colorizeSegmentation(const Mat &score, Mat &segm);
+
+int main(int argc, char** argv)
+{
+ CommandLineParser parser(argc, argv, keys);
+ parser.about("Use this script to run semantic segmentation deep learning networks using OpenCV.");
+ if (argc == 1 || parser.has("help"))
+ {
+ parser.printMessage();
+ return 0;
+ }
+
+ float scale = parser.get<float>("scale");
+ Scalar mean = parser.get<Scalar>("mean");
+ bool swapRB = parser.get<bool>("rgb");
+ CV_Assert(parser.has("width"), parser.has("height"));
+ int inpWidth = parser.get<int>("width");
+ int inpHeight = parser.get<int>("height");
+ String model = parser.get<String>("model");
+ String config = parser.get<String>("config");
+ String framework = parser.get<String>("framework");
+ int backendId = parser.get<int>("backend");
+ int targetId = parser.get<int>("target");
+
+ // Open file with classes names.
+ if (parser.has("classes"))
+ {
+ std::string file = parser.get<String>("classes");
+ std::ifstream ifs(file.c_str());
+ if (!ifs.is_open())
+ CV_Error(Error::StsError, "File " + file + " not found");
+ std::string line;
+ while (std::getline(ifs, line))
+ {
+ classes.push_back(line);
+ }
+ }
+
+ // Open file with colors.
+ if (parser.has("colors"))
+ {
+ std::string file = parser.get<String>("colors");
+ std::ifstream ifs(file.c_str());
+ if (!ifs.is_open())
+ CV_Error(Error::StsError, "File " + file + " not found");
+ std::string line;
+ while (std::getline(ifs, line))
+ {
+ std::istringstream colorStr(line.c_str());
+
+ Vec3b color;
+ for (int i = 0; i < 3 && !colorStr.eof(); ++i)
+ colorStr >> color[i];
+ colors.push_back(color);
+ }
+ }
+
+ CV_Assert(parser.has("model"));
+ //! [Read and initialize network]
+ Net net = readNet(model, config, framework);
+ net.setPreferableBackend(backendId);
+ net.setPreferableTarget(targetId);
+ //! [Read and initialize network]
+
+ // Create a window
+ static const std::string kWinName = "Deep learning semantic segmentation in OpenCV";
+ namedWindow(kWinName, WINDOW_NORMAL);
+
+ //! [Open a video file or an image file or a camera stream]
+ VideoCapture cap;
+ if (parser.has("input"))
+ cap.open(parser.get<String>("input"));
+ else
+ cap.open(0);
+ //! [Open a video file or an image file or a camera stream]
+
+ // Process frames.
+ Mat frame, blob;
+ while (waitKey(1) < 0)
+ {
+ cap >> frame;
+ if (frame.empty())
+ {
+ waitKey();
+ break;
+ }
+
+ //! [Create a 4D blob from a frame]
+ blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
+ //! [Create a 4D blob from a frame]
+
+ //! [Set input blob]
+ net.setInput(blob);
+ //! [Set input blob]
+ //! [Make forward pass]
+ Mat score = net.forward();
+ //! [Make forward pass]
+
+ Mat segm;
+ colorizeSegmentation(score, segm);
+
+ resize(segm, segm, frame.size(), 0, 0, INTER_NEAREST);
+ addWeighted(frame, 0.1, segm, 0.9, 0.0, frame);
+
+ // Put efficiency information.
+ std::vector<double> layersTimes;
+ double freq = getTickFrequency() / 1000;
+ double t = net.getPerfProfile(layersTimes) / freq;
+ std::string label = format("Inference time: %.2f ms", t);
+ putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+
+ imshow(kWinName, frame);
+ if (!classes.empty())
+ showLegend();
+ }
+ return 0;
+}
+
+void colorizeSegmentation(const Mat &score, Mat &segm)
+{
+ const int rows = score.size[2];
+ const int cols = score.size[3];
+ const int chns = score.size[1];
+
+ if (colors.empty())
+ {
+ // Generate colors.
