Python cv2.THRESH_OTSU Examples

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 

def skeletonize(image_in):
    '''Inputs and grayscale image and outputs a binary skeleton image'''
    size = np.size(image_in)
    skel = np.zeros(image_in.shape, np.uint8)

    ret, image_edit = cv2.threshold(image_in, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))
    done = False

    while not done:
        eroded = cv2.erode(image_edit, element)
        temp = cv2.dilate(eroded, element)
        temp = cv2.subtract(image_edit, temp)
        skel = cv2.bitwise_or(skel, temp)
        image_edit = eroded.copy()

        zeros = size - cv2.countNonZero(image_edit)
        if zeros == size:
            done = True

    return skel 

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 

def crop_row_detect(image_in):
    
    save_image('0_image_in', image_in)
    
    ### Grayscale Transform ###
    image_edit = grayscale_transform(image_in)
    save_image('1_image_gray', image_edit)
        
    ### Binarization ###
    _, image_edit = cv2.threshold(image_edit, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    save_image('2_image_bin', image_edit)
    
    ### Stripping ###
    crop_points = strip_process(image_edit)
    save_image('8_crop_points', crop_points)
    
    ### Hough Transform ###
    crop_lines = crop_point_hough(crop_points)
    save_image('9_image_hough', cv2.addWeighted(image_in, 1, crop_lines, 1, 0.0))
    
    return crop_lines 

def DeleteNotArea(self, in_img):
        input_gray = cv2.cvtColor(in_img, cv2.COLOR_BGR2GRAY)
        w = in_img.shape[1]
        h = in_img.shape[0]
        tmp_mat = in_img[int(h * 0.1):int(h * 0.85), int(w * 0.15):int(w * 0.85)]

        plateType = getPlateType(tmp_mat, True)

        if plateType == 'BLUE':
            tmp = in_img[int(h * 0.1):int(h * 0.85), int(w * 0.15):int(w * 0.85)]
            threadHoldV = ThresholdOtsu(tmp)
            _, img_threshold = cv2.threshold(input_gray, threadHoldV, 255, cv2.THRESH_BINARY)
        elif plateType == 'YELLOW':
            tmp = in_img[int(h * 0.1):int(h * 0.85), int(w * 0.15):int(w * 0.85)]
            threadHoldV = ThresholdOtsu(tmp)
            _, img_threshold = cv2.threshold(input_gray, threadHoldV, 255, cv2.THRESH_BINARY_INV)
        else:
            _, img_threshold = cv2.threshold(input_gray, 10, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)

        top, bottom = clearLiuDing(img_threshold, 0, img_threshold.shape[0] - 1)
        posLeft, posRight, flag = bFindLeftRightBound1(img_threshold)

        if flag:
            in_img = in_img[int(top):int(bottom), int(posLeft):int(w)] 

def colorSearch(self, src, color, out_rect):
        """

        :param src:
        :param color:
        :param out_rect: minAreaRect
        :return: binary
        """
        color_morph_width = 10
        color_morph_height = 2

        match_gray = colorMatch(src, color, False)

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

        element = cv2.getStructuringElement(cv2.MORPH_RECT, (color_morph_width, color_morph_height))
        src_threshold = cv2.morphologyEx(src_threshold, cv2.MORPH_CLOSE, element)

        out = src_threshold.copy()

        _, contours, _ = cv2.findContours(src_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

        for cnt in contours:
            mr = cv2.minAreaRect(cnt)
            if self.verifySizes(mr):
                out_rect.append(mr)

        return out 

def param_filter(self, frame):
        # Apply pre-blur according to trackbar value.
        if self.pre_blur_val == 1:
            frame = cv2.GaussianBlur(frame, (5, 5), 0)
        elif self.pre_blur_val == 2:
            frame = cv2.medianBlur(frame, 5)

        # Apply a thresholding method according to trackbar value.
        if self.thresh_flag:
            _, frame = cv2.threshold(frame, 127, 255, cv2.THRESH_BINARY)
        else:
            _, frame = cv2.threshold(frame, 127, 255, cv2.THRESH_OTSU)

        # Apply post-blur according to trackbar value.
        if self.post_blur_val:
            frame = cv2.medianBlur(frame, 5)

        return frame


    # Apply filterrs to frame according to contour parameters. 

def thresholding(probs: np.ndarray, threshold: float=-1) -> np.ndarray:
    """
    Computes the binary mask of the detected Page from the probabilities output by network.

