mirror of
https://github.com/opencv/opencv.git
synced 2025-06-08 01:53:19 +08:00
Unite deep learning object detection samples
This commit is contained in:
Dmitry Kurtaev
parent
667f5b655a
commit
e8d94ea87c
@ -18,40 +18,26 @@ VIDEO DEMO:
|
|||||||
Source Code
|
Source Code
|
||||||
-----------
|
-----------
|
||||||
|
|
||||||
The latest version of sample source code can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/yolo_object_detection.cpp).
|
Use a universal sample for object detection models written
|
||||||
|
[in C++](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.cpp) and
|
||||||
|
[in Python](https://github.com/opencv/opencv/blob/master/samples/dnn/object_detection.py) languages
|
||||||
|
|
||||||
@include dnn/yolo_object_detection.cpp
|
Usage examples
|
||||||
|
--------------
|
||||||
How to compile in command line with pkg-config
|
|
||||||
----------------------------------------------
|
|
||||||
|
|
||||||
@code{.bash}
|
|
||||||
|
|
||||||
# g++ `pkg-config --cflags opencv` `pkg-config --libs opencv` yolo_object_detection.cpp -o yolo_object_detection
|
|
||||||
|
|
||||||
@endcode
|
|
||||||
|
|
||||||
Execute in webcam:
|
Execute in webcam:
|
||||||
|
|
||||||
@code{.bash}
|
@code{.bash}
|
||||||
|
|
||||||
$ yolo_object_detection -camera_device=0 -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
|
$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392
|
||||||
|
|
||||||
@endcode
|
@endcode
|
||||||
|
|
||||||
Execute with image:
|
Execute with image or video file:
|
||||||
|
|
||||||
@code{.bash}
|
@code{.bash}
|
||||||
|
|
||||||
$ yolo_object_detection -source=[PATH-IMAGE] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
|
$ example_dnn_object_detection --config=[PATH-TO-DARKNET]/cfg/yolo.cfg --model=[PATH-TO-DARKNET]/yolo.weights --classes=object_detection_classes_pascal_voc.txt --width=416 --height=416 --scale=0.00392 --input[PATH-TO-IMAGE-OR-VIDEO-FILE]
|
||||||
|
|
||||||
@endcode
|
|
||||||
|
|
||||||
Execute in video file:
|
|
||||||
|
|
||||||
@code{.bash}
|
|
||||||
|
|
||||||
$ yolo_object_detection -source=[PATH-TO-VIDEO] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names
|
|
||||||
|
|
||||||
@endcode
|
@endcode
|
||||||
|
|
||||||
|
|||||||
@ -222,7 +222,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
|
|||||||
/** @brief Returns index of output blob in output array.
|
/** @brief Returns index of output blob in output array.
|
||||||
* @see inputNameToIndex()
|
* @see inputNameToIndex()
|
||||||
*/
|
*/
|
||||||
virtual int outputNameToIndex(String outputName);
|
CV_WRAP virtual int outputNameToIndex(String outputName);
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* @brief Ask layer if it support specific backend for doing computations.
|
* @brief Ask layer if it support specific backend for doing computations.
|
||||||
|
|||||||
20
samples/dnn/README.md
Normal file
20
samples/dnn/README.md
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
# OpenCV deep learning module samples
|
||||||
|
|
||||||
|
## Model Zoo
|
||||||
|
|
||||||
|
### Object detection
|
||||||
|
|
||||||
|
| Model | Scale | Size WxH| Mean subtraction | Channels order |
|
||||||
|
|---------------|-------|-----------|--------------------|-------|
|
||||||
|
| [MobileNet-SSD, Caffe](https://github.com/chuanqi305/MobileNet-SSD/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | BGR |
|
||||||
|
| [OpenCV face detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector) | `1.0` | `300x300` | `104 177 123` | BGR |
|
||||||
|
| [SSDs from TensorFlow](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB |
|
||||||
|
| [YOLO](https://pjreddie.com/darknet/yolo/) | `0.00392 (1/255)` | `416x416` | `0 0 0` | RGB |
|
||||||
|
| [VGG16-SSD](https://github.com/weiliu89/caffe/tree/ssd) | `1.0` | `300x300` | `104 117 123` | BGR |
|
||||||
|
| [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801, 115.9465, 122.7717` | BGR |
|
||||||
|
| [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | 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)
|
||||||
@ -1,93 +0,0 @@
|
|||||||
#include <opencv2/dnn.hpp>
|
|
||||||
#include <opencv2/dnn/all_layers.hpp>
|
|
||||||
#include <opencv2/imgproc.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace dnn;
|
|
||||||
|
|
||||||
const char* keys =
|
|
||||||
"{ help h | | print help message }"
|
|
||||||
"{ proto p | | path to .prototxt }"
|
|
||||||
"{ model m | | path to .caffemodel }"
|
|
||||||
"{ image i | | path to input image }"
|
|
||||||
"{ conf c | 0.8 | minimal confidence }";
|
|
||||||
|
|
||||||
const char* classNames[] = {
|
|
||||||
"__background__",
|
|
||||||
"aeroplane", "bicycle", "bird", "boat",
|
|
||||||
"bottle", "bus", "car", "cat", "chair",
|
|
||||||
"cow", "diningtable", "dog", "horse",
|
|
||||||
"motorbike", "person", "pottedplant",
|
|
||||||
"sheep", "sofa", "train", "tvmonitor"
|
|
||||||
};
|
|
||||||
|
|
||||||
static const int kInpWidth = 800;
|
|
||||||
static const int kInpHeight = 600;
|
|
||||||
|
|
||||||
int main(int argc, char** argv)
|
|
||||||
{
|
|
||||||
// Parse command line arguments.
|
|
||||||
CommandLineParser parser(argc, argv, keys);
|
|
||||||
parser.about("This sample is used to run Faster-RCNN and R-FCN object detection "
|
|
||||||
"models with OpenCV. You can get required models from "
|
|
||||||
"https://github.com/rbgirshick/py-faster-rcnn (Faster-RCNN) and from "
|
|
||||||
"https://github.com/YuwenXiong/py-R-FCN (R-FCN). Corresponding .prototxt "
|
|
||||||
"files may be found at https://github.com/opencv/opencv_extra/tree/master/testdata/dnn.");
|
|
||||||
if (argc == 1 || parser.has("help"))
|
|
||||||
{
|
|
||||||
parser.printMessage();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
String protoPath = parser.get<String>("proto");
|
|
||||||
String modelPath = parser.get<String>("model");
|
|
||||||
String imagePath = parser.get<String>("image");
|
|
||||||
float confThreshold = parser.get<float>("conf");
|
|
||||||
CV_Assert(!protoPath.empty(), !modelPath.empty(), !imagePath.empty());
|
|
||||||
|
|
||||||
// Load a model.
|
|
||||||
Net net = readNetFromCaffe(protoPath, modelPath);
|
|
||||||
|
|
||||||
Mat img = imread(imagePath);
|
|
||||||
resize(img, img, Size(kInpWidth, kInpHeight));
|
|
||||||
Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
|
|
||||||
Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
|
|
||||||
|
|
||||||
net.setInput(blob, "data");
|
|
||||||
net.setInput(imInfo, "im_info");
|
|
||||||
|
|
||||||
// Draw detections.
|
|
||||||
Mat detections = net.forward();
|
|
||||||
const float* data = (float*)detections.data;
|
|
||||||
for (size_t i = 0; i < detections.total(); i += 7)
|
|
||||||
{
|
|
||||||
// An every detection is a vector [id, classId, confidence, left, top, right, bottom]
|
|
||||||
float confidence = data[i + 2];
|
|
||||||
if (confidence > confThreshold)
|
|
||||||
{
|
|
||||||
int classId = (int)data[i + 1];
|
|
||||||
int left = max(0, min((int)data[i + 3], img.cols - 1));
|
|
||||||
int top = max(0, min((int)data[i + 4], img.rows - 1));
|
|
||||||
int right = max(0, min((int)data[i + 5], img.cols - 1));
|
|
||||||
int bottom = max(0, min((int)data[i + 6], img.rows - 1));
|
|
||||||
|
|
||||||
// Draw a bounding box.
