Python cv2.RETR_EXTERNAL Examples

def contour_filter(self, frame):
        _, contours, _ = cv2.findContours(frame,
            cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        new_frame = np.zeros(frame.shape, np.uint8)
        for i, contour in enumerate(contours):
            c_area = cv2.contourArea(contour)
            if self.contour_min_area <= c_area <= self.contour_max_area:
                mask = np.zeros(frame.shape, np.uint8)
                cv2.drawContours(mask, contours, i, 255, cv2.FILLED)
                mask = cv2.bitwise_and(frame, mask)
                new_frame = cv2.bitwise_or(new_frame, mask)
        frame = new_frame

        if self.contour_disp_flag:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
            cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)

        return frame


    # A number of methods corresponding to the various trackbars available. 

def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)

#-----------------
# MAIN FUNCTION
#----------------- 

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 

def mask_to_bbox(mask, image, num_class, area_threhold=0, out_path=None, out_file_name=None):
  bbox_list = []
  im = copy.copy(image)
  mask = mask.astype(np.uint8)
  for i in range(1, num_class, 1):
    c_bbox_list = []
    c_mask = np.zeros_like(mask)
    c_mask[np.where(mask==i)] = 255
    bimg , countours, hier = cv2.findContours(c_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in countours:
      area = cv2.contourArea(cnt)
      if area < area_threhold:
        continue
      epsilon = 0.005 * cv2.arcLength(cnt,True)
      approx = cv2.approxPolyDP(cnt,epsilon,True)
      (x, y, w, h) = cv2.boundingRect(approx)
      c_bbox_list.append([x,  y, x+w, y+h])
      if out_path is not None:
        color = COLOR_LIST[i-1]
        im=cv2.rectangle(im, pt1=(x, y), pt2=(x+w, y+h),color=color, thickness=2)
    bbox_list.append(c_bbox_list)
  if out_path is not None:
    outf = os.path.join(out_path, out_file_name)
    cv2.imwrite(outf, im)
  return bbox_list 

def find_corners_of_largest_polygon(img):
	"""Finds the 4 extreme corners of the largest contour in the image."""
	opencv_version = cv2.__version__.split('.')[0]
	if opencv_version == '3':
		_, contours, h = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # Find contours
	else:
		contours, h = cv2.findContours(img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # Find contours
	contours = sorted(contours, key=cv2.contourArea, reverse=True)  # Sort by area, descending
	polygon = contours[0]  # Largest image

	# Use of `operator.itemgetter` with `max` and `min` allows us to get the index of the point
	# Each point is an array of 1 coordinate, hence the [0] getter, then [0] or [1] used to get x and y respectively.

	# Bottom-right point has the largest (x + y) value
	# Top-left has point smallest (x + y) value
	# Bottom-left point has smallest (x - y) value
	# Top-right point has largest (x - y) value
	bottom_right, _ = max(enumerate([pt[0][0] + pt[0][1] for pt in polygon]), key=operator.itemgetter(1))
	top_left, _ = min(enumerate([pt[0][0] + pt[0][1] for pt in polygon]), key=operator.itemgetter(1))
	bottom_left, _ = min(enumerate([pt[0][0] - pt[0][1] for pt in polygon]), key=operator.itemgetter(1))
	top_right, _ = max(enumerate([pt[0][0] - pt[0][1] for pt in polygon]), key=operator.itemgetter(1))

	# Return an array of all 4 points using the indices
	# Each point is in its own array of one coordinate
	return [polygon[top_left][0], polygon[top_right][0], polygon[bottom_right][0], polygon[bottom_left][0]] 

def mask2poly_single(binary_mask):
    """