    :param probs: array in range [0, 1] of shape HxWx2
    :param threshold: threshold between [0 and 1], if negative Otsu's adaptive threshold will be used
    :return: binary mask
    """

    if threshold < 0:  # Otsu's thresholding
        probs = np.uint8(probs * 255)
        #TODO Correct that weird gaussianBlur
        probs = cv2.GaussianBlur(probs, (5, 5), 0)

        thresh_val, bin_img = cv2.threshold(probs, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        mask = np.uint8(bin_img / 255)
    else:
        mask = np.uint8(probs > threshold)

    return mask 

def compare_ssim_debug(image_a, image_b, color=(255, 0, 0)):
    """
    Args:
        image_a, image_b: opencv image or PIL.Image
        color: (r, g, b) eg: (255, 0, 0) for red

    Refs:
        https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/
    """
    ima, imb = conv2cv(image_a), conv2cv(image_b)
    score, diff = compare_ssim(ima, imb, full=True)
    diff = (diff * 255).astype('uint8')
    _, thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
    cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)

    cv2color = tuple(reversed(color))
    im = ima.copy()
    for c in cnts:
        x, y, w, h = cv2.boundingRect(c)
        cv2.rectangle(im, (x, y), (x+w, y+h), cv2color, 2)
    # todo: show image
    cv2pil(im).show()
    return im 

def extracttext(imgpath, preprocess):
    if imgpath.startswith('http://') or imgpath.startswith('https://') or imgpath.startswith('ftp://'):
        image = url_to_image(imgpath)
    else:
        image = cv2.imread(imgpath)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    if preprocess == "thresh":
        gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    elif preprocess == "blur":
        gray = cv2.medianBlur(gray, 3)

    filename = "{}.png".format(os.getpid())
    cv2.imwrite(filename, gray)
    text = pytesseract.image_to_string(Image.open(filename))

    os.remove(filename)
    return {"text": text} 

def remove_background(img):
        """ Remove noise using OTSU's method.

        :param img: The image to be processed
        :return: The normalized image
        """

        img = img.astype(np.uint8)
        # Binarize the image using OTSU's algorithm. This is used to find the center
        # of mass of the image, and find the threshold to remove background noise
        threshold, _ = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

        # Remove noise - anything higher than the threshold. Note that the image is still grayscale
        img[img > threshold] = 255

        return img 

def binarize_image(input_image, threshold_value=177):
    """
    Convert input_image to binary representation

    :param input_image: image
    :type input_image: numpy.ndarray
    :param threshold_value: value to be used as a
                          threshold
    :type threshold_value: int
    :return: image in binary form
    :rtype: numpy.ndarray
    """
    gray_image = grayscale_image(input_image)
    bin_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
    _, bin_image = cv2.threshold(bin_image,
                                 threshold_value,
                                 255,
                                 cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return bin_image 

def clean(img):
    """Process an image"""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    (__, img_bw) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

    ctrs, __ = cv2.findContours(img_bw.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # take largest contour
    ctr = sorted(ctrs, key=lambda ctr: (cv2.boundingRect(ctr)[2] * cv2.boundingRect(ctr)[3]),
                 reverse=True)[0]
    # Get bounding box
    x, y, w, h = cv2.boundingRect(ctr)

    # Getting ROI
    roi = img_bw[y:y + h, x:x + w]
    return skeletize(crop(roi, IMAGE_SIZE)) 

def getPaperFromImage(img):
    gImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    bImg = cv2.medianBlur(src = gImg, ksize = 51)

    threshold, _ = cv2.threshold(src = bImg, thresh = 0, maxval = 255, type = cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    cannyImg = cv2.Canny(image = bImg, threshold1 = 0.5 * threshold, threshold2 = threshold)

    _, contours, _ = cv2.findContours(image = cannyImg.copy(), mode = cv2.RETR_TREE, method = cv2.CHAIN_APPROX_SIMPLE)

    maxRect = Rect(0, 0, 0, 0)
    for contour in contours:
        x, y, w, h = cv2.boundingRect(points = contour)
        currentArea = w * h
        if currentArea > maxRect.getArea():
            maxRect.set(x, y, w, h)

    return img[maxRect.y : maxRect.y + maxRect.h, maxRect.x : maxRect.x + maxRect.w] 

def main():
    threshold = 0
    max_value = 255

    image = cv2.imread("../data/7.1.08.tiff", 0)

