Python cv2.RETR_LIST Examples

def _find_size_candidates(self, image):
        binary_image = self._filter_image(image)

        _, contours, _ = cv2.findContours(binary_image,
                                          cv2.RETR_LIST,
                                          cv2.CHAIN_APPROX_SIMPLE)

        size_candidates = []
        for contour in contours:
            bounding_rect = cv2.boundingRect(contour)
            contour_area = cv2.contourArea(contour)
            if self._is_valid_contour(contour_area, bounding_rect):
                candidate = (bounding_rect[2] + bounding_rect[3]) / 2
                size_candidates.append(candidate)

        return size_candidates 

def find_squares(img):
    img = cv2.GaussianBlur(img, (5, 5), 0)
    squares = []
    for gray in cv2.split(img):
        for thrs in xrange(0, 255, 26):
            if thrs == 0:
                bin = cv2.Canny(gray, 0, