Python cv2.convertScaleAbs方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def laplacian(filepathname):
    v = cv2.imread(filepathname)
    s = cv2.cvtColor(v, cv2.COLOR_BGR2GRAY)
    s = cv2.Laplacian(s, cv2.CV_16S, ksize=3)
    s = cv2.convertScaleAbs(s)
    cv2.imshow('nier',s)
    return s

    # ret, binary = cv2.threshold(s,40,255,cv2.THRESH_BINARY)
    # contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    # for c in contours:
    #     x,y,w,h = cv2.boundingRect(c)
    #     if w>5 and h>10:
    #         cv2.rectangle(v,(x,y),(x+w,y+h),(155,155,0),1)
    # cv2.imshow('nier2',v)

    # cv2.waitKey()
    # cv2.destroyAllWindows() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def prediction(self, image):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.GaussianBlur(image, (21, 21), 0)
        if self.avg is None:
            self.avg = image.copy().astype(float)
        cv2.accumulateWeighted(image, self.avg, 0.5)
        frameDelta = cv2.absdiff(image, cv2.convertScaleAbs(self.avg))
        thresh = cv2.threshold(
                frameDelta, DELTA_THRESH, 255,
                cv2.THRESH_BINARY)[1]
        thresh = cv2.dilate(thresh, None, iterations=2)
        cnts = cv2.findContours(
                thresh.copy(), cv2.RETR_EXTERNAL,
                cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        self.avg = image.copy().astype(float)
        return cnts 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def adjust_brightness(img, brightness_factor):
    """Adjust brightness of an Image.
    Args:
        img (numpy ndarray): numpy ndarray to be adjusted.
        brightness_factor (float):  How much to adjust the brightness. Can be
            any non negative number. 0 gives a black image, 1 gives the
            original image while 2 increases the brightness by a factor of 2.
    Returns:
        numpy ndarray: Brightness adjusted image.
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
    table = np.array([ i*brightness_factor for i in range (0,256)]).clip(0,255).astype('uint8')
    # same thing but a bit slower
    # cv2.convertScaleAbs(img, alpha=brightness_factor, beta=0)
    if img.shape[2]==1:
        return cv2.LUT(img, table)[:,:,np.newaxis]
    else:
        return cv2.LUT(img, table) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def adjust_brightness(img, brightness_factor):
    """Adjust brightness of an Image.
    Args:
        img (numpy ndarray): numpy ndarray to be adjusted.
        brightness_factor (float):  How much to adjust the brightness. Can be
            any non negative number. 0 gives a black image, 1 gives the
            original image while 2 increases the brightness by a factor of 2.
    Returns:
        numpy ndarray: Brightness adjusted image.
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
    table = np.array([ i*brightness_factor for i in range (0,256)]).clip(0,255).astype('uint8')
    # same thing but a bit slower
    # cv2.convertScaleAbs(img, alpha=brightness_factor, beta=0)
    if img.shape[2] == 1:
        return cv2.LUT(img, table)[:,:,np.newaxis]
    else:
        return cv2.LUT(img, table) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def sobelOperT(self, img, blursize, morphW, morphH):
        '''
            No different with sobelOper ? 
        '''
        blur = cv2.GaussianBlur(img, (blursize, blursize), 0, 0, cv2.BORDER_DEFAULT)

        if len(blur.shape) == 3:
            gray = cv2.cvtColor(blur, cv2.COLOR_RGB2GRAY)
        else:
            gray = blur

        x = cv2.Sobel(gray, cv2.CV_16S, 1, 0, 3)
        absX = cv2.convertScaleAbs(x)
        grad = cv2.addWeighted(absX, 1, 0, 0, 0)

        _, threshold = cv2.threshold(grad, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)

        element = cv2.getStructuringElement(cv2.MORPH_RECT, (morphW, morphH))
        threshold = cv2.morphologyEx(threshold, cv2.MORPH_CLOSE, element)

        return threshold 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def sobel(filepathname):
    v = cv2.imread(filepathname)
    s = cv2.cvtColor(v,cv2.COLOR_BGR2GRAY)
    x, y = cv2.Sobel(s,cv2.CV_16S,1,0), cv2.Sobel(s,cv2.CV_16S,0,1)
    s = cv2.convertScaleAbs(cv2.subtract(x,y))
    s = cv2.blur(s,(9,9))
    cv2.imshow('nier',s)
    return s

