diff --git a/ReleaseNotesTmp.html.orig b/ReleaseNotesTmp.html.orig deleted file mode 100644 index b84f9d4a7..000000000 --- a/ReleaseNotesTmp.html.orig +++ /dev/null @@ -1,76 +0,0 @@ -<<<<<<< HEAD -Stereo Vision Toolkit v1.3.1 - -Changes: -- Add ML detector using YOLO or Tensorflow -- Add loading of stereo image pair from image files (.png, .jpg, .jpeg) -- Add toggle to show epipolar lines for checking calibration -- Add RGBD streaming using shared memory (for use with I3DRStereo3D Unity package) -- Add I3DR Titania stereo camera support -- Add Vimba camera integration -- Add colour image capture to Basler cameras -- Add point cloud texture options (depth colourmap or image) -- Add colour to loading/saving stereo videos -- Add about dialogue to show software version -- Add separate camera capture and processing threads -- Add video saving of individual camera images or disparity -- Add WLS filter to OpenCV BM and SGBM matchers -- Add hostname and hostID to license message if I3DRSGM license is not found -- Add new camera serials -- Change load/save YAML calibration to use ROS perception supported YAML (changed from OpenCV FileStorage yaml) -- Upgrade to OpenCV v4.5.0 -- Upgrade to CUDA 11.1 -- Upgrade to I3DRSGM v1.0.11 -- Upgrade to using Pylon v6.1.1.18345 library for Basler cameras - -Bug fixes: -- Fix YAML file generates invalid Q matrix -- Fix missing link to online documentation -- Fix overwriting image during save -- Fix non rectified images saved with '_rect' suffix -- Fix maximum point cloud z value being set incorrectly -- Fix camera capture delay when processing load is high -- Fix I3DRSGM failed to initalise when switching to camera with different resolution - -Known issues: -- ROS perception YAML's data arrays must be on single line in YAML file. -- WLS filter causes speckle filter to sometimes be disabled. -======= -Stereo Vision Toolkit v1.3.1 - -Changes: -- Add ML detector using YOLO or Tensorflow -- Add loading of stereo image pair from image files (.png, .jpg, .jpeg) -- Add toggle to show epipolar lines for checking calibration -- Add RGBD streaming using shared memory (for use with I3DRStereo3D Unity package) -- Add I3DR Titania stereo camera support -- Add Vimba camera integration -- Add colour image capture to Basler cameras -- Add point cloud texture options (depth colourmap or image) -- Add colour to loading/saving stereo videos -- Add about dialogue to show software version -- Add separate camera capture and processing threads -- Add video saving of individual camera images or disparity -- Add WLS filter to OpenCV BM and SGBM matchers -- Add hostname and hostID to license message if I3DRSGM license is not found -- Add new camera serials -- Change load/save YAML calibration to use ROS perception supported YAML (changed from OpenCV FileStorage yaml) -- Upgrade to OpenCV v4.5.0 -- Upgrade to CUDA 11.1 -- Upgrade to I3DRSGM v1.0.11 -- Upgrade to using Pylon v6.1.1.18345 library for Basler cameras - -Bug fixes: -- Fix YAML file generates invalid Q matrix -- Fix missing link to online documentation -- Fix overwriting image during save -- Fix non rectified images saved with '_rect' suffix -- Fix maximum point cloud z value being set incorrectly -- Fix camera capture delay when processing load is high -- Fix I3DRSGM failed to initalise when switching to camera with different resolution - -Known issues: -- ROS perception YAML's data arrays must be on single line in YAML file. -- WLS filter causes speckle filter to sometimes be disabled. ->>>>>>> dev -- Stereo videos are saved uncompressed to avoid lossy compression which would cause issues when loading stereo videos. This will create large file sizes. \ No newline at end of file diff --git a/resources/ml/coco/coco.names.orig b/resources/ml/coco/coco.names.orig deleted file mode 100644 index 5d037108a..000000000 --- a/resources/ml/coco/coco.names.orig +++ /dev/null @@ -1,162 +0,0 @@ -<<<<<<< HEAD -person -bicycle -car -motorbike -aeroplane -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 -sofa -pottedplant -bed -diningtable -toilet -tvmonitor -laptop -mouse -remote -keyboard -cell phone -microwave -oven -toaster -sink -refrigerator -book -clock -vase -scissors -teddy bear -hair drier -======= -person -bicycle -car -motorbike -aeroplane -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 -sofa -pottedplant -bed -diningtable -toilet -tvmonitor -laptop -mouse -remote -keyboard -cell phone -microwave -oven -toaster -sink -refrigerator -book -clock -vase -scissors -teddy bear -hair drier ->>>>>>> dev -toothbrush \ No newline at end of file diff --git a/resources/ml/coco/yolov4-tiny.cfg.orig b/resources/ml/coco/yolov4-tiny.cfg.orig deleted file mode 100644 index 48a29d922..000000000 --- a/resources/ml/coco/yolov4-tiny.cfg.orig +++ /dev/null @@ -1,564 +0,0 @@ -<<<<<<< HEAD -[net] -# Testing -#batch=1 -#subdivisions=1 -# Training -batch=64 -subdivisions=1 -width=416 -height=416 -channels=3 -momentum=0.9 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.00261 -burn_in=1000 -max_batches = 500200 -policy=steps -steps=400000,450000 -scales=.1,.1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -################################## - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - - -[yolo] -mask = 3,4,5 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms -beta_nms=0.6 - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 23 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - -[yolo] -mask = 1,2,3 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms -======= -[net] -# Testing -#batch=1 -#subdivisions=1 -# Training -batch=64 -subdivisions=1 -width=416 -height=416 -channels=3 -momentum=0.9 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.00261 -burn_in=1000 -max_batches = 500200 -policy=steps -steps=400000,450000 -scales=.1,.1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -################################## - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - - -[yolo] -mask = 3,4,5 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms -beta_nms=0.6 - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 23 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - -[yolo] -mask = 1,2,3 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms ->>>>>>> dev -beta_nms=0.6 \ No newline at end of file diff --git a/src/aboutdialog.cpp.orig b/src/aboutdialog.cpp.orig deleted file mode 100644 index f0efd831d..000000000 --- a/src/aboutdialog.cpp.orig +++ /dev/null @@ -1,41 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#include "aboutdialog.h" -#include "ui_aboutdialog.h" - -AboutDialog::AboutDialog(QWidget *parent) - : QDialog(parent), ui(new Ui::AboutDialog) { - ui->setupUi(this); - -} - -void AboutDialog::setVersion(QString version){ - ui->lblVersion->setText(version); -} - -AboutDialog::~AboutDialog() { delete ui; } -======= -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#include "aboutdialog.h" -#include "ui_aboutdialog.h" - -AboutDialog::AboutDialog(QWidget *parent) - : QDialog(parent), ui(new Ui::AboutDialog) { - ui->setupUi(this); - -} - -void AboutDialog::setVersion(QString version){ - ui->lblVersion->setText(version); -} - -AboutDialog::~AboutDialog() { delete ui; } ->>>>>>> dev diff --git a/src/aboutdialog.h.orig b/src/aboutdialog.h.orig deleted file mode 100644 index 14ab5b52a..000000000 --- a/src/aboutdialog.h.orig +++ /dev/null @@ -1,73 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#ifndef ABOUTDIALOG_H -#define ABOUTDIALOG_H - -#include -#include - -namespace Ui { -class AboutDialog; -} - -//! About dialog -/*! - Dialog for show information about the software -*/ -class AboutDialog : public QDialog -{ - Q_OBJECT - -public: - explicit AboutDialog(QWidget *parent = 0); - void setVersion(QString version); - ~AboutDialog(); - -private: - //! QT UI dialog - Ui::AboutDialog *ui; - -}; - -#endif // CALIBRATEFROMIMAGESDIALOG_H -======= -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#ifndef ABOUTDIALOG_H -#define ABOUTDIALOG_H - -#include -#include - -namespace Ui { -class AboutDialog; -} - -//! About dialog -/*! - Dialog for show information about the software -*/ -class AboutDialog : public QDialog -{ - Q_OBJECT - -public: - explicit AboutDialog(QWidget *parent = 0); - void setVersion(QString version); - ~AboutDialog(); - -private: - //! QT UI dialog - Ui::AboutDialog *ui; - -}; - -#endif // CALIBRATEFROMIMAGESDIALOG_H ->>>>>>> dev diff --git a/src/aboutdialog.ui.orig b/src/aboutdialog.ui.orig deleted file mode 100644 index c1a524943..000000000 --- a/src/aboutdialog.ui.orig +++ /dev/null @@ -1,103 +0,0 @@ -<<<<<<< HEAD - - - AboutDialog - - - - 0 - 0 - 200 - 61 - - - - - 0 - 0 - - - - About - - - - - - - 0 - 0 - - - - Version Placeholder - - - Qt::AlignLeading|Qt::AlignLeft|Qt::AlignVCenter - - - - - - - (C) Industrial 3D Robotics 2021 - - - - - - - - -======= - - - AboutDialog - - - - 0 - 0 - 200 - 61 - - - - - 0 - 0 - - - - About - - - - - - - 0 - 0 - - - - Version Placeholder - - - Qt::AlignLeading|Qt::AlignLeft|Qt::AlignVCenter - - - - - - - (C) Industrial 3D Robotics 2021 - - - - - - - - ->>>>>>> dev diff --git a/src/camera/cameracontrol/cameracontrol/cameracontrol.ino.orig b/src/camera/cameracontrol/cameracontrol/cameracontrol.ino.orig deleted file mode 100644 index f14a37750..000000000 --- a/src/camera/cameracontrol/cameracontrol/cameracontrol.ino.orig +++ /dev/null @@ -1,235 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#define PHOBOS_USB_TRIGGER_1 2 -#define PHOBOS_USB_TRIGGER_2 2 - -#define PHOBOS_GIGE_TRIGGER_1 3 -#define PHOBOS_GIGE_TRIGGER_2 2 - -#define TITANIA_USB_TRIGGER_1 1 -#define TITANIA_USB_TRIGGER_2 3 - -//Choose trigger pins based on the camera being used -#define CAMERA_TRIGGER_1 TITANIA_USB_TRIGGER_1 -#define CAMERA_TRIGGER_2 TITANIA_USB_TRIGGER_2 - -//Comment out line when not using the IMU -#define USE_IMU -#ifdef USE_IMU - #include - #include - #include - #include - - //Setup imu device - Adafruit_BNO055 bno = Adafruit_BNO055(55); -#endif - -double frame_delay; // amount of time between triggered (1/fps) -int trigger_time = 10; // time for trigger to be registered by camera -double fps = 5; // inital fps -String inString = ""; // string to hold input -unsigned long lastTriggerTime = 0; -unsigned long timeSinceTrigger; -#ifdef USE_IMU - bool imuInit = false; -#endif - -void setup() { - Serial.begin(115200); - #ifdef USE_IMU - // Initialise the IMU sensor - if(!bno.begin()) - { - // Problem detecting the BNO055 ... check your connections - // serial will return -1000 in all axis in loop to indicate the problem - imuInit = false; - } else { - bno.setExtCrystalUse(true); - imuInit = true; - } - #endif - pinMode(CAMERA_TRIGGER_1, OUTPUT); - pinMode(CAMERA_TRIGGER_2, OUTPUT); - pinMode(LED_BUILTIN,OUTPUT); - - lastTriggerTime = millis(); -} - -void loop() { - // Check time since last trigger - timeSinceTrigger = millis() - lastTriggerTime; - // Calculate frame delay from fps (can change based on user input) - frame_delay = 1000/fps; - - // Wait for time to trigger the camera (without stopping the program) - if (timeSinceTrigger > frame_delay){ - // Triggger camera (pulse high and then low - digitalWrite(LED_BUILTIN, HIGH); - digitalWrite(CAMERA_TRIGGER_1, HIGH); - digitalWrite(CAMERA_TRIGGER_2, HIGH); - lastTriggerTime = millis(); - delay(trigger_time); // Wait small time for high to be registered by camera - digitalWrite(LED_BUILTIN, LOW); - digitalWrite(CAMERA_TRIGGER_1, LOW); - digitalWrite(CAMERA_TRIGGER_2, LOW); - } - - // Read trigger frame rate from serial - if (Serial.available() > 0) { //Only read if data is available - int inChar = Serial.read(); - if (isDigit(inChar)) { - // Convert the incoming byte to a char and add it to the string: - inString += (char)inChar; - } - // If you get a newline, print the string, then the string's value: - if (inChar == '\n') { - fps = inString.toInt(); - // clear the string for new input: - inString = ""; - } - #ifdef USE_IMU - } else { - if (imuInit){ - // Get a new IMU sensor event - sensors_event_t event; - bno.getEvent(&event); - - // Display the floating point data - Serial.print(event.orientation.x, 4); - Serial.print(","); - Serial.print(event.orientation.y, 4); - Serial.print(","); - Serial.print(event.orientation.z, 4); - Serial.println(); - } else { - // IMU failed to initalise return -1000 in all axis - Serial.println("-1000,-1000,-1000"); - } - } - #else - } - #endif -} -======= -/* -* Copyright I3D Robotics Ltd, 2020 -* Author: Ben Knight (bknight@i3drobotics.com) -*/ - -#define PHOBOS_USB_TRIGGER_1 2 -#define PHOBOS_USB_TRIGGER_2 2 - -#define PHOBOS_GIGE_TRIGGER_1 3 -#define PHOBOS_GIGE_TRIGGER_2 2 - -#define TITANIA_USB_TRIGGER_1 1 -#define TITANIA_USB_TRIGGER_2 3 - -//Choose trigger pins based on the camera being used -#define CAMERA_TRIGGER_1 TITANIA_USB_TRIGGER_1 -#define CAMERA_TRIGGER_2 TITANIA_USB_TRIGGER_2 - -//Comment out line when not using the IMU -#define USE_IMU -#ifdef USE_IMU - #include - #include - #include - #include - - //Setup imu device - Adafruit_BNO055 bno = Adafruit_BNO055(55); -#endif - -double frame_delay; // amount of time between triggered (1/fps) -int trigger_time = 10; // time for trigger to be registered by camera -double fps = 5; // inital fps -String inString = ""; // string to hold input -unsigned long lastTriggerTime = 0; -unsigned long timeSinceTrigger; -#ifdef USE_IMU - bool imuInit = false; -#endif - -void setup() { - Serial.begin(115200); - #ifdef USE_IMU - // Initialise the IMU sensor - if(!bno.begin()) - { - // Problem detecting the BNO055 ... check your connections - // serial will return -1000 in all axis in loop to indicate the problem - imuInit = false; - } else { - bno.setExtCrystalUse(true); - imuInit = true; - } - #endif - pinMode(CAMERA_TRIGGER_1, OUTPUT); - pinMode(CAMERA_TRIGGER_2, OUTPUT); - pinMode(LED_BUILTIN,OUTPUT); - - lastTriggerTime = millis(); -} - -void loop() { - // Check time since last trigger - timeSinceTrigger = millis() - lastTriggerTime; - // Calculate frame delay from fps (can change based on user input) - frame_delay = 1000/fps; - - // Wait for time to trigger the camera (without stopping the program) - if (timeSinceTrigger > frame_delay){ - // Triggger camera (pulse high and then low - digitalWrite(LED_BUILTIN, HIGH); - digitalWrite(CAMERA_TRIGGER_1, HIGH); - digitalWrite(CAMERA_TRIGGER_2, HIGH); - lastTriggerTime = millis(); - delay(trigger_time); // Wait small time for high to be registered by camera - digitalWrite(LED_BUILTIN, LOW); - digitalWrite(CAMERA_TRIGGER_1, LOW); - digitalWrite(CAMERA_TRIGGER_2, LOW); - } - - // Read trigger frame rate from serial - if (Serial.available() > 0) { //Only read if data is available - int inChar = Serial.read(); - if (isDigit(inChar)) { - // Convert the incoming byte to a char and add it to the string: - inString += (char)inChar; - } - // If you get a newline, print the string, then the string's value: - if (inChar == '\n') { - fps = inString.toInt(); - // clear the string for new input: - inString = ""; - } - #ifdef USE_IMU - } else { - if (imuInit){ - // Get a new IMU sensor event - sensors_event_t event; - bno.getEvent(&event); - - // Display the floating point data - Serial.print(event.orientation.x, 4); - Serial.print(","); - Serial.print(event.orientation.y, 4); - Serial.print(","); - Serial.print(event.orientation.z, 4); - Serial.println(); - } else { - // IMU failed to initalise return -1000 in all axis - Serial.println("-1000,-1000,-1000"); - } - } - #else - } - #endif -} ->>>>>>> dev diff --git a/src/camera/cameracontrol/imucontrol/imucontrol/imucontrol.ino.orig b/src/camera/cameracontrol/imucontrol/imucontrol/imucontrol.ino.orig deleted file mode 100644 index 2fd49ae60..000000000 --- a/src/camera/cameracontrol/imucontrol/imucontrol/imucontrol.ino.orig +++ /dev/null @@ -1,87 +0,0 @@ -<<<<<<< HEAD -#include -#include -#include -#include - -Adafruit_BNO055 bno = Adafruit_BNO055(55); - -void setup(void) -{ - Serial.begin(9600); - Serial.println("Orientation Sensor Test"); Serial.println(""); - - /* Initialise the sensor */ - if(!bno.begin()) - { - /* There was a problem detecting the BNO055 ... check your connections */ - Serial.print("Ooops, no BNO055 detected ... Check your wiring or I2C ADDR!"); - while(1); - } - - delay(1000); - - bno.setExtCrystalUse(true); -} - -void loop(void) -{ - /* Get a new sensor event */ - sensors_event_t event; - bno.getEvent(&event); - - /* Display the floating point data */ - Serial.print("X: "); - Serial.print(event.orientation.x, 4); - Serial.print("\tY: "); - Serial.print(event.orientation.y, 4); - Serial.print("\tZ: "); - Serial.print(event.orientation.z, 4); - Serial.println(""); - - delay(100); -} -======= -#include -#include -#include -#include - -Adafruit_BNO055 bno = Adafruit_BNO055(55); - -void setup(void) -{ - Serial.begin(9600); - Serial.println("Orientation Sensor Test"); Serial.println(""); - - /* Initialise the sensor */ - if(!bno.begin()) - { - /* There was a problem detecting the BNO055 ... check your connections */ - Serial.print("Ooops, no BNO055 detected ... Check your wiring or I2C ADDR!"); - while(1); - } - - delay(1000); - - bno.setExtCrystalUse(true); -} - -void loop(void) -{ - /* Get a new sensor event */ - sensors_event_t event; - bno.getEvent(&event); - - /* Display the floating point data */ - Serial.print("X: "); - Serial.print(event.orientation.x, 4); - Serial.print("\tY: "); - Serial.print(event.orientation.y, 4); - Serial.print("\tZ: "); - Serial.print(event.orientation.z, 4); - Serial.println(""); - - delay(100); -} ->>>>>>> dev diff --git a/src/camera/cameravimba.cpp.orig b/src/camera/cameravimba.cpp.orig deleted file mode 100644 index 9dd51cd35..000000000 --- a/src/camera/cameravimba.cpp.orig +++ /dev/null @@ -1,1211 +0,0 @@ -<<<<<<< HEAD -#include "cameravimba.h" - -CameraVimba::CameraVimba(QObject *parent) : QObject(parent), system(VimbaSystem::GetInstance()) -{ -} - - -void CameraVimba::assignThread(QThread *thread) -{ - moveToThread(thread); - connect(this, SIGNAL(finished()), thread, SLOT(quit())); - connect(this, SIGNAL(finished()), this, SLOT(deleteLater())); - connect(thread, SIGNAL(finished()), thread, SLOT(deleteLater())); - thread->start(); -} - -void CameraVimba::close() { - - qDebug() << "Closing camera"; - - stopCapture(); - camera->Close(); - - connected = false; -} - -bool CameraVimba::initCamera(std::string camera_serial,int binning, bool trigger, int fps) -{ - this->camera_serial = camera_serial; - this->binning = binning; - this->trigger = trigger; - this->fps = fps; - connected = false; - - CameraPtrVector cameras; - system.GetCameras(cameras); - - bool cameraFind = false; - for (const CameraPtr &trial_camera : cameras){ - std::string serial; - trial_camera->GetSerialNumber(serial); - - if(serial == camera_serial){ - cameraFind = true; - camera = trial_camera; - break; - } - } - - if (!cameraFind){ - std::cerr << "Failed to find camera with name: " << camera_serial << std::endl; - return false; - } - - // Print the model name of the camera. - std::string model; - camera->GetModel(model); - qDebug() << "Using device " << model.c_str(); - - auto error = camera->Open(VmbAccessModeFull); - - if (error == VmbErrorSuccess){ - - bool success = changeFPS(fps); - if(!success) - qDebug() << "Failed to set FPS"; - - // Currently binning is not supported - //bool error = setBinning(binning); - success = enableTrigger(trigger); - if(!success) - qDebug() << "Failed to set trigger mode"; - - frame_observer = shared_ptr(new FrameObserver(camera)); - connected = true; - - } else { - qDebug() << "Failed to open camera"; - connected = false; - } - - return connected; -} - -void CameraVimba::startCapture(void){ - - if(capturing) return; - - auto res = camera->StartContinuousImageAcquisition(15, frame_observer); - - if(res != VmbErrorSuccess){ - qDebug() << "Couldn't start capture!" << res; - }else{ - connect(frame_observer.get(), SIGNAL( frameReady(int) ), this, SLOT( onFrame(int) ) ); - capturing = true; - } -} - -void CameraVimba::stopCapture(void){ - if(!capturing) return; - - camera->StopContinuousImageAcquisition(); - disconnect(frame_observer.get(), SIGNAL( frameReady(int) ), this, SLOT( onFrame(int) ) ); - capturing = false; -} - -void CameraVimba::onFrame(int status){ - if(!connected){ - return; - } - - // Pick up frame - FramePtr pFrame = frame_observer->getFrame(); - if( pFrame == nullptr){ - qDebug() << "frame pointer is NULL, late frame ready message"; - return; - } - - // See if it is not corrupt - if( status == VmbFrameStatusComplete ){ - VmbUchar_t *pBuffer; - VmbErrorType err = pFrame.get()->GetImage(pBuffer); - if( VmbErrorSuccess != err ) - { - qDebug() << "Failed to get image pointer"; - return; - } - - VmbPixelFormatType ePixelFormat = VmbPixelFormatMono8; - err = pFrame->GetPixelFormat( ePixelFormat ); - if ( err != VmbErrorSuccess) - { - qDebug() << "Failed to get pixel format"; - return; - } - - if (( ePixelFormat != VmbPixelFormatMono8 )) - { - qDebug() << "Invalid pixel format"; - return; - } - - VmbUint32_t nImageSize = 0; - err = pFrame->GetImageSize( nImageSize ); - if ( err != VmbErrorSuccess ) - { - qDebug() << "Failed to get image size"; - return; - } - - VmbUint32_t nWidth = 0; - err = pFrame->GetWidth( nWidth ); - if ( err != VmbErrorSuccess ){ - qDebug() << "Failed to get image width"; - return; - } - - VmbUint32_t nHeight = 0; - err = pFrame->GetHeight( nHeight ); - if ( err != VmbErrorSuccess ) - { - qDebug() << "Failed to get image height"; - return; - } - - VmbUchar_t *pImage = nullptr; - err = pFrame->GetImage( pImage ); - if ( err == VmbErrorSuccess) - { - frame_mutex.lock(); - cv::Mat image_temp = cv::Mat(static_cast(nHeight), static_cast(nWidth), CV_8UC1, pImage ); - image_temp.copyTo(image); - frame_mutex.unlock(); - - } - - camera->QueueFrame(pFrame); - } - - emit captured(); -} - -VmbError_t CameraVimba::getStringFeature(std::string name, std::string &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -VmbError_t CameraVimba::getIntFeature(std::string name, long long &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - VmbInt64_t out; - feature->GetValue(out); - res = static_cast(out); - } - - return error; -} - -VmbError_t CameraVimba::getBoolFeature(std::string name, bool &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -VmbError_t CameraVimba::getDoubleFeature(std::string name, double &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -void CameraVimba::getImageSize(int &width, int &height, int &bitdepth) -{ - //get image size - long long h = 0, w = 0; - auto err_h = getIntFeature("Height", h); - auto err_w = getIntFeature("Width", w); - - std::string format; - auto err_fmt = getStringFeature("PixelSize", format); - - if (err_h == VmbErrorSuccess && err_w == VmbErrorSuccess && err_fmt == VmbErrorSuccess){ - height = static_cast(h); - width = static_cast(w); - if(format == "Bpp8") - bitdepth = 8; - else if(format == "Bpp16") - bitdepth = 16; - } else { - qDebug() << "Failed to get width / height"; - } -} - -bool CameraVimba::changeFPS(int fps){ - bool res = true; - bool capture_state = capturing; - - if(capture_state){ - stopCapture(); - } - // unlimited frame rate if set to 0 - if (fps > 0){ - // Order is important! - res &= enableFPS(true); - if(!res) - qDebug() << "Failed to enable FPS"; - res &= setFPS(fps); - if(!res) - qDebug() << "Failed to set FPS"; - - this->fps = fps; - } else { - res &= enableFPS(false); - this->fps = fps; - } - if(capture_state){ - startCapture(); - } - return res; -} - -bool CameraVimba::setFPS(int fps){ - FeaturePtr feature; - qDebug() << "Setting fps: " << fps; - camera->GetFeatureByName("AcquisitionFrameRate", feature); - auto error = feature->SetValue(static_cast(fps)); - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableFPS(bool enable){ - if (enable){ - qDebug() << "Enabling fps"; - } else { - qDebug() << "Disabling fps"; - } - - FeaturePtr feature; - camera->GetFeatureByName("AcquisitionFrameRateEnable", feature); - auto error = feature->SetValue(enable); - - bool state; - feature->GetValue(state); - - return (error == VmbErrorSuccess) && (state == enable); -} - -bool CameraVimba::enableTrigger(bool enable){ - FeaturePtr feature; - VmbError_t error; - std::string state; - std::string requested_trigger_mode; - std::string requested_line_selector; - std::string requested_line_mode; - std::string requested_trigger_source; - std::string requested_trigger_selector; - bool success = true; - - if(enable){ - qDebug() << "Enabling trigger"; - requested_trigger_mode = "On"; - requested_trigger_source = "Line0"; - requested_trigger_selector = "FrameStart"; - requested_line_selector = "Line0"; - requested_line_mode = "Input"; - } else { - qDebug() << "Disabling trigger"; - requested_trigger_mode = "Off"; - requested_trigger_source = "Software"; - requested_trigger_selector = "FrameStart"; - requested_line_selector = "Line0"; - requested_line_mode = "Input"; - } - - if (enable){ - /* - camera->GetFeatureByName("LineSelector", feature); - error = feature->SetValue(requested_line_selector.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_line_selector); - - if (!success){ - qDebug() << "Failed to set line selector"; - return success; - } - - camera->GetFeatureByName("LineMode", feature); - error = feature->SetValue(requested_line_mode.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_line_mode); - - if (!success){ - qDebug() << "Failed to set line mode"; - return success; - } - */ - } - - camera->GetFeatureByName("TriggerSelector", feature); - error = feature->SetValue(requested_trigger_selector.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_selector); - - if (!success){ - qDebug() << "Failed to set trigger selector"; - return success; - } - - camera->GetFeatureByName("TriggerSource", feature); - error = feature->SetValue(requested_trigger_source.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_source); - - if (!success){ - qDebug() << "Failed to set trigger source"; - return success; - } - - camera->GetFeatureByName("TriggerMode", feature); - error = feature->SetValue(requested_trigger_mode.