    :param binary_mask:
    :return:
    """
    try:
        contours, hierarchy = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        # contour_lens = np.array(list(map(len, contours)))
        # max_id = contour_lens.argmax()
        # max_contour = contours[max_id]
        max_contour = max(contours, key=len)
        rect = cv2.minAreaRect(max_contour)
        poly = cv2.boxPoints(rect)
        # poly = TuplePoly2Poly(poly)
    except:
        import pdb
        pdb.set_trace()
    return poly 

def _rois_from_img(self, img):

        if len(self._mask.shape) == 3:
            self._mask = cv2.cvtColor(self._mask, cv2.COLOR_BGR2GRAY)
        if CV_VERSION == 3:
            _, contours, hiera = cv2.findContours(np.copy(self._mask), RETR_EXTERNAL, CHAIN_APPROX_SIMPLE)
        else:
            contours, hiera = cv2.findContours(np.copy(self._mask), RETR_EXTERNAL, CHAIN_APPROX_SIMPLE)

        rois = []
        for i,c in enumerate(contours):
            tmp_mask = np.zeros_like(self._mask)
            cv2.drawContours(tmp_mask, [c],0, 1)

            value = int(np.median(self._mask[tmp_mask > 0]))

            rois.append(ROI(c, i+1, value))

        return rois 

def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert entity.shape[1] == 1, "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert entity.shape[1] == 1, "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

def sobelSecSearchPart(self, bound, refpoint, out):
        bound_threshold = self.sobelOperT(bound, 3, 6, 2)

        tempBoundThread = bound_threshold.copy()
        clearLiuDingOnly(tempBoundThread)

        posLeft, posRight, flag = bFindLeftRightBound(tempBoundThread)
        if flag:
            if posRight != 0 and posLeft != 0 and posLeft < posRight:
                posY = int(bound_threshold.shape[0] * 0.5)
                for i in range(posLeft + int(bound_threshold.shape[0] * 0.1), posRight - 4):
                    bound_threshold[posY, i] = 255
            for i in range(bound_threshold.shape[0]):
                bound_threshold[i, posLeft] = 0
                bound_threshold[i, posRight] = 0

        _, contours, _ = cv2.findContours(bound_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        for it in contours:
            mr = cv2.minAreaRect(it)
            if self.verifySizes(mr):
                tmp = (mr[0][0] + refpoint[0], mr[0][1] + refpoint[1])
                out.append((tmp, mr[1], mr[2])) 

def sobelFrtSearch(self, src):
        out_rects = []

        src_threshold = self.sobelOper(src, self.m_GaussianBlurSize, self.m_MorphSizeWidth, self.m_MorphSizeHeight)
        _, contours, _ = cv2.findContours(src_threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

        for it in contours:
            mr = cv2.minAreaRect(it)

            if self.verifySizes(mr):
                safeBoundRect, flag = self.calcSafeRect(mr, src)
                if not flag:
                    continue
                out_rects.append(safeBoundRect)

        return out_rects 

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 _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

def segment(image, threshold=25):
    global bg
    # find the absolute difference between background and current frame
    diff = cv2.absdiff(bg.astype("uint8"), image)

    # threshold the diff image so that we get the foreground
    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]

    # get the contours in the thresholded image
    (_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # return None, if no contours detected
    if len(cnts) == 0:
        return
    else:
        # based on contour area, get the maximum contour which is the hand
        segmented = max(cnts, key=cv2.contourArea)
        return (thresholded, segmented)

#--------------------------------------------------------------
# To count the number of fingers in the segmented hand region
#-------------------------------------------------------------- 

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 find_tissue_cnts(bw_img):
    """ Fint contours of tissues
    Parameters
    ----------
    bw_img : np.array
        2D binary image.
    Returns
    -------
    cnts: list
        List of all contours coordinates of tissues.
    """

    cnts, _ = cv2.findContours(img_as_ubyte(bw_img), mode=cv2.RETR_EXTERNAL,
                               method=cv2.CHAIN_APPROX_NONE)

    return cnts 

def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

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 _mask_post_processing(mask, center_pos, size, track_mask_threshold):
    target_mask = (mask > track_mask_threshold)
    target_mask = target_mask.astype(np.uint8)
    if cv2.__version__[-5] == '4':
        contours, _ = cv2.findContours(target_mask,
                                       cv2.RETR_EXTERNAL,
                                       cv2.CHAIN_APPROX_NONE)
    else:
        _, contours, _ = cv2.findContours(
                target_mask,
                cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    cnt_area = [cv2.contourArea(cnt) for cnt in contours]
    if len(contours) != 0 and np.max(cnt_area) > 100:
        contour = contours[np.argmax(cnt_area)] 
        polygon = contour.reshape(-1, 2)
        prbox = cv2.boxPoints(cv2.minAreaRect(polygon))
        rbox_in_img = prbox
    else:  # empty mask
        location = cxy_wh_2_rect(center_pos, size)
        rbox_in_img = np.array([[location[0], location[1]],
                    [location[0] + location[2], location[1]],
                    [location[0] + location[2], location[1] + location[3]],
                    [location[0], location[1] + location[3]]])
    return rbox_in_img 