|
|
||||||
rectangle(img, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
|
|
||||||
|
|
||||||
// Put a label with a class name and confidence.
|
|
||||||
String label = cv::format("%s, %.3f", classNames[classId], confidence);
|
|
||||||
int baseLine;
|
|
||||||
Size labelSize = cv::getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
||||||
|
|
||||||
top = max(top, labelSize.height);
|
|
||||||
rectangle(img, Point(left, top - labelSize.height),
|
|
||||||
Point(left + labelSize.width, top + baseLine),
|
|
||||||
Scalar(255, 255, 255), FILLED);
|
|
||||||
putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
imshow("frame", img);
|
|
||||||
waitKey();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
@ -1,132 +0,0 @@
|
|||||||
# This script is used to demonstrate MobileNet-SSD network using OpenCV deep learning module.
|
|
||||||
#
|
|
||||||
# It works with model taken from https://github.com/chuanqi305/MobileNet-SSD/ that
|
|
||||||
# was trained in Caffe-SSD framework, https://github.com/weiliu89/caffe/tree/ssd.
|
|
||||||
# Model detects objects from 20 classes.
|
|
||||||
#
|
|
||||||
# Also TensorFlow model from TensorFlow object detection model zoo may be used to
|
|
||||||
# detect objects from 90 classes:
|
|
||||||
# http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz
|
|
||||||
# Text graph definition must be taken from opencv_extra:
|
|
||||||
# https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/ssd_mobilenet_v1_coco.pbtxt
|
|
||||||
import numpy as np
|
|
||||||
import argparse
|
|
||||||
|
|
||||||
try:
|
|
||||||
import cv2 as cv
|
|
||||||
except ImportError:
|
|
||||||
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
|
|
||||||
'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
|
|
||||||
|
|
||||||
inWidth = 300
|
|
||||||
inHeight = 300
|
|
||||||
WHRatio = inWidth / float(inHeight)
|
|
||||||
inScaleFactor = 0.007843
|
|
||||||
meanVal = 127.5
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
parser = argparse.ArgumentParser(
|
|
||||||
description='Script to run MobileNet-SSD object detection network '
|
|
||||||
'trained either in Caffe or TensorFlow frameworks.')
|
|
||||||
parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used")
|
|
||||||
parser.add_argument("--prototxt", default="MobileNetSSD_deploy.prototxt",
|
|
||||||
help='Path to text network file: '
|
|
||||||
'MobileNetSSD_deploy.prototxt for Caffe model or '
|
|
||||||
'ssd_mobilenet_v1_coco.pbtxt from opencv_extra for TensorFlow model')
|
|
||||||
parser.add_argument("--weights", default="MobileNetSSD_deploy.caffemodel",
|
|
||||||
help='Path to weights: '
|
|
||||||
'MobileNetSSD_deploy.caffemodel for Caffe model or '
|
|
||||||
'frozen_inference_graph.pb from TensorFlow.')
|
|
||||||
parser.add_argument("--num_classes", default=20, type=int,
|
|
||||||
help="Number of classes. It's 20 for Caffe model from "
|
|
||||||
"https://github.com/chuanqi305/MobileNet-SSD/ and 90 for "
|
|
||||||
"TensorFlow model from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz")
|
|
||||||
parser.add_argument("--thr", default=0.2, type=float, help="confidence threshold to filter out weak detections")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
if args.num_classes == 20:
|
|
||||||
net = cv.dnn.readNetFromCaffe(args.prototxt, args.weights)
|
|
||||||
swapRB = False
|
|
||||||
classNames = { 0: 'background',
|
|
||||||
1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat',
|
|
||||||
5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair',
|
|
||||||
10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse',
|
|
||||||
14: 'motorbike', 15: 'person', 16: 'pottedplant',
|
|
||||||
17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor' }
|
|
||||||
else:
|
|
||||||
assert(args.num_classes == 90)
|
|
||||||
net = cv.dnn.readNetFromTensorflow(args.weights, args.prototxt)
|
|
||||||
swapRB = True
|
|
||||||
classNames = { 0: 'background',
|
|
||||||
1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus',
|
|
||||||
7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant',
|
|
||||||
13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat',
|
|
||||||
18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear',
|
|
||||||
24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag',
|
|
||||||
32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard',
|
|
||||||
37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove',
|
|
||||||
41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle',
|
|
||||||
46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon',
|
|
||||||
51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange',
|
|
||||||
56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut',
|
|
||||||
61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed',
|
|
||||||
67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse',
|
|
||||||
75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven',
|
|
||||||
80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock',
|
|
||||||
86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush' }
|
|
||||||
|
|
||||||
if args.video:
|
|
||||||
cap = cv.VideoCapture(args.video)
|
|
||||||
else:
|
|
||||||
cap = cv.VideoCapture(0)
|
|
||||||
|
|
||||||
while True:
|
|
||||||
# Capture frame-by-frame
|
|
||||||
ret, frame = cap.read()
|
|
||||||
blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), (meanVal, meanVal, meanVal), swapRB)
|
|
||||||
net.setInput(blob)
|
|
||||||
detections = net.forward()
|
|
||||||
|
|
||||||
cols = frame.shape[1]
|
|
||||||
rows = frame.shape[0]
|
|
||||||
|
|
||||||
if cols / float(rows) > WHRatio:
|
|
||||||
cropSize = (int(rows * WHRatio), rows)
|
|
||||||
else:
|
|
||||||
cropSize = (cols, int(cols / WHRatio))
|
|
||||||
|
|
||||||
y1 = int((rows - cropSize[1]) / 2)
|
|
||||||
y2 = y1 + cropSize[1]
|
|
||||||
x1 = int((cols - cropSize[0]) / 2)
|
|
||||||
x2 = x1 + cropSize[0]
|
|
||||||
frame = frame[y1:y2, x1:x2]
|
|
||||||
|
|
||||||
cols = frame.shape[1]
|
|
||||||
rows = frame.shape[0]
|
|
||||||
|
|
||||||
for i in range(detections.shape[2]):
|
|
||||||
confidence = detections[0, 0, i, 2]
|
|
||||||
if confidence > args.thr:
|
|
||||||
class_id = int(detections[0, 0, i, 1])
|
|
||||||
|
|
||||||
xLeftBottom = int(detections[0, 0, i, 3] * cols)
|
|
||||||
yLeftBottom = int(detections[0, 0, i, 4] * rows)
|
|
||||||
xRightTop = int(detections[0, 0, i, 5] * cols)
|
|
||||||
yRightTop = int(detections[0, 0, i, 6] * rows)
|
|
||||||
|
|
||||||
cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
|
|
||||||
(0, 255, 0))
|
|
||||||
if class_id in classNames:
|
|
||||||
label = classNames[class_id] + ": " + str(confidence)
|
|
||||||
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
|
||||||
|
|
||||||
yLeftBottom = max(yLeftBottom, labelSize[1])
|
|
||||||
cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
|
|
||||||
(xLeftBottom + labelSize[0], yLeftBottom + baseLine),
|
|
||||||
(255, 255, 255), cv.FILLED)
|
|
||||||
cv.putText(frame, label, (xLeftBottom, yLeftBottom),
|
|
||||||
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
|
|
||||||
|
|
||||||
cv.imshow("detections", frame)
|
|
||||||
if cv.waitKey(1) >= 0:
|
|
||||||
break
|
|
||||||
255
samples/dnn/object_detection.cpp
Normal file
255
samples/dnn/object_detection.cpp
Normal file
@ -0,0 +1,255 @@
|
|||||||
|
#include <opencv2/opencv.hpp>
|
||||||
|
#include <fstream>
|
||||||
|
#include <iostream>
|
||||||
|
#include <sstream>
|
||||||
|
|
||||||
|
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 to label detected objects. }"
|
||||||
|
"{ 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. }"
|
||||||
|
"{ thr | .5 | Confidence threshold. }"
|
||||||
|
"{ opencl | | Enable OpenCL }";
|
||||||
|
|
||||||
|
using namespace cv;
|
||||||
|
using namespace dnn;
|
||||||
|
|
||||||
|
float confThreshold;
|
||||||
|
std::vector<std::string> classes;
|
||||||
|
|
||||||
|
void loadClasses(const std::string& file);
|
||||||
|
|
||||||
|
Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = "");
|
||||||
|
|
||||||
|
void postprocess(Mat& frame, const Mat& out, Net& net);
|
||||||
|
|
||||||
|
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
|
||||||
|
|
||||||
|
void callback(int pos, void* userdata);
|
||||||
|
|
||||||
|
int main(int argc, char** argv)
|
||||||
|
{
|
||||||
|
CommandLineParser parser(argc, argv, keys);
|
||||||
|
parser.about("Use this script to run object detection deep learning networks using OpenCV.");
|
||||||
|
if (argc == 1 || parser.has("help"))
|
||||||
|
{
|
||||||
|
parser.printMessage();
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
confThreshold = parser.get<float>("thr");
|
||||||
|
float scale = parser.get<float>("scale");
|
||||||
|
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"))
|
||||||
|
{
|
||||||
|
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 (ifs >> line)
|
||||||
|
{
|
||||||
|
classes.push_back(line);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Load a model.