+ colors.push_back(Vec3b());
+ for (int i = 1; i < chns; ++i)
+ {
+ Vec3b color;
+ for (int j = 0; j < 3; ++j)
+ color[j] = (colors[i - 1][j] + rand() % 256) / 2;
+ colors.push_back(color);
+ }
+ }
+ else if (chns != (int)colors.size())
+ {
+ CV_Error(Error::StsError, format("Number of output classes does not match "
+ "number of colors (%d != %d)", chns, colors.size()));
+ }
+
+ Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
+ Mat maxVal(rows, cols, CV_32FC1, score.data);
+ for (int ch = 1; ch < chns; ch++)
+ {
+ for (int row = 0; row < rows; row++)
+ {
+ const float *ptrScore = score.ptr<float>(0, ch, row);
+ uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
+ float *ptrMaxVal = maxVal.ptr<float>(row);
+ for (int col = 0; col < cols; col++)
+ {
+ if (ptrScore[col] > ptrMaxVal[col])
+ {
+ ptrMaxVal[col] = ptrScore[col];
+ ptrMaxCl[col] = (uchar)ch;
+ }
+ }
+ }
+ }
+
+ segm.create(rows, cols, CV_8UC3);
+ for (int row = 0; row < rows; row++)
+ {
+ const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
+ Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
+ for (int col = 0; col < cols; col++)
+ {
+ ptrSegm[col] = colors[ptrMaxCl[col]];
+ }
+ }
+}
+
+void showLegend()
+{
+ static const int kBlockHeight = 30;
+ static Mat legend;
+ if (legend.empty())
+ {
+ const int numClasses = (int)classes.size();
+ if ((int)colors.size() != numClasses)
+ {
+ CV_Error(Error::StsError, format("Number of output classes does not match "
+ "number of labels (%d != %d)", colors.size(), classes.size()));
+ }
+ legend.create(kBlockHeight * numClasses, 200, CV_8UC3);
+ for (int i = 0; i < numClasses; i++)
+ {
+ Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
+ block.setTo(colors[i]);
+ putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
+ }
+ namedWindow("Legend", WINDOW_NORMAL);
+ imshow("Legend", legend);
+ }
+}
--- /dev/null
+import cv2 as cv
+import argparse
+import numpy as np
+import sys
+
+backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
+
+parser = argparse.ArgumentParser(description='Use this script to run semantic segmentation deep learning networks using OpenCV.')
+parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
+parser.add_argument('--model', required=True,
+ help='Path to a binary file of model contains trained weights. '
+ 'It could be a file with extensions .caffemodel (Caffe), '
+ '.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet)')
+parser.add_argument('--config',
+ help='Path to a text file of model contains network configuration. '
+ 'It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet)')
+parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
+ help='Optional name of an origin framework of the model. '
+ 'Detect it automatically if it does not set.')
+parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
+parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
+ 'An every color is represented with three values from 0 to 255 in BGR channels order.')
+parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0],
+ help='Preprocess input image by subtracting mean values. '
+ 'Mean values should be in BGR order.')
+parser.add_argument('--scale', type=float, default=1.0,
+ help='Preprocess input image by multiplying on a scale factor.')
+parser.add_argument('--width', type=int, required=True,
+ help='Preprocess input image by resizing to a specific width.')
+parser.add_argument('--height', type=int, required=True,
+ help='Preprocess input image by resizing to a specific height.')
+parser.add_argument('--rgb', action='store_true',
+ help='Indicate that model works with RGB input images instead BGR ones.')
+parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
+ help="Choose one of computation backends: "
+ "%d: default C++ backend, "
+ "%d: Halide language (http://halide-lang.org/), "
+ "%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
+parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
+ help='Choose one of target computation devices: '
+ '%d: CPU target (by default), '
+ '%d: OpenCL' % targets)
+args = parser.parse_args()
+
+np.random.seed(324)
+
+# Load names of classes
+classes = None
+if args.classes:
+ with open(args.classes, 'rt') as f:
+ classes = f.read().rstrip('\n').split('\n')
+
+# Load colors
+colors = None
+if args.colors:
+ with open(args.colors, 'rt') as f:
+ colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
+
+legend = None
+def showLegend(classes):
+ global legend
+ if not classes is None and legend is None:
+ blockHeight = 30
+ assert(len(classes) == len(colors))
+
+ legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
+ for i in range(len(classes)):
+ block = legend[i * blockHeight:(i + 1) * blockHeight]
+ block[:,:] = colors[i]
+ cv.putText(block, classes[i], (0, blockHeight/2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
+
+ cv.namedWindow('Legend', cv.WINDOW_NORMAL)
+ cv.imshow('Legend', legend)
+ classes = None
+
+# Load a network
+net = cv.dnn.readNet(args.model, args.config, args.framework)
+net.setPreferableBackend(args.backend)
+net.setPreferableTarget(args.target)
+
+winName = 'Deep learning image classification in OpenCV'
+cv.namedWindow(winName, cv.WINDOW_NORMAL)
+
+cap = cv.VideoCapture(args.input if args.input else 0)
+legend = None
+while cv.waitKey(1) < 0:
+ hasFrame, frame = cap.read()
+ if not hasFrame:
+ cv.waitKey()
+ break
+
+ # Create a 4D blob from a frame.
+ blob = cv.dnn.blobFromImage(frame, args.scale, (args.width, args.height), args.mean, args.rgb, crop=False)
+
+ # Run a model
+ net.setInput(blob)
+ score = net.forward()
+
+ numClasses = score.shape[1]
+ height = score.shape[2]
+ width = score.shape[3]
+
+ # Draw segmentation
+ if not colors:
+ # Generate colors
+ colors = [np.array([0, 0, 0], np.uint8)]
+ for i in range(1, numClasses):
+ colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
+
+ classIds = np.argmax(score[0], axis=0)
+ segm = np.stack([colors[idx] for idx in classIds.flatten()])
+ segm = segm.reshape(height, width, 3)
+
+ segm = cv.resize(segm, (frame.shape[1], frame.shape[0]), interpolation=cv.INTER_NEAREST)
+ frame = (0.1 * frame + 0.9 * segm).astype(np.uint8)
+
+ # Put efficiency information.
+ t, _ = net.getPerfProfile()
+ label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
+ cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+ showLegend(classes)
+
+ cv.imshow(winName, frame)
+++ /dev/null
-/*
-Sample of using OpenCV dnn module with Torch ENet model.
-*/
-
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-using namespace cv;
-using namespace cv::dnn;
-
-#include <fstream>
-#include <iostream>
-#include <cstdlib>
-#include <sstream>
-using namespace std;
-
-const String keys =
- "{help h || Sample app for loading ENet Torch model. "
- "The model and class names list can be downloaded here: "
- "https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa }"
- "{model m || path to Torch .net model file (model_best.net) }"
- "{image i || path to image file }"
- "{result r || path to save output blob (optional, binary format, NCHW order) }"
- "{show s || whether to show all output channels or not}"
- "{o_blob || output blob's name. If empty, last blob's name in net is used}";
-
-static const int kNumClasses = 20;
-
-static const String classes[] = {
- "Background", "Road", "Sidewalk", "Building", "Wall", "Fence", "Pole",
- "TrafficLight", "TrafficSign", "Vegetation", "Terrain", "Sky", "Person",