    # ret, binary = cv2.threshold(s,40,255,cv2.THRESH_BINARY)
    # contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    # for c in contours:
    #     x,y,w,h = cv2.boundingRect(c)
    #     if w>5 and h>10:
    #         cv2.rectangle(v,(x,y),(x+w,y+h),(155,155,0),1)
    # cv2.imshow('nier2',v)

    # cv2.waitKey()
    # cv2.destroyAllWindows() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def blur_mask(img):
    assert isinstance(img, numpy.ndarray), 'img_col must be a numpy array'
    assert img.ndim == 3, 'img_col must be a color image ({0} dimensions currently)'.format(img.ndim)
    msk, val, blurry = main.blur_detector(img)
    logger.debug('inverting img_fft')
    msk = cv2.convertScaleAbs(255-(255*msk/numpy.max(msk)))
    msk[msk < 50] = 0
    msk[msk > 127] = 255
    logger.debug('removing border')
    msk = remove_border(msk)
    logger.debug('applying erosion and dilation operators')
    msk = morphology(msk)
    logger.debug('evaluation complete')
    result = numpy.sum(msk)/(255.0*msk.size)
    logger.info('{0}% of input image is blurry'.format(int(100*result)))
    return msk, result, blurry 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def compute_energy_matrix(img): 
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
 
    # Compute X derivative of the image 
    sobel_x = cv2.Sobel(gray,cv2.CV_64F, 1, 0, ksize=3) 
 
    # Compute Y derivative of the image 
    sobel_y = cv2.Sobel(gray,cv2.CV_64F, 0, 1, ksize=3) 
 
    abs_sobel_x = cv2.convertScaleAbs(sobel_x) 
    abs_sobel_y = cv2.convertScaleAbs(sobel_y) 
 
    # Return weighted summation of the two images i.e. 0.5*X + 0.5*Y 
    return cv2.addWeighted(abs_sobel_x, 0.5, abs_sobel_y, 0.5, 0) 
 
# Find vertical seam in the input image 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def preprocess(image):
	# load the image
	image = cv2.imread(args["image"])

	#resize image
	image = cv2.resize(image,None,fx=0.7, fy=0.7, interpolation = cv2.INTER_CUBIC)

	#convert to grayscale
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

	#calculate x & y gradient
	gradX = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 1, dy = 0, ksize = -1)
	gradY = cv2.Sobel(gray, ddepth = cv2.CV_32F, dx = 0, dy = 1, ksize = -1)

	# subtract the y-gradient from the x-gradient
	gradient = cv2.subtract(gradX, gradY)
	gradient = cv2.convertScaleAbs(gradient)

	# blur the image
	blurred = cv2.blur(gradient, (3, 3))

	# threshold the image
	(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
	thresh = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	return thresh 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def detect_shirt(self):
        
        
        #self.dst=cv2.inRange(self.norm_rgb,np.array([self.lb,self.lg,self.lr],np.uint8),np.array([self.b,self.g,self.r],np.uint8))
        self.dst=cv2.inRange(self.norm_rgb,np.array([20,20,20],np.uint8),np.array([255,110,80],np.uint8))
        cv2.threshold(self.dst,0,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY)
        fg=cv2.erode(self.dst,None,iterations=2)
        #cv2.imshow("fore",fg)  
        bg=cv2.dilate(self.dst,None,iterations=3)
        _,bg=cv2.threshold(bg, 1,128,1)
        #cv2.imshow("back",bg)
        
        mark=cv2.add(fg,bg)
        mark32=np.int32(mark)
        cv2.watershed(self.norm_rgb,mark32)
        self.m=cv2.convertScaleAbs(mark32)
        _,self.m=cv2.threshold(self.m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        #cv2.imshow("final_tshirt",self.m)
        
        cntr,h=cv2.findContours(self.m,cv2.cv.CV_RETR_EXTERNAL,cv2.cv.CV_CHAIN_APPROX_SIMPLE)
               
        return self.m,cntr 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def normalized(self):
               
