def hand_pose(model,move_text):
#Camera and font initialization
cap=cv2.VideoCapture(1)
array=[]
array1=[]
#The main event loop
while(cap.isOpened()):
move=''
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray.copy(),100,255,cv2.THRESH_TOZERO)
_,contours,hierarchy = cv2.findContours(th1.copy(),cv2.RETR_EXTERNAL, 2)
cnt=ut.getMaxContour(contours,4000)
if cnt!=None:
gesture,res=ut.getGestureImg(cnt,img,th1,model)
cv2.imshow('PredictedGesture',gesture)
move=move_text[res]
array.append(move)
if len(array)==30:
array1.append(move)
print move
if len(array1)>=5:
a=''.join(array1)
tts.TTS(a)
array1=[]
array=[]
cv2.imshow('Frame',img)
k = 0xFF & cv2.waitKey(10)
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
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]
img_ycrcb = cv2.cvtColor(img, 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
if cnt != None:
gesture, label = ut.getGestureImg(
cnt, img, mask, model, word_map, FORMAT
) # passing the trained model for prediction and fetching the result
if (label != None):
if (self.temp == 0):
previouslabel = label
if previouslabel == label:
previouslabel = label
self.temp += 1
else:
temp = 0
if (temp == STABILIZATION_QTY):
# if(label=='P'):
# label=" "
# text= text + label
# if(label=='Q'):
# words = re.split(" +",text)
# words.pop()
# text = " ".join(words)
#text=previousText
# 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)
print label
self.sendMessage(label)
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]
img_ycrcb = cv2.cvtColor(img, 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
if cnt!=None:
gesture,label=ut.getGestureImg(cnt,img,mask,model,word_map,FORMAT) # passing the trained model for prediction and fetching the result
if(label!=None):
if(self.temp==0):
previouslabel=label
if previouslabel == label:
previouslabel=label
self.temp+=1
else :
temp=0
if(temp==STABILIZATION_QTY):
# if(label=='P'):
# label=" "
# text= text + label
# if(label=='Q'):
# words = re.split(" +",text)
# words.pop()
# text = " ".join(words)
#text=previousText
# 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)
print label
self.sendMessage(label)
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
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
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)
if cnt != None:
gesture, res = ut.getGestureImg(cnt, img, th1, model)
cv2.imshow('PredictedGesture',
cv2.imread('TrainData2/' + res + '_1.jpg'))
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.circle(img, (cx, cy), 5, [0, 0, 255], -1)
#cv2.putText(img,res,(x,y), font,1,(255,255,255),2,cv2.LINE_AA)
hull = cv2.convexHull(cnt, returnPoints=False)
defects = cv2.convexityDefects(cnt, hull)
if defects != None:
for i in range(defects.shape[0]):
s, e, f, d = defects[i, 0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
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")
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
if cnt!=None:
gesture,label=ut.getGestureImg(cnt,img1,mask,model) # passing the trained model for prediction and fetching the result
if(label!=None):
if(temp==0):
previouslabel=label
if previouslabel==label :
previouslabel=label
temp+=1
else :
temp=0
if(temp==40):
if(label=='P'):
label=" "
text= text + label
if(label=='Q'):
words = re.split(" +",text)
thresh=thresh[0]-15
break
while(cap.isOpened()):
t=time.time()
_,img=cap.read()
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,th1 =cv2.threshold(gray,thresh,255,cv2.THRESH_TOZERO)
cv2.imshow('threshold',th1)
_,contours,hierarchy = cv2.findContours(th1.copy(),cv2.RETR_EXTERNAL, 2)