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_mode); - - if (!success){ - qDebug() << "Failed to set trigger mode"; - qDebug() << state.c_str(); - return success; - } - - return success; -} - -bool CameraVimba::changeBinning(int binning){ - close(); - return (initCamera(this->camera_serial,binning,this->trigger,this->fps)); -} - -double CameraVimba::getFPS(void){ - FeaturePtr feature; - double fps; - auto success = getDoubleFeature("AcquisitionFrameRate", fps); - if(success != VmbErrorSuccess){ - fps = -1; - } - return fps; -} - - -bool CameraVimba::setBinning(int binning) -{ - FeaturePtr feature; - bool success = true; - camera->GetFeatureByName("BinningHorizontal", feature); - // Camera doesn't support binning - if(feature.get() == nullptr) return false; - - success &= (feature->SetValue(binning) == VmbErrorSuccess); - camera->GetFeatureByName("BinningVertical", feature); - success &= (feature->SetValue(binning) == VmbErrorSuccess); - - return success; -} - -bool CameraVimba::setExposure(double exposure) -{ - FeaturePtr feature; - //int fps = static_cast(getFPS()); - camera.get()->GetFeatureByName("ExposureTime", feature); - double exposure_scaled = exposure * 1000; - qDebug() << "Setting exposure to: " << exposure_scaled; - qDebug() << "Vimba exposure to: " << exposure_scaled; - auto error = feature->SetValue(exposure_scaled); // microseconds - - if(error != VmbErrorSuccess){ - qDebug() << "Failed to set exposure"; - }else{ - /* - if(fps < 30 && 1.0/(1000.0*exposure) < 1.0/30){ - changeFPS(30); - } - */ - } - - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableAutoExposure(bool enable) -{ - std::string exposure_mode = "Off"; - if(enable) exposure_mode = "Continuous"; - - FeaturePtr feature; - - if (enable){ - qDebug() << "Enabling auto exposure"; - } else { - qDebug() << "Disabling auto exposure"; - } - - camera->GetFeatureByName("ExposureAuto", feature); - auto error = feature->SetValue(exposure_mode.c_str()); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableAutoGain(bool enable) -{ - - if (enable){ - qDebug() << "Enabling auto gain"; - } else { - qDebug() << "Disabling auto gain"; - } - - std::string gain_mode = "Off"; - if(enable) gain_mode = "Continuous"; - - FeaturePtr feature; - camera->GetFeatureByName("GainAuto", feature); - auto error = feature->SetValue(gain_mode.c_str()); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::setGain(int gain) -{ - - enableAutoGain(false); - - FeaturePtr feature; - camera.get()->GetFeatureByName("Gain", feature); - - qDebug() << "Setting gain to: " << gain; - - double min_gain, max_gain; - feature->GetRange(min_gain, max_gain); - - qDebug() << "Vimba gain to: " << max_gain*static_cast(gain)/100.0; - - auto error = feature->SetValue(max_gain*static_cast(gain)/100.0); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::capture(void) -{ - bool res = false; - QElapsedTimer timer; - timer.start(); - - if (connected){ - // get image from cameras - AVT::VmbAPI::FramePtr pFrame; - VmbErrorType capture_err; - VmbFrameStatusType status = VmbFrameStatusIncomplete; - - //qDebug() << "Init: " << timer.nsecsElapsed() / 1e9; - - capture_err = camera->AcquireSingleImage( pFrame, 5000 ); - //timer.start(); - - if ( capture_err == VmbErrorSuccess) - { - capture_err = pFrame->GetReceiveStatus( status ); - if ( capture_err == VmbErrorSuccess - && status == VmbFrameStatusComplete) - { - VmbPixelFormatType ePixelFormat = VmbPixelFormatMono8; - capture_err = pFrame->GetPixelFormat( ePixelFormat ); - if ( capture_err == VmbErrorSuccess) - { - if ( ( ePixelFormat != VmbPixelFormatMono8 )) - { - //capture_err = VmbErrorInvalidValue; - qDebug() << "Invalid pixel format"; - res = false; - } - else - { - VmbUint32_t nImageSize = 0; - capture_err = pFrame->GetImageSize( nImageSize ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUint32_t nWidth = 0; - capture_err = pFrame->GetWidth( nWidth ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUint32_t nHeight = 0; - capture_err = pFrame->GetHeight( nHeight ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUchar_t *pImage = nullptr; - capture_err = pFrame->GetImage( pImage ); - if ( capture_err == VmbErrorSuccess) - { - cv::Mat image_temp = cv::Mat(nHeight, nWidth, CV_8UC1, pImage ); - image_temp.copyTo(image); - res = true; - } - } - } - } - - } - } else { - qDebug() << "Failed to get pixel format"; - res = false; - } - } else { - qDebug() << "Failed to acquire frame or incomplete"; - res = false; - } - } else { - if(capture_err == VmbErrorTimeout && trigger){ - qDebug() << "Waiting for trigger?"; - } - qDebug() << "Failed to acquire image"; - res = false; - } - } else { - qDebug() << "Camera is not connected or is initalising"; - std::cerr << "Camera is not connected or is initalising" << std::endl; - res = false; - } - - qDebug() << "Capture time: " << timer.nsecsElapsed() / 1e9; - - return res; -} - -bool CameraVimba::getImage(cv::Mat &out) { - cv::Mat temp; - frame_mutex.lock(); - if (image.cols == 0 || image.rows == 0){ - qDebug() << "Global image result buffer size is incorrect"; - qDebug() << "cols: " << image.cols << " rows: " << image.rows; - return false; - } - image.copyTo(out); - frame_mutex.unlock(); - return true; -} - -CameraVimba::~CameraVimba(void) -{ - close(); - emit finished(); -} -======= -#include "cameravimba.h" - -CameraVimba::CameraVimba(QObject *parent) : QObject(parent), system(VimbaSystem::GetInstance()) -{ -} - - -void CameraVimba::assignThread(QThread *thread) -{ - moveToThread(thread); - connect(this, SIGNAL(finished()), thread, SLOT(quit())); - connect(this, SIGNAL(finished()), this, SLOT(deleteLater())); - connect(thread, SIGNAL(finished()), thread, SLOT(deleteLater())); - thread->start(); -} - -void CameraVimba::close() { - - qDebug() << "Closing camera"; - - stopCapture(); - camera->Close(); - - connected = false; -} - -bool CameraVimba::initCamera(std::string camera_serial,int binning, bool trigger, int fps) -{ - this->camera_serial = camera_serial; - this->binning = binning; - this->trigger = trigger; - this->fps = fps; - connected = false; - - CameraPtrVector cameras; - system.GetCameras(cameras); - - bool cameraFind = false; - for (const CameraPtr &trial_camera : cameras){ - std::string serial; - trial_camera->GetSerialNumber(serial); - - if(serial == camera_serial){ - cameraFind = true; - camera = trial_camera; - break; - } - } - - if (!cameraFind){ - std::cerr << "Failed to find camera with name: " << camera_serial << std::endl; - return false; - } - - // Print the model name of the camera. - std::string model; - camera->GetModel(model); - qDebug() << "Using device " << model.c_str(); - - auto error = camera->Open(VmbAccessModeFull); - - if (error == VmbErrorSuccess){ - - bool success = changeFPS(fps); - if(!success) - qDebug() << "Failed to set FPS"; - - // Currently binning is not supported - //bool error = setBinning(binning); - success = enableTrigger(trigger); - if(!success) - qDebug() << "Failed to set trigger mode"; - - frame_observer = shared_ptr(new FrameObserver(camera)); - connected = true; - - } else { - qDebug() << "Failed to open camera"; - connected = false; - } - - return connected; -} - -void CameraVimba::startCapture(void){ - - if(capturing) return; - - auto res = camera->StartContinuousImageAcquisition(15, frame_observer); - - if(res != VmbErrorSuccess){ - qDebug() << "Couldn't start capture!" << res; - }else{ - connect(frame_observer.get(), SIGNAL( frameReady(int) ), this, SLOT( onFrame(int) ) ); - capturing = true; - } -} - -void CameraVimba::stopCapture(void){ - if(!capturing) return; - - camera->StopContinuousImageAcquisition(); - disconnect(frame_observer.get(), SIGNAL( frameReady(int) ), this, SLOT( onFrame(int) ) ); - capturing = false; -} - -void CameraVimba::onFrame(int status){ - if(!connected){ - return; - } - - // Pick up frame - FramePtr pFrame = frame_observer->getFrame(); - if( pFrame == nullptr){ - qDebug() << "frame pointer is NULL, late frame ready message"; - return; - } - - // See if it is not corrupt - if( status == VmbFrameStatusComplete ){ - VmbUchar_t *pBuffer; - VmbErrorType err = pFrame.get()->GetImage(pBuffer); - if( VmbErrorSuccess != err ) - { - qDebug() << "Failed to get image pointer"; - return; - } - - VmbPixelFormatType ePixelFormat = VmbPixelFormatMono8; - err = pFrame->GetPixelFormat( ePixelFormat ); - if ( err != VmbErrorSuccess) - { - qDebug() << "Failed to get pixel format"; - return; - } - - if (( ePixelFormat != VmbPixelFormatMono8 )) - { - qDebug() << "Invalid pixel format"; - return; - } - - VmbUint32_t nImageSize = 0; - err = pFrame->GetImageSize( nImageSize ); - if ( err != VmbErrorSuccess ) - { - qDebug() << "Failed to get image size"; - return; - } - - VmbUint32_t nWidth = 0; - err = pFrame->GetWidth( nWidth ); - if ( err != VmbErrorSuccess ){ - qDebug() << "Failed to get image width"; - return; - } - - VmbUint32_t nHeight = 0; - err = pFrame->GetHeight( nHeight ); - if ( err != VmbErrorSuccess ) - { - qDebug() << "Failed to get image height"; - return; - } - - VmbUchar_t *pImage = nullptr; - err = pFrame->GetImage( pImage ); - if ( err == VmbErrorSuccess) - { - frame_mutex.lock(); - cv::Mat image_temp = cv::Mat(static_cast(nHeight), static_cast(nWidth), CV_8UC1, pImage ); - image_temp.copyTo(image); - frame_mutex.unlock(); - - } - - camera->QueueFrame(pFrame); - } - - emit captured(); -} - -VmbError_t CameraVimba::getStringFeature(std::string name, std::string &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -VmbError_t CameraVimba::getIntFeature(std::string name, long long &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - VmbInt64_t out; - feature->GetValue(out); - res = static_cast(out); - } - - return error; -} - -VmbError_t CameraVimba::getBoolFeature(std::string name, bool &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -VmbError_t CameraVimba::getDoubleFeature(std::string name, double &res){ - - FeaturePtr feature; - auto error = camera->GetFeatureByName(name.c_str(), feature); - if(error == VmbErrorSuccess){ - feature->GetValue(res); - } - - return error; -} - -void CameraVimba::getImageSize(int &width, int &height, int &bitdepth) -{ - //get image size - long long h = 0, w = 0; - auto err_h = getIntFeature("Height", h); - auto err_w = getIntFeature("Width", w); - - std::string format; - auto err_fmt = getStringFeature("PixelSize", format); - - if (err_h == VmbErrorSuccess && err_w == VmbErrorSuccess && err_fmt == VmbErrorSuccess){ - height = static_cast(h); - width = static_cast(w); - if(format == "Bpp8") - bitdepth = 8; - else if(format == "Bpp16") - bitdepth = 16; - } else { - qDebug() << "Failed to get width / height"; - } -} - -bool CameraVimba::changeFPS(int fps){ - bool res = true; - bool capture_state = capturing; - - if(capture_state){ - stopCapture(); - } - // unlimited frame rate if set to 0 - if (fps > 0){ - // Order is important! - res &= enableFPS(true); - if(!res) - qDebug() << "Failed to enable FPS"; - res &= setFPS(fps); - if(!res) - qDebug() << "Failed to set FPS"; - - this->fps = fps; - } else { - res &= enableFPS(false); - this->fps = fps; - } - if(capture_state){ - startCapture(); - } - return res; -} - -bool CameraVimba::setFPS(int fps){ - FeaturePtr feature; - qDebug() << "Setting fps: " << fps; - camera->GetFeatureByName("AcquisitionFrameRate", feature); - auto error = feature->SetValue(static_cast(fps)); - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableFPS(bool enable){ - if (enable){ - qDebug() << "Enabling fps"; - } else { - qDebug() << "Disabling fps"; - } - - FeaturePtr feature; - camera->GetFeatureByName("AcquisitionFrameRateEnable", feature); - auto error = feature->SetValue(enable); - - bool state; - feature->GetValue(state); - - return (error == VmbErrorSuccess) && (state == enable); -} - -bool CameraVimba::enableTrigger(bool enable){ - FeaturePtr feature; - VmbError_t error; - std::string state; - std::string requested_trigger_mode; - std::string requested_line_selector; - std::string requested_line_mode; - std::string requested_trigger_source; - std::string requested_trigger_selector; - bool success = true; - - if(enable){ - qDebug() << "Enabling trigger"; - requested_trigger_mode = "On"; - requested_trigger_source = "Line0"; - requested_trigger_selector = "FrameStart"; - requested_line_selector = "Line0"; - requested_line_mode = "Input"; - } else { - qDebug() << "Disabling trigger"; - requested_trigger_mode = "Off"; - requested_trigger_source = "Software"; - requested_trigger_selector = "FrameStart"; - requested_line_selector = "Line0"; - requested_line_mode = "Input"; - } - - if (enable){ - /* - camera->GetFeatureByName("LineSelector", feature); - error = feature->SetValue(requested_line_selector.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_line_selector); - - if (!success){ - qDebug() << "Failed to set line selector"; - return success; - } - - camera->GetFeatureByName("LineMode", feature); - error = feature->SetValue(requested_line_mode.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_line_mode); - - if (!success){ - qDebug() << "Failed to set line mode"; - return success; - } - */ - } - - camera->GetFeatureByName("TriggerSelector", feature); - error = feature->SetValue(requested_trigger_selector.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_selector); - - if (!success){ - qDebug() << "Failed to set trigger selector"; - return success; - } - - camera->GetFeatureByName("TriggerSource", feature); - error = feature->SetValue(requested_trigger_source.