#        t1=time.time()
        b=self.down[:,:,0]
        g=self.down[:,:,1]
        r=self.down[:,:,2]
        
        sum=b+g+r
        
        
        self.norm[:,:,0]=b/sum*255.0
        self.norm[:,:,1]=g/sum*255.0
        self.norm[:,:,2]=r/sum*255.0
        
 #       print "conversion time",time.time()-t1
        
        #self.norm=cv2.merge([self.norm1,self.norm2,self.norm3])
        self.norm_rgb=cv2.convertScaleAbs(self.norm)
        #self.norm.dtype=np.uint8
        return self.norm_rgb 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def draw_mask(img, mask, thickness=3, color=(255, 0, 0)):
    def _get_edge(mask, thickness=3):
        dtype = mask.dtype
        x=cv2.Sobel(np.float32(mask),cv2.CV_16S,1,0, ksize=thickness) 
        y=cv2.Sobel(np.float32(mask),cv2.CV_16S,0,1, ksize=thickness)
        absX=cv2.convertScaleAbs(x)
        absY=cv2.convertScaleAbs(y)  
        edge = cv2.addWeighted(absX,0.5,absY,0.5,0)
        return edge.astype(dtype)
    
    img = img.copy()
    canvas = np.zeros(img.shape, img.dtype) + color
    img[mask > 0] = img[mask > 0] * 0.8 + canvas[mask > 0] * 0.2
    edge = _get_edge(mask, thickness)
    img[edge > 0] = img[edge > 0] * 0.2 + canvas[edge > 0] * 0.8
    return img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def differentialDerivativeOpenCv():
    img = cv2.imread("E:\\TestImages\\person.jpg")

    # 转换成单通道灰度图
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
    # 进行微分计算后，可能会出现负值，将每个像素加上最小负数的绝对值
    absX = cv2.convertScaleAbs(x)  # 转回uint8
    absY = cv2.convertScaleAbs(y)
    # img = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)

    cv2.imshow("First order differential X", absX)
    cv2.imshow("First order differential Y", absY)
    cv2.waitKey(0)
    cv2.destroyAllWindows() 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def get_init_process_img(roi_img):
    """
    对图片进行初始化处理，包括，梯度化，高斯模糊，二值化，腐蚀，膨胀和边缘检测
    :param roi_img: ndarray
    :return: ndarray
    """
    h = cv2.Sobel(roi_img, cv2.CV_32F, 0, 1, -1)
    v = cv2.Sobel(roi_img, cv2.CV_32F, 1, 0, -1)
    img = cv2.add(h, v)
    img = cv2.convertScaleAbs(img)
    img = cv2.GaussianBlur(img, (3, 3), 0)
    ret, img = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY)
    kernel = np.ones((1, 1), np.uint8)
    img = cv2.erode(img, kernel, iterations=1)
    img = cv2.dilate(img, kernel, iterations=2)
    img = cv2.erode(img, kernel, iterations=1)
    img = cv2.dilate(img, kernel, iterations=2)
    img = auto_canny(img)
    return img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import convertScaleAbs [as 别名]
def mainLoop(self):
        frame = self.webcam.get_frame()
        f1 = imutils.resize(frame, width = 256)
        #crop_frame = frame[100:228,200:328]
        self.data_buffer.append(f1)
        self.run_color()
        #print(frame)
        
        #if len(self.vidmag_frames) > 0:
            #print(self.vidmag_frames[0])
        cv2.putText(frame, "FPS "+str(float("{:.2f}".format(self.fps))),
                       (20,420), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0),2)
            
        #frame[100:228,200:328] = cv2.convertScaleAbs(self.vidmag_frames[-1])
        cv2.imshow("Original",frame)
        #f2 = imutils.resize(cv2.convertScaleAbs(self.vidmag_frames[-1]), width = 640)
        f2 = imutils.resize(cv2.convertScaleAbs(self.frame_out), width = 640)
            
        cv2.imshow("Color amplification",f2)
            
            
        self.key_handler()  #if not the GUI cant show anything