How to use touch_text method in robotframework-ioslibrary

Best Python code snippet using robotframework-ioslibrary_python

on_world_touch_demo.py

Source:on_world_touch_demo.py

1#!/usr/bin/env python2'''3example to show optical flow4USAGE: video_correlation.py [<video_source>]5Keys:6    ESC    - exit7'''8# Python 2/3 compatibility9from __future__ import print_function10import numpy as np11import cv2 as cv12# from cv2 import cuda13import EasyPySpin14#transforms15import queue16import mediapipe as mp17import sys18np.random.seed(42)19#global parameters20touch_threshold = 5 #vector magnitude. The lower, the more sensitive detection21centripetal_threshold = 2 #vector variance. The higher, the less possible hand motion is detected as speckle motion (false positive)22forceScale = int(5) #scalar. The bigger, the longer vectors look. does not affect flow23lineWidth = int(5) # line width of vectors.24mp_hands = mp.solutions.hands25mp_drawing = mp.solutions.drawing_utils26mp_drawing_styles = mp.solutions.drawing_styles27#configuration28    #laser torch + diffuser, on partition: touch_threshold = 3, centripetal_threshold = 129    #laser projector + concave only, on partition:  touch_threshold = 3, centripetal_threshold = 130    #laser projector + concave + diffuser, on partition:  touch_threshold = 3.5, centripetal_threshold = 2. can cover larger area, but SNR is lower -> var is large when stepping on floor.31#laser projector + concave only, on wall: active_threshold = 6, touch_threshold = 7, centripetal_threshold = 0.532def draw_touching_point(wrist, index, width, height):33    blank_image = np.zeros((int(height),int(width),3), np.uint8)34    35    #drawing parameters36    white = (255,255,255)37    wrist_co = (int(wrist.x*width),int(wrist.y*height))38    # print('wrist',wrist_co)39    index_co = (int(index.x*width),int(index.y*height))40    coeff = 341    ellipse_center = (int(coeff*wrist_co[0] + (1-coeff)*index_co[0]),42                    int(coeff*wrist_co[1] + (1-coeff)*index_co[1]))43    longeraxis = int(np.sqrt((index_co[0]-ellipse_center[0])**244                    +(index_co[1]-ellipse_center[1])**2))45    axesLength = (longeraxis,30)46    angle = np.arctan2(wrist_co[1]-index_co[1],wrist_co[0]-index_co[0])47    angle = int(angle*180/np.pi)48    # print(angle)49    # # wrist50    # image = cv.circle(image, wrist_co, 5, (0,255,0), 5)51    # # index_fingertip52    # image = cv.circle(image, index_co, 5, (0,255,0), 5)53    #ellipse54    blank_image = cv.ellipse(blank_image, ellipse_center,axesLength,angle,0,360,white,-1)55    cropped_image = blank_image[97:394,119:515]56    dim = (2048,1536)57    resized = cv.resize(cropped_image, dim, interpolation = cv.INTER_AREA)58    resized = cv.cvtColor(resized,cv.COLOR_BGR2GRAY).astype("float32")59    return resized, index_co60def nothing(x):61    pass62def cart2pol(z):63    rho = np.sqrt(z[0]**2 + z[1]**2)64    phi = np.arctan2(z[1],z[0])65    return(rho, phi)66def touch_detection(flow,yy,xx,isTouched,img_mask):67    y, x = flow.shape[:2] 68    69    isEnter = False70    isExit = False71    flow_array_original = flow72    flow_array = []73    for iy in range(y):74        for ix in range(x):75            if img_mask[yy[iy],xx[ix]] == 0:76                mag, angle = cart2pol(flow_array_original[iy][ix])77                if mag > touch_threshold:78                    flow_array.append([mag,angle])79    80    if flow_array == []:81        flow_array.append([0,0])82    flow_array = np.array(flow_array)83    flow_array_var = np.var(flow_array[:,1])84    flow_array_magnitude = flow_array[:,0].mean()85    if flow_array_magnitude > touch_threshold and flow_array_var > centripetal_threshold:86        touch_text = "Speckle motion: touched" 87        if (isTouched == False):88            isEnter = True89        isTouched = True90    else:91        touch_text = "Speckle motion: no touch" 92        if (isTouched == True):93            isExit = True94        isTouched = False95        96    # touch_text = "var:"+str(round(flow_array_var,2))+" mag:"+str(round(flow_array_magnitude,2))97    return touch_text, isTouched, isEnter, isExit98def draw_flow(img, flow,yy,xx,touch_text,img_mask):99    h, w = img.shape[:2]100    y, x = flow.shape[:2] 101    mask = np.zeros((h,w,3), np.uint8)102   103    for iy in range(y):104        for ix in range(x):105            if img_mask[yy[iy],xx[ix]] == 0:106                start_point = (xx[ix],yy[iy])107                end_point = (xx[ix]+forceScale*int(flow[iy][ix][0]),yy[iy]+forceScale*int(flow[iy][ix][1]))108                # print(start_point,end_point)109                cv.arrowedLine(mask, start_point,end_point, (0, 255, 0),lineWidth)110    masked_image = cv.addWeighted(img,1,mask,1,0)111    112    #test display113    font = cv.FONT_HERSHEY_SIMPLEX114    # org115    org = (600, 100)116    # fontScale117    fontScale = 2118    # Blue color in BGR119    color = (0, 255, 0)120    # Line thickness of 2 px121    thickness = 5122    123    masked_image = cv.putText(masked_image, touch_text, org, font, 124                   fontScale, color, thickness, cv.LINE_AA)125    return masked_image126cv.namedWindow('Touch Detection',cv.WINDOW_NORMAL)127def main():128    try:129        fn = sys.argv[1]130        cam = cv.VideoCapture(fn)131        mode = 'video'132    except IndexError:133        fn = 0134        mode = 'camera'135        cam = EasyPySpin.VideoCapture(0)136        cap = cv.VideoCapture(0, cv.CAP_DSHOW)137        138    _ret, prev = cam.read()139    gpu_previous = cv.cuda_GpuMat()140    gpu_previous.upload(prev)141    142    interval = 1143    while (cam.isOpened() == False):144        _ret, prev = cam.read()145        gpu_previous.upload(prev)146    147    frameWidth = int(cam.get(cv.CAP_PROP_FRAME_WIDTH))148    frameHeight = int(cam.get(cv.CAP_PROP_FRAME_HEIGHT))149    width = cap.get(cv.CAP_PROP_FRAME_WIDTH)150    height = cap.get(cv.CAP_PROP_FRAME_HEIGHT)151    print(width, height)152    gpu_cur = cv.cuda_GpuMat()153    gpu_ref = cv.cuda_GpuMat()154    gpu_ref = gpu_previous155    count = 0156    no_row_grids = 15#30157    no_col_grids = 30#60158    flow = np.zeros([no_row_grids,no_col_grids,2])159    # flow_val = []160    nc = frameWidth # 2048161    nr = frameHeight # 1536162    vel_est_delta = 1 #delta of frames over which velocity is estimated163    yy = np.arange(0,nr,int(nr/no_row_grids))164    xx = np.arange(0,nc,int(nc/no_col_grids))165    queue_vel = queue.Queue(vel_est_delta)166    intensity_mask = cv.imread('29/29_mask.png')167    intensity_mask = cv.cvtColor(intensity_mask,cv.COLOR_BGR2GRAY).astype("float32")168    img_mask = 255*intensity_mask169    img_mask = img_mask.clip(0, 255).astype("uint8")170    UI = np.zeros((480,640,3), np.uint8) #297,396171    UI.fill(50)172    isTouched = False173    isEnter = False174    isExit = False175    with mp_hands.Hands(176        model_complexity=0,177        min_detection_confidence=0.5,178        min_tracking_confidence=0.5) as hands:179        while (cam.isOpened()):180            _ret, img = cam.read()181            gpu_cur.upload(img)182            success, image = cap.read()183            if not success:184                print("Ignoring empty camera frame.")185                # If loading a video, use 'break' instead of 'continue'.186                break187            # To improve performance, optionally mark the image as not writeable to188            # pass by reference.189            image.flags.writeable = False190            image = cv.cvtColor(image, cv.COLOR_BGR2RGB)191            results = hands.process(image)192            # Draw landmark annotation on the image.193            image.flags.writeable = True194            image = cv.cvtColor(image, cv.COLOR_RGB2BGR)195            # print(bool(results.multi_hand_landmarks))196            if results.multi_hand_landmarks:197                for hand_landmarks in results.multi_hand_landmarks:198                    # mp_drawing.draw_landmarks(199                    # image,200                    # hand_landmarks,201                    # mp_hands.HAND_CONNECTIONS,202                    # mp_drawing_styles.get_default_hand_landmarks_style(),203                    # mp_drawing_styles.get_default_hand_connections_style())204                    hand_mask, index_co = draw_touching_point(hand_landmarks.landmark[0],205                                                    hand_landmarks.landmark[8],width,height)206                    # print(index_co,'index_co')207                    #GREEN208                    # image = cv.circle(image, index_co, 5, (0,255,0), 5)209                    210                    #RED211                    if isEnter == True:212                        UI = cv.circle(UI, index_co, 10, (0,0,255), -1)213                        print('red',index_co)214                    # if isExit == True:215                    #     UI.fill(50)216                    img_mask = 255*(intensity_mask+hand_mask)217                    img_mask = img_mask.clip(0, 255).astype("uint8")218            219            cv.imshow('Mask', cv.resize(img_mask, (640,480), interpolation = cv.INTER_AREA))220            cv.imshow('UI', cv.resize(UI[97:394,119:515], (800,600), interpolation = cv.INTER_AREA))221            222        223            try:224                gpu_flow_create = cv.cuda_FarnebackOpticalFlow.create(3, 0.5, False, 15, 3, 5, 1.2, 0,)225                gpu_flow = cv.cuda_FarnebackOpticalFlow.calc(gpu_flow_create,gpu_ref,gpu_cur,None,)226            except IndexError:227                continue228            flow = gpu_flow.download()229            flow_sparse = flow[::int(nr/no_row_grids),::int(nc/no_col_grids),:]230            count = count + interval231            232            cv.imshow('Webcam',image)233            if count>=2*vel_est_delta:234                touch_text, isTouched, isEnter, isExit = touch_detection(flow_sparse,yy,xx,isTouched,img_mask)235                display_img = draw_flow(cv.cvtColor(img,cv.COLOR_GRAY2BGR),236                                        flow_sparse,yy,xx,touch_text,img_mask)237                cv.imshow('Touch Detection',display_img)238            239            gpu_ref.upload(img)240            ch = cv.waitKey(5)241            if ch == 27:242                break243            if ch == ord('p'):244                cv.waitKey(-1)#pause245            if ch == ord('z'):246                UI.fill(50)247        print('Done. frame count:',count)248if __name__ == '__main__':249    print(__doc__)250    main()...

