How to use the imutils.perspective.four_point_transform function in imutils

def detectCharacterCandidates(self, region):
		# apply a 4-point transform to extract the license plate
		plate = perspective.four_point_transform(self.image, region)
		cv2.imshow("Perspective Transform", imutils.resize(plate, width=400))

		# extract the Value component from the HSV color space and apply adaptive thresholding
		# to reveal the characters on the license plate
		V = cv2.split(cv2.cvtColor(plate, cv2.COLOR_BGR2HSV))[2]
		thresh = threshold_adaptive(V, 29, offset=15).astype("uint8") * 255
		thresh = cv2.bitwise_not(thresh)

		# resize the license plate region to a canonical size
		plate = imutils.resize(plate, width=400)
		thresh = imutils.resize(thresh, width=400)
		cv2.imshow("LP Threshold", thresh)

		# perform a connected components analysis and initialize the mask to store the locations
		# of the character candidates
		labels = measure.label(thresh, neighbors=8, background=0)

def detectCharacterCandidates(self, region):
		# apply a 4-point transform to extract the license plate
		plate = perspective.four_point_transform(self.image, region)
		cv2.imshow("Perspective Transform", imutils.resize(plate, width=400))

		# extract the Value component from the HSV color space and apply adaptive thresholding
		# to reveal the characters on the license plate
		V = cv2.split(cv2.cvtColor(plate, cv2.COLOR_BGR2HSV))[2]
		thresh = threshold_adaptive(V, 29, offset=15).astype("uint8") * 255
		thresh = cv2.bitwise_not(thresh)

		# resize the license plate region to a canonical size
		plate = imutils.resize(plate, width=400)
		thresh = imutils.resize(thresh, width=400)
		cv2.imshow("Thresh", thresh)

		# perform a connected components analysis and initialize the mask to store the locations
		# of the character candidates
		labels = measure.label(thresh, neighbors=8, background=0)

# calc the perimeter
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02*peri, True)
        if len(approx) == 4:
            docCnts = approx
            break
try:
    docArea = cv2.contourArea(docCnts)
    if docArea <= 300000 or docArea >= 1000000:
        raise ge.PaperDetectionError(
            'Error in finding paper contour. Area of docCnts is {}'.format(docArea))
except ge.PaperDetectionError as e:
    sys.exit(e.message)

# apply perspective transform to the shape
paper = four_point_transform(image, docCnts.reshape(4, 2))
warped = four_point_transform(gray, docCnts.reshape(4, 2))
# binarisation of image
# instead of otsu thresholding
# we have used adaptive thresholding

thresh = cv2.adaptiveThreshold(
    warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)

# find contours in threshholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                        cv2.CHAIN_APPROX_SIMPLE)

# filter out contours with parents
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# throw new error
try:

# 将轮廓按大小降序排序
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

    # 对排序后的轮廓循环处理
    for c in cnts:
        # 获取近似的轮廓
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)

        # 如果我们的近似轮廓有四个顶点，那么就认为找到了答题卡
        if len(approx) == 4:
            docCnt = approx
            break
# 对原始图像和灰度图都进行四点透视变换
paper = four_point_transform(image, docCnt.reshape(4, 2))
warped = four_point_transform(gray, docCnt.reshape(4, 2))

# # 对灰度图应用大津二值化算法
# thresh = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
#
# # 在二值图像中查找轮廓，然后初始化题目对应的轮廓列表
# cnts = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
# cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# questionCnts = []
#
# # 对每一个轮廓进行循环处理
# for q, c in enumerate(cnts):
#
#     # 计算轮廓的边界框，然后利用边界框数据计算宽高比
#     (x, y, w, h) = cv2.boundingRect(c)
#     ar = w / float(h)
#     # print 'w' + str(w), 'h' + str(h)

area = sideOne*sideTwo
                # find the largest rectangle within all contours
                if area > largestArea:
                    largestArea = area
                    largestRect = box
            

        # draw contour of the found rectangle on  the original image
        if largestArea > frameArea*0.02:
            cv2.drawContours(frame,[largestRect],0,(0,0,255),2)
            


        #if largestRect is not None:
            # cut and warp interesting area
            warped = four_point_transform(mask, [largestRect][0])
            
            # show an image if rectangle was found
            #cv2.imshow("Warped", cv2.bitwise_not(warped))
            
            # use function to detect the sign on the found rectangle
            detectedTrafficSign = identifyTrafficSign(warped)
            #print(detectedTrafficSign)