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_source); - - if (!success){ - qDebug() << "Failed to set trigger source"; - return success; - } - - camera->GetFeatureByName("TriggerMode", feature); - error = feature->SetValue(requested_trigger_mode.c_str()); - feature->GetValue(state); - success &= (error == VmbErrorSuccess) && (state == requested_trigger_mode); - - if (!success){ - qDebug() << "Failed to set trigger mode"; - qDebug() << state.c_str(); - return success; - } - - return success; -} - -bool CameraVimba::changeBinning(int binning){ - close(); - return (initCamera(this->camera_serial,binning,this->trigger,this->fps)); -} - -double CameraVimba::getFPS(void){ - FeaturePtr feature; - double fps; - auto success = getDoubleFeature("AcquisitionFrameRate", fps); - if(success != VmbErrorSuccess){ - fps = -1; - } - return fps; -} - - -bool CameraVimba::setBinning(int binning) -{ - FeaturePtr feature; - bool success = true; - camera->GetFeatureByName("BinningHorizontal", feature); - // Camera doesn't support binning - if(feature.get() == nullptr) return false; - - success &= (feature->SetValue(binning) == VmbErrorSuccess); - camera->GetFeatureByName("BinningVertical", feature); - success &= (feature->SetValue(binning) == VmbErrorSuccess); - - return success; -} - -bool CameraVimba::setExposure(double exposure) -{ - FeaturePtr feature; - //int fps = static_cast(getFPS()); - camera.get()->GetFeatureByName("ExposureTime", feature); - double exposure_scaled = exposure * 1000; - qDebug() << "Setting exposure to: " << exposure_scaled; - qDebug() << "Vimba exposure to: " << exposure_scaled; - auto error = feature->SetValue(exposure_scaled); // microseconds - - if(error != VmbErrorSuccess){ - qDebug() << "Failed to set exposure"; - }else{ - /* - if(fps < 30 && 1.0/(1000.0*exposure) < 1.0/30){ - changeFPS(30); - } - */ - } - - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableAutoExposure(bool enable) -{ - std::string exposure_mode = "Off"; - if(enable) exposure_mode = "Continuous"; - - FeaturePtr feature; - - if (enable){ - qDebug() << "Enabling auto exposure"; - } else { - qDebug() << "Disabling auto exposure"; - } - - camera->GetFeatureByName("ExposureAuto", feature); - auto error = feature->SetValue(exposure_mode.c_str()); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::enableAutoGain(bool enable) -{ - - if (enable){ - qDebug() << "Enabling auto gain"; - } else { - qDebug() << "Disabling auto gain"; - } - - std::string gain_mode = "Off"; - if(enable) gain_mode = "Continuous"; - - FeaturePtr feature; - camera->GetFeatureByName("GainAuto", feature); - auto error = feature->SetValue(gain_mode.c_str()); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::setGain(int gain) -{ - - enableAutoGain(false); - - FeaturePtr feature; - camera.get()->GetFeatureByName("Gain", feature); - - qDebug() << "Setting gain to: " << gain; - - double min_gain, max_gain; - feature->GetRange(min_gain, max_gain); - - qDebug() << "Vimba gain to: " << max_gain*static_cast(gain)/100.0; - - auto error = feature->SetValue(max_gain*static_cast(gain)/100.0); - - return error == VmbErrorSuccess; -} - -bool CameraVimba::capture(void) -{ - bool res = false; - QElapsedTimer timer; - timer.start(); - - if (connected){ - // get image from cameras - AVT::VmbAPI::FramePtr pFrame; - VmbErrorType capture_err; - VmbFrameStatusType status = VmbFrameStatusIncomplete; - - //qDebug() << "Init: " << timer.nsecsElapsed() / 1e9; - - capture_err = camera->AcquireSingleImage( pFrame, 5000 ); - //timer.start(); - - if ( capture_err == VmbErrorSuccess) - { - capture_err = pFrame->GetReceiveStatus( status ); - if ( capture_err == VmbErrorSuccess - && status == VmbFrameStatusComplete) - { - VmbPixelFormatType ePixelFormat = VmbPixelFormatMono8; - capture_err = pFrame->GetPixelFormat( ePixelFormat ); - if ( capture_err == VmbErrorSuccess) - { - if ( ( ePixelFormat != VmbPixelFormatMono8 )) - { - //capture_err = VmbErrorInvalidValue; - qDebug() << "Invalid pixel format"; - res = false; - } - else - { - VmbUint32_t nImageSize = 0; - capture_err = pFrame->GetImageSize( nImageSize ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUint32_t nWidth = 0; - capture_err = pFrame->GetWidth( nWidth ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUint32_t nHeight = 0; - capture_err = pFrame->GetHeight( nHeight ); - if ( capture_err == VmbErrorSuccess ) - { - VmbUchar_t *pImage = nullptr; - capture_err = pFrame->GetImage( pImage ); - if ( capture_err == VmbErrorSuccess) - { - cv::Mat image_temp = cv::Mat(nHeight, nWidth, CV_8UC1, pImage ); - image_temp.copyTo(image); - res = true; - } - } - } - } - - } - } else { - qDebug() << "Failed to get pixel format"; - res = false; - } - } else { - qDebug() << "Failed to acquire frame or incomplete"; - res = false; - } - } else { - if(capture_err == VmbErrorTimeout && trigger){ - qDebug() << "Waiting for trigger?"; - } - qDebug() << "Failed to acquire image"; - res = false; - } - } else { - qDebug() << "Camera is not connected or is initalising"; - std::cerr << "Camera is not connected or is initalising" << std::endl; - res = false; - } - - qDebug() << "Capture time: " << timer.nsecsElapsed() / 1e9; - - return res; -} - -bool CameraVimba::getImage(cv::Mat &out) { - cv::Mat temp; - frame_mutex.lock(); - if (image.cols == 0 || image.rows == 0){ - qDebug() << "Global image result buffer size is incorrect"; - qDebug() << "cols: " << image.cols << " rows: " << image.rows; - return false; - } - image.copyTo(out); - frame_mutex.unlock(); - return true; -} - -CameraVimba::~CameraVimba(void) -{ - close(); - emit finished(); -} ->>>>>>> dev diff --git a/src/camera/cameravimba.h.orig b/src/camera/cameravimba.h.orig deleted file mode 100644 index a0a23b3ed..000000000 --- a/src/camera/cameravimba.h.orig +++ /dev/null @@ -1,297 +0,0 @@ -<<<<<<< HEAD -#ifndef CAMERAVIMBA_H -#define CAMERAVIMBA_H - -#include -#include -#include -#include -#include - -#include -#include - -using namespace AVT::VmbAPI; - -// Constructor for the FrameObserver class -class FrameObserver : public QObject, public IFrameObserver{ - Q_OBJECT -public: - explicit FrameObserver(CameraPtr pCamera) : IFrameObserver(pCamera){} - - // Frame callback notifies about incoming frames - void FrameReceived(const FramePtr pFrame){ - bool bQueueDirectly = true; - VmbFrameStatusType eReceiveStatus; - auto receive_error = pFrame->GetReceiveStatus( eReceiveStatus ); - int num_recievers = receivers(SIGNAL(frameReady(int))); - - if( num_recievers!= 0 && receive_error == VmbErrorSuccess ){ - // Lock the frame queue - frame_mutex.lock(); - frame_queue.push( pFrame ); - // Unlock frame queue - frame_mutex.unlock(); - // Emit the frame received signal - emit frameReady(eReceiveStatus); - bQueueDirectly = false; - - }else{ - m_pCamera->QueueFrame( pFrame ); - } - } - - FramePtr getFrame(void){ - // Lock the frame queue - frame_mutex.lock(); - // Pop frame from queue - FramePtr res; - if( !frame_queue.empty() ) - { - res = frame_queue.front(); - frame_queue.pop(); - } - // Unlock frame queue - frame_mutex.unlock(); - return res; - } - - void clearFrameQueue(){ - // Lock the frame queue - frame_mutex.lock(); - // Clear the frame queue and release the memory - std::queue empty; - std::swap( frame_queue, empty ); - // Unlock the frame queue - frame_mutex.unlock(); - } - -signals: - void frameReady(int status); - -private: - std::queue frame_queue; - QMutex frame_mutex; -}; - -class CameraVimba : public QObject -{ - Q_OBJECT -public: - explicit CameraVimba(QObject *parent = nullptr); - bool isAvailable(); - void close(); - bool initCamera(std::string camera_serial, int binning, bool trigger, int fps); - void assignThread(QThread *thread); - void getImageSize(int &image_width, int &image_height, int &bit_depth); - bool setFrame16(); - bool setFrame8(); - bool setMaximumResolution(); - bool setPTP(bool enable); - bool setExposure(double exposure); - bool enableAutoExposure(bool enable); - bool enableAutoGain(bool enable); - bool setGain(int gain); - bool setPacketSize(int packetSize); - bool setInterPacketDelay(int interPacketDelay); - bool setBinning(int binning); - bool enableTrigger(bool enable); - - bool changeFPS(int fps); - bool changeBinning(int binning); - bool changePacketSize(int packet_size); - bool setFPS(int fps); - bool enableFPS(bool enable); - - ~CameraVimba(void); - -signals: - void captured(); - void finished(); - -private: - VimbaSystem &system; - CameraPtr camera; - cv::Mat image; - cv::Mat channels[3]; - cv::Mat image_buffer; - enum format {Y800, Y16}; - format image_format; - bool connected = false; - int binning = 1; - bool trigger = false; - int fps = 30; - std::string camera_serial; - int max_timeout = 2000; - QMutex frame_mutex; - bool capturing = false; - - shared_ptr frame_observer; - FramePtrVector frame_buffer; - - VmbError_t getStringFeature(std::string feature, std::string &res); - VmbError_t getBoolFeature(std::string feature, bool &res); - VmbError_t getDoubleFeature(std::string feature, double &res); - VmbError_t getIntFeature(std::string feature, long long &res); - double getFPS(); - -public slots: - bool capture(void); - bool getImage(cv::Mat &out); - - void startCapture(); - void stopCapture(); -private slots: - void onFrame(int); -}; - -#endif // CAMERAVIMBA_H -======= -#ifndef CAMERAVIMBA_H -#define CAMERAVIMBA_H - -#include -#include -#include -#include -#include - -#include -#include - -using namespace AVT::VmbAPI; - -// Constructor for the FrameObserver class -class FrameObserver : public QObject, public IFrameObserver{ - Q_OBJECT -public: - explicit FrameObserver(CameraPtr pCamera) : IFrameObserver(pCamera){} - - // Frame callback notifies about incoming frames - void FrameReceived(const FramePtr pFrame){ - bool bQueueDirectly = true; - VmbFrameStatusType eReceiveStatus; - auto receive_error = pFrame->GetReceiveStatus( eReceiveStatus ); - int num_recievers = receivers(SIGNAL(frameReady(int))); - - if( num_recievers!= 0 && receive_error == VmbErrorSuccess ){ - // Lock the frame queue - frame_mutex.lock(); - frame_queue.push( pFrame ); - // Unlock frame queue - frame_mutex.unlock(); - // Emit the frame received signal - emit frameReady(eReceiveStatus); - bQueueDirectly = false; - - }else{ - m_pCamera->QueueFrame( pFrame ); - } - } - - FramePtr getFrame(void){ - // Lock the frame queue - frame_mutex.lock(); - // Pop frame from queue - FramePtr res; - if( !frame_queue.empty() ) - { - res = frame_queue.front(); - frame_queue.pop(); - } - // Unlock frame queue - frame_mutex.unlock(); - return res; - } - - void clearFrameQueue(){ - // Lock the frame queue - frame_mutex.lock(); - // Clear the frame queue and release the memory - std::queue empty; - std::swap( frame_queue, empty ); - // Unlock the frame queue - frame_mutex.unlock(); - } - -signals: - void frameReady(int status); - -private: - std::queue frame_queue; - QMutex frame_mutex; -}; - -class CameraVimba : public QObject -{ - Q_OBJECT -public: - explicit CameraVimba(QObject *parent = nullptr); - bool isAvailable(); - void close(); - bool initCamera(std::string camera_serial, int binning, bool trigger, int fps); - void assignThread(QThread *thread); - void getImageSize(int &image_width, int &image_height, int &bit_depth); - bool setFrame16(); - bool setFrame8(); - bool setMaximumResolution(); - bool setPTP(bool enable); - bool setExposure(double exposure); - bool enableAutoExposure(bool enable); - bool enableAutoGain(bool enable); - bool setGain(int gain); - bool setPacketSize(int packetSize); - bool setInterPacketDelay(int interPacketDelay); - bool setBinning(int binning); - bool enableTrigger(bool enable); - - bool changeFPS(int fps); - bool changeBinning(int binning); - bool changePacketSize(int packet_size); - bool setFPS(int fps); - bool enableFPS(bool enable); - - ~CameraVimba(void); - -signals: - void captured(); - void finished(); - -private: - VimbaSystem &system; - CameraPtr camera; - cv::Mat image; - cv::Mat channels[3]; - cv::Mat image_buffer; - enum format {Y800, Y16}; - format image_format; - bool connected = false; - int binning = 1; - bool trigger = false; - int fps = 30; - std::string camera_serial; - int max_timeout = 2000; - QMutex frame_mutex; - bool capturing = false; - - shared_ptr frame_observer; - FramePtrVector frame_buffer; - - VmbError_t getStringFeature(std::string feature, std::string &res); - VmbError_t getBoolFeature(std::string feature, bool &res); - VmbError_t getDoubleFeature(std::string feature, double &res); - VmbError_t getIntFeature(std::string feature, long long &res); - double getFPS(); - -public slots: - bool capture(void); - bool getImage(cv::Mat &out); - - void startCapture(); - void stopCapture(); -private slots: - void onFrame(int); -}; - -#endif // CAMERAVIMBA_H ->>>>>>> dev diff --git a/src/detection/boundingbox.h.orig b/src/detection/boundingbox.h.orig deleted file mode 100644 index ddfec334e..000000000 --- a/src/detection/boundingbox.h.orig +++ /dev/null @@ -1,43 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ -#ifndef BOUNDINGBOX_H -#define BOUNDINGBOX_H - -#include -#include - -typedef struct{ - QRect rect = QRect(0,0,0,0); - QString classname = ""; - int occluded = 0; - bool truncated = false; - int classid = 0; - double confidence = 0; -} BoundingBox; - -#endif // BOUNDINGBOX_H -======= -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ -#ifndef BOUNDINGBOX_H -#define BOUNDINGBOX_H - -#include -#include - -typedef struct{ - QRect rect = QRect(0,0,0,0); - QString classname = ""; - int occluded = 0; - bool truncated = false; - int classid = 0; - double confidence = 0; -} BoundingBox; - -#endif // BOUNDINGBOX_H ->>>>>>> dev diff --git a/src/detection/detectoropencv.cpp.orig b/src/detection/detectoropencv.cpp.orig deleted file mode 100644 index 79c206b84..000000000 --- a/src/detection/detectoropencv.cpp.orig +++ /dev/null @@ -1,883 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#include "detectoropencv.h" - -DetectorOpenCV::DetectorOpenCV(QObject *parent) : QObject(parent) -{ - -} - -void DetectorOpenCV::assignThread(QThread *thread){ - qDebug() << "Moving detector to thread"; - thread_ = thread; - this->moveToThread(thread_); - connect(this, SIGNAL(finished()), thread_, SLOT(quit())); - //connect(this, SIGNAL(finished()), this, SLOT(deleteLater())); - connect(thread_, SIGNAL(finished()), thread_, SLOT(deleteLater())); - thread_->start(); - thread_->setPriority(QThread::LowestPriority); -} - -void DetectorOpenCV::setImageSize(int width, int height){ - if(width > 0 && height > 0){ - input_width = width; - input_height = height; - } -} - -void DetectorOpenCV::readNamesFile(std::string class_file){ - - class_names.clear(); - - // Load names of classes - std::ifstream ifs(class_file.c_str()); - std::string line; - int i=0; - while (std::getline(ifs, line)){ - class_names.push_back(line); - std::cout << "Added detection class: " << i++ << " " << class_names.back() << std::endl; - } -} - -void DetectorOpenCV::setChannels(int channels){ - input_channels = channels; -} - -void DetectorOpenCV::setTarget(int target){ - preferable_target = target; - - if(preferable_target == cv::dnn::DNN_TARGET_OPENCL){ - // Check for GPU - cv::ocl::Context context; - - if(!cv::ocl::haveOpenCL()){ - std::cout << "OpenCL is not available. Falling back to CPU" << std::endl; - preferable_target = cv::dnn::DNN_TARGET_CPU; - } - - // Attempt to use a GPU - if(context.create(cv::ocl::Device::TYPE_GPU)){ - std::cout << "Found OpenCL capable GPU - we're going to use it!" << std::endl; - cv::ocl::Device(context.device(1)); - } - } -#ifdef