import numpy as np
import util as ut
import svm_train as st
import re
from picamera.array import PiRGBArray
from picamera import PiCamera
import io, time
print("Opening ASL Model!")
try:
direct = cwd + "/ASLClassifier.dat"
print(direct)
model = st.load(direct)
print("SVM Loaded successfully..")
except:
model = st.trainSVM(17)
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)
font = cv2.FONT_HERSHEY_SIMPLEX
text = " "
temp = 0
previouslabel = None
previousText = " "
label = None
for frame in camera.capture_continuous(
#Importing Opencv and Numpy
import cv2
import numpy as np
#Importing our dependencies
import util as ut
import svm_train as st
import hand_util as hu
import time
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
model = st.trainSVM(9, 20, 'TrainData2')
kernel = np.ones((5, 5), np.uint8)
#Camera and font initialization
cam = int(raw_input("Enter Camera Index : "))
cap = cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
#The main event loop
while (cap.isOpened()):
t = time.time()
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, th1 = cv2.threshold(gray.copy(), 130, 255, cv2.THRESH_BINARY)
th1 = cv2.erode(th1, kernel, iterations=2)
cv2.imshow('thresh', th1)
_, contours, hierarchy = cv2.findContours(th1.copy(), cv2.RETR_EXTERNAL, 2)
cnt = ut.getMaxContour(contours, 4000)
feature.append(feature_tmp)
if lines[-1] == '+':
label.append(1)
elif lines[-1] == '-':
label.append(-1)
f.close()
return np.mat(feature), np.mat(label).T
print ("step 1: load data...")
dataSet, labels = load_data('data.txt')
train_x = dataSet
train_y = labels
test_x = dataSet
test_y = labels
## step 2: training...
print ("step 2: training...")
C = 0.001
toler = 0.001
maxIter = 500
svmClassifier = SVM.trainSVM(train_x, train_y, C, toler, maxIter, kernelOption = ('linear', 0))
print("b: ", svmClassifier.b)
## step 3: testing
print ("step 3: testing...")
accuracy = SVM.testSVM(svmClassifier, test_x, test_y)
## step 4: show the result
print ("step 4: show the result...")
print ('The classify accuracy is: %.3f%%' % (accuracy * 100))
SVM.showSVM(svmClassifier)
#other dependencies
from pymouse import PyMouse
from pykeyboard import PyKeyboard
import moosegesture as mges
#PyMouse the library to control mouse movements from python
m1 = PyMouse()
k1 = PyKeyboard()
#capturing device
cam = int(raw_input("Enter Camera Index : "))
cap = cv2.VideoCapture(cam)
#training the svm
model = st.trainSVM(3, 40, 'TrainData')
#initilizing values
thresh = 120
frame_count = 0
color = (0, 0, 255)
res = ut.get_screen_res()
w_screen = int(res['w']) + 200
h_screen = int(res['h']) + 200
font = cv2.FONT_HERSHEY_SIMPLEX
#loop 1 to calculate the mean threshhold
while (cap.isOpened()):
# for fps calc
t = time.time()
import cv2
import numpy as np
import util as ut
import svm_train as st
import time
from pygame import mixer
import threading
model=st.trainSVM(5,20,'TrainData')
move_text={'0':'ZERO','1':'ONE','2':'TWO','3':'THREE','4':'FOUR','5':'FIVE','6':'SIX','7':'SEVEN','8':'EIGHT','9':'NINE'}
aud_fil=['0.mp3','1.mp3','2.mp3','3.mp3','4.mp3','5.mp3','6.mp3','7.mp3','8.mp3','9.mp3']
cam = 0
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
mixer.init()
while(cap.isOpened()):
global lock
lock=threading.Lock()
move=''
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
_,th1 = cv2.threshold(gray.copy(),150,255,cv2.THRESH_TOZERO)
cv2.imshow('thresh',th1)
_,contours,_ = cv2.findContours(th1.copy(),cv2.RETR_EXTERNAL, 2)
cnt=ut.getMaxContour(contours,4000)
if cnt!=None:
_,res=ut.getGestureImg(cnt,img,th1,model)
cv2.imshow('PredictedGesture',cv2.imread('TrainData/'+res+'_1.jpg'))
move=move_text[res]
mixer.music.load('Aud/'+aud_fil[int(res)])
import cv2
import numpy as np
import util as ut
import svm_train as st
import re
model=st.trainSVM(17)
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
cam=int(raw_input("Enter Camera number: "))
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
def nothing(x) :
pass
text= " "
temp=0
previouslabel=None
previousText=" "
label = None
while(cap.isOpened()):
_,img=cap.read()
cv2.rectangle(img,(900,100),(1300,500),(255,0,0),3) # bounding box which captures ASL sign to be detected by the system
img1=img[100:500,900:1300]
img_ycrcb = cv2.cvtColor(img1, cv2.COLOR_BGR2YCR_CB)
blur = cv2.GaussianBlur(img_ycrcb,(11,11),0)
skin_ycrcb_min = np.array((0, 138, 67))
skin_ycrcb_max = np.array((255, 173, 133))
mask = cv2.inRange(blur, skin_ycrcb_min, skin_ycrcb_max) # detecting the hand in the bounding box using skin detection
contours,hierarchy = cv2.findContours(mask.copy(),cv2.RETR_EXTERNAL, 2)
cnt=ut.getMaxContour(contours,4000) # using contours to capture the skin filtered image of the hand
def camera():
print("Opening ASL Model!")