- "Rider", "Car", "Truck", "Bus", "Train", "Motorcycle", "Bicycle"
-};
-
-static const Vec3b colors[] = {
- Vec3b(0, 0, 0), Vec3b(244, 126, 205), Vec3b(254, 83, 132), Vec3b(192, 200, 189),
- Vec3b(50, 56, 251), Vec3b(65, 199, 228), Vec3b(240, 178, 193), Vec3b(201, 67, 188),
- Vec3b(85, 32, 33), Vec3b(116, 25, 18), Vec3b(162, 33, 72), Vec3b(101, 150, 210),
- Vec3b(237, 19, 16), Vec3b(149, 197, 72), Vec3b(80, 182, 21), Vec3b(141, 5, 207),
- Vec3b(189, 156, 39), Vec3b(235, 170, 186), Vec3b(133, 109, 144), Vec3b(231, 160, 96)
-};
-
-static void showLegend();
-
-static void colorizeSegmentation(const Mat &score, Mat &segm);
-
-int main(int argc, char **argv)
-{
- CommandLineParser parser(argc, argv, keys);
-
- if (parser.has("help") || argc == 1)
- {
- parser.printMessage();
- return 0;
- }
-
- String modelFile = parser.get<String>("model");
- String imageFile = parser.get<String>("image");
-
- if (!parser.check())
- {
- parser.printErrors();
- return 0;
- }
-
- String resultFile = parser.get<String>("result");
-
- //! [Read model and initialize network]
- dnn::Net net = dnn::readNetFromTorch(modelFile);
-
- //! [Prepare blob]
- Mat img = imread(imageFile), input;
- if (img.empty())
- {
- std::cerr << "Can't read image from the file: " << imageFile << std::endl;
- exit(-1);
- }
-
- Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false); //Convert Mat to batch of images
- //! [Prepare blob]
-
- //! [Set input blob]
- net.setInput(inputBlob); //set the network input
- //! [Set input blob]
-
- TickMeter tm;
-
- String oBlob = net.getLayerNames().back();
- if (!parser.get<String>("o_blob").empty())
- {
- oBlob = parser.get<String>("o_blob");
- }
-
- //! [Make forward pass]
- tm.start();
- Mat result = net.forward(oBlob);
- tm.stop();
-
- if (!resultFile.empty()) {
- CV_Assert(result.isContinuous());
-
- ofstream fout(resultFile.c_str(), ios::out | ios::binary);
- fout.write((char*)result.data, result.total() * sizeof(float));
- fout.close();
- }
-
- std::cout << "Output blob: " << result.size[0] << " x " << result.size[1] << " x " << result.size[2] << " x " << result.size[3] << "\n";
- std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
-
- if (parser.has("show"))
- {
- Mat segm, show;
- colorizeSegmentation(result, segm);
- showLegend();
-
- cv::resize(segm, segm, img.size(), 0, 0, cv::INTER_NEAREST);
- addWeighted(img, 0.1, segm, 0.9, 0.0, show);
-
- imshow("Result", show);
- waitKey();
- }
- return 0;
-} //main
-
-static void showLegend()
-{
- static const int kBlockHeight = 30;
-
- cv::Mat legend(kBlockHeight * kNumClasses, 200, CV_8UC3);
- for(int i = 0; i < kNumClasses; i++)
- {
- cv::Mat block = legend.rowRange(i * kBlockHeight, (i + 1) * kBlockHeight);
- block.setTo(colors[i]);
- putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
- }
- imshow("Legend", legend);
-}
-
-static void colorizeSegmentation(const Mat &score, Mat &segm)
-{
- const int rows = score.size[2];
- const int cols = score.size[3];
- const int chns = score.size[1];
-
- Mat maxCl = Mat::zeros(rows, cols, CV_8UC1);
- Mat maxVal(rows, cols, CV_32FC1, score.data);
- for (int ch = 1; ch < chns; ch++)
- {
- for (int row = 0; row < rows; row++)
- {
- const float *ptrScore = score.ptr<float>(0, ch, row);
- uint8_t *ptrMaxCl = maxCl.ptr<uint8_t>(row);
- float *ptrMaxVal = maxVal.ptr<float>(row);
- for (int col = 0; col < cols; col++)
- {
- if (ptrScore[col] > ptrMaxVal[col])
- {
- ptrMaxVal[col] = ptrScore[col];
- ptrMaxCl[col] = (uchar)ch;
- }
- }
- }
- }
-
- segm.create(rows, cols, CV_8UC3);
- for (int row = 0; row < rows; row++)
- {
- const uchar *ptrMaxCl = maxCl.ptr<uchar>(row);
- Vec3b *ptrSegm = segm.ptr<Vec3b>(row);
- for (int col = 0; col < cols; col++)
- {
- ptrSegm[col] = colors[ptrMaxCl[col]];
- }
- }
-}
projects / platform / upstream / opencv.git / commitdiff