trackgesture.py 文件源码

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)

    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

#%%