def binaryMask(frame, x0, y0, width, height, flip, framecount, plot):
global model, saveImg
cv2.rectangle(frame, (x0,y0),(x0+width,y0+height),(0,255,0),1)
#roi = cv2.UMat(frame[y0:y0+height, x0:x0+width])
roi = frame[y0:y0+height, x0:x0+width]
if flip:
roi = cv2.flip(roi, 1)
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),2)
th3 = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,2)
ret, res = cv2.threshold(th3, minValue, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
res = cv2.resize(res, (40, 40))
guess = ""
if saveImg == True:
saveROIImg(res, flip)
guess = myNN.guessGesture(model, res)
plot = np.zeros((512,512,3), np.uint8)
plot = myNN.update(plot)
return res, guess, plot
def binaryMask(frame, x0, y0, width, height):
global guessGesture, visualize, mod, lastgesture, saveImg
cv2.rectangle(frame, (x0, y0), (x0 + width, y0 + height), (0, 255, 0), 1)
roi = frame[y0:y0 + height, x0:x0 + width]
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 2)
th3 = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 11, 2)
ret, res = cv2.threshold(th3, minValue, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
guess_res = "Bin"
if saveImg == True:
saveROIImg(res)
elif guessGesture == True:
retgesture, guess_res = myNN.guessGesture(mod, res)
if lastgesture != retgesture:
lastgesture = retgesture
elif visualize == True:
layer = int(raw_input("Enter which layer to visualize "))
cv2.waitKey(1)
myNN.visualizeLayers(mod, res, layer)
visualize = False
return res, guess_res
def binaryMask(roi, framecount):
global guess_gesture, visualize, mod, save_image
gray = cv.cvtColor(roi, cv.COLOR_BGR2GRAY)
blur = cv.GaussianBlur(gray, (5, 5), 2)
th3 = cv.adaptiveThreshold(blur, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C,
cv.THRESH_BINARY_INV, 3, 1)
ret, res = cv.threshold(th3, thresh_minvalue, 255,
cv.THRESH_BINARY_INV+cv.THRESH_OTSU)
if guess_gesture and (not framecount % 5):
# t = threading.Thread(target=nn.guessGesture, args=[mod, res])
# t.start()
nn.guessGesture(mod, res)
elif visualize:
pass
# layer = int(input("Enter which layer to visualize"))
# cv.waitKey(0)
# nn.visualizeLayers(mod, res, layer)
# visualize = False
return res
def skinMask(frame, x0, y0, width, height):
global guessGesture, visualize, mod, lastgesture, saveImg
# HSV values
low_range = np.array([0, 50, 80])
upper_range = np.array([30, 200, 255])
cv2.rectangle(frame, (x0, y0), (x0 + width, y0 + height), (0, 255, 0), 1)
roi = frame[y0:y0 + height, x0:x0 + width]
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
#Apply skin color range
mask = cv2.inRange(hsv, low_range, upper_range)
mask = cv2.erode(mask, skinkernel, iterations=1)
mask = cv2.dilate(mask, skinkernel, iterations=1)
#blur
mask = cv2.GaussianBlur(mask, (15, 15), 1)
#cv2.imshow("Blur", mask)
#bitwise and mask original frame
res = cv2.bitwise_and(roi, roi, mask=mask)
# color to grayscale
res = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
retgesture = "^______^Skin"
if saveImg == True:
saveROIImg(res)
elif guessGesture == True:
retgesture = myNN.guessGesture(mod, res)
if lastgesture != retgesture:
lastgesture = retgesture
print myNN.output[lastgesture]
time.sleep(0.01)
#guessGesture = False
elif visualize == True:
layer = int(raw_input("Enter which layer to visualize "))
cv2.waitKey(0)
myNN.visualizeLayers(mod, res, layer)
visualize = False
return res, retgesture
def binaryMask(frame, x0, y0, width, height):
global guessGesture, visualize, mod, lastgesture, saveImg
cv2.rectangle(frame, (x0, y0), (x0 + width, y0 + height), (0, 255, 0), 1)
roi = frame[y0:y0 + height, x0:x0 + width]
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 2)
#blur = cv2.bilateralFilter(roi,9,75,75)
th3 = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 11, 2)
ret, res = cv2.threshold(th3, minValue, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
#ret, res = cv2.threshold(blur, minValue, 255, cv2.THRESH_BINARY +cv2.THRESH_OTSU)
retgesture = "^______^Bin"
if saveImg == True:
saveROIImg(res)
elif guessGesture == True:
retgesture = myNN.guessGesture(mod, res)
if lastgesture != retgesture:
lastgesture = retgesture
#print lastgesture
## Checking for only PUNCH gesture here
## Run this app in Prediction Mode and keep Chrome browser on focus with Internet Off
## And have fun :) with Dino
if lastgesture == 3:
jump = ''' osascript -e 'tell application "System Events" to key code 49' '''
#jump = ''' osascript -e 'tell application "System Events" to key down (49)' '''
os.system(jump)
print myNN.output[lastgesture] + "= Dino JUMP!"