def find_bounding_box(pane, bounding_box_lower_thresholds, bounding_box_upper_thresholds, sort=True):
    # thresholds: turple
    # dimension_resized: turple
    
    segmented_pictures = []
    rect_coordinates = []
    
    
    width_lower_threshold, height_lower_threshold = bounding_box_lower_thresholds
    width_upper_threshold, height_upper_threshold = bounding_box_upper_thresholds
    
    contours, hierarchy = cv2.findContours(pane, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    
    for contour in contours:
        (x, y, w, h) = cv2.boundingRect(contour)
        if h > height_lower_threshold and w > width_lower_threshold and h <= height_upper_threshold and w <= width_upper_threshold:
            rect_coordinates.append((x, y, w, h))
        
        else:
            continue 
    if sort:
        x_coordinates = [x for (x,y,w,h) in rect_coordinates]
        rect_coordinates= [e for _,e in sorted(zip(x_coordinates,rect_coordinates))]
    return rect_coordinates 

def _findContours(self):
        contours = []
        masks = self.masks.detach().numpy()
        for mask in masks:
            mask = cv2.UMat(mask)
            contour, hierarchy = cv2_util.findContours(
                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
            )

            reshaped_contour = []
            for entity in contour:
                assert len(entity.shape) == 3
                assert (
                    entity.shape[1] == 1
                ), "Hierarchical contours are not allowed"
                reshaped_contour.append(entity.reshape(-1).tolist())
            contours.append(reshaped_contour)
        return contours 

def mask2poly_single(binary_mask):
    """

    :param binary_mask:
    :return:
    """
    # try:
    contours, hierarchy = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    max_contour = max(contours, key=len)
    rect = cv2.minAreaRect(max_contour)
    poly = cv2.boxPoints(rect)
    poly = TuplePoly2Poly(poly)

    return poly 

def mask_to_bbox(mask, image, num_class, out_path=None, out_file_name=None):
  bbox_list = []
  im = copy.copy(image)
  mask = mask.astype(np.uint8)
  for i in range(1, num_class, 1):
    c_bbox_list = []
    c_mask = np.zeros_like(mask)
    c_mask[np.where(mask==i)] = 255
    bimg , countours, hier = cv2.findContours(c_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in countours:
      area = cv2.contourArea(cnt)
      if area < 50:
        continue
      epsilon = 0.005 * cv2.arcLength(cnt,True)
      approx = cv2.approxPolyDP(cnt,epsilon,True)
      (x, y, w, h) = cv2.boundingRect(approx)
      c_bbox_list.append([x,  y, x+w, y+h])
      if out_path is not None:
        color = COLOR_LIST[i-1]
        im=cv2.rectangle(im, pt1=(x, y), pt2=(x+w, y+h),color=color, thickness=2)
    bbox_list.append(c_bbox_list)
  if out_path is not None:
    outf = os.path.join(out_path, out_file_name)
    print(outf)
    cv2.imwrite(outf, im)
  return bbox_list 

def get_contours(self):
        """
        Method to find contours in an image and crop them and return a list with cropped contours
        """

        images = []
        main_image = self.img
        orig_image = main_image.copy()

        # convert to greyscale and apply Gaussian filtering
        main_image = cv2.cvtColor(src=main_image, code=cv2.COLOR_BGR2GRAY)
        main_image = cv2.GaussianBlur(src=main_image, ksize=(5, 5), sigmaX=0)

        # threshold the image
        _, main_image = cv2.threshold(src=main_image, thresh=127, maxval=255, type=cv2.THRESH_BINARY)

        # find contours in the image
        contours, _ = cv2.findContours(image=main_image.copy(), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)