    # when applying OTSU threshold, set threshold to 0.

    _, output1 = cv2.threshold(image, threshold, max_value, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    _, output2 = cv2.threshold(image, threshold, max_value, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    _, output3 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
    _, output4 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TOZERO_INV + cv2.THRESH_OTSU)
    _, output5 = cv2.threshold(image, threshold, max_value, cv2.THRESH_TRUNC + cv2.THRESH_OTSU)

    images = [image, output1, output2, output3, output4, output5]
    titles = ["Orignals", "Binary", "Binary Inverse", "TOZERO", "TOZERO INV", "TRUNC"]

    for i in range(6):
        plt.subplot(3, 2, i + 1)
        plt.imshow(images[i], cmap='gray')
        plt.title(titles[i])

    plt.show() 

def binary_image(self,img):
        # 应用5种不同的阈值方法
        # ret, th1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
        # ret, th2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
        # ret, th3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
        # ret, th4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
        # ret, th5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)
        # titles = ['Gray', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
        # images = [img_gray, th1, th2, th3, th4, th5]
        # 使用Matplotlib显示
        # for i in range(6):
        #     plt.subplot(2, 3, i + 1)
        #     plt.imshow(images[i], 'gray')
        #     plt.title(titles[i], fontsize=8)
        #     plt.xticks([]), plt.yticks([])  # 隐藏坐标轴
        # plt.show()

        # Otsu阈值
        _, th = cv2.threshold(img, 0, 255, cv2.THRESH_TOZERO + cv2.THRESH_OTSU)
        cv2.imshow('Binary image', th)
        return th

    # 边缘检测 

def otsu_copy(self, img):
        img_r = np.zeros(img.shape)
        img1 = img[:, :, 0]
        img2 = img[:, :, 1]
        img3 = img[:, :, 2]
        # print(img.min())
        # print(img[:,:,0].min())
        # blur = cv2.GaussianBlur(img,(5,5))
        # ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

        img_r[:, :, 0] = threshold1
        img_r[:, :, 1] = threshold1
        img_r[:, :, 2] = threshold1
        return img_r 

def find_marker_ellipses(im):
    im_gray = cvtColor(im, COLOR_BGR2GRAY)
    im_blur = GaussianBlur(im_gray, (3, 3), 0)
    ret, th = threshold(im_blur, 0, 255, THRESH_BINARY_INV + THRESH_OTSU)
    imgEdge, contours, hierarchy = findContours(th, RETR_TREE, CHAIN_APPROX_NONE)
    points = []
    origins = []
    ellipses = []
    id_point_candidates = []
    small_point_candidates = []
    for cnt in contours:
        if contour_sanity_check(cnt, im.shape[0], point_d=0.02):
            id_point_candidates.append(cnt)
        elif contour_sanity_check(cnt, im.shape[0], point_d=0.01):
            small_point_candidates.append(cnt)
    for cnt in id_point_candidates:
        x, y, w, h = boundingRect(cnt)
        ellipse = fitEllipse(cnt)
        points.append(im_gray[y:y + h, x:x + w])
        origins.append((x, y))
        ellipses.append(ellipse)
    return points, origins, ellipses 

def find_contours(self):
        src = self.cv_read_img(self.src_file)
        if src is None:
            return

        dst = cv.GaussianBlur(src, (3, 3), 0)
        gray = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)
        ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
        cloneImage, contous, heriachy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
        for i,contou in enumerate(contous):
            cv.drawContours(src, contous, i, (0, 0, 255), 1)

        # 轮廓
        self.decode_and_show_dst(src)

        # 轮廓覆盖
        for i,contou in enumerate(contous):
            cv.drawContours(src, contous, i, (0, 0, 255), -1)
        self.decode_and_show_dst(src)
    #人脸识别 正脸 需要下载xml模型 haarcascade_frontalface_alt.xml 

def threshold(self):
        src = self.cv_read_img(self.src_file)
        if src is None:
            return

        gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

        # 这个函数的第一个参数就是原图像，原图像应该是灰度图。
        # 第二个参数就是用来对像素值进行分类的阈值。
        # 第三个参数就是当像素值高于（有时是小于）阈值时应该被赋予的新的像素值
        # 第四个参数来决定阈值方法，见threshold_simple()
        # ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)
        ret, dst = cv.threshold(gray, 127, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
        self.decode_and_show_dst(dst)

    # Canny边缘检测 

def get_proto_objects_map(self, use_otsu=True):
        """Returns the proto-objects map of an RGB image

            This method generates a proto-objects map of an RGB image.
            Proto-objects are saliency hot spots, generated by thresholding
            the saliency map.