main.py

Source:main.py

1from gwc_python.core import GestureWorksCore2from gwc_python.GWCUtils import TOUCHREMOVED, TOUCHADDED3from direct.gui.OnscreenImage import OnscreenImage4from direct.gui.OnscreenText import OnscreenText5from direct.showbase.ShowBase import ShowBase6from direct.task import Task7from pandac.PandaModules import LineSegs 8from pandac.PandaModules import deg2Rad9from pandac.PandaModules import NodePath10from pandac.PandaModules import Vec311from panda3d.core import TransparencyAttrib12from panda3d.core import TextNode13SCREEN_WIDTH = 192014SCREEN_HEIGHT = 108015class MultitouchApp(ShowBase):16    17    def __init__(self, gw):18        self.gw = gw19        self.active_points = {}20        self.touch_images = {}21        self.touch_text = {}22        23        ShowBase.__init__(self)24        self.build()25        self.taskMgr.add(self.updateGestureworks, "updateGestureworksTask")26        27    def processTouchEvents(self, touches):28        for touch in touches:29            if touch.status != TOUCHREMOVED:30                self.active_points.update({touch.point_id: touch})31            else:32                self.active_points.pop(touch.point_id)33    34    def clearScreen(self):35        for single_image in self.touch_images:36            self.touch_images[single_image].destroy()37        for single_text in self.touch_text:38            self.touch_text[single_text].destroy()              39 40    def drawTouchPoints(self):41        self.win42        for touch in self.active_points.values():43            44            # We need to convert Gestureworks coordinates to Panda3D coordinates45            touch_x = float((touch.position.x - SCREEN_WIDTH/2) / SCREEN_WIDTH) * 446            touch_y = float((SCREEN_HEIGHT/2 - touch.position.y) / SCREEN_HEIGHT) * 247            if touch.status != TOUCHREMOVED:48                # Draw circles49                self.touch_images[touch.point_id] = OnscreenImage('ring_black.png', pos=(touch_x,0,touch_y), scale=.05)50                self.touch_images[touch.point_id].setTransparency(TransparencyAttrib.MAlpha)51                52                # Draw the touchpoint info53                label = 'ID: %d\nX: %d | Y: %d' %(touch.point_id,touch.position.x,touch.position.y)54                self.touch_text[touch.point_id] = OnscreenText(label,pos=(touch_x+0.1,touch_y), scale=.05,align=TextNode.ALeft)                 55 56    def updateGestureworks(self, task):            57        self.gw.processFrame()58        point_events = gw.consumePointEvents()59        self.processTouchEvents(point_events)60        self.clearScreen();61        self.drawTouchPoints()62        return Task.cont63    64    def build(self):65        if not self.gw.registerWindow('Panda'):66            print('Unable to register touch window')67            exit()68 69if __name__ == '__main__':70    gw = GestureWorksCore('C:\\GestureworksCore\\GestureworksCore32.dll')71    if not gw.loaded_dll: 72        print 'Unable to load GestureWorksCore'73        exit()74    gw.initializeGestureWorks(SCREEN_WIDTH, SCREEN_HEIGHT)75    app = MultitouchApp(gw)...