WITH_CUDA - else if(preferable_target == cv::dnn::DNN_TARGET_CUDA || preferable_target == cv::dnn::DNN_TARGET_CUDA_FP16){ - try - { - // Check for GPU - if (cv::cuda::getCudaEnabledDeviceCount() <= 0) { - qDebug() << "CUDA is not available. Falling back to CPU"; - preferable_target = cv::dnn::DNN_TARGET_CPU; - }else{ - qDebug() << "NVIDIA GPU detected."; - } - } - catch( cv::Exception& e ) - { - const char* err_msg = e.what(); - std::cout << "exception caught: " << err_msg << std::endl; - qDebug() << "CUDA is not available. Falling back to CPU"; - preferable_target = cv::dnn::DNN_TARGET_CPU; - } - } -#endif -} - -void DetectorOpenCV::loadNetwork(std::string names_file, std::string cfg_file, std::string model_file){ - // Load the names - readNamesFile(names_file); - - // Infer network type automatically - net = cv::dnn::readNet(model_file, cfg_file); - - // Should default to DNN_BACKEND_OPENCV (otherwise Intel inference engine) - if(preferable_target == cv::dnn::DNN_TARGET_CUDA || preferable_target == cv::dnn::DNN_TARGET_CUDA_FP16){ - net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); - qDebug() << "Set backend and target to CUDA"; - }else{ - net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV); - } - - // Network target, e.g. CUDA, CPU, etc - net.setPreferableTarget(preferable_target); - - getOutputClassNames(); - - ready = true; -} - -void DetectorOpenCV::getOutputClassNames() -{ - output_names.clear(); - - //Get the indices of the output layers, i.e. the layers with unconnected outputs - std::vector outLayers = net.getUnconnectedOutLayers(); - - //get the names of all the layers in the network - auto layersNames = net.getLayerNames(); - - // Get the names of the output layers in names - output_names.resize(outLayers.size()); - - for (size_t i = 0; i < outLayers.size(); ++i){ - output_names.at(i) = layersNames.at(static_cast(outLayers[i]) - 1); - } - -} - -void DetectorOpenCV::postProcessTensorflow(cv::Mat& frame, const std::vector& outputs, std::vector &filtered_boxes){ - std::vector classIds; - std::vector confidences; - std::vector boxes; - - auto detections = outputs.at(0); - const int numDetections = detections.size[2]; - - std::cout << "Outputs_size: " << detections.total() << std::endl; - std::cout << "Number of detections: " << numDetections << std::endl; - - // batch id, class id, confidence, bbox (x4) - detections = detections.reshape(1, static_cast(detections.total() / 7)); - - // There are top-k (= 100 typical) detections, most of which should have - // more or less zero confidence. - for (int i = 0; i < numDetections; ++i) - { - float confidence = detections.at(i, 2); - - if (confidence > confThreshold ) - { - - // Extract the bounding box - int classId = static_cast(detections.at(i, 1)); - int left = static_cast(frame.cols * detections.at(i, 3)); - int top = static_cast(frame.rows * detections.at(i, 4)); - int right = static_cast(frame.cols * detections.at(i, 5)); - int bottom = static_cast(frame.rows * detections.at(i, 6)); - - BoundingBox bbox; - bbox.rect.setLeft(std::max(0, std::min(left, frame.cols - 1))); - bbox.rect.setTop(std::max(0, std::min(top, frame.rows - 1))); - bbox.rect.setRight(std::max(0, std::min(right, frame.cols - 1))); - bbox.rect.setBottom(std::max(0, std::min(bottom, frame.rows - 1))); - bbox.confidence = static_cast(confidence); - bbox.classid = classId; - bbox.classname = QString::fromStdString(class_names.at(static_cast(bbox.classid))); - - /* - std::cout << "Found (" << bbox.classid << ") " << bbox.classname.toStdString() - << " at" << " (" << bbox.rect.center().x() << ", " << bbox.rect.center().y() - << "), conf: " << bbox.confidence - << ", size (" << bbox.rect.width() << "x" << bbox.rect.height() << ")" - << std::endl; - */ - - filtered_boxes.push_back(bbox); - }else{ - // - } - } -} - -void DetectorOpenCV::postProcess(cv::Mat& frame, const std::vector& outputs, std::vector &filtered_boxes) -{ - std::vector classIds; - std::vector confidences; - std::vector boxes; - - // Debug: this should be three because there are three scales that Yolo searches over - //std::cout << "Outputs: " << outputs.size() << std::endl; - - for (size_t i = 0; i < outputs.size(); ++i) - { - // Scan through all the bounding boxes output from the network and keep only the - // ones with high confidence scores. Assign the box's class label as the class - // with the highest score for the box. - float* data = reinterpret_cast(outputs[i].data); - for (int j = 0; j < outputs[i].rows; ++j, data += outputs[i].cols) - { - cv::Mat scores = outputs[i].row(j).colRange(5, outputs[i].cols); - cv::Point classIdPoint; - double confidence; - // Get the value and location of the maximum score - minMaxLoc(scores, nullptr, &confidence, nullptr, &classIdPoint); - - if (confidence > 0) - { - - // Output is a percentage of the frame width/height - // so it doesn't matter that we're transforming boxes - // between the resized and full-size image. - int centerX = static_cast(data[0] * frame.cols); - int centerY = static_cast(data[1] * frame.rows); - int width = static_cast(data[2] * frame.cols); - int height = static_cast(data[3] * frame.rows); - int left = centerX - width / 2; - int top = centerY - height / 2; - - classIds.push_back(classIdPoint.x); - confidences.push_back(static_cast(confidence)); - boxes.push_back(cv::Rect(left, top, width, height)); - - }else{ - if(confidence == 0.0) - continue; - - std::cout << "Detected " - << class_names.at(static_cast(classIdPoint.x)) - << " with low confidence: " << confidence << std::endl; - - } - } - } - - std::vector indices; - - // Perform non maximum suppression to eliminate redundant overlapping boxes with - // lower confidences - - // We set the confidence threshold to zero here, we'll filter the boxes out later. - // This lets us provide some feedback to the user if their threshold is too high. - cv::dnn::NMSBoxes(boxes, confidences, static_cast(confThreshold), static_cast(nmsThreshold), indices); - - for (size_t i = 0; i < indices.size(); ++i) - { - auto idx = static_cast(indices.at(i)); - - BoundingBox box; - cv::Rect rect = boxes.at(idx); - - box.confidence = static_cast(confidences.at(idx)); - box.classid = classIds.at(idx); - box.classname = QString::fromStdString(class_names.at(static_cast(box.classid))); - - // Darknet predicts box centres and half-width/height, so the - // box can go outside the image. Clamp to the image size: - QPoint top_left = {std::max(0, rect.x), std::max(0, rect.y)}; - QPoint bottom_right = top_left + QPoint({rect.width, rect.height}); - - bottom_right.setX(std::min(bottom_right.x(), frame.cols)); - bottom_right.setY(std::min(bottom_right.y(), frame.rows)); - - box.rect.setBottomRight(bottom_right); - box.rect.setTopLeft(top_left); - - /* - std::cout << "Found " << box.classname.toStdString() - << " at" << " (" << box.rect.center().x() << ", " << box.rect.center().y() - << "), conf: " << box.confidence - << ", size (" << box.rect.width() << "x" << box.rect.height() << ")" - << std::endl; - */ - - filtered_boxes.push_back(box); - - } - - return; -} - -std::vector DetectorOpenCV::infer(cv::Mat image){ - - std::vector detections; - running = true; - - // Assume we have an alpha image if 4 channels - if(image.channels() == 4){ - cv::cvtColor(image, image, cv::COLOR_BGRA2BGR); - } - - if(convert_depth && image.elemSize() == 2){ - double minval, maxval; - cv::minMaxIdx(image, &minval, &maxval); - - double range = maxval-minval; - double scale_factor = 255.0/range; - - image.convertTo(image, CV_32FC1); - image -= minval; - image *= scale_factor; - image.convertTo(image, CV_8UC1); - } - - if(convert_grayscale && image.channels() == 1){ - cv::cvtColor(image, image, cv::COLOR_GRAY2RGB); - } - - if(image.channels() != input_channels){ - std::cout << "Input channel mismatch. Expecting " - << input_channels - << " channels but image has " << image.channels() - << " channels."; - - return detections; - } - - // Make sure we service the GUI thread as well, as - // this thread will run in a very tight loop - QCoreApplication::processEvents(); - if(framework == FRAMEWORK_TENSORFLOW){ - detections = inferTensorflow(image); - }else if(framework == FRAMEWORK_DARKNET){ - detections = inferDarknet(image); - } - QCoreApplication::processEvents(); - - running = false; - - return detections; - -} - -std::vector DetectorOpenCV::inferTensorflow(cv::Mat image){ - - std::vector results; - - auto mean = cv::Scalar(0,0,0); - if(image.channels() == 1){ - mean = cv::Scalar(0); - } - - /* - // Check for 16-bit - double scale_factor = 1/255.0; - - if(image.elemSize() == 2){ - scale_factor = 1/65535.0; - } - */ - - auto input_size = cv::Size(image.cols, image.rows); - - bool swap_rb = false; // BGR->RGB? - bool crop = false; // Use the entire image - - // No normalising! The model will handle it. - auto blob = cv::dnn::blobFromImage(image, 1.0, input_size, mean, swap_rb, crop); - - //Sets the input to the network - net.setInput(blob); - - // Runs the forward pass to get output of the output layers - std::vector outputs; - net.forward(outputs, output_names); - - postProcessTensorflow(image, outputs, results); - - std::vector layersTimes; - double freq = cv::getTickFrequency() / 1000; - processing_time = net.getPerfProfile(layersTimes) / freq; - - return results; -} - -std::vector DetectorOpenCV::inferDarknet(cv::Mat image){ - - std::vector results; - - auto mean = cv::Scalar(0,0,0); - if(image.channels() == 1){ - mean = cv::Scalar(0); - } - - // Check for 16-bit - double scale_factor = 1/255.0; - if(image.elemSize() == 2){ - scale_factor = 1/65535.0; - } - - auto input_size = cv::Size(input_width, input_height); - - bool swap_rb = true; // BGR->RGB? - bool crop = false; // Use the entire image - auto blob = cv::dnn::blobFromImage(image, scale_factor, input_size, mean, swap_rb, crop); - - //Sets the input to the network - net.setInput(blob); - - // Runs the forward pass to get output of the output layers - std::vector outputs; - net.forward(outputs, output_names); - - // Put efficiency information. The function getPerfProfile returns the - // overall time for inference(t) and the timings for each of the layers(in layersTimes) - std::vector layersTimes; - double freq = cv::getTickFrequency() / 1000; - processing_time = net.getPerfProfile(layersTimes) / freq; - - // Remove the bounding boxes with low confidence - postProcess(image, outputs, results); - - return results; -} - -void DetectorOpenCV::annotateImage(cv::Mat &frame, std::vector boxes, cv::Scalar colour, cv::Scalar font_colour){ - for(auto &box : boxes){ - //Draw a rectangle displaying the bounding box - - auto top_left = cv::Point(box.rect.left(), box.rect.top()); - - cv::rectangle(frame, top_left, - cv::Point(box.rect.right(), box.rect.bottom()), - colour); - - //Get the label for the class name and its confidence - std::string label = cv::format("%.2f", box.confidence); - - //Display the label at the top of the bounding box - int baseLine; - cv::Size labelSize = getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); - cv::putText(frame, label, top_left, cv::FONT_HERSHEY_SIMPLEX, 0.5, font_colour); - } -} - -DetectorOpenCV::~DetectorOpenCV(void){ - emit finished(); -} -======= -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#include "detectoropencv.h" - -DetectorOpenCV::DetectorOpenCV(QObject *parent) : QObject(parent) -{ - -} - -void DetectorOpenCV::assignThread(QThread *thread){ - qDebug() << "Moving detector to thread"; - thread_ = thread; - this->moveToThread(thread_); - connect(this, SIGNAL(finished()), thread_, SLOT(quit())); - //connect(this, SIGNAL(finished()), this, SLOT(deleteLater())); - connect(thread_, SIGNAL(finished()), thread_, SLOT(deleteLater())); - thread_->start(); - thread_->setPriority(QThread::LowestPriority); -} - -void DetectorOpenCV::setImageSize(int width, int height){ - if(width > 0 && height > 0){ - input_width = width; - input_height = height; - } -} - -void DetectorOpenCV::readNamesFile(std::string class_file){ - - class_names.clear(); - - // Load names of classes - std::ifstream ifs(class_file.c_str()); - std::string line; - int i=0; - while (std::getline(ifs, line)){ - class_names.push_back(line); - std::cout << "Added detection class: " << i++ << " " << class_names.back() << std::endl; - } -} - -void DetectorOpenCV::setChannels(int channels){ - input_channels = channels; -} - -void DetectorOpenCV::setTarget(int target){ - preferable_target = target; - - if(preferable_target == cv::dnn::DNN_TARGET_OPENCL){ - // Check for GPU - cv::ocl::Context context; - - if(!cv::ocl::haveOpenCL()){ - std::cout << "OpenCL is not available. Falling back to CPU" << std::endl; - preferable_target = cv::dnn::DNN_TARGET_CPU; - } - - // Attempt to use a GPU - if(context.create(cv::ocl::Device::TYPE_GPU)){ - std::cout << "Found OpenCL capable GPU - we're going to use it!" << std::endl; - cv::ocl::Device(context.device(1)); - } - } -#ifdef WITH_CUDA - else if(preferable_target == cv::dnn::DNN_TARGET_CUDA || preferable_target == cv::dnn::DNN_TARGET_CUDA_FP16){ - try - { - // Check for GPU - if (cv::cuda::getCudaEnabledDeviceCount() <= 0) { - qDebug() << "CUDA is not available. Falling back to CPU"; - preferable_target = cv::dnn::DNN_TARGET_CPU; - }else{ - qDebug() << "NVIDIA GPU detected."; - } - } - catch( cv::Exception& e ) - { - const char* err_msg = e.what(); - std::cout << "exception caught: " << err_msg << std::endl; - qDebug() << "CUDA is not available. Falling back to CPU"; - preferable_target = cv::dnn::DNN_TARGET_CPU; - } - } -#endif -} - -void DetectorOpenCV::loadNetwork(std::string names_file, std::string cfg_file, std::string model_file){ - // Load the names - readNamesFile(names_file); - - // Infer network type automatically - net = cv::dnn::readNet(model_file, cfg_file); - - // Should default to DNN_BACKEND_OPENCV (otherwise Intel inference engine) - if(preferable_target == cv::dnn::DNN_TARGET_CUDA || preferable_target == cv::dnn::DNN_TARGET_CUDA_FP16){ - net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); - qDebug() << "Set backend and target to CUDA"; - }else{ - net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV); - } - - // Network target, e.g. CUDA, CPU, etc - net.setPreferableTarget(preferable_target); - - getOutputClassNames(); - - ready = true; -} - -void DetectorOpenCV::getOutputClassNames() -{ - output_names.clear(); - - //Get the indices of the output layers, i.e. the layers with unconnected outputs - std::vector outLayers = net.getUnconnectedOutLayers(); - - //get the names of all the layers in the network - auto layersNames = net.getLayerNames(); - - // Get the names of the output layers in names - output_names.resize(outLayers.size()); - - for (size_t i = 0; i < outLayers.size(); ++i){ - output_names.at(i) = layersNames.at(static_cast(outLayers[i]) - 1); - } - -} - -void DetectorOpenCV::postProcessTensorflow(cv::Mat& frame, const std::vector& outputs, std::vector &filtered_boxes){ - std::vector classIds; - std::vector confidences; - std::vector boxes; - - auto detections = outputs.at(0); - const int numDetections = detections.size[2]; - - std::cout << "Outputs_size: " << detections.total() << std::endl; - std::cout << "Number of detections: " << numDetections << std::endl; - - // batch id, class id, confidence, bbox (x4) - detections = detections.reshape(1, static_cast(detections.total() / 7)); - - // There are top-k (= 100 typical) detections, most of which should have - // more or less zero confidence. - for (int i = 0; i < numDetections; ++i) - { - float confidence = detections.at(i, 2); - - if (confidence > confThreshold ) - { - - // Extract the bounding box - int classId = static_cast(detections.at(i, 1)); - int left = static_cast(frame.cols * detections.at(i, 3)); - int top = static_cast(frame.rows * detections.at(i, 4)); - int right = static_cast(frame.cols * detections.at(i, 5)); - int bottom = static_cast(frame.rows * detections.at(i, 6)); - - BoundingBox bbox; - bbox.rect.setLeft(std::max(0, std::min(left, frame.cols - 1))); - bbox.rect.setTop(std::max(0, std::min(top, frame.rows - 1))); - bbox.rect.setRight(std::max(0, std::min(right, frame.cols - 1))); - bbox.rect.setBottom(std::max(0, std::min(bottom, frame.rows - 1))); - bbox.confidence = static_cast(confidence); - bbox.classid = classId; - bbox.classname = QString::fromStdString(class_names.at(static_cast(bbox.classid))); - - /* - std::cout << "Found (" << bbox.classid << ") " << bbox.classname.toStdString() - << " at" << " (" << bbox.rect.center().x() << ", " << bbox.rect.center().y() - << "), conf: " << bbox.confidence - << ", size (" << bbox.rect.width() << "x" << bbox.rect.height() << ")" - << std::endl; - */ - - filtered_boxes.push_back(bbox); - }else{ - // - } - } -} - -void DetectorOpenCV::postProcess(cv::Mat& frame, const std::vector& outputs, std::vector &filtered_boxes) -{ - std::vector classIds; - std::vector confidences; - std::vector boxes; - - // Debug: this should be three because there are three scales that Yolo searches over - //std::cout << "Outputs: " << outputs.size() << std::endl; - - for (size_t i = 0; i < outputs.size(); ++i) - { - // Scan through all the bounding boxes output from the network and keep only the - // ones with high confidence scores. Assign the box's class label as the class - // with the highest score for the box. - float* data = reinterpret_cast(outputs[i].data); - for (int j = 0; j < outputs[i].rows; ++j, data += outputs[i].cols) - { - cv::Mat scores = outputs[i].row(j).colRange(5, outputs[i].cols); - cv::Point classIdPoint; - double confidence; - // Get the value and location of the maximum score - minMaxLoc(scores, nullptr, &confidence, nullptr, &classIdPoint); - - if (confidence > 0) - { - - // Output is a percentage of the frame width/height - // so it doesn't matter that we're transforming boxes - // between the resized and full-size image. - int centerX = static_cast(data[0] * frame.cols); - int centerY = static_cast(data[1] * frame.rows); - int width = static_cast(data[2] * frame.cols); - int height = static_cast(data[3] * frame.rows); - int left = centerX - width / 2; - int top = centerY - height / 2; - - classIds.push_back(classIdPoint.x); - confidences.push_back(static_cast(confidence)); - boxes.push_back(cv::Rect(left, top, width, height)); - - }else{ - if(confidence == 0.0) - continue; - - std::cout << "Detected " - << class_names.at(static_cast(classIdPoint.x)) - << " with low confidence: " << confidence << std::endl; - - } - } - } - - std::vector indices; - - // Perform non maximum suppression to eliminate redundant overlapping boxes with - // lower confidences - - // We set the confidence threshold to zero here, we'll filter the boxes out later. - // This lets us provide some feedback to the user if their threshold is too high. - cv::dnn::NMSBoxes(boxes, confidences, static_cast(confThreshold), static_cast(nmsThreshold), indices); - - for (size_t i = 0; i < indices.size(); ++i) - { - auto idx = static_cast(indices.at(i)); - - BoundingBox box; - cv::Rect rect = boxes.at(idx); - - box.confidence = static_cast(confidences.at(idx)); - box.classid = classIds.at(idx); - box.classname = QString::fromStdString(class_names.at(static_cast(box.classid))); - - // Darknet predicts box centres and half-width/height, so the - // box can go outside the image. Clamp to the image size: - QPoint top_left = {std::max(0, rect.x), std::max(0, rect.y)}; - QPoint bottom_right = top_left + QPoint({rect.width, rect.height}); - - bottom_right.setX(std::min(bottom_right.x(), frame.cols)); - bottom_right.setY(std::min(bottom_right.y(), frame.rows)); - - box.rect.setBottomRight(bottom_right); - box.rect.setTopLeft(top_left); - - /* - std::cout << "Found " << box.classname.toStdString() - << " at" << " (" << box.rect.center().x() << ", " << box.rect.center().y() - << "), conf: " << box.confidence - << ", size (" << box.rect.width() << "x" << box.rect.height() << ")" - << std::endl; - */ - - filtered_boxes.push_back(box); - - } - - return; -} - -std::vector DetectorOpenCV::infer(cv::Mat image){ - - std::vector detections; - running = true; - - // Assume we have an alpha image if 4 channels - if(image.channels() == 4){ - cv::cvtColor(image, image, cv::COLOR_BGRA2BGR); - } - - if(convert_depth && image.elemSize() == 2){ - double minval, maxval; - cv::minMaxIdx(image, &minval, &maxval); - - double range = maxval-minval; - double scale_factor = 255.0/range; - - image.convertTo(image, CV_32FC1); - image -= minval; - image *= scale_factor; - image.convertTo(image, CV_8UC1); - } - - if(convert_grayscale && image.channels() == 1){ - cv::cvtColor(image, image, cv::COLOR_GRAY2RGB); - } - - if(image.channels() != input_channels){ - std::cout << "Input channel mismatch. Expecting " - << input_channels - << " channels but image has " << image.channels() - << " channels."; - - return detections; - } - - // Make sure we service the GUI thread as well, as - // this thread will run in a very tight loop - QCoreApplication::processEvents(); - if(framework == FRAMEWORK_TENSORFLOW){ - detections = inferTensorflow(image); - }else if(framework == FRAMEWORK_DARKNET){ - detections = inferDarknet(image); - } - QCoreApplication::processEvents(); - - running = false; - - return detections; - -} - -std::vector DetectorOpenCV::inferTensorflow(cv::Mat image){ - - std::vector results; - - auto mean = cv::Scalar(0,0,0); - if(image.channels() == 1){ - mean = cv::Scalar(0); - } - - /* - // Check for 16-bit - double scale_factor = 1/255.0; - - if(image.elemSize() == 2){ - scale_factor = 1/65535.0; - } - */ - - auto input_size = cv::Size(image.cols, image.rows); - - bool swap_rb = false; // BGR->RGB? - bool crop = false; // Use the entire image - - // No normalising! The model will handle it. - auto blob = cv::dnn::blobFromImage(image, 1.0, input_size, mean, swap_rb, crop); - - //Sets the input to the network - net.setInput(blob); - - // Runs the forward pass to get output of the output layers - std::vector outputs; - net.forward(outputs, output_names); - - postProcessTensorflow(image, outputs, results); - - std::vector layersTimes; - double freq = cv::getTickFrequency() / 1000; - processing_time = net.getPerfProfile(layersTimes) / freq; - - return results; -} - -std::vector DetectorOpenCV::inferDarknet(cv::Mat image){ - - std::vector results; - - auto mean = cv::Scalar(0,0,0); - if(image.channels() == 1){ - mean = cv::Scalar(0); - } - - // Check for 16-bit - double scale_factor = 1/255.0; - if(image.elemSize() == 2){ - scale_factor = 1/65535.0; - } - - auto input_size = cv::Size(input_width, input_height); - - bool swap_rb = true; // BGR->RGB? - bool crop = false; // Use the entire image - auto blob = cv::dnn::blobFromImage(image, scale_factor, input_size, mean, swap_rb, crop); - - //Sets the input to the network - net.setInput(blob); - - // Runs the forward pass to get output of the output layers - std::vector outputs; - net.forward(outputs, output_names); - - // Put efficiency information. The function getPerfProfile returns the - // overall time for inference(t) and the timings for each of the layers(in layersTimes) - std::vector layersTimes; - double freq = cv::getTickFrequency() / 1000; - processing_time = net.getPerfProfile(layersTimes) / freq; - - // Remove the bounding boxes with low confidence - postProcess(image, outputs, results); - - return results; -} - -void DetectorOpenCV::annotateImage(cv::Mat &frame, std::vector boxes, cv::Scalar colour, cv::Scalar font_colour){ - for(auto &box : boxes){ - //Draw a rectangle displaying the bounding box - - auto top_left = cv::Point(box.rect.left(), box.rect.top()); - - cv::rectangle(frame, top_left, - cv::Point(box.rect.right(), box.rect.bottom()), - colour); - - //Get the label for the class name and its confidence - std::string label = cv::format("%.2f", box.confidence); - - //Display the label at the top of the bounding box - int baseLine; - cv::Size labelSize = getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); - cv::putText(frame, label, top_left, cv::FONT_HERSHEY_SIMPLEX, 0.5, font_colour); - } -} - -DetectorOpenCV::~DetectorOpenCV(void){ - emit finished(); -} ->>>>>>> dev diff --git a/src/detection/detectoropencv.h.orig b/src/detection/detectoropencv.h.orig deleted file mode 100644 index 0651b6fb7..000000000 --- a/src/detection/detectoropencv.h.orig +++ /dev/null @@ -1,221 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ -#ifndef DETECTOROPENCV_H -#define DETECTOROPENCV_H - -#include -#include -#include -#include - -#include -#include -#include - -#include - -#include -#include -#include -#include - -typedef enum { - FRAMEWORK_TENSORFLOW, - FRAMEWORK_DARKNET, - FRAMEWORK_ONNX, - FRAMEWORK_PYTORCH -} model_framework; - -class DetectorOpenCV : public QObject { - -Q_OBJECT - -public: - DetectorOpenCV(QObject *parent = nullptr); - - void setImageSize(int width, int height); - - void loadNetwork(std::string names_file, std::string cfg_file, std::string model_file); - - void annotateImage(cv::Mat &image, - std::vector boxes, - cv::Scalar colour = cv::Scalar(0,0,255), - cv::Scalar font_colour = cv::Scalar(255,255,255)); - - std::vector inferDarknet(cv::Mat image); - std::vector inferTensorflow(cv::Mat image); - - double getConfidenceThreshold(void){ return confThreshold;} - double getNMSThreshold(void){ return nmsThreshold;} - - void assignThread(QThread* thread); - int getChannels(void){return input_channels;} - int getInputWidth(void){return input_width;} - int getInputHeight(void){return input_height;} - bool isReady(void){return ready;} - double getProcessingTime(void){return processing_time;} - int getNumClasses(void){return static_cast(class_names.size());} - std::vector getClassNames(void){return class_names;} - bool isRunning(void){return running;} - ~DetectorOpenCV(); - -public slots: - std::vector infer(cv::Mat image); - void setFramework(model_framework framework){this->framework = framework;} - - void setConfidenceThresholdPercent(int thresh_pc){confThreshold = std::max(0.0, thresh_pc / 100.);} - void setConfidenceThreshold(double thresh){confThreshold = std::max(0.0, thresh);} - - void setNMSThresholdPercent(int thresh){nmsThreshold = std::max(0.0, thresh / 100.0);} - void setNMSThreshold(double thresh){nmsThreshold = std::max(0.0, thresh);} - - void setConvertGrayscale(bool convert){convert_grayscale = convert;} - void setConvertDepth(bool convert){convert_depth = convert;} - void setTarget(int target); - void setChannels(int channels); - -private: - - void postProcess(cv::Mat& frame, const std::vector& outs, std::vector &filtered_outputs); - void readNamesFile(std::string class_file = "coco.names"); - void getOutputClassNames(void); - - bool convert_grayscale = true; - bool convert_depth = true; - double processing_time; - double confThreshold = 0.5; // Confidence threshold - double nmsThreshold = 0.4; // Non-maximum suppression threshold - int input_width = 416; // Width of network's input image - int input_height = 416; // Height of network's input image - int input_channels = 3; - int preferable_target = cv::dnn::DNN_TARGET_OPENCL; - model_framework framework = FRAMEWORK_DARKNET; - bool ready = false; - bool running = false; - QThread* thread_; - - std::vector class_names; - std::vector output_names; - cv::dnn::Net net; - void postProcessTensorflow(cv::Mat &frame, const std::vector &outputs, std::vector &filtered_boxes); - -signals: - void finished(); - void objectsDetected(); -}; - -#endif // DETECTOROPENCV_H -======= -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ -#ifndef DETECTOROPENCV_H -#define DETECTOROPENCV_H - -#include -#include -#include -#include - -#include -#include -#include - -#include - -#include -#include -#include -#include - -typedef enum { - FRAMEWORK_TENSORFLOW, - FRAMEWORK_DARKNET, - FRAMEWORK_ONNX, - FRAMEWORK_PYTORCH -} model_framework; - -class DetectorOpenCV : public QObject { - -Q_OBJECT - -public: - DetectorOpenCV(QObject *parent = nullptr); - - void setImageSize(int width, int height); - - void loadNetwork(std::string names_file, std::string cfg_file, std::string model_file); - - void annotateImage(cv::Mat &image, - std::vector boxes, - cv::Scalar colour = cv::Scalar(0,0,255), - cv::Scalar font_colour = cv::Scalar(255,255,255)); - - std::vector inferDarknet(cv::Mat image); - std::vector inferTensorflow(cv::Mat image); - - double getConfidenceThreshold(void){ return confThreshold;} - double getNMSThreshold(void){ return nmsThreshold;} - - void assignThread(QThread* thread); - int getChannels(void){return input_channels;} - int getInputWidth(void){return input_width;} - int getInputHeight(void){return input_height;} - bool isReady(void){return ready;} - double getProcessingTime(void){return processing_time;} - int getNumClasses(void){return static_cast(class_names.size());} - std::vector getClassNames(void){return class_names;} - bool isRunning(void){return running;} - ~DetectorOpenCV(); - -public slots: - std::vector infer(cv::Mat image); - void setFramework(model_framework framework){this->framework = framework;} - - void setConfidenceThresholdPercent(int thresh_pc){confThreshold = std::max(0.0, thresh_pc / 100.);} - void setConfidenceThreshold(double thresh){confThreshold = std::max(0.0, thresh);} - - void setNMSThresholdPercent(int thresh){nmsThreshold = std::max(0.0, thresh / 100.0);} - void setNMSThreshold(double thresh){nmsThreshold = std::max(0.0, thresh);} - - void setConvertGrayscale(bool convert){convert_grayscale = convert;} - void setConvertDepth(bool convert){convert_depth = convert;} - void setTarget(int target); - void setChannels(int channels); - -private: - - void postProcess(cv::Mat& frame, const std::vector& outs, std::vector &filtered_outputs); - void readNamesFile(std::string class_file = "coco.names"); - void getOutputClassNames(void); - - bool convert_grayscale = true; - bool convert_depth = true; - double processing_time; - double confThreshold = 0.5; // Confidence threshold - double nmsThreshold = 0.4; // Non-maximum suppression threshold - int input_width = 416; // Width of network's input image - int input_height = 416; // Height of network's input image - int input_channels = 3; - int preferable_target = cv::dnn::DNN_TARGET_OPENCL; - model_framework framework = FRAMEWORK_DARKNET; - bool ready = false; - bool running = false; - QThread* thread_; - - std::vector class_names; - std::vector output_names; - cv::dnn::Net net; - void postProcessTensorflow(cv::Mat &frame, const std::vector &outputs, std::vector &filtered_boxes); - -signals: - void finished(); - void objectsDetected(); -}; - -#endif // DETECTOROPENCV_H ->>>>>>> dev diff --git a/src/detection/detectorsetupdialog.cpp.orig b/src/detection/detectorsetupdialog.cpp.orig deleted file mode 100644 index 442f03a08..000000000 --- a/src/detection/detectorsetupdialog.cpp.orig +++ /dev/null @@ -1,635 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#include "detectorsetupdialog.h" -#include "ui_detectorsetupdialog.h" - -DetectorSetupDialog::DetectorSetupDialog(QWidget *parent) : - QDialog(parent), - ui(new Ui::DetectorSetupDialog) -{ - ui->setupUi(this); - - connect(ui->cfgPathButton, SIGNAL(clicked()), this, SLOT(setCfgFile())); - connect(ui->cfgPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->weightPathButton, SIGNAL(clicked(bool)), this, SLOT(setWeightsFile())); - connect(ui->weightPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->namesPathButton, SIGNAL(clicked(bool)), this, SLOT(setNamesFile())); - connect(ui->frameworkComboBox, SIGNAL(currentIndexChanged(QString)), this, SLOT(setFramework())); - connect(ui->targetComboBox, SIGNAL(currentIndexChanged(QString)), this, SLOT(setTarget())); - connect(ui->namesPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->convertGrayscaleCheckbox, SIGNAL(clicked(bool)), this, SLOT(setConvertGrayscale())); - connect(ui->convertDepthCheckbox, SIGNAL(clicked(bool)), this, SLOT(setConvertDepth())); - - settings = new QSettings("I3Dr", "Stereo Vision Toolkit"); - - cfg_file = settings->value("model_cfg", "").toString(); - weight_file = settings->value("model_weights", "").toString(); - names_file = settings->value("model_names", "").toString(); - - convert_grayscale = settings->value("model_convert_grayscale", true).toBool(); - ui->convertGrayscaleCheckbox->setChecked(convert_grayscale); - - convert_depth = settings->value("model_convert_depth", true).toBool(); - ui->convertDepthCheckbox->setChecked(convert_depth); - - image_width = settings->value("model_width", 0).toInt(); - image_height = settings->value("model_height", 0).toInt(); - image_channels = settings->value("model_channels", 3).toInt(); - - framework = static_cast(settings->value("model_framework", 0).toInt()); - if(framework == FRAMEWORK_DARKNET){ - ui->frameworkComboBox->setCurrentText("Darknet (YOLO)"); - ui->imageHeightLabel->show(); - ui->imageWidthLabel->show(); - }else if(framework == FRAMEWORK_TENSORFLOW){ - ui->frameworkComboBox->setCurrentText("Tensorflow"); - ui->imageHeightLabel->hide(); - ui->imageWidthLabel->hide(); - } - - target = settings->value("model_target", cv::dnn::DNN_TARGET_CPU).toInt(); - if(target == cv::dnn::DNN_TARGET_CPU){ - ui->targetComboBox->setCurrentText("CPU"); - }else if(target == cv::dnn::DNN_TARGET_OPENCL){ - ui->targetComboBox->setCurrentText("OpenCL"); - }else if(target == cv::dnn::DNN_TARGET_OPENCL_FP16){ - ui->targetComboBox->setCurrentText("OpenCL FP16"); - } -#ifdef WITH_CUDA - else if(target == cv::dnn::DNN_TARGET_CUDA){ - ui->targetComboBox->setCurrentText("CUDA"); - }else if(target == cv::dnn::DNN_TARGET_CUDA_FP16){ - ui->targetComboBox->setCurrentText("CUDA FP16"); - } -#endif - updateFields(); - checkForm(); - -} - -void DetectorSetupDialog::updateFields(){ - ui->cfgPathLineEdit->setText(cfg_file); - ui->weightPathLineEdit->setText(weight_file); - ui->namesPathLineEdit->setText(names_file); - - ui->imageWidthLabel->setText(QString::number(image_width)); - ui->imageHeightLabel->setText(QString::number(image_height)); - ui->imageChannelsLabel->setText(QString::number(image_channels)); -} - -void DetectorSetupDialog::setConvertGrayscale(void){ - convert_grayscale = ui->convertGrayscaleCheckbox->isChecked(); - settings->setValue("model_convert_grayscale", convert_grayscale); -} - -bool DetectorSetupDialog::getConvertGrayscale(void){ - return convert_grayscale; -} - -void DetectorSetupDialog::setConvertDepth(void){ - convert_depth = ui->convertDepthCheckbox->isChecked(); - settings->setValue("model_convert_depth", convert_depth); -} - -bool DetectorSetupDialog::getConvertDepth(void){ - return convert_depth; -} - -void DetectorSetupDialog::checkForm(void){ - - ui->buttonBox->button(QDialogButtonBox::Ok)->setDisabled(true); - - cfg_file = ui->cfgPathLineEdit->text(); - weight_file = ui->weightPathLineEdit->text(); - names_file = ui->namesPathLineEdit->text(); - - if(cfg_file == "") return; - if(weight_file == "") return; - if(names_file == "") return; - - if(!QFile(cfg_file).exists()){ - qDebug() << "Config file doesn't exist"; - return; - }else if(!getParamsFromConfig()){ - return; - } - - if(!QFile(weight_file).exists()){ - qDebug() << "Weight file doesn't exist"; - return; - } - - if(!QFile(names_file).exists()){ - qDebug() << "Names file doesn't exist"; - return; - } - - // At this point, all good. - ui->buttonBox->button(QDialogButtonBox::Ok)->setEnabled(true); - settings->setValue("model_width", image_width); - settings->setValue("model_height", image_height); - settings->setValue("modelchannels", image_channels); - - settings->setValue("model_cfg", cfg_file); - settings->setValue("model_weights", weight_file); - settings->setValue("model_names", names_file); - -} - -bool DetectorSetupDialog::getParamsFromConfig(void){ - - qDebug() << "Checking config file"; - - if(framework == FRAMEWORK_DARKNET){ - QSettings darknet_settings(cfg_file, QSettings::IniFormat); - - darknet_settings.beginGroup("net"); - - auto keys = darknet_settings.childKeys(); - - if(!darknet_settings.contains("width")){ - qDebug() << "No width parameter"; - return false; - } - if(!darknet_settings.contains("height")){ - qDebug() << "No height parameter"; - return false; - } - if(!darknet_settings.contains("channels")){ - qDebug() << "No channels parameter"; - return false; - } - - auto width = darknet_settings.value("width").toInt(); - auto height = darknet_settings.value("height").toInt(); - auto channels = darknet_settings.value("channels").toInt(); - - darknet_settings.endGroup(); - - qDebug() << width << height << channels; - - if(width > 0 && height > 0 && channels > 0){ - - qDebug() << width << height << channels; - - image_width = width; - image_height = height; - image_channels = channels; - - }else{ - return false; - } - - }else if(framework == FRAMEWORK_TENSORFLOW){ - // In theory we can parse the .pbtxt file to figure out - // the input layer parameters, but that either means bringing in - // protobuf or loading the entire network via OpenCV. - - } - - updateFields(); - - return true; - -} - -QString DetectorSetupDialog::openFile(QString title, QString search_path, QString filter){ - QString openDir; - - if(search_path == ""){ - openDir = settings->value("project_folder").toString(); - }else{ - openDir = QFileInfo(search_path).absoluteDir().absolutePath(); - } - - auto path = QFileDialog::getOpenFileName(this, title, - openDir, - QString("All files (*.*);;%1").arg(filter), - &filter); - - return path; -} - -void DetectorSetupDialog::setCfgFile(void){ - - QString filter, title; - - if(framework == FRAMEWORK_DARKNET){ - filter = tr("Config (*.cfg)"); - title = tr("Select darknet config file"); - }else if(framework == FRAMEWORK_TENSORFLOW){ - filter = tr("Config (*.pbtxt)"); - title = tr("Select tensorflow config file"); - }else{ - return; - } - - auto path = openFile(title, cfg_file, filter); - - if(path != ""){ - ui->cfgPathLineEdit->setText(path); - } - - checkForm(); -} - - -void DetectorSetupDialog::setWeightsFile(void){ - - QString filter, title; - - if(framework == FRAMEWORK_DARKNET){ - filter = tr("Weights (*.weights)"); - title = tr("Select darknet weights file"); - }else if(framework == FRAMEWORK_TENSORFLOW){ - filter = tr("Config (*.pb)"); - title = tr("Select tensorflow frozen graph"); - }else{ - return; - } - - auto path = openFile(title, weight_file, filter); - - if(path != ""){ - ui->weightPathLineEdit->setText(path); - } - - checkForm(); -} - -void DetectorSetupDialog::setFramework(void){ - if(ui->frameworkComboBox->currentText().startsWith("Darknet")){ - framework = FRAMEWORK_DARKNET; - settings->setValue("model_framework", framework); - }else if(ui->frameworkComboBox->currentText().startsWith("Tensorflow")){ - framework = FRAMEWORK_TENSORFLOW; - settings->setValue("model_framework", framework); - } -} - -void DetectorSetupDialog::setTarget(void){ - if(ui->targetComboBox->currentText() == "CPU"){ - target = cv::dnn::DNN_TARGET_CPU; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "OpenCL"){ - target = cv::dnn::DNN_TARGET_OPENCL; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "OpenCL FP16"){ - target = cv::dnn::DNN_TARGET_OPENCL_FP16; - settings->setValue("model_target", target); - } -#ifdef WITH_CUDA - else if(ui->targetComboBox->currentText() == "CUDA"){ - target = cv::dnn::DNN_TARGET_CUDA; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "CUDA FP16"){ - target = cv::dnn::DNN_TARGET_CUDA_FP16; - settings->setValue("model_target", target); - } -#endif -} - -int DetectorSetupDialog::getTarget(void){ - return target; -} - -void DetectorSetupDialog::setNamesFile(void){ - - QString filter = tr("Names (*.names)"); - QString title = tr("Select darknet names file"); - - auto path = openFile(title, names_file, filter); - - if(path != ""){ - ui->namesPathLineEdit->setText(path); - } - - checkForm(); -} - -DetectorSetupDialog::~DetectorSetupDialog() -{ - delete ui; -} -======= -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#include "detectorsetupdialog.h" -#include "ui_detectorsetupdialog.h" - -DetectorSetupDialog::DetectorSetupDialog(QWidget *parent) : - QDialog(parent), - ui(new Ui::DetectorSetupDialog) -{ - ui->setupUi(this); - - connect(ui->cfgPathButton, SIGNAL(clicked()), this, SLOT(setCfgFile())); - connect(ui->cfgPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->weightPathButton, SIGNAL(clicked(bool)), this, SLOT(setWeightsFile())); - connect(ui->weightPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->namesPathButton, SIGNAL(clicked(bool)), this, SLOT(setNamesFile())); - connect(ui->frameworkComboBox, SIGNAL(currentIndexChanged(QString)), this, SLOT(setFramework())); - connect(ui->targetComboBox, SIGNAL(currentIndexChanged(QString)), this, SLOT(setTarget())); - connect(ui->namesPathLineEdit, SIGNAL(editingFinished()), this, SLOT(checkForm())); - connect(ui->convertGrayscaleCheckbox, SIGNAL(clicked(bool)), this, SLOT(setConvertGrayscale())); - connect(ui->convertDepthCheckbox, SIGNAL(clicked(bool)), this, SLOT(setConvertDepth())); - - settings = new QSettings("I3Dr", "Stereo Vision Toolkit"); - - cfg_file = settings->value("model_cfg", "").toString(); - weight_file = settings->value("model_weights", "").toString(); - names_file = settings->value("model_names", "").toString(); - - convert_grayscale = settings->value("model_convert_grayscale", true).toBool(); - ui->convertGrayscaleCheckbox->setChecked(convert_grayscale); - - convert_depth = settings->value("model_convert_depth", true).toBool(); - ui->convertDepthCheckbox->setChecked(convert_depth); - - image_width = settings->value("model_width", 0).toInt(); - image_height = settings->value("model_height", 0).toInt(); - image_channels = settings->value("model_channels", 3).toInt(); - - framework = static_cast(settings->value("model_framework", 0).toInt()); - if(framework == FRAMEWORK_DARKNET){ - ui->frameworkComboBox->setCurrentText("Darknet (YOLO)"); - ui->imageHeightLabel->show(); - ui->imageWidthLabel->show(); - }else if(framework == FRAMEWORK_TENSORFLOW){ - ui->frameworkComboBox->setCurrentText("Tensorflow"); - ui->imageHeightLabel->hide(); - ui->imageWidthLabel->hide(); - } - - target = settings->value("model_target", cv::dnn::DNN_TARGET_CPU).toInt(); - if(target == cv::dnn::DNN_TARGET_CPU){ - ui->targetComboBox->setCurrentText("CPU"); - }else if(target == cv::dnn::DNN_TARGET_OPENCL){ - ui->targetComboBox->setCurrentText("OpenCL"); - }else if(target == cv::dnn::DNN_TARGET_OPENCL_FP16){ - ui->targetComboBox->setCurrentText("OpenCL FP16"); - } -#ifdef WITH_CUDA - else if(target == cv::dnn::DNN_TARGET_CUDA){ - ui->targetComboBox->setCurrentText("CUDA"); - }else if(target == cv::dnn::DNN_TARGET_CUDA_FP16){ - ui->targetComboBox->setCurrentText("CUDA FP16"); - } -#endif - updateFields(); - checkForm(); - -} - -void DetectorSetupDialog::updateFields(){ - ui->cfgPathLineEdit->setText(cfg_file); - ui->weightPathLineEdit->setText(weight_file); - ui->namesPathLineEdit->setText(names_file); - - ui->imageWidthLabel->setText(QString::number(image_width)); - ui->imageHeightLabel->setText(QString::number(image_height)); - ui->imageChannelsLabel->setText(QString::number(image_channels)); -} - -void DetectorSetupDialog::setConvertGrayscale(void){ - convert_grayscale = ui->convertGrayscaleCheckbox->isChecked(); - settings->setValue("model_convert_grayscale", convert_grayscale); -} - -bool DetectorSetupDialog::getConvertGrayscale(void){ - return convert_grayscale; -} - -void DetectorSetupDialog::setConvertDepth(void){ - convert_depth = ui->convertDepthCheckbox->isChecked(); - settings->setValue("model_convert_depth", convert_depth); -} - -bool DetectorSetupDialog::getConvertDepth(void){ - return convert_depth; -} - -void DetectorSetupDialog::checkForm(void){ - - ui->buttonBox->button(QDialogButtonBox::Ok)->setDisabled(true); - - cfg_file = ui->cfgPathLineEdit->text(); - weight_file = ui->weightPathLineEdit->text(); - names_file = ui->namesPathLineEdit->text(); - - if(cfg_file == "") return; - if(weight_file == "") return; - if(names_file == "") return; - - if(!QFile(cfg_file).exists()){ - qDebug() << "Config file doesn't exist"; - return; - }else if(!getParamsFromConfig()){ - return; - } - - if(!QFile(weight_file).exists()){ - qDebug() << "Weight file doesn't exist"; - return; - } - - if(!QFile(names_file).exists()){ - qDebug() << "Names file doesn't exist"; - return; - } - - // At this point, all good. - ui->buttonBox->button(QDialogButtonBox::Ok)->setEnabled(true); - settings->setValue("model_width", image_width); - settings->setValue("model_height", image_height); - settings->setValue("modelchannels", image_channels); - - settings->setValue("model_cfg", cfg_file); - settings->setValue("model_weights", weight_file); - settings->setValue("model_names", names_file); - -} - -bool DetectorSetupDialog::getParamsFromConfig(void){ - - qDebug() << "Checking config file"; - - if(framework == FRAMEWORK_DARKNET){ - QSettings darknet_settings(cfg_file, QSettings::IniFormat); - - darknet_settings.beginGroup("net"); - - auto keys = darknet_settings.childKeys(); - - if(!darknet_settings.contains("width")){ - qDebug() << "No width parameter"; - return false; - } - if(!darknet_settings.contains("height")){ - qDebug() << "No height parameter"; - return false; - } - if(!darknet_settings.contains("channels")){ - qDebug() << "No channels parameter"; - return false; - } - - auto width = darknet_settings.value("width").toInt(); - auto height = darknet_settings.value("height").toInt(); - auto channels = darknet_settings.value("channels").toInt(); - - darknet_settings.endGroup(); - - qDebug() << width << height << channels; - - if(width > 0 && height > 0 && channels > 0){ - - qDebug() << width << height << channels; - - image_width = width; - image_height = height; - image_channels = channels; - - }else{ - return false; - } - - }else if(framework == FRAMEWORK_TENSORFLOW){ - // In theory we can parse the .pbtxt file to figure out - // the input layer parameters, but that either means bringing in - // protobuf or loading the entire network via OpenCV. - - } - - updateFields(); - - return true; - -} - -QString DetectorSetupDialog::openFile(QString title, QString search_path, QString filter){ - QString openDir; - - if(search_path == ""){ - openDir = settings->value("project_folder").toString(); - }else{ - openDir = QFileInfo(search_path).absoluteDir().absolutePath(); - } - - auto path = QFileDialog::getOpenFileName(this, title, - openDir, - QString("All files (*.*);;%1").arg(filter), - &filter); - - return path; -} - -void DetectorSetupDialog::setCfgFile(void){ - - QString filter, title; - - if(framework == FRAMEWORK_DARKNET){ - filter = tr("Config (*.cfg)"); - title = tr("Select darknet config file"); - }else if(framework == FRAMEWORK_TENSORFLOW){ - filter = tr("Config (*.pbtxt)"); - title = tr("Select tensorflow config file"); - }else{ - return; - } - - auto path = openFile(title, cfg_file, filter); - - if(path != ""){ - ui->cfgPathLineEdit->setText(path); - } - - checkForm(); -} - - -void DetectorSetupDialog::setWeightsFile(void){ - - QString filter, title; - - if(framework == FRAMEWORK_DARKNET){ - filter = tr("Weights (*.weights)"); - title = tr("Select darknet weights file"); - }else if(framework == FRAMEWORK_TENSORFLOW){ - filter = tr("Config (*.pb)"); - title = tr("Select tensorflow frozen graph"); - }else{ - return; - } - - auto path = openFile(title, weight_file, filter); - - if(path != ""){ - ui->weightPathLineEdit->setText(path); - } - - checkForm(); -} - -void DetectorSetupDialog::setFramework(void){ - if(ui->frameworkComboBox->currentText().startsWith("Darknet")){ - framework = FRAMEWORK_DARKNET; - settings->setValue("model_framework", framework); - }else if(ui->frameworkComboBox->currentText().startsWith("Tensorflow")){ - framework = FRAMEWORK_TENSORFLOW; - settings->setValue("model_framework", framework); - } -} - -void DetectorSetupDialog::setTarget(void){ - if(ui->targetComboBox->currentText() == "CPU"){ - target = cv::dnn::DNN_TARGET_CPU; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "OpenCL"){ - target = cv::dnn::DNN_TARGET_OPENCL; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "OpenCL FP16"){ - target = cv::dnn::DNN_TARGET_OPENCL_FP16; - settings->setValue("model_target", target); - } -#ifdef WITH_CUDA - else if(ui->targetComboBox->currentText() == "CUDA"){ - target = cv::dnn::DNN_TARGET_CUDA; - settings->setValue("model_target", target); - }else if(ui->targetComboBox->currentText() == "CUDA FP16"){ - target = cv::dnn::DNN_TARGET_CUDA_FP16; - settings->setValue("model_target", target); - } -#endif -} - -int DetectorSetupDialog::getTarget(void){ - return target; -} - -void DetectorSetupDialog::setNamesFile(void){ - - QString filter = tr("Names (*.names)"); - QString title = tr("Select darknet names file"); - - auto path = openFile(title, names_file, filter); - - if(path != ""){ - ui->namesPathLineEdit->setText(path); - } - - checkForm(); -} - -DetectorSetupDialog::~DetectorSetupDialog() -{ - delete ui; -} ->>>>>>> dev diff --git a/src/detection/detectorsetupdialog.h.orig b/src/detection/detectorsetupdialog.h.orig deleted file mode 100644 index bb12b67af..000000000 --- a/src/detection/detectorsetupdialog.h.orig +++ /dev/null @@ -1,151 +0,0 @@ -<<<<<<< HEAD -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#ifndef DETECTORSETUPDIALOG_H -#define DETECTORSETUPDIALOG_H - -#include -#include -#include -#include -#include -#include - -#include -#include - -namespace Ui { -class DetectorSetupDialog; -} - -class DetectorSetupDialog : public QDialog -{ - Q_OBJECT - -public: - explicit DetectorSetupDialog(QWidget *parent = nullptr); - int getWidth(void){return image_width;} - int getHeight(void){return image_height;} - int getChannels(void){return image_channels;} - QString getNames(void){return names_file;} - QString getWeights(void){return weight_file;} - QString getCfg(void){return cfg_file;} - bool getConvertGrayscale(void); - bool getConvertDepth(void); - model_framework getFramework(void){return framework;} - int getTarget(); - ~DetectorSetupDialog(); - -private slots: - - void setCfgFile(); - void setNamesFile(); - void setWeightsFile(); - void setFramework(); - void setTarget(); - void setConvertGrayscale(void); - void setConvertDepth(void); - - void checkForm(); - - -private: - Ui::DetectorSetupDialog *ui; - bool getParamsFromConfig(); - void updateFields(); - - QSettings* settings; - - QString cfg_file; - QString weight_file; - QString names_file; - int image_width = 320; - int image_height = 240; - int image_channels = 3; // default - int target = 0; - bool convert_grayscale = true; - bool convert_depth = true; - model_framework framework = FRAMEWORK_TENSORFLOW; - QString openFile(QString title, QString search_path="", QString filter=""); -}; - -#endif // DETECTORSETUPDIALOG_H -======= -/* -* Copyright Deeplabel, used with permission of the author -* Author: Josh Veitch-Michaelis (jveitch@i3drobotics.com) -*/ - -#ifndef DETECTORSETUPDIALOG_H -#define DETECTORSETUPDIALOG_H - -#include -#include -#include -#include -#include -#include - -#include -#include - -namespace Ui { -class DetectorSetupDialog; -} - -class DetectorSetupDialog : public QDialog -{ - Q_OBJECT - -public: - explicit DetectorSetupDialog(QWidget *parent = nullptr); - int getWidth(void){return image_width;} - int getHeight(void){return image_height;} - int getChannels(void){return image_channels;} - QString getNames(void){return names_file;} - QString getWeights(void){return weight_file;} - QString getCfg(void){return cfg_file;} - bool getConvertGrayscale(void); - bool getConvertDepth(void); - model_framework getFramework(void){return framework;} - int getTarget(); - ~DetectorSetupDialog(); - -private slots: - - void setCfgFile(); - void setNamesFile(); - void setWeightsFile(); - void setFramework(); - void setTarget(); - void setConvertGrayscale(void); - void setConvertDepth(void); - - void checkForm(); - - -private: - Ui::DetectorSetupDialog *ui; - bool getParamsFromConfig(); - void updateFields(); - - QSettings* settings; - - QString cfg_file; - QString weight_file; - QString names_file; - int image_width = 320; - int image_height = 240; - int image_channels = 3; // default - int target = 0; - bool convert_grayscale = true; - bool convert_depth = true; - model_framework framework = FRAMEWORK_TENSORFLOW; - QString openFile(QString title, QString search_path="", QString filter=""); -}; - -#endif // DETECTORSETUPDIALOG_H ->>>>>>> dev diff --git a/src/detection/detectorsetupdialog.ui.orig b/src/detection/detectorsetupdialog.ui.orig deleted file mode 100644 index 4566fb5e2..000000000 --- a/src/detection/detectorsetupdialog.ui.orig +++ /dev/null @@ -1,545 +0,0 @@ -<<<<<<< HEAD - - - DetectorSetupDialog - - - - 0 - 0 - 500 - 314 - - - - - 500 - 0 - - - - Configure object detector - - - - - - - - Weight file - - - - - - - - Darknet (YOLO) - - - - - Tensorflow - - - - - - - - - CPU - - - - - OpenCL - - - - - OpenCL FP16 - - - - - CUDA - - - - - CUDA FP16 - - - - - - - - ... - - - - - - - Config file - - - - - - - ./ml/coco/yolov4-tiny.weights - - - - - - - - - - - - - - - Channels - - - - - - - Convert grayscale images to RGB - - - true - - - - - - - Type - - - - - - - Image Height - - - - - - - - - - - - - - - ... - - - - - - - Loaded Network Parameters: - - - - - - - - - - - - - - - ... - - - - - - - ./ml/coco/yolov4-tiny.cfg - - - - - - - Names file - - - - - - - ./ml/coco/coco.names - - - - - - - Image Width - - - - - - - Target - - - - - - - Convert 16-bit images to 8-bit - - - true - - - - - - - - - Qt::Horizontal - - - QDialogButtonBox::Cancel|QDialogButtonBox::Ok - - - - - - - cfgPathLineEdit - cfgPathButton - weightPathLineEdit - weightPathButton - namesPathLineEdit - namesPathButton - - - - - buttonBox - accepted() - DetectorSetupDialog - accept() - - - 248 - 254 - - - 157 - 274 - - - - - buttonBox - rejected() - DetectorSetupDialog - reject() - - - 316 - 260 - - - 286 - 274 - - - - - -======= - - - DetectorSetupDialog - - - - 0 - 0 - 500 - 314 - - - - - 500 - 0 - - - - Configure object detector - - - - - - - - Weight file - - - - - - - - Darknet (YOLO) - - - - - Tensorflow - - - - - - - - - CPU - - - - - OpenCL - - - - - OpenCL FP16 - - - - - CUDA - - - - - CUDA FP16 - - - - - - - - ... - - - - - - - Config file - - - - - - - ./ml/coco/yolov4-tiny.weights - - - - - - - - - - - - - - - Channels - - - - - - - Convert grayscale images to RGB - - - true - - - - - - - Type - - - - - - - Image Height - - - - - - - - - - - - - - - ... - - - - - - - Loaded Network Parameters: - - - - - - - - - - - - - - - ... - - - - - - - ./ml/coco/yolov4-tiny.cfg - - - - - - - Names file - - - - - - - ./ml/coco/coco.names - - - - - - - Image Width - - - - - - - Target - - - - - - - Convert 16-bit images to 8-bit - - - true - - - - - - - - - Qt::Horizontal - - - QDialogButtonBox::Cancel|QDialogButtonBox::Ok - - - - - - - cfgPathLineEdit - cfgPathButton - weightPathLineEdit - weightPathButton - namesPathLineEdit - namesPathButton - - - - - buttonBox - accepted() - DetectorSetupDialog - accept() - - - 248 - 254 - - - 157 - 274 - - - - - buttonBox - rejected() - DetectorSetupDialog - reject() - - - 316 - 260 - - - 286 - 274 - - - - - ->>>>>>> dev