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
-
-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
+Usage examples
+--------------
Execute in webcam:
@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
-
-@endcode
-
-Execute with image:
-
-@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
@endcode
-Execute in video file:
+Execute with image or 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
+$ 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
/** @brief Returns index of output blob in output array.
* @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.
--- /dev/null
+# 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)
+++ /dev/null
-#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;
-}
+++ /dev/null
-# 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
--- /dev/null
+#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();
+}
--- /dev/null
+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)
--- /dev/null
+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
--- /dev/null
+aeroplane
+bicycle
+bird
+boat
+bottle
+bus
+car
+cat
+chair
+cow
+diningtable
+dog
+horse
+motorbike
+person
+pottedplant
+sheep
+sofa
+train
+tvmonitor
+++ /dev/null
-#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
+++ /dev/null
-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
+++ /dev/null
-#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
+++ /dev/null
-#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
+++ /dev/null
-// 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
projects / platform / upstream / opencv.git / commitdiff