# time.sleep(0.01 )
# guessGesture = False
elif visualize == True:
layer = int(raw_input("Enter which layer to visualize "))
cv2.waitKey(1)
myNN.visualizeLayers(mod, res, layer)
visualize = False
return res, retgesture
def skinMask(frame, x0, y0, width, height):
global mod, lastgesture
# HSV values
low_range = np.array([0, 50, 80])
upper_range = np.array([30, 200, 255])
cv2.rectangle(frame, (x0, y0), (x0 + width, y0 + height), (0, 255, 0), 1)
roi = frame[y0:y0 + height, x0:x0 + width]
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
#Apply skin color range
mask = cv2.inRange(hsv, low_range, upper_range)
mask = cv2.erode(mask, skinkernel, iterations=1)
mask = cv2.dilate(mask, skinkernel, iterations=1)
#blur
mask = cv2.GaussianBlur(mask, (15, 15), 1)
#cv2.imshow("Blur", mask)
#bitwise and mask original frame
res = cv2.bitwise_and(roi, roi, mask=mask)
# color to grayscale
res = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
retgesture = myNN.guessGesture(mod, res)
if lastgesture != retgesture:
lastgesture = retgesture
if lastgesture == 4:
jump = ''' osascript -e 'tell application "System Events" to key code 49' '''
#jump = ''' osascript -e 'tell application "System Events" to key down (49)' '''
os.system(jump)
print myNN.output[lastgesture] + "= Dino JUMP!"
if lastgesture == -1:
print "Nothing"
else:
print myNN.output[lastgesture]
time.sleep(0.01)
return res, lastgesture
def binaryMask(frame, x0, y0, width, height):
global guessGesture, visualize, mod, lastgesture, saveImg
cv2.rectangle(frame, (x0, y0), (x0 + width, y0 + height), (0, 255, 0), 1)
roi = frame[y0:y0 + height, x0:x0 + width]
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 2)
th3 = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 11, 2)
ret, res = cv2.threshold(th3, minValue, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
if guessGesture == True:
retgesture = myNN.guessGesture(mod, res)
if lastgesture != retgesture:
lastgesture = retgesture
print myNN.output[lastgesture]
time.sleep(0.01)
return res
def Guess(self,frame, x0, y0, width, height):
low_range = np.array([0, 50, 80])
upper_range = np.array([30, 200, 255])
cv2.rectangle(frame, (x0,y0),(x0+width,y0+height),(0,255,0),1)
roi = frame[y0:y0+height, x0:x0+width]
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, low_range, upper_range)
mask = cv2.erode(mask, self.skinkernel, iterations = 1)
mask = cv2.dilate(mask, self.skinkernel, iterations = 1)
mask = cv2.GaussianBlur(mask, (15,15), 1)
res = cv2.bitwise_and(roi, roi, mask = mask)
res = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
return myNN.guessGesture(self.mod,res)