|
||||||
|
CV_Assert(parser.has("model"));
|
||||||
|
Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
|
||||||
|
|
||||||
|
if (parser.get<bool>("opencl"))
|
||||||
|
{
|
||||||
|
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
||||||
|
}
|
||||||
|
|
||||||
|
// Create a window
|
||||||
|
static const std::string kWinName = "Deep learning object detection in OpenCV";
|
||||||
|
namedWindow(kWinName, WINDOW_NORMAL);
|
||||||
|
int initialConf = confThreshold * 100;
|
||||||
|
createTrackbar("Confidence threshold", kWinName, &initialConf, 99, callback);
|
||||||
|
|
||||||
|
// 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);
|
||||||
|
|
||||||
|
// Process frames.
|
||||||
|
Mat frame, blob;
|
||||||
|
while (waitKey(1) < 0)
|
||||||
|
{
|
||||||
|
cap >> frame;
|
||||||
|
if (frame.empty())
|
||||||
|
{
|
||||||
|
waitKey();
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
|
||||||
|
// Create a 4D blob from a frame.
|
||||||
|
Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
|
||||||
|
inpHeight > 0 ? inpHeight : frame.rows);
|
||||||
|
blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);
|
||||||
|
|
||||||
|
// Run a model.
|
||||||
|
net.setInput(blob);
|
||||||
|
if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
|
||||||
|
{
|
||||||
|
resize(frame, frame, inpSize);
|
||||||
|
Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
|
||||||
|
net.setInput(imInfo, "im_info");
|
||||||
|
}
|
||||||
|
Mat out = net.forward();
|
||||||
|
|
||||||
|
postprocess(frame, out, net);
|
||||||
|
|
||||||
|
// Put efficiency information.
|
||||||
|
std::vector<double> layersTimes;
|
||||||
|
double t = net.getPerfProfile(layersTimes);
|
||||||
|
std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency());
|
||||||
|
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
|
||||||
|
|
||||||
|
imshow(kWinName, frame);
|
||||||
|
}
|
||||||
|
return 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
void postprocess(Mat& frame, const Mat& out, Net& net)
|
||||||
|
{
|
||||||
|
static std::vector<int> outLayers = net.getUnconnectedOutLayers();
|
||||||
|
static std::string outLayerType = net.getLayer(outLayers[0])->type;
|
||||||
|
|
||||||
|
float* data = (float*)out.data;
|
||||||
|
if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
|
||||||
|
{
|
||||||
|
// Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||||
|
// detections and an every detection is a vector of values
|
||||||
|
// [batchId, classId, confidence, left, top, right, bottom]
|
||||||
|
for (size_t i = 0; i < out.total(); i += 7)
|
||||||
|
{
|
||||||
|
float confidence = data[i + 2];
|
||||||
|
if (confidence > confThreshold)
|
||||||
|
{
|
||||||
|
int left = data[i + 3];
|
||||||
|
int top = data[i + 4];
|
||||||
|
int right = data[i + 5];
|
||||||
|
int bottom = data[i + 6];
|
||||||
|
int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
|
||||||
|
drawPred(classId, confidence, left, top, right, bottom, frame);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else if (outLayerType == "DetectionOutput")
|
||||||
|
{
|
||||||
|
// Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||||
|
// detections and an every detection is a vector of values
|
||||||
|
// [batchId, classId, confidence, left, top, right, bottom]
|
||||||
|
for (size_t i = 0; i < out.total(); i += 7)
|
||||||
|
{
|
||||||
|
float confidence = data[i + 2];
|
||||||
|
if (confidence > confThreshold)
|
||||||
|
{
|
||||||
|
int left = (int)(data[i + 3] * frame.cols);
|
||||||
|
int top = (int)(data[i + 4] * frame.rows);
|
||||||
|
int right = (int)(data[i + 5] * frame.cols);
|
||||||
|
int bottom = (int)(data[i + 6] * frame.rows);
|
||||||
|
int classId = (int)(data[i + 1]) - 1; // Skip 0th background class id.
|
||||||
|
drawPred(classId, confidence, left, top, right, bottom, frame);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else if (outLayerType == "Region")
|
||||||
|
{
|
||||||
|
// Network produces output blob with a shape NxC where N is a number of
|
||||||
|
// detected objects and C is a number of classes + 4 where the first 4
|
||||||
|
// numbers are [center_x, center_y, width, height]
|
||||||
|
for (int i = 0; i < out.rows; ++i, data += out.cols)
|
||||||
|
{
|
||||||
|
Mat confidences = out.row(i).colRange(5, out.cols);
|
||||||
|
Point classIdPoint;
|
||||||
|
double confidence;
|
||||||
|
minMaxLoc(confidences, 0, &confidence, 0, &classIdPoint);
|
||||||
|
if (confidence > confThreshold)
|
||||||
|
{
|
||||||
|
int classId = classIdPoint.x;
|
||||||
|
int centerX = (int)(data[0] * frame.cols);
|
||||||
|
int centerY = (int)(data[1] * frame.rows);
|
||||||
|
int width = (int)(data[2] * frame.cols);
|
||||||
|
int height = (int)(data[3] * frame.rows);
|
||||||
|
int left = centerX - width / 2;
|
||||||
|
int top = centerY - height / 2;
|
||||||
|
drawPred(classId, confidence, left, top, left + width, top + height, frame);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
else
|
||||||
|
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
|
||||||
|
}
|
||||||
|
|
||||||
|
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
|
||||||
|
{
|
||||||
|
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
|
||||||
|
|
||||||
|
std::string label = format("%.2f", conf);
|
||||||
|
if (!classes.empty())
|
||||||
|
{
|
||||||
|
CV_Assert(classId < (int)classes.size());
|
||||||
|
label = classes[classId] + ": " + label;
|
||||||
|
}
|
||||||
|
|
||||||
|
int baseLine;
|
||||||
|
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
||||||
|
|
||||||
|
top = max(top, labelSize.height);
|
||||||
|
rectangle(frame, Point(left, top - labelSize.height),
|
||||||
|
Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
|
||||||
|
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
|
||||||
|
}
|
||||||
|
|
||||||
|
void callback(int pos, void*)
|
||||||
|
{
|
||||||
|
confThreshold = pos * 0.01;
|
||||||
|
}
|
||||||
|
|
||||||
|
Net readNet(const std::string& model, const std::string& config, const std::string& framework)
|
||||||
|
{
|
||||||
|
std::string modelExt = model.substr(model.find('.'));
|
||||||
|
if (framework == "caffe" || modelExt == ".caffemodel")
|
||||||
|
return readNetFromCaffe(config, model);
|
||||||
|
else if (framework == "tensorflow" || modelExt == ".pb")
|
||||||
|
return readNetFromTensorflow(model, config);
|
||||||
|
else if (framework == "torch" || modelExt == ".t7" || modelExt == ".net")
|
||||||
|
return readNetFromTorch(model);
|
||||||
|
else if (framework == "darknet" || modelExt == ".weights")
|
||||||
|
return readNetFromDarknet(config, model);
|
||||||
|
else
|
||||||
|
CV_Error(Error::StsError, "Cannot determine an origin framework of model from file " + model);
|
||||||
|
return Net();
|
||||||
|
}
|
||||||
161
samples/dnn/object_detection.py
Normal file
161
samples/dnn/object_detection.py
Normal file
@ -0,0 +1,161 @@
|
|||||||
|
import cv2 as cv
|
||||||
|
import argparse
|
||||||
|
import sys
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description='Use this script to run object detection 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 to label detected objects.')