config.py

Source:config.py

1import os2# config3NUM_BLOCKS = 14SHUFFLE_BAGS = True5# Parameter for randomize the number of balloons per pop range (aka bag)6RANDOMIZE_BALLOON_NUM = False7BALLOON_SETUP = [{'range': [0, 8], 'number_of_balloons':8},8                 {'range': [0, 16], 'number_of_balloons':8},9                 {'range': [8, 16], 'number_of_balloons':8}]10PRACTICE_SETUP = [{'range': [10,10], 'number_of_balloons':0},11                 {'range': [3,3], 'number_of_balloons':0},12                 {'range': [6,6], 'number_of_balloons':0}]13NUM_BALLOONS = 1814NUM_BAGS = len(BALLOON_SETUP)15BALLOONS_PER_BAG = NUM_BALLOONS/NUM_BAGS16TOUCH=False17TOUCH_TEXT = ["Touch left side\n","Touch right side\n"]18TOUCH_INST = ['left side of the screen', 'right side of the screen']19TASK_DIR = "."20INST2_IMG_PATH = os.path.join("inst", "INST2.png")21RESP_KEYS = ['F', 'J']22CONT_KEY = ['SPACEBAR']23CONT_KEY_STR = "Spacebar"24KEY_TEXT = RESP_KEYS25REWARD_LOW = 0.0526REWARD_HIGH = 0.2527#starting value in bank28GRAND_TOTAL = 1.0029RST_WIDTH = 60030FEEDBACK_TIME = 0.7531ISI = 0.2532INTER_PUMP_DURATION = 0.033REWARD_SLIDE_DURATION = 0.2534PUMP_DURATION = 0.2535COLLECT_DURATION = 0.536BALLOON_GROWTH_DURATION = 0.237POP_ANIMATION_DURATION = 1.038BALLOON_START_SIZE = 10039BALLOON_EXPLODE_SIZE = (500, 500)40FLIP_BART = False41INC_BALLOON_SIZE = 542TRIAN_SIZE = 1043CROSS_COLOR = (1.0, 1.0, 1.0, 1.0)44CROSS_FONTSIZE = 9045BANK_WIDTH = 15046BANK_HEIGHT = 15047POP_SIZE = (600, 600)48AIR_PUMP_WIDTH = 10049AIR_PUMP_HEIGHT = 10050NOZZLE_WIDTH = 451NOZZLE_HEIGHT = 4052FEEDBACK_FONT_SIZE = 9053INST_FONT_SIZE = 2054CROSS_FONT_SIZE = 7555FONT_SIZE = 3056LABEL_FONT_SIZE = 3057SKIP_SIZE = [200, 50]58SKIP_FONT_SIZE = 2259# font sizes for labels60TOTAL_FONT_SIZE = 3561TRIAL_FONT_SIZE = 3062FMRI = False63FMRI_TR = ["5"]64FMRI_TECH_KEYS = ['ENTER']65FMRI_TR_DUR = .866INIT_TR_WAIT = 6.067POST_TR_WAIT = 16.068POST_CHECK_TR_DUR = 3.0*FMRI_TR_DUR69TIME_BETWEEN_HAPPY = 1570TIME_JITTER_HAPPY = 1071HAPPY_FONT_SIZE = 2572HAPPY_INC_BASE = .0273HAPPY_INC_START = .274HAPPY_MOD = 20.75HAPPY_RANGE = 1076NON_PRESS_INT = .177PRESS_INT = .01678SLIDER_WIDTH = 100079RESP_HAPPY = ["F", "J"]80EEG = False...

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