            # write the description of the sign on the original image
            cv2.putText(frame, detectedTrafficSign, tuple(largestRect[0]), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)
        
        # show original image
        cv2.imshow("Original", frame)
        
        # if the `q` key was pressed, break from the loop

for c in cnts:
                # approximate the contour
                peri = cv2.arcLength(c, True)
                approx = cv2.approxPolyDP(c, 0.02 * peri, True)

                # if the contour has four vertices, then we have found
                # the thermostat display
                if len(approx) == 4:
                        displayCnt = approx
                        break

        # extract the sign borders, apply a perspective transform
        # to it
        # A common task in computer vision and image processing is to perform 
        # a 4-point perspective transform of a ROI in an image and obtain a top-down, "birds eye view" of the ROI
        warped = four_point_transform(gray, displayCnt.reshape(4, 2))
        output = four_point_transform(image, displayCnt.reshape(4, 2))

        # draw a red square on the image
        cv2.drawContours(image, [displayCnt], -1, (0, 0, 255), 5)

        # threshold the warped image, then apply a series of morphological
        # operations to cleanup the thresholded image
        # cv2.THRESH_OTSU. it automatically calculates a threshold value from image histogram 
        # for a bimodal image
        thresh = cv2.threshold(warped, 0, 255, 
                cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 5))
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)


        # (roiH, roiW) = roi.shape

def getRotateRect(img, cooridnate_lists, WIDTH = 100, HEIGHT = 100):
        
        warped_img_lists = []
        i = 0 
        
        for coordinate in cooridnate_lists :
            warped = perspective.four_point_transform(img, coordinate)
            
            warped_resize = cv2.resize(warped, (WIDTH, HEIGHT), interpolation=cv2.INTER_CUBIC)
            
            # plt.imshow(warped, cmap = 'gray', interpolation = 'bicubic')
            # plt.xticks([]), plt.yticks([])
            # plt.show()
            cv2.imshow("Vaga - %d"%i, warped_resize)
            
            warped_img_lists.append(warped_resize)

            i+=1
        return warped_img_lists

else:
                # count error with PID to move to the sign direction
                pid = pidController(center[0], halfFrameWidth, 0.5, 0, 0)
 
                # if error with "-", then we need to slow down left motor, else - right
                if pid < 0:
                    moveMotors(MAX_MOTOR_SPEED + pid, MAX_MOTOR_SPEED + pid*0.1)
                else:
                    moveMotors(MAX_MOTOR_SPEED - pid*0.1, MAX_MOTOR_SPEED - pid)
            
            # draw contour of the found rectangle on  the original image   
            cv2.drawContours(frame,[largestRect],0,(0,0,255),2)
            
            # cut and warp interesting area
            warped = four_point_transform(mask, [largestRect][0])
            
            # show an image if rectangle was found
            cv2.imshow("Warped", cv2.bitwise_not(warped))
			
	    # use function to detect the sign on the found rectangle
            detectedTrafficSign = identifyTrafficSign(warped)
            print('detectedTrafficSign', detectedTrafficSign)
            if detectedTrafficSign is not None:
                lastDetectedTrafficSign = detectedTrafficSign
            print('lastDetectedTrafficSign', lastDetectedTrafficSign)
	    # write the description of the sign on the original image
            cv2.putText(frame, detectedTrafficSign, tuple(largestRect[0]), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 255, 0), 2)
        
        # if there is no blue rectangular on the frame, then stop
        else:
            moveMotors(127,127) #127 is mapped to 0 on Arduino

blurred = cv2.GaussianBlur(table_cell, (5, 5), 0)
            #cv2.rectangle(color_image,(vx_p,hy_p),(vx_c+vw_c,hy_c+hh_c),(255,0,0),2)

            thresholded = cv2.threshold(blurred, 128, 255,
                                cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

            im2, contours, hierarchy = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            contours = sorted(contours, key=cv2.contourArea, reverse=True)

            rect = cv2.minAreaRect(contours[0])
            box = cv2.boxPoints(rect)
            box = np.int0(box)
            extracted = four_point_transform(table_cell.copy(), box.reshape(4, 2))[1:-1,1:-1] #remove 1 px from each side
            ret,extracted = cv2.threshold(extracted,165,255,cv2.THRESH_BINARY)
            extracted_columns.append(extracted)

            # cv2.drawContours(color_image, [contours[0]], -1, (0,255,0), 3)


        extracted_rows_columns.append(extracted_columns)

    #show_wait_destroy("horizontal_lines_img",color_image)
    return extracted_rows_columns