|
||||||
|
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,
|
||||||
|
help='Preprocess input image by resizing to a specific width.')
|
||||||
|
parser.add_argument('--height', type=int,
|
||||||
|
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('--thr', type=float, default=0.5, help='Confidence threshold')
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Load names of classes
|
||||||
|
classes = None
|
||||||
|
if args.classes:
|
||||||
|
with open(args.classes, 'rt') as f:
|
||||||
|
classes = f.read().rstrip('\n').split('\n')
|
||||||
|
|
||||||
|
# Load a network
|
||||||
|
modelExt = args.model[args.model.find('.'):]
|
||||||
|
if args.framework == 'caffe' or modelExt == '.caffemodel':
|
||||||
|
net = cv.dnn.readNetFromCaffe(args.config, args.model)
|
||||||
|
elif args.framework == 'tensorflow' or modelExt == '.pb':
|
||||||
|
net = cv.dnn.readNetFromTensorflow(args.model, args.config)
|
||||||
|
elif args.framework == 'torch' or modelExt in ['.t7', '.net']:
|
||||||
|
net = cv.dnn.readNetFromTorch(args.model)
|
||||||
|
elif args.framework == 'darknet' or modelExt == '.weights':
|
||||||
|
net = cv.dnn.readNetFromDarknet(args.config, args.model)
|
||||||
|
else:
|
||||||
|
print('Cannot determine an origin framework of model from file %s' % args.model)
|
||||||
|
sys.exit(0)
|
||||||
|
|
||||||
|
confThreshold = args.thr
|
||||||
|
|
||||||
|
def postprocess(frame, out):
|
||||||
|
frameHeight = frame.shape[0]
|
||||||
|
frameWidth = frame.shape[1]
|
||||||
|
|
||||||
|
def drawPred(classId, conf, left, top, right, bottom):
|
||||||
|
# Draw a bounding box.
|
||||||
|
cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
|
||||||
|
|
||||||
|
label = '%.2f' % confidence
|
||||||
|
|
||||||
|
# Print a label of class.
|
||||||
|
if classes:
|
||||||
|
assert(classId < len(classes))
|
||||||
|
label = '%s: %s' % (classes[classId], label)
|
||||||
|
|
||||||
|
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
||||||
|
top = max(top, labelSize[1])
|
||||||
|
cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
|
||||||
|
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
|
||||||
|
|
||||||
|
layerNames = net.getLayerNames()
|
||||||
|
lastLayerId = net.getLayerId(layerNames[-1])
|
||||||
|
lastLayer = net.getLayer(lastLayerId)
|
||||||
|
|
||||||
|
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
|
||||||
|
# Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||||
|
# detections and an every detection is a vector of values
|
||||||
|
# [batchId, classId, confidence, left, top, right, bottom]
|
||||||
|
for detection in out[0, 0]:
|
||||||
|
confidence = detection[2]
|
||||||
|
if confidence > confThreshold:
|
||||||
|
left = int(detection[3])
|
||||||
|
top = int(detection[4])
|
||||||
|
right = int(detection[5])
|
||||||
|
bottom = int(detection[6])
|
||||||
|
classId = int(detection[1]) - 1 # Skip background label
|
||||||
|
drawPred(classId, confidence, left, top, right, bottom)
|
||||||
|
elif lastLayer.type == 'DetectionOutput':
|
||||||
|
# Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||||
|
# detections and an every detection is a vector of values
|
||||||
|
# [batchId, classId, confidence, left, top, right, bottom]
|
||||||
|
for detection in out[0, 0]:
|
||||||
|
confidence = detection[2]
|
||||||
|
if confidence > confThreshold:
|
||||||
|
left = int(detection[3] * frameWidth)
|
||||||
|
top = int(detection[4] * frameHeight)
|
||||||
|
right = int(detection[5] * frameWidth)
|
||||||
|
bottom = int(detection[6] * frameHeight)
|
||||||
|
classId = int(detection[1]) - 1 # Skip background label
|
||||||
|
drawPred(classId, confidence, left, top, right, bottom)
|
||||||
|
elif lastLayer.type == 'Region':
|
||||||
|
# Network produces output blob with a shape NxC where N is a number of
|
||||||
|
# detected objects and C is a number of classes + 4 where the first 4
|
||||||
|
# numbers are [center_x, center_y, width, height]
|
||||||
|
for detection in out:
|
||||||
|
confidences = detection[5:]
|
||||||
|
classId = np.argmax(confidences)
|
||||||
|
confidence = confidences[classId]
|
||||||
|
if confidence > confThreshold:
|
||||||
|
center_x = int(detection[0] * frameWidth)
|
||||||
|
center_y = int(detection[1] * frameHeight)
|
||||||
|
width = int(detection[2] * frameWidth)
|
||||||
|
height = int(detection[3] * frameHeight)
|
||||||
|
left = center_x - width / 2
|
||||||
|
top = center_y - height / 2
|
||||||
|
drawPred(classId, confidence, left, top, left + width, top + height)
|
||||||
|
|
||||||
|
# Process inputs
|
||||||
|
winName = 'Deep learning object detection in OpenCV'
|
||||||
|
cv.namedWindow(winName, cv.WINDOW_NORMAL)
|
||||||
|
|
||||||
|
def callback(pos):
|
||||||
|
global confThreshold
|
||||||
|
confThreshold = pos / 100.0
|
||||||
|
|
||||||
|
cv.createTrackbar('Confidence threshold, %', winName, int(confThreshold * 100), 99, callback)
|
||||||
|
|
||||||
|
cap = cv.VideoCapture(args.input if args.input else 0)
|
||||||
|
while cv.waitKey(1) < 0:
|
||||||
|
hasFrame, frame = cap.read()
|
||||||
|
if not hasFrame:
|
||||||
|
cv.waitKey()
|
||||||
|
break
|
||||||
|
|
||||||
|
frameHeight = frame.shape[0]
|
||||||
|
frameWidth = frame.shape[1]