        # get rectangles containing each contour
        bboxes = [cv2.boundingRect(array=contour) for contour in contours]

        for bbox in bboxes:
            x, y, width, height = bbox[:4]
            images.append(orig_image[y:y + height, x:x + width])

        return images 

def extract_main_table(gray_image):
    inverted = cv2.bitwise_not(gray_image)
    blurred = cv2.GaussianBlur(inverted, (5, 5), 0)

    thresholded = cv2.threshold(blurred, 0, 255,
                                cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

    if DEBUG:
        show_wait_destroy("thresholded",thresholded)

    cnts = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[1]# if imutils.is_cv2() else cnts[1]
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    rect = cv2.minAreaRect(cnts[0])
    box = cv2.boxPoints(rect)
    box = np.int0(box)

    extracted = four_point_transform(gray_image.copy(), box.reshape(4, 2))

    if DEBUG:
        color_image = cv2.cvtColor(gray_image.copy(), cv2.COLOR_GRAY2BGR)
        cv2.drawContours(color_image,[box],0,(0,0,255),2)
        cv2.drawContours(color_image, [cnts[0]], -1, (0, 255, 0), 2)


    return extracted 

def motion_detector(self, img):
        occupied = False
        # resize the frame, convert it to grayscale, and blur it
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (15, 15), 0)
     
        if self.avg is None:
            print("[INFO] starting background model...")
            self.avg = gray.copy().astype("float")
     
        # accumulate the weighted average between the current frame and
        # previous frames, then compute the difference between the current
        # frame and running average
        cv2.accumulateWeighted(gray, self.avg, 0.5)
        frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg))
        # threshold the delta image, dilate the thresholded image to fill
        # in holes, then find contours on thresholded image
        thresh = cv2.threshold(frameDelta, 5, 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 = cnts[1]
     
        # loop over the contours
        for c in cnts:
            # if the contour is too small, ignore it
            if cv2.contourArea(c) < 5000:
                pass
            occupied = True
     
        return occupied 

def seg2poly_old(rle):
    # TODO: debug for this function
    """
    This function transform a single encoded RLE to a single poly
    :param seg: RlE
    :return: poly
    """
    # try:
    #     binary_mask = mask_utils.decode(rle)
    #     contours, hierarchy = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL)
    #     contour_lens = np.array(list(map(len, contours)))
    #     max_id = contour_lens.argmax()
    #     max_contour = contours[max_id]
    #     rect = cv2.minAreaRect(max_contour)
    #     poly = cv2.boxPoints(rect)
    #     return poly
    # except:
    #     return -1
    try:
        binary_mask = mask_utils.decode(rle)
        contours, hierarchy = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        # len is not appropriate
        # contour_lens = np.array(list(map(len, contours)))
        # max_id = contour_lens.argmax()
        contour_areas = np.array(list(map(cv2.contourArea, contours)))
        max_id = contour_areas.argmax()
        max_contour = contours[max_id]
        rect = cv2.minAreaRect(max_contour)
        poly = cv2.boxPoints(rect)
        poly = TuplePoly2Poly(poly)
        return poly
    except:
        return [] 

def biggest_contour(im):
    im2, contours, hierarchy = cv2.findContours(np.copy(im), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    biggest, maxarea = None, 0
    for cnt in contours:
        a = cv2.contourArea(cnt)
        if a > maxarea:
            biggest, maxarea = cnt, a
    return biggest 

def get_contours(name, small, pagemask, masktype):

    mask = get_mask(name, small, pagemask, masktype)

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

    contours_out = []

    for contour in contours:

        rect = cv2.boundingRect(contour)
        xmin, ymin, width, height = rect

        if (width < TEXT_MIN_WIDTH or
                height < TEXT_MIN_HEIGHT or
                width < TEXT_MIN_ASPECT*height):
            continue

        tight_mask = make_tight_mask(contour, xmin, ymin, width, height)

        if tight_mask.sum(axis=0).max() > TEXT_MAX_THICKNESS:
            continue

        contours_out.append(ContourInfo(contour, rect, tight_mask))

    if DEBUG_LEVEL >= 2:
        visualize_contours(name, small, contours_out)

    return contours_out