try:
direct = cwd + "/ASLClassifier.dat"
print(direct)
model = st.load(direct)
print("SVM Loaded successfully..")
except:
model = st.trainSVM(17)
#cap=cv2.VideoCapture(0)
#cap=PiCamera(0)
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
time.sleep(0.1)
#rgbFrame = PiRGBArray(camera, size = camera.resolution)
#frame1 = captureProcessFrame(camera, rgbFrame, 5)
#frameCount = 0
font = cv2.FONT_HERSHEY_SIMPLEX
text = " "
temp = 0
previouslabel = None # Past label
previousText = " " # Past text
label = None # current label
for frame in camera.capture_continuous(
rawCapture, format="bgr",
use_video_port=True): # Capture pi camera frames
stream = frame.array # store frame input as array
rawCapture.truncate(0) # take optional size
img = stream # store stream as temp img
cv2.rectangle(img, (300, 300), (100, 100), (0, 255, 0),
0) # create rectangle one screen
img1 = img[100:300, 100:300] # image stream ratio
img_ycrcb = cv2.cvtColor(img1,
cv2.COLOR_BGR2YCR_CB) # color format settings
blur = cv2.GaussianBlur(
img_ycrcb, (11, 11), 0
) # Gaussian blur added to screen helps deal with frams quality and performance
skin_ycrcb_min = np.array((0, 138, 67)) # color spaces min
skin_ycrcb_max = np.array((255, 173, 133)) # color spaces max
mask = cv2.inRange(
blur, skin_ycrcb_min, skin_ycrcb_max
) # detecting the hand in the bounding box using skin detection
imgres, contours, hierarchy = cv2.findContours(
mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # track motion points
cnt = ut.getMaxContour(contours, 4000) #store total motion points
if cnt is not None: # is there something there?
gesture, label = ut.getGestureImg(
cnt, img1, mask, model
) # passing the trained model for prediction and fetching the result
if (
label != None
): # is the model finding something in the box that matches a label?
if (temp == 0): # if temp is 0
previouslabel = label # last label now label
elif previouslabel == label: # still same gesture?
previouslabel = label # track again
temp += 1 # increse temp gesture count
else: # otherwise
temp = 0 # temp gesture back to 0
if (temp == 40): # has it been on screen for 40 temp counts?
if (label == 'P'): # create sentence space if user does P
label = " " # lable space
text = text + label # add results to text
if (label == 'Q'): # Wipe with Q(QUIT)
words = re.split(" +", text) # split result
words.pop() # push text off
text = " ".join(words) # fresh text
#text=previousText
print(text) # print out your last gestures before quiting
#cv2.imshow('PredictedGesture',gesture) # showing the best match or prediction
cv2.putText(
img, label, (50, 150), font, 8, (0, 125, 155),
2) # displaying the predicted letter on the main screen
cv2.putText(img, text, (50, 450), font, 3, (0, 0, 255), 2)
cv2.imshow('Frame', img) # show on screen
#cv2.imshow('Mask',mask)
key = cv2.waitKey(1) & 0xFF # wait for q to quit
if key == ord("q"): # is Q?
#cap.release()
cv2.destroyAllWindows() # close OpenCV session
camera.close()
break # stop while
return render_template("index.html")
"""
import cv2
import numpy as np
import util as ut
import svm_train as st
import time
import hand_util as hu
cam=int(raw_input("Enter Camera Index : "))
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
model=st.trainSVM(3,40,'TrainData')
font = cv2.FONT_HERSHEY_SIMPLEX
thresh=120
frame_count=0
color=(0,0,255)
while(cap.isOpened()):
t=time.time()
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.rectangle(img,(270,165),(370,315),color,3)
fps=int(1/(time.time()-t))
cv2.putText(img,"FPS: "+str(fps),(50,50), font,1,(255,255,255),2,cv2.LINE_AA)
cv2.imshow('Frame',img)
import ssl
from SimpleWebSocketServer import WebSocket, SimpleWebSocketServer, SimpleSSLWebSocketServer
from optparse import OptionParser
import base64
# import ASL as asl
import svm_train as st
# test
import os, sys
from PIL import Image
STABILIZATION_QTY = 20
NUM_SAMPLES = 15
FORMAT = 'JPG'
model, word_map = st.trainSVM(NUM_SAMPLES, FORMAT)
class SimpleEcho(WebSocket):
count = 0
temp = 0
previouslabel = None
previousText = " "
label = None
def process(self, img):
cv2.imshow('original_image', img)
# _,img=cap.read()
# cv2.rectangle(img,(900,100),(1300,500),(255,0,0),3) # bounding box which captures ASL sign to be detected by the system
# img1=img[100:500,900:1300]
import cv2
import numpy as np
import util as ut
import svm_train as st
import re
model = st.trainSVM(3)
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
cam = 0