            :param use_otsu: flag whether to use Otsu thresholding (True) or
                             a hardcoded threshold value (False)
            :returns: proto-objects map
        """
        saliency = self.get_saliency_map()

        if use_otsu:
            _, img_objects = cv2.threshold(np.uint8(saliency*255), 0, 255,
                                           cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        else:
            thresh = np.mean(saliency)*255*3
            _, img_objects = cv2.threshold(np.uint8(saliency*255), thresh, 255,
                                           cv2.THRESH_BINARY)
        return img_objects 

def extractGrid(img,
                nvertical,
                nhorizontal,
                threshold1 = 50,
                threshold2 = 150,
                apertureSize = 3,
                hough_threshold_step=20,
                hough_threshold_min=50,
                hough_threshold_max=150):
    """Finds the grid lines in a board image.
    :param img: board image
    :param nvertical: number of vertical lines
    :param nhorizontal: number of horizontal lines
    :returns: a pair (horizontal, vertical). Both elements are lists with the lines' positions.
    """


    w, h, _ = img.shape
    close_threshold_v = (w / nvertical) / 4
    close_threshold_h = (h / nhorizontal) / 4


    im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    thresh, im_bw = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    im_canny = cv2.Canny(im_bw, threshold1, threshold2, apertureSize=apertureSize)

    for i in range((hough_threshold_max - hough_threshold_min + 1) / hough_threshold_step):
        lines = cv2.HoughLines(im_canny, 1, np.pi / 180, hough_threshold_max - (hough_threshold_step * i))
        if lines is None:
            continue

        lines = [Line(l[0], l[1]) for l in lines[0]]
        horizontal, vertical = partitionLines(lines)
        vertical = filterCloseLines(vertical, horizontal=False, threshold=close_threshold_v)
        horizontal = filterCloseLines(horizontal, horizontal=True, threshold=close_threshold_h)

        if len(vertical) >= nvertical and \
           len(horizontal) >= nhorizontal:
            return (horizontal, vertical) 

def text_recognition(post):
    meme_name = "temp"  # We would just call all the temporary images as "temp"
    request.urlretrieve(post.url, filename=meme_name)  # Here we are downloading the appropriate image (png, jpg, jpeg, bmp)
    image = cv2.imread(meme_name)  # We load up the image using opencv
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # Turning the image to grayscale
    gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]  # Making a threshold for the image, text will be more apparent
    gray = cv2.medianBlur(gray, 3)  # Adding some blur, useful for really noisy images
    filename = "{}-ocr.png".format(meme_name)  # Making the temporary, ready file for text recognition
    cv2.imwrite(filename, gray)  # Now, we will save the image
    img = Image.open(filename)  # Open the processed image with pillow
    recognized_text = pytesseract.image_to_string(img).encode('utf-8')  # As a means of exceptions, need to read out as UTF-8, so no encoding errors would occur
    os.remove(filename)  # Deleting all temporary image
    os.remove(meme_name)
    return recognized_text  # Returns the recognized text in UTF-8 encodign and with capital and lower letters 

def detect_shirt2(self):
        self.hsv=cv2.cvtColor(self.norm_rgb,cv.CV_BGR2HSV)
        self.hue,s,_=cv2.split(self.hsv)
        
        _,self.dst=cv2.threshold(self.hue,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        self.fg=cv2.erode(self.dst,None,iterations=3)
        self.bg=cv2.dilate(self.dst,None,iterations=1)
        _,self.bg=cv2.threshold(self.bg,1,128,1)
        mark=cv2.add(self.fg,self.bg)
        mark32=np.int32(mark)
        cv2.watershed(self.norm_rgb,mark32)
        
        m=cv2.convertScaleAbs(mark32)
        _,m=cv2.threshold(m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        
        cntr,h=cv2.findContours(m,cv.CV_RETR_EXTERNAL,cv.CV_CHAIN_APPROX_SIMPLE)
        print len(cntr)
        #print cntr[0].shape
        #cntr[1].dtype=np.float32
        #ret=cv2.contourArea(np.array(cntr[1]))
        #print ret
        #cntr[0].dtype=np.uint8
        cv2.drawContours(m,cntr,-1,(255,255,255),3)
        cv2.imshow("mask_fg",self.fg)
        cv2.imshow("mask_bg",self.bg)
        cv2.imshow("mark",m) 

def binary_pic(name_list):
    for image in name_list:
        temp_image = cv2.imread(image)
        #print image
        GrayImage=cv2.cvtColor(temp_image,cv2.COLOR_BGR2GRAY)
        ret,thresh1=cv2.threshold(GrayImage,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
        single_name = image.split('t/')[1]
        #print single_name
        cv2.imwrite('../data/tmp/'+single_name,thresh1) 

def _binarize(img):
    """
    Uses Otsu' adaptive thresholding to convert the image to black/white pixels based on its immediate surroundings
    """
    img = _filter_blur(img)
    _, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return img 