|
||||||
|
|
||||||
|
# Create a 4D blob from a frame.
|
||||||
|
inpWidth = args.width if args.width else frameWidth
|
||||||
|
inpHeight = args.height if args.height else frameHeight
|
||||||
|
blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
|
||||||
|
|
||||||
|
# Run a model
|
||||||
|
net.setInput(blob)
|
||||||
|
if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
|
||||||
|
frame = cv.resize(frame, (inpWidth, inpHeight))
|
||||||
|
net.setInput(np.array([inpHeight, inpWidth, 1.6], dtype=np.float32), 'im_info');
|
||||||
|
out = net.forward()
|
||||||
|
|
||||||
|
postprocess(frame, out)
|
||||||
|
|
||||||
|
# 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, 0, 0))
|
||||||
|
|
||||||
|
cv.imshow(winName, frame)
|
||||||
90
samples/dnn/object_detection_classes_coco.txt
Normal file
90
samples/dnn/object_detection_classes_coco.txt
Normal file
@ -0,0 +1,90 @@
|
|||||||
|
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
|
||||||
20
samples/dnn/object_detection_classes_pascal_voc.txt
Normal file
20
samples/dnn/object_detection_classes_pascal_voc.txt
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
aeroplane
|
||||||
|
bicycle
|
||||||
|
bird
|
||||||
|
boat
|
||||||
|
bottle
|
||||||
|
bus
|
||||||
|
car
|
||||||
|
cat
|
||||||
|
chair
|
||||||
|
cow
|
||||||
|
diningtable
|
||||||
|
dog
|
||||||
|
horse
|
||||||
|
motorbike
|
||||||
|
person
|
||||||
|
pottedplant
|
||||||
|
sheep
|
||||||
|
sofa
|
||||||
|
train
|
||||||
|
tvmonitor
|
||||||
@ -1,164 +0,0 @@
|
|||||||
#include <opencv2/dnn.hpp>
|
|
||||||
#include <opencv2/imgproc.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
#include <iostream>
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace std;
|
|
||||||
using namespace cv::dnn;
|
|
||||||
|
|
||||||
const size_t inWidth = 300;
|
|
||||||
const size_t inHeight = 300;
|
|
||||||
const double inScaleFactor = 1.0;
|
|
||||||
const Scalar meanVal(104.0, 177.0, 123.0);
|
|
||||||
|
|
||||||
const char* about = "This sample uses Single-Shot Detector "
|
|
||||||
"(https://arxiv.org/abs/1512.02325) "
|
|
||||||
"with ResNet-10 architecture to detect faces on camera/video/image.\n"
|
|
||||||
"More information about the training is available here: "
|
|
||||||
"<OPENCV_SRC_DIR>/samples/dnn/face_detector/how_to_train_face_detector.txt\n"
|
|
||||||
".caffemodel model's file is available here: "
|
|
||||||
"<OPENCV_SRC_DIR>/samples/dnn/face_detector/res10_300x300_ssd_iter_140000.caffemodel\n"
|
|
||||||
".prototxt file is available here: "
|
|
||||||
"<OPENCV_SRC_DIR>/samples/dnn/face_detector/deploy.prototxt\n";
|
|
||||||
|
|
||||||
const char* params
|
|
||||||
= "{ help | false | print usage }"
|
|
||||||
"{ proto | | model configuration (deploy.prototxt) }"
|
|
||||||
"{ model | | model weights (res10_300x300_ssd_iter_140000.caffemodel) }"
|
|
||||||
"{ camera_device | 0 | camera device number }"
|
|
||||||
"{ video | | video or image for detection }"
|
|
||||||
"{ opencl | false | enable OpenCL }"
|
|
||||||
"{ min_confidence | 0.5 | min confidence }";
|
|
||||||
|
|
||||||
int main(int argc, char** argv)
|
|
||||||
{
|
|
||||||
CommandLineParser parser(argc, argv, params);
|
|
||||||
|
|
||||||
if (parser.get<bool>("help"))
|
|
||||||
{
|
|
||||||
cout << about << endl;
|
|
||||||
parser.printMessage();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
String modelConfiguration = parser.get<string>("proto");
|
|
||||||
String modelBinary = parser.get<string>("model");
|
|
||||||
|
|
||||||
//! [Initialize network]
|
|
||||||
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
|
|
||||||
//! [Initialize network]
|
|
||||||
|
|
||||||
if (net.empty())
|
|
||||||
{
|
|
||||||
cerr << "Can't load network by using the following files: " << endl;
|
|
||||||
cerr << "prototxt: " << modelConfiguration << endl;
|
|
||||||
cerr << "caffemodel: " << modelBinary << endl;
|
|
||||||
cerr << "Models are available here:" << endl;
|
|
||||||
cerr << "<OPENCV_SRC_DIR>/samples/dnn/face_detector" << endl;
|
|
||||||
cerr << "or here:" << endl;
|
|
||||||
cerr << "https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector" << endl;
|
|
||||||
exit(-1);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (parser.get<bool>("opencl"))
|
|
||||||
{
|
|
||||||
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
||||||
}
|
|
||||||
|
|
||||||
VideoCapture cap;
|
|
||||||
if (parser.get<String>("video").empty())
|
|
||||||
{
|
|
||||||
int cameraDevice = parser.get<int>("camera_device");
|
|
||||||
cap = VideoCapture(cameraDevice);
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't find camera: " << cameraDevice << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
cap.open(parser.get<String>("video"));
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
for(;;)
|
|
||||||
{
|
|
||||||
Mat frame;
|
|
||||||
cap >> frame; // get a new frame from camera/video or read image
|
|
||||||
|
|
||||||
if (frame.empty())
|
|
||||||
{
|
|
||||||
waitKey();
|
|
||||||
break;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (frame.channels() == 4)
|
|
||||||
cvtColor(frame, frame, COLOR_BGRA2BGR);
|
|
||||||
|
|
||||||
//! [Prepare blob]
|
|
||||||
Mat inputBlob = blobFromImage(frame, inScaleFactor,
|
|
||||||
Size(inWidth, inHeight), meanVal, false, 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]
|
|
||||||
Mat detection = net.forward("detection_out"); //compute output
|
|
||||||
//! [Make forward pass]
|
|
||||||
|
|
||||||
vector<double> layersTimings;
|
|
||||||
double freq = getTickFrequency() / 1000;
|
|
||||||
double time = net.getPerfProfile(layersTimings) / freq;
|
|
||||||
|
|
||||||
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
|
|
||||||
|
|
||||||
ostringstream ss;
|
|
||||||
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
|
|
||||||
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
|
|
||||||
|
|
||||||
float confidenceThreshold = parser.get<float>("min_confidence");
|
|
||||||
for(int i = 0; i < detectionMat.rows; i++)
|
|
||||||
{
|
|
||||||
float confidence = detectionMat.at<float>(i, 2);
|
|
||||||
|
|
||||||
if(confidence > confidenceThreshold)
|
|
||||||
{
|
|
||||||
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
|
|
||||||
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
|
|
||||||
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
|
|
||||||
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
|
|
||||||
|
|
||||||
Rect object((int)xLeftBottom, (int)yLeftBottom,
|
|
||||||
(int)(xRightTop - xLeftBottom),
|
|
||||||
(int)(yRightTop - yLeftBottom));
|
|
||||||
|
|
||||||
rectangle(frame, object, Scalar(0, 255, 0));
|
|
||||||
|
|
||||||
ss.str("");
|
|
||||||
ss << confidence;
|
|
||||||
String conf(ss.str());
|
|
||||||
String label = "Face: " + conf;
|
|
||||||
int baseLine = 0;
|
|
||||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
||||||
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
|
|
||||||
Size(labelSize.width, labelSize.height + baseLine)),
|
|
||||||
Scalar(255, 255, 255), FILLED);
|
|
||||||
putText(frame, label, Point(xLeftBottom, yLeftBottom),
|
|
||||||
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
imshow("detections", frame);
|
|
||||||
if (waitKey(1) >= 0) break;
|
|
||||||
}
|
|
||||||
|
|
||||||
return 0;
|
|
||||||
} // main
|
|
||||||
@ -1,55 +0,0 @@
|
|||||||
import numpy as np