cap = cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
def nothing(x):
pass
text = " "
temp = 0
previouslabel = None
previousText = " "
label = None
while (cap.isOpened()):
_, img = cap.read()
cv2.rectangle(
img, (900, 100), (1300, 500), (255, 0, 0),
3) # bounding box which captures ASL sign to be detected by the system
img1 = img[100:500, 900:1300]
img_ycrcb = cv2.cvtColor(img1, cv2.COLOR_BGR2YCR_CB)
blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)
import sys
import cv2
import numpy as np
import util as ut
import svm_train as st
import re
st.trainSVM(17)
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
def nothing(x):
pass
text = " "
temp = 0
previouslabel = None
previousText = " "
label = None
while (cap.isOpened()):
_, img = cap.read()
cv2.rectangle(
img, (900, 100), (1300, 500), (255, 0, 0),
3) # bounding box which captures ASL sign to be detected by the system
img1 = img[100:500, 900:1300]
img_ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)
skin_ycrcb_min = np.array((0, 138, 67))
#Importing Opencv and Numpy
import cv2
import numpy as np
#Importing our dependencies
import util as ut
import svm_train as st
import time
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
model=st.trainSVM(9,20,'TrainData2')
move_text={'1':'GRAB','2':'Bless','3':'Rock','4':'Stop','5':'ThumbsUp','6':'Victory','7':'Stop2','8':'Left','9':'Right'}
#Camera and font initialization
cam=int(raw_input("Enter Camera Index : "))
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
#The main event loop
while(cap.isOpened()):
move=''
t=time.time()
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray.copy(),150,255,cv2.THRESH_TOZERO)
import cv2
import numpy as np
import util as ut
import svm_train as st
import time
model=st.trainSVM(9)
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
cam=int(raw_input("Enter Camera Index : "))
cap=cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
while(cap.isOpened()):
t=time.time()
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray.copy(),20,255,cv2.THRESH_BINARY)
_,contours,hierarchy = cv2.findContours(th1.copy(),cv2.RETR_EXTERNAL, 2)
cnt=ut.getMaxContour(contours,4000)
if cnt!=None:
gesture=ut.getGestureImg(cnt,img,th1,model)
cv2.imshow('PredictedGesture',gesture)
fps=int(1/(time.time()-t))
cv2.putText(img,"FPS: "+str(fps),(50,50), font,1,(255,255,255),2,cv2.LINE_AA)
cv2.imshow('Frame',img)
k = 0xFF & cv2.waitKey(10)
if k == 27:
break
import cv2
import numpy as np
import util as ut
import svm_train as st
import re
model = st.trainSVM(1) # pass the number of starting character (Eg A-> 1)
#create and train SVM model each time coz bug in opencv 3.1.0 svm.load() https://github.com/Itseez/opencv/issues/4969
# cam=int(input("Enter Camera number: "))
cap = cv2.VideoCapture(0) #camera 0 captures the images
font = cv2.FONT_HERSHEY_SIMPLEX
def nothing(x):
pass
label = None
while (cap.isOpened()):
# cnt = []
# cv2.waitKey(5000)
_, img = cap.read()
cv2.rectangle(
img, (300, 200), (800, 600), (255, 0, 0),
3) # bounding box which captures ASL sign to be detected by the system
# sleep(2000)
img1 = img[300:600, 200:800] #Image within the rectangle is cropped out
import cv2
import numpy as np
import util as ut
import svm_train as st
import re
model = st.trainSVM(26)
cam = int(input("Enter Camera number: "))
cap = cv2.VideoCapture(cam)
font = cv2.FONT_HERSHEY_SIMPLEX
def nothing(x):
pass
text = " "
temp = 0
previouslabel = None
previousText = " "
label = None
while (cap.isOpened()):
_, img = cap.read()
img = cv2.flip(img, 1)
cv2.rectangle(
img, (450, 100), (640, 350), (255, 0, 0),
2) # bounding box which captures ASL sign to be detected by the system
img1 = img[100:350, 450:640]
img_ycrcb = cv2.cvtColor(img1, cv2.COLOR_BGR2YCR_CB)
blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)
from SimpleWebSocketServer import WebSocket, SimpleWebSocketServer, SimpleSSLWebSocketServer
from optparse import OptionParser
import base64
# import ASL as asl
import svm_train as st
# test
import os,sys
from PIL import Image
STABILIZATION_QTY=20
NUM_SAMPLES=15
FORMAT='JPG'
model,word_map=st.trainSVM(NUM_SAMPLES,FORMAT)
class SimpleEcho(WebSocket):
count = 0
temp=0
previouslabel=None
previousText=" "
label = None
def process(self,img):
cv2.imshow('original_image',img)
# _,img=cap.read()
# cv2.rectangle(img,(900,100),(1300,500),(255,0,0),3) # bounding box which captures ASL sign to be detected by the system
# img1=img[100:500,900:1300]