def extractGrid(img,
                nvertical,
                nhorizontal,
                threshold1 = 50,
                threshold2 = 150,
                apertureSize = 3,
                hough_threshold_step=20,
                hough_threshold_min=50,
                hough_threshold_max=150):
    """Finds the grid lines in a board image.
    :param img: board image
    :param nvertical: number of vertical lines
    :param nhorizontal: number of horizontal lines
    :returns: a pair (horizontal, vertical). Both elements are lists with the lines' positions.
    """


    w, h, _ = img.shape
    close_threshold_v = (w / nvertical) / 4
    close_threshold_h = (h / nhorizontal) / 4


    im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    thresh, im_bw = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    im_canny = cv2.Canny(im_bw, threshold1, threshold2, apertureSize=apertureSize)

    for i in range((hough_threshold_max - hough_threshold_min + 1) / hough_threshold_step):
        lines = cv2.HoughLines(im_canny, 1, np.pi / 180, hough_threshold_max - (hough_threshold_step * i))
        if lines is None:
            continue

        lines = [Line(l[0], l[1]) for l in lines[0]]
        horizontal, vertical = partitionLines(lines)
        vertical = filterCloseLines(vertical, horizontal=False, threshold=close_threshold_v)
        horizontal = filterCloseLines(horizontal, horizontal=True, threshold=close_threshold_h)

        if len(vertical) >= nvertical and \
           len(horizontal) >= nhorizontal:
            return (horizontal, vertical) 

def getSignatureFromPage(img):
    imgSize = np.shape(img)

    gImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # The values for edge detection can be approximated using Otsu's Algorithm
    # Reference - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.402.5899&rep=rep1&type=pdf
    threshold, _ = cv2.threshold(src = gImg, thresh = 0, maxval = 255, type = cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    cannyImg = cv2.Canny(image = gImg, threshold1 = 0.5 * threshold, threshold2 = threshold)

    # Close the image to fill blank spots so blocks of text that are close together (like the signature) are easier to detect
    # Signature usually are wider and shorter so the strcturing elements used for closing will have this ratio
    kernel = cv2.getStructuringElement(shape = cv2.MORPH_RECT, ksize = (30, 1))
    cannyImg = cv2.morphologyEx(src = cannyImg, op = cv2.MORPH_CLOSE, kernel = kernel)

    # findContours is a distructive function so the image pased is only a copy
    _, contours, _ = cv2.findContours(image = cannyImg.copy(), mode = cv2.RETR_TREE, method = cv2.CHAIN_APPROX_SIMPLE)

    maxRect = Rect(0, 0, 0, 0)
    for contour in contours:
        x, y, w, h = cv2.boundingRect(points = contour)
        currentArea = w * h
        if currentArea > maxRect.getArea():
            maxRect.set(x, y, w, h)

    # Increase the bounding box to get a better view of the signature
    maxRect.addPadding(imgSize = imgSize, padding = 10)

    return img[maxRect.y : maxRect.y + maxRect.h, maxRect.x : maxRect.x + maxRect.w] 

def convert_to_linedrawing(self, luminous_image_data):
        linedrawing = cv2.threshold(luminous_image_data, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
        return linedrawing 

def extractGrid(img,
                nvertical,
                nhorizontal,
                threshold1 = 50,
                threshold2 = 150,
                apertureSize = 3,
                hough_threshold_step=20,
                hough_threshold_min=50,
                hough_threshold_max=150):
    """Finds the grid lines in a board image.
    :param img: board image
    :param nvertical: number of vertical lines
    :param nhorizontal: number of horizontal lines
    :returns: a pair (horizontal, vertical). Both elements are lists with the lines' positions.
    """


    w, h, _ = img.shape
    close_threshold_v = (w / nvertical) / 4
    close_threshold_h = (h / nhorizontal) / 4


    im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    thresh, im_bw = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    im_canny = cv2.Canny(im_bw, threshold1, threshold2, apertureSize=apertureSize)

    for i in range((hough_threshold_max - hough_threshold_min + 1) / hough_threshold_step):
        lines = cv2.HoughLines(im_canny, 1, np.pi / 180, hough_threshold_max - (hough_threshold_step * i))
        if lines is None:
            continue

        lines = [Line(l[0], l[1]) for l in lines[0]]
        horizontal, vertical = partitionLines(lines)
        vertical = filterCloseLines(vertical, horizontal=False, threshold=close_threshold_v)
        horizontal = filterCloseLines(horizontal, horizontal=True, threshold=close_threshold_h)

        if len(vertical) >= nvertical and \
           len(horizontal) >= nhorizontal:
            return (horizontal, vertical)