|
|
||||||
import argparse
|
|
||||||
import cv2 as cv
|
|
||||||
try:
|
|
||||||
import cv2 as cv
|
|
||||||
except ImportError:
|
|
||||||
raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, '
|
|
||||||
'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)')
|
|
||||||
|
|
||||||
from cv2 import dnn
|
|
||||||
|
|
||||||
inWidth = 300
|
|
||||||
inHeight = 300
|
|
||||||
confThreshold = 0.5
|
|
||||||
|
|
||||||
prototxt = 'face_detector/deploy.prototxt'
|
|
||||||
caffemodel = 'face_detector/res10_300x300_ssd_iter_140000.caffemodel'
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
net = dnn.readNetFromCaffe(prototxt, caffemodel)
|
|
||||||
cap = cv.VideoCapture(0)
|
|
||||||
while True:
|
|
||||||
ret, frame = cap.read()
|
|
||||||
cols = frame.shape[1]
|
|
||||||
rows = frame.shape[0]
|
|
||||||
|
|
||||||
net.setInput(dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (104.0, 177.0, 123.0), False, False))
|
|
||||||
detections = net.forward()
|
|
||||||
|
|
||||||
perf_stats = net.getPerfProfile()
|
|
||||||
|
|
||||||
print('Inference time, ms: %.2f' % (perf_stats[0] / cv.getTickFrequency() * 1000))
|
|
||||||
|
|
||||||
for i in range(detections.shape[2]):
|
|
||||||
confidence = detections[0, 0, i, 2]
|
|
||||||
if confidence > confThreshold:
|
|
||||||
xLeftBottom = int(detections[0, 0, i, 3] * cols)
|
|
||||||
yLeftBottom = int(detections[0, 0, i, 4] * rows)
|
|
||||||
xRightTop = int(detections[0, 0, i, 5] * cols)
|
|
||||||
yRightTop = int(detections[0, 0, i, 6] * rows)
|
|
||||||
|
|
||||||
cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),
|
|
||||||
(0, 255, 0))
|
|
||||||
label = "face: %.4f" % confidence
|
|
||||||
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
|
||||||
|
|
||||||
cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]),
|
|
||||||
(xLeftBottom + labelSize[0], yLeftBottom + baseLine),
|
|
||||||
(255, 255, 255), cv.FILLED)
|
|
||||||
cv.putText(frame, label, (xLeftBottom, yLeftBottom),
|
|
||||||
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
|
|
||||||
|
|
||||||
cv.imshow("detections", frame)
|
|
||||||
if cv.waitKey(1) != -1:
|
|
||||||
break
|
|
||||||
@ -1,187 +0,0 @@
|
|||||||
#include <opencv2/dnn.hpp>
|
|
||||||
#include <opencv2/dnn/shape_utils.hpp>
|
|
||||||
#include <opencv2/imgproc.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
#include <iostream>
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace std;
|
|
||||||
using namespace cv::dnn;
|
|
||||||
|
|
||||||
const size_t inWidth = 300;
|
|
||||||
const size_t inHeight = 300;
|
|
||||||
const float inScaleFactor = 0.007843f;
|
|
||||||
const float meanVal = 127.5;
|
|
||||||
const char* classNames[] = {"background",
|
|
||||||
"aeroplane", "bicycle", "bird", "boat",
|
|
||||||
"bottle", "bus", "car", "cat", "chair",
|
|
||||||
"cow", "diningtable", "dog", "horse",
|
|
||||||
"motorbike", "person", "pottedplant",
|
|
||||||
"sheep", "sofa", "train", "tvmonitor"};
|
|
||||||
|
|
||||||
const String keys
|
|
||||||
= "{ help | false | print usage }"
|
|
||||||
"{ proto | MobileNetSSD_deploy.prototxt | model configuration }"
|
|
||||||
"{ model | MobileNetSSD_deploy.caffemodel | model weights }"
|
|
||||||
"{ camera_device | 0 | camera device number }"
|
|
||||||
"{ camera_width | 640 | camera device width }"
|
|
||||||
"{ camera_height | 480 | camera device height }"
|
|
||||||
"{ video | | video or image for detection}"
|
|
||||||
"{ out | | path to output video file}"
|
|
||||||
"{ min_confidence | 0.2 | min confidence }"
|
|
||||||
"{ opencl | false | enable OpenCL }"
|
|
||||||
;
|
|
||||||
|
|
||||||
int main(int argc, char** argv)
|
|
||||||
{
|
|
||||||
CommandLineParser parser(argc, argv, keys);
|
|
||||||
parser.about("This sample uses MobileNet Single-Shot Detector "
|
|
||||||
"(https://arxiv.org/abs/1704.04861) "
|
|
||||||
"to detect objects on camera/video/image.\n"
|
|
||||||
".caffemodel model's file is available here: "
|
|
||||||
"https://github.com/chuanqi305/MobileNet-SSD\n"
|
|
||||||
"Default network is 300x300 and 20-classes VOC.\n");
|
|
||||||
|
|
||||||
if (parser.get<bool>("help"))
|
|
||||||
{
|
|
||||||
parser.printMessage();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
String modelConfiguration = parser.get<String>("proto");
|
|
||||||
String modelBinary = parser.get<String>("model");
|
|
||||||
CV_Assert(!modelConfiguration.empty() && !modelBinary.empty());
|
|
||||||
|
|
||||||
//! [Initialize network]
|
|
||||||
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
|
|
||||||
//! [Initialize network]
|
|
||||||
|
|
||||||
if (parser.get<bool>("opencl"))
|
|
||||||
{
|
|
||||||
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
||||||
}
|
|
||||||
|
|
||||||
if (net.empty())
|
|
||||||
{
|
|
||||||
cerr << "Can't load network by using the following files: " << endl;
|
|
||||||
cerr << "prototxt: " << modelConfiguration << endl;
|
|
||||||
cerr << "caffemodel: " << modelBinary << endl;
|
|
||||||
cerr << "Models can be downloaded here:" << endl;
|
|
||||||
cerr << "https://github.com/chuanqi305/MobileNet-SSD" << endl;
|
|
||||||
exit(-1);
|
|
||||||
}
|
|
||||||
|
|
||||||
VideoCapture cap;
|
|
||||||
if (!parser.has("video"))
|
|
||||||
{
|
|
||||||
int cameraDevice = parser.get<int>("camera_device");
|
|
||||||
cap = VideoCapture(cameraDevice);
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't find camera: " << cameraDevice << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
|
|
||||||
cap.set(CAP_PROP_FRAME_WIDTH, parser.get<int>("camera_width"));
|
|
||||||
cap.set(CAP_PROP_FRAME_HEIGHT, parser.get<int>("camera_height"));
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
cap.open(parser.get<String>("video"));
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
//Acquire input size
|
|
||||||
Size inVideoSize((int) cap.get(CAP_PROP_FRAME_WIDTH),
|
|
||||||
(int) cap.get(CAP_PROP_FRAME_HEIGHT));
|
|
||||||
|
|
||||||
double fps = cap.get(CAP_PROP_FPS);
|
|
||||||
int fourcc = static_cast<int>(cap.get(CAP_PROP_FOURCC));
|
|
||||||
VideoWriter outputVideo;
|
|
||||||
outputVideo.open(parser.get<String>("out") ,
|
|
||||||
(fourcc != 0 ? fourcc : VideoWriter::fourcc('M','J','P','G')),
|
|
||||||
(fps != 0 ? fps : 10.0), inVideoSize, true);
|
|
||||||
|
|
||||||
for(;;)
|
|
||||||
{
|
|
||||||
Mat frame;
|
|
||||||
cap >> frame; // get a new frame from camera/video or read image
|
|
||||||
|
|
||||||
if (frame.empty())
|
|
||||||
{
|
|
||||||
waitKey();
|
|
||||||
break;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (frame.channels() == 4)
|
|
||||||
cvtColor(frame, frame, COLOR_BGRA2BGR);
|
|
||||||
|
|
||||||
//! [Prepare blob]
|
|
||||||
Mat inputBlob = blobFromImage(frame, inScaleFactor,
|
|
||||||
Size(inWidth, inHeight),
|
|
||||||
Scalar(meanVal, meanVal, meanVal),
|
|
||||||
false, false); //Convert Mat to batch of images
|
|
||||||
//! [Prepare blob]
|
|
||||||
|
|
||||||
//! [Set input blob]
|
|
||||||
net.setInput(inputBlob); //set the network input
|
|
||||||
//! [Set input blob]
|
|
||||||
|
|
||||||
//! [Make forward pass]
|
|
||||||
Mat detection = net.forward(); //compute output
|
|
||||||
//! [Make forward pass]
|
|
||||||
|
|
||||||
vector<double> layersTimings;
|
|
||||||
double freq = getTickFrequency() / 1000;
|
|
||||||
double time = net.getPerfProfile(layersTimings) / freq;
|
|
||||||