cnts=ut.getContourBiggerThan(contours,minArea=3000,maxArea=40000)
line=[]
for cnt in cnts:
x,y,w,h = cv2.boundingRect(cnt)
_,resp=ut.getGestureImg(cnt,img,th1,model)
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
line.append((cx,cy))
cv2.circle(img,(cx,cy),5,[0,255,0],-1)
cv2.putText(img,resp,(x,y), font,1,(255,255,255),2,cv2.LINE_AA)
if len(line)==2:
pt1=line[0]
pt2=line[1]
ang=int(ut.getSlope(pt1,pt2))
cv2.putText(img,'Angle-> '+str(ang),(400,50), font,1,(255,255,255),2,cv2.LINE_AA)
if ang>0:
cv2.putText(img,'RIGHT',((pt1[0]+pt2[0])/2,(pt1[1]+pt2[1])/2), font,3,(255,255,255),2,cv2.LINE_AA)
else:
cv2.putText(img,'LEFT',((pt1[0]+pt2[0])/2,(pt1[1]+pt2[1])/2), font,3,(255,255,255),2,cv2.LINE_AA)
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
cap.release()
cv2.destroyAllWindows()
print img_ycrcb
blur = cv2.GaussianBlur(img_ycrcb, (11, 11), 0)
print blur
skin_ycrcb_min = np.array([0, 48, 80])
print skin_ycrcb_min
skin_ycrcb_max = np.array([20, 255, 255])
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
print cnt
if type(cnt) != type(None):
gesture, label = ut.getGestureImg(cnt, img1, mask, model)
#label=unichr(64+int(model.predict(st.hog_single(img)) )) # passing the trained model for prediction and fetching the result
if (label != None):
if (temp == 0):
previouslabel = label
if previouslabel == label:
previouslabel = label
temp += 1
else:
temp = 0
if (temp == 40):
if (label == 'P'):
label = " "
text = text + label
if (label == 'Q'):
temp = 0
previouslabel = None
previousText = " "
label = None
text = ""
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)
cv2.imshow('thresh', th1)
_, contours, hierarchy = cv2.findContours(th1.copy(), cv2.RETR_EXTERNAL, 2)
cnt = ut.getMaxContour(contours, 4000)
if cnt.any() != None:
gesture, label = ut.getGestureImg(cnt, img, th1, model)
if label != None:
if temp == 0:
previouslabel = label
if previouslabel == label:
previouslabel = label
temp += 1
else:
temp = 0
if (temp == 40):
if (label == 'P'):
label = " "
text += label
if (label == 'Q'):
words = re.split(" +", text)
def start_webcam():
cap = cv2.VideoCapture(0)
cap.set(4, 1024)
font = cv2.FONT_HERSHEY_SIMPLEX
text = " "
temp = 0
previouslabel = None
previousText = " "
label = None
flag = True
words = ""
def rescale_frame(frame, percent):
scale_percent = 100
width = int(frame.shape[1] * scale_percent / 100)
height = int(frame.shape[0] * percent / 100)
dim = (width, height)
return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
while (flag):
_, img = cap.read()
img = rescale_frame(img, percent=94)
cv2.rectangle(
img, (620, 100), (1000, 500), (255, 0, 0), 3
) # bounding box which captures ASL sign to be detected by the system
img1 = img[100:500, 620:1000]
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
# cnt!= None
if cnt is not None:
gesture, label = ut.getGestureImg(
cnt, img1, mask, model
) # passing the trained model for prediction and fetching the result
if (label != None):
if (temp == 0):
previouslabel = label
if previouslabel == label:
previouslabel = label
temp += 1
else:
temp = 0
if (temp == 30):
if (label == 'Q'):
label = " "
text = text + label
if (label == 'W'):
words = list(text)
print(words)
words.pop()
words.pop()
text = "".join(words)
# text=previousText
print(text)
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)
cv2.imshow('Mask', mask)
k = 0xFF & cv2.waitKey(10)
if k == 27:
flag = False
cv2.imshow('Frame', img)
cap.release()
words = list(text)
print(words)
words = "".join(words)
text_inil = "[Kullanıcı 1] :"
deneme = text_inil + words + "\n "
text61.insert(INSERT, deneme.upper())
print(deneme)
print('aaaaaaaaaa')
