python类CV_16S的实例源码

def img_sobel_binary(im, blur_sz):

    # ??????????????
    img_blur = cv2.GaussianBlur(im,blur_sz,0)
    if len(img_blur.shape) == 3:
        blur_gray = cv2.cvtColor(img_blur,cv2.COLOR_BGR2GRAY) 
    else:
        blur_gray = img_blur

    # ??Sobel????
    sobelx = cv2.Sobel(blur_gray,cv2.CV_16S,1,0,ksize=3)
    abs_sobelx = np.absolute(sobelx)
    sobel_8u = np.uint8(abs_sobelx)
    img_show_hook("Sobel??", sobel_8u)

    # OTSU??????    
    ret, thd = cv2.threshold(sobel_8u, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)    
    thd_abs = cv2.convertScaleAbs(thd)
    bgimg = cv2.addWeighted(thd_abs, 1, 0, 0, 0)

    img_show_hook("OTSU????", bgimg)

    return bgimg

def img_sobel_binary(im, blur_sz):

    # ??????????????
    img_blur = cv2.GaussianBlur(im,blur_sz,0)
    if len(img_blur.shape) == 3:
        blur_gray = cv2.cvtColor(img_blur,cv2.COLOR_BGR2GRAY) 
    else:
        blur_gray = img_blur

    # ??Sobel????
    sobelx = cv2.Sobel(blur_gray,cv2.CV_16S,1,0,ksize=3)
    abs_sobelx = np.absolute(sobelx)
    sobel_8u = np.uint8(abs_sobelx)
    img_show_hook("Sobel??", sobel_8u)

    # OTSU??????    
    ret, thd = cv2.threshold(sobel_8u, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)    
    thd_abs = cv2.convertScaleAbs(thd)
    bgimg = cv2.addWeighted(thd_abs, 1, 0, 0, 0)

    img_show_hook("OTSU????", bgimg)

    return bgimg

def gradient_img(colorsrc):
    '''
        http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html
    '''
    SCALE = 1
    DELTA = 0
    DDEPTH = cv2.CV_16S  ## to avoid overflow

    graysrc = cv2.cvtColor(colorsrc, cv2.cv.CV_BGR2GRAY)
    graysrc = cv2.GaussianBlur(graysrc, (3, 3), 0)

    ## gradient X ##
    gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
    gradx = cv2.convertScaleAbs(gradx)

    ## gradient Y ##
    grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
    grady = cv2.convertScaleAbs(grady)

    grad = cv2.addWeighted(gradx, 0.5, grady, 0.5, 0)

    return grad

def edgedetect(channel):
    sobelx = cv2.Sobel(channel, cv2.CV_16S, 1, 0, ksize=3)
    sobely = cv2.Sobel(channel, cv2.CV_16S, 0, 1, ksize=3)
    sobel = np.hypot(sobelx, sobely)
    sobel[sobel > 255] = 255

    return sobel

def getRGBS(img, PLOT = False):

    image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

    # grab the image channels, initialize the tuple of colors,
    # the figure and the flattened feature vector   
    features = []
    featuresSobel = []
    Grayscale = cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY)
    histG = cv2.calcHist([Grayscale], [0], None, [16], [0, 256])
    histG = histG / histG.sum()
    features.extend(histG[:,0].tolist())


    grad_x = np.abs(cv2.Sobel(Grayscale, cv2.CV_16S, 1, 0, ksize = 3, scale = 1, delta = 0, borderType = cv2.BORDER_DEFAULT))
    grad_y = np.abs(cv2.Sobel(Grayscale, cv2.CV_16S, 0, 1, ksize = 3, scale = 1, delta = 0, borderType = cv2.BORDER_DEFAULT))
    abs_grad_x = cv2.convertScaleAbs(grad_x)
    abs_grad_y = cv2.convertScaleAbs(grad_y)
    dst = cv2.addWeighted(abs_grad_x,0.5,abs_grad_y,0.5,0)
    histSobel = cv2.calcHist([dst], [0], None, [16], [0, 256])
    histSobel = histSobel / histSobel.sum()
    features.extend(histSobel[:,0].tolist())

    Fnames = []
    Fnames.extend(["Color-Gray"+str(i) for i in range(8)])
    Fnames.extend(["Color-GraySobel"+str(i) for i in range(8)])

    return features, Fnames

def gradient_img(colorsrc):
    '''
        http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html
    '''
    SCALE = 1
    DELTA = 0
    DDEPTH = cv2.CV_16S  ## to avoid overflow

    # grayscale image
    if len(colorsrc.shape)==2:
        graysrc = cv2.GaussianBlur(colorsrc, (3, 3), 0)

        ## gradient X ##
        gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
        gradx = cv2.convertScaleAbs(gradx)