|
|
||||||
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
|
|
||||||
|
|
||||||
if (!outputVideo.isOpened())
|
|
||||||
{
|
|
||||||
putText(frame, format("FPS: %.2f ; time: %.2f ms", 1000.f/time, time),
|
|
||||||
Point(20,20), 0, 0.5, Scalar(0,0,255));
|
|
||||||
}
|
|
||||||
else
|
|
||||||
cout << "Inference time, ms: " << time << endl;
|
|
||||||
|
|
||||||
float confidenceThreshold = parser.get<float>("min_confidence");
|
|
||||||
for(int i = 0; i < detectionMat.rows; i++)
|
|
||||||
{
|
|
||||||
float confidence = detectionMat.at<float>(i, 2);
|
|
||||||
|
|
||||||
if(confidence > confidenceThreshold)
|
|
||||||
{
|
|
||||||
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
|
|
||||||
|
|
||||||
int left = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
|
|
||||||
int top = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
|
|
||||||
int right = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
|
|
||||||
int bottom = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
|
|
||||||
|
|
||||||
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
|
|
||||||
String label = format("%s: %.2f", classNames[objectClass], confidence);
|
|
||||||
int baseLine = 0;
|
|
||||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
||||||
top = max(top, labelSize.height);
|
|
||||||
rectangle(frame, Point(left, top - labelSize.height),
|
|
||||||
Point(left + labelSize.width, top + baseLine),
|
|
||||||
Scalar(255, 255, 255), FILLED);
|
|
||||||
putText(frame, label, Point(left, top),
|
|
||||||
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
if (outputVideo.isOpened())
|
|
||||||
outputVideo << frame;
|
|
||||||
|
|
||||||
imshow("detections", frame);
|
|
||||||
if (waitKey(1) >= 0) break;
|
|
||||||
}
|
|
||||||
|
|
||||||
return 0;
|
|
||||||
} // main
|
|
||||||
@ -1,156 +0,0 @@
|
|||||||
#include <opencv2/dnn.hpp>
|
|
||||||
#include <opencv2/dnn/shape_utils.hpp>
|
|
||||||
#include <opencv2/imgproc.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
#include <iostream>
|
|
||||||
|
|
||||||
using namespace cv;
|
|
||||||
using namespace std;
|
|
||||||
using namespace cv::dnn;
|
|
||||||
|
|
||||||
const char* classNames[] = {"background",
|
|
||||||
"aeroplane", "bicycle", "bird", "boat",
|
|
||||||
"bottle", "bus", "car", "cat", "chair",
|
|
||||||
"cow", "diningtable", "dog", "horse",
|
|
||||||
"motorbike", "person", "pottedplant",
|
|
||||||
"sheep", "sofa", "train", "tvmonitor"};
|
|
||||||
|
|
||||||
const char* about = "This sample uses Single-Shot Detector "
|
|
||||||
"(https://arxiv.org/abs/1512.02325) "
|
|
||||||
"to detect objects on camera/video/image.\n"
|
|
||||||
".caffemodel model's file is available here: "
|
|
||||||
"https://github.com/weiliu89/caffe/tree/ssd#models\n"
|
|
||||||
"Default network is 300x300 and 20-classes VOC.\n";
|
|
||||||
|
|
||||||
const char* params
|
|
||||||
= "{ help | false | print usage }"
|
|
||||||
"{ proto | | model configuration }"
|
|
||||||
"{ model | | model weights }"
|
|
||||||
"{ camera_device | 0 | camera device number}"
|
|
||||||
"{ video | | video or image for detection}"
|
|
||||||
"{ min_confidence | 0.5 | min confidence }";
|
|
||||||
|
|
||||||
int main(int argc, char** argv)
|
|
||||||
{
|
|
||||||
cv::CommandLineParser parser(argc, argv, params);
|
|
||||||
|
|
||||||
if (parser.get<bool>("help"))
|
|
||||||
{
|
|
||||||
cout << about << endl;
|
|
||||||
parser.printMessage();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
String modelConfiguration = parser.get<string>("proto");
|
|
||||||
String modelBinary = parser.get<string>("model");
|
|
||||||
|
|
||||||
//! [Initialize network]
|
|
||||||
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
|
|
||||||
//! [Initialize network]
|
|
||||||
|
|
||||||
if (net.empty())
|
|
||||||
{
|
|
||||||
cerr << "Can't load network by using the following files: " << endl;
|
|
||||||
cerr << "prototxt: " << modelConfiguration << endl;
|
|
||||||
cerr << "caffemodel: " << modelBinary << endl;
|
|
||||||
cerr << "Models can be downloaded here:" << endl;
|
|
||||||
cerr << "https://github.com/weiliu89/caffe/tree/ssd#models" << endl;
|
|
||||||
exit(-1);
|
|
||||||
}
|
|
||||||
|
|
||||||
VideoCapture cap;
|
|
||||||
if (parser.get<String>("video").empty())
|
|
||||||
{
|
|
||||||
int cameraDevice = parser.get<int>("camera_device");
|
|
||||||
cap = VideoCapture(cameraDevice);
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't find camera: " << cameraDevice << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
cap.open(parser.get<String>("video"));
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
for (;;)
|
|
||||||
{
|
|
||||||
cv::Mat frame;
|
|
||||||
cap >> frame; // get a new frame from camera/video or read image
|
|
||||||
|
|
||||||
if (frame.empty())
|
|
||||||
{
|
|
||||||
waitKey();
|
|
||||||
break;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (frame.channels() == 4)
|
|
||||||
cvtColor(frame, frame, COLOR_BGRA2BGR);
|
|
||||||
|
|
||||||
//! [Prepare blob]
|
|
||||||
Mat inputBlob = blobFromImage(frame, 1.0f, Size(300, 300), Scalar(104, 117, 123), false, 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]
|
|
||||||
Mat detection = net.forward("detection_out"); //compute output
|
|
||||||
//! [Make forward pass]
|
|
||||||
|
|
||||||
vector<double> layersTimings;
|
|
||||||
double freq = getTickFrequency() / 1000;
|
|
||||||
double time = net.getPerfProfile(layersTimings) / freq;
|
|
||||||
ostringstream ss;
|
|
||||||
ss << "FPS: " << 1000/time << " ; time: " << time << " ms";
|
|
||||||
putText(frame, ss.str(), Point(20,20), 0, 0.5, Scalar(0,0,255));
|
|
||||||
|
|
||||||
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
|
|
||||||
|
|
||||||
float confidenceThreshold = parser.get<float>("min_confidence");
|
|
||||||
for(int i = 0; i < detectionMat.rows; i++)
|
|
||||||
{
|
|
||||||
float confidence = detectionMat.at<float>(i, 2);
|
|
||||||
|
|
||||||
if(confidence > confidenceThreshold)
|
|
||||||
{
|
|
||||||
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
|
|
||||||
|
|
||||||
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
|
|
||||||
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
|
|
||||||
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
|
|
||||||
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
|
|
||||||
|
|
||||||
ss.str("");
|
|
||||||
ss << confidence;
|
|
||||||
String conf(ss.str());
|
|
||||||
|
|
||||||
Rect object(xLeftBottom, yLeftBottom,
|
|
||||||
xRightTop - xLeftBottom,
|
|
||||||
yRightTop - yLeftBottom);
|
|
||||||
|
|
||||||
rectangle(frame, object, Scalar(0, 255, 0));
|
|
||||||
String label = String(classNames[objectClass]) + ": " + conf;
|
|
||||||
int baseLine = 0;
|
|
||||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
||||||
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
|
|
||||||
Size(labelSize.width, labelSize.height + baseLine)),
|
|
||||||
Scalar(255, 255, 255), FILLED);
|
|
||||||
putText(frame, label, Point(xLeftBottom, yLeftBottom),
|
|
||||||
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
imshow("detections", frame);
|
|
||||||
if (waitKey(1) >= 0) break;
|
|
||||||
}
|
|
||||||
|
|
||||||
return 0;
|
|
||||||
} // main
|
|
||||||
@ -1,185 +0,0 @@
|
|||||||
// Brief Sample of using OpenCV dnn module in real time with device capture, video and image.
