std::string keys =
"{ help h | | Print help message. }"
"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | models.yml | An optional path to file with preprocessing parameters }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ initial_width | 0 | Preprocess input image by initial resizing to a specific width.}"
"{ initial_height | 0 | Preprocess input image by initial resizing to a specific height.}"
"{ std | 0.0 0.0 0.0 | Preprocess input image by dividing on a standard deviation.}"
"{ crop | false | Preprocess input image by center cropping.}"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ needSoftmax | false | Use Softmax to post-process the output of the net.}"
"{ classes | | Optional path to a text file with names of classes. }"
"{ backend | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA, "
"6: WebNN }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
;
using namespace
dnn;
std::vector<std::string> classes;
int
main
(
int
argc,
char
** argv)
const
std::string modelName = parser.
get
<
String
>(
"@alias"
);
const
std::string zooFile = parser.
get
<
String
>(
"zoo"
);
keys += genPreprocArguments(modelName, zooFile);
parser.
about
(
"Use this script to run classification deep learning networks using OpenCV."
);
if
(argc == 1 || parser.
has
(
"help"
))
return
0;
int
rszWidth = parser.
get
<
int
>(
"initial_width"
);
int
rszHeight = parser.
get
<
int
>(
"initial_height"
);
float
scale = parser.
get
<
float
>(
"scale"
);
bool
swapRB = parser.
get
<
bool
>(
"rgb"
);
bool
crop = parser.
get
<
bool
>(
"crop"
);
int
inpWidth = parser.
get
<
int
>(
"width"
);
int
inpHeight = parser.
get
<
int
>(
"height"
);
int
backendId = parser.
get
<
int
>(
"backend"
);
int
targetId = parser.
get
<
int
>(
"target"
);
bool
needSoftmax = parser.
get
<
bool
>(
"needSoftmax"
);
std::cout<<
"mean: "
<<mean<<std::endl;
std::cout<<
"std: "
<<
std
<<std::endl;
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);
return
1;
Net net = readNet(model, config, framework);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
static
const
std::string kWinName =
"Deep learning image classification in OpenCV"
;
namedWindow(kWinName, WINDOW_NORMAL);
if
(parser.
has
(
"input"
))
while
(waitKey(1) < 0)
cap >> frame;
if
(frame.empty())
waitKey();
break
;
if
(rszWidth != 0 && rszHeight != 0)
resize(frame, frame,
Size
(rszWidth, rszHeight));
blobFromImage(frame, blob, scale,
Size
(inpWidth, inpHeight), mean, swapRB, crop);
if
(
std
.val[0] != 0.0 &&
std
.val[1] != 0.0 &&
std
.val[2] != 0.0)
divide(blob,
std
, blob);
net.setInput(blob);
int
classId;
double
confidence;
Mat
prob = net.forward();
double
t1;
prob = net.forward();
for
(
int
i = 0; i < 200; i++) {
prob = net.forward();
minMaxLoc(prob.
reshape
(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.
x
;
if
(needSoftmax ==
true
)
float
maxProb = 0.0;
float
sum = 0.0;
maxProb = *std::max_element(prob.
begin
<
float
>(), prob.
end
<
float
>());
cv::exp(prob-maxProb, softmaxProb);
sum = (float)
cv::sum
(softmaxProb)[0];
softmaxProb /= sum;
minMaxLoc(softmaxProb.
reshape
(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.
x
;
std::string label = format(
"Inference time of 1 round: %.2f ms"
, t1);
std::string label2 = format(
"Average time of 200 rounds: %.2f ms"
, timeRecorder.
getTimeMilli
()/200);
putText(frame, label,
Point
(0, 15), FONT_HERSHEY_SIMPLEX, 0.5,
Scalar
(0, 255, 0));
putText(frame, label2,
Point
(0, 35), FONT_HERSHEY_SIMPLEX, 0.5,
Scalar
(0, 255, 0));
label = format(
"%s: %.4f"
, (classes.empty() ? format(
"Class #%d"
, classId).c_str() :
classes[classId].c_str()),
confidence);
putText(frame, label,
Point
(0, 55), FONT_HERSHEY_SIMPLEX, 0.5,
Scalar
(0, 255, 0));
imshow(kWinName, frame);
return
0;
Designed for command line parsing.
Definition
utility.hpp:890
T get(const String &name, bool space_delete=true) const
Access arguments by name.
Definition
utility.hpp:956
void about(const String &message)
Set the about message.
void printErrors() const
Print list of errors occurred.
void printMessage() const
Print help message.
bool has(const String &name) const
Check if field was provided in the command line.
bool check() const
Check for parsing errors.
n-dimensional dense array class
Definition
mat.hpp:828
Mat reshape(int cn, int rows=0) const
Changes the shape and/or the number of channels of a 2D matrix without copying the data.
MatIterator_< _Tp > end()
Returns the matrix iterator and sets it to the after-last matrix element.
MatIterator_< _Tp > begin()
Returns the matrix iterator and sets it to the first matrix element.
_Tp x
x coordinate of the point
Definition
types.hpp:201
Template class for specifying the size of an image or rectangle.
Definition
types.hpp:335
a Class to measure passing time.
Definition
utility.hpp:326
void start()
starts counting ticks.
Definition
utility.hpp:335
void stop()
stops counting ticks.
Definition
utility.hpp:341
void reset()
resets internal values.
Definition
utility.hpp:430
double getTimeMilli() const
returns passed time in milliseconds.
Definition
utility.hpp:365
Class for video capturing from video files, image sequences or cameras.
Definition
videoio.hpp:735
virtual bool open(const String &filename, int apiPreference=CAP_ANY)
Opens a video file or a capturing device or an IP video stream for video capturing.
Scalar sum(InputArray src)
Calculates the sum of array elements.
std::string String
Definition
cvstd.hpp:151
#define CV_Error(code, msg)
Call the error handler.
Definition
base.hpp:335
#define CV_Assert(expr)
Checks a condition at runtime and throws exception if it fails.
Definition
base.hpp:359
int main(int argc, char *argv[])
Definition
highgui_qt.cpp:3