        ## gradient Y ##
        grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
        grady = cv2.convertScaleAbs(grady)

        grad = cv2.addWeighted(gradx, 0.5, grady, 0.5, 0)

        return grad

    # multi-channel image
    else:
        gradx_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        grady_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        for index in range(colorsrc.shape[2]):
            graysrc=colorsrc[:,:,index]
            graysrc = cv2.GaussianBlur(graysrc, (3, 3), 0)

            ## gradient X ##
            gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
            gradx = cv2.convertScaleAbs(gradx)
            gradx_total=gradx_total+gradx

            ## gradient Y ##
            grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
            grady = cv2.convertScaleAbs(grady)
            grady_total = grady_total + grady

            grad = cv2.addWeighted(gradx_total, 0.5, grady_total, 0.5, 0)

        return grad

def gradient_img(colorsrc):
    '''
        http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html
    '''
    SCALE = 1
    DELTA = 0
    DDEPTH = cv2.CV_16S  ## to avoid overflow

    # grayscale image
    if len(colorsrc.shape)==2:
        graysrc = cv2.GaussianBlur(colorsrc, (3, 3), 0)

        ## gradient X ##
        gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
        gradx = cv2.convertScaleAbs(gradx)

        ## gradient Y ##
        grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
        grady = cv2.convertScaleAbs(grady)

        grad = cv2.addWeighted(gradx, 0.5, grady, 0.5, 0)

        return grad

    # multi-channel image
    else:
        gradx_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        grady_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        for index in range(colorsrc.shape[2]):
            graysrc=colorsrc[:,:,index]
            graysrc = cv2.GaussianBlur(graysrc, (3, 3), 0)

            ## gradient X ##
            gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
            gradx = cv2.convertScaleAbs(gradx)
            gradx_total=gradx_total+gradx

            ## gradient Y ##
            grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
            grady = cv2.convertScaleAbs(grady)
            grady_total = grady_total + grady

            grad = cv2.addWeighted(gradx_total, 0.5, grady_total, 0.5, 0)

        return grad

def gradient_img(colorsrc):
    '''
        http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html
    '''
    SCALE = 1
    DELTA = 0
    DDEPTH = cv2.CV_16S  ## to avoid overflow

    # grayscale image
    if len(colorsrc.shape)==2:
        graysrc = cv2.GaussianBlur(colorsrc, (3, 3), 0)

        ## gradient X ##
        gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
        gradx = cv2.convertScaleAbs(gradx)

        ## gradient Y ##
        grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
        grady = cv2.convertScaleAbs(grady)

        grad = cv2.addWeighted(gradx, 0.5, grady, 0.5, 0)

        return grad

    # multi-channel image
    else:
        gradx_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        grady_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        for index in range(colorsrc.shape[2]):
            graysrc=colorsrc[:,:,index]
            graysrc = cv2.GaussianBlur(graysrc, (3, 3), 0)

            ## gradient X ##
            gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
            gradx = cv2.convertScaleAbs(gradx)
            gradx_total=gradx_total+gradx

            ## gradient Y ##
            grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
            grady = cv2.convertScaleAbs(grady)
            grady_total = grady_total + grady

            grad = cv2.addWeighted(gradx_total, 0.5, grady_total, 0.5, 0)

        return grad

def gradient_img(colorsrc):
    '''
        http://docs.opencv.org/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html
    '''
    SCALE = 1
    DELTA = 0
    DDEPTH = cv2.CV_16S  ## to avoid overflow

    # grayscale image
    if len(colorsrc.shape)==2:
        graysrc = cv2.GaussianBlur(colorsrc, (3, 3), 0)

        ## gradient X ##
        gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
        gradx = cv2.convertScaleAbs(gradx)

        ## gradient Y ##
        grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
        grady = cv2.convertScaleAbs(grady)

        grad = cv2.addWeighted(gradx, 0.5, grady, 0.5, 0)

        return grad

    # multi-channel image
    else:
        gradx_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        grady_total = np.zeros((colorsrc.shape[0], colorsrc.shape[1]))
        for index in range(colorsrc.shape[2]):
            graysrc=colorsrc[:,:,index]
            graysrc = cv2.GaussianBlur(graysrc, (3, 3), 0)

            ## gradient X ##
            gradx = cv2.Sobel(graysrc, DDEPTH, 1, 0, ksize=3, scale=SCALE, delta=DELTA)
            gradx = cv2.convertScaleAbs(gradx)
            gradx_total=gradx_total+gradx

            ## gradient Y ##
            grady = cv2.Sobel(graysrc, DDEPTH, 0, 1, ksize=3, scale=SCALE, delta=DELTA)
            grady = cv2.convertScaleAbs(grady)
            grady_total = grady_total + grady

            grad = cv2.addWeighted(gradx_total, 0.5, grady_total, 0.5, 0)

        return grad