|
|
||||||
// VIDEO DEMO: https://www.youtube.com/watch?v=NHtRlndE2cg
|
|
||||||
|
|
||||||
#include <opencv2/dnn.hpp>
|
|
||||||
#include <opencv2/dnn/shape_utils.hpp>
|
|
||||||
#include <opencv2/imgproc.hpp>
|
|
||||||
#include <opencv2/highgui.hpp>
|
|
||||||
#include <fstream>
|
|
||||||
#include <iostream>
|
|
||||||
|
|
||||||
using namespace std;
|
|
||||||
using namespace cv;
|
|
||||||
using namespace cv::dnn;
|
|
||||||
|
|
||||||
static const char* about =
|
|
||||||
"This sample uses You only look once (YOLO)-Detector (https://arxiv.org/abs/1612.08242) to detect objects on camera/video/image.\n"
|
|
||||||
"Models can be downloaded here: https://pjreddie.com/darknet/yolo/\n"
|
|
||||||
"Default network is 416x416.\n"
|
|
||||||
"Class names can be downloaded here: https://github.com/pjreddie/darknet/tree/master/data\n";
|
|
||||||
|
|
||||||
static const char* params =
|
|
||||||
"{ help | false | print usage }"
|
|
||||||
"{ cfg | | model configuration }"
|
|
||||||
"{ model | | model weights }"
|
|
||||||
"{ camera_device | 0 | camera device number}"
|
|
||||||
"{ source | | video or image for detection}"
|
|
||||||
"{ out | | path to output video file}"
|
|
||||||
"{ fps | 3 | frame per second }"
|
|
||||||
"{ style | box | box or line style draw }"
|
|
||||||
"{ min_confidence | 0.24 | min confidence }"
|
|
||||||
"{ class_names | | File with class names, [PATH-TO-DARKNET]/data/coco.names }";
|
|
||||||
|
|
||||||
int main(int argc, char** argv)
|
|
||||||
{
|
|
||||||
CommandLineParser parser(argc, argv, params);
|
|
||||||
|
|
||||||
if (parser.get<bool>("help"))
|
|
||||||
{
|
|
||||||
cout << about << endl;
|
|
||||||
parser.printMessage();
|
|
||||||
return 0;
|
|
||||||
}
|
|
||||||
|
|
||||||
String modelConfiguration = parser.get<String>("cfg");
|
|
||||||
String modelBinary = parser.get<String>("model");
|
|
||||||
|
|
||||||
//! [Initialize network]
|
|
||||||
dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
|
|
||||||
//! [Initialize network]
|
|
||||||
|
|
||||||
if (net.empty())
|
|
||||||
{
|
|
||||||
cerr << "Can't load network by using the following files: " << endl;
|
|
||||||
cerr << "cfg-file: " << modelConfiguration << endl;
|
|
||||||
cerr << "weights-file: " << modelBinary << endl;
|
|
||||||
cerr << "Models can be downloaded here:" << endl;
|
|
||||||
cerr << "https://pjreddie.com/darknet/yolo/" << endl;
|
|
||||||
exit(-1);
|
|
||||||
}
|
|
||||||
|
|
||||||
VideoCapture cap;
|
|
||||||
VideoWriter writer;
|
|
||||||
int codec = CV_FOURCC('M', 'J', 'P', 'G');
|
|
||||||
double fps = parser.get<float>("fps");
|
|
||||||
if (parser.get<String>("source").empty())
|
|
||||||
{
|
|
||||||
int cameraDevice = parser.get<int>("camera_device");
|
|
||||||
cap = VideoCapture(cameraDevice);
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't find camera: " << cameraDevice << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
cap.open(parser.get<String>("source"));
|
|
||||||
if(!cap.isOpened())
|
|
||||||
{
|
|
||||||
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
|
|
||||||
return -1;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
if(!parser.get<String>("out").empty())
|
|
||||||
{
|
|
||||||
writer.open(parser.get<String>("out"), codec, fps, Size((int)cap.get(CAP_PROP_FRAME_WIDTH),(int)cap.get(CAP_PROP_FRAME_HEIGHT)), 1);
|
|
||||||
}
|
|
||||||
|
|
||||||
vector<String> classNamesVec;
|
|
||||||
ifstream classNamesFile(parser.get<String>("class_names").c_str());
|
|
||||||
if (classNamesFile.is_open())
|
|
||||||
{
|
|
||||||
string className = "";
|
|
||||||
while (std::getline(classNamesFile, className))
|
|
||||||
classNamesVec.push_back(className);
|
|
||||||
}
|
|
||||||
|
|
||||||
String object_roi_style = parser.get<String>("style");
|
|
||||||
|
|
||||||
for(;;)
|
|
||||||
{
|
|
||||||
Mat frame;
|
|
||||||
cap >> frame; // get a new frame from camera/video or read image
|
|
||||||
|
|
||||||
if (frame.empty())
|
|
||||||
{
|
|
||||||
waitKey();
|
|
||||||
break;
|
|
||||||
}
|
|
||||||
|
|
||||||
if (frame.channels() == 4)
|
|
||||||
cvtColor(frame, frame, COLOR_BGRA2BGR);
|
|
||||||
|
|
||||||
//! [Prepare blob]
|
|
||||||
Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, 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]
|
|
||||||
Mat detectionMat = net.forward("detection_out"); //compute output
|
|
||||||
//! [Make forward pass]
|
|
||||||
|
|
||||||
vector<double> layersTimings;
|
|
||||||
double tick_freq = getTickFrequency();
|
|
||||||
double time_ms = net.getPerfProfile(layersTimings) / tick_freq * 1000;
|
|
||||||
putText(frame, format("FPS: %.2f ; time: %.2f ms", 1000.f / time_ms, time_ms),
|
|
||||||
Point(20, 20), 0, 0.5, Scalar(0, 0, 255));
|
|
||||||
|
|
||||||
float confidenceThreshold = parser.get<float>("min_confidence");
|
|
||||||
for (int i = 0; i < detectionMat.rows; i++)
|
|
||||||
{
|
|
||||||
const int probability_index = 5;
|
|
||||||
const int probability_size = detectionMat.cols - probability_index;
|
|
||||||
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
|
|
||||||
|
|
||||||
size_t objectClass = max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
|
|
||||||
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
|
|
||||||
|
|
||||||
if (confidence > confidenceThreshold)
|
|
||||||
{
|
|
||||||
float x_center = detectionMat.at<float>(i, 0) * frame.cols;
|
|
||||||
float y_center = detectionMat.at<float>(i, 1) * frame.rows;
|
|
||||||
float width = detectionMat.at<float>(i, 2) * frame.cols;
|
|
||||||
float height = detectionMat.at<float>(i, 3) * frame.rows;
|
|
||||||
Point p1(cvRound(x_center - width / 2), cvRound(y_center - height / 2));
|
|
||||||
Point p2(cvRound(x_center + width / 2), cvRound(y_center + height / 2));
|
|
||||||
Rect object(p1, p2);
|
|
||||||
|
|
||||||
Scalar object_roi_color(0, 255, 0);
|
|
||||||
|
|
||||||
if (object_roi_style == "box")
|
|
||||||
{
|
|
||||||
rectangle(frame, object, object_roi_color);
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
Point p_center(cvRound(x_center), cvRound(y_center));
|
|
||||||
line(frame, object.tl(), p_center, object_roi_color, 1);
|
|
||||||
}
|
|
||||||
|
|
||||||
String className = objectClass < classNamesVec.size() ? classNamesVec[objectClass] : cv::format("unknown(%d)", objectClass);
|
|
||||||
String label = format("%s: %.2f", className.c_str(), confidence);
|
|
||||||
int baseLine = 0;
|
|
||||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
|
||||||
rectangle(frame, Rect(p1, Size(labelSize.width, labelSize.height + baseLine)),
|
|
||||||
object_roi_color, FILLED);
|
|
||||||
putText(frame, label, p1 + Point(0, labelSize.height),
|
|
||||||
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
|
|
||||||
}
|
|
||||||
}
|
|
||||||
if(writer.isOpened())
|
|
||||||
{
|
|
||||||
writer.write(frame);
|
|
||||||
}
|
|
||||||
|
|
||||||
imshow("YOLO: Detections", frame);
|
|
||||||
if (waitKey(1) >= 0) break;
|
|
||||||
}
|
|
||||||
|
|
||||||
return 0;
|
|
||||||
} // main
|
|
||||||
Loading…
Reference in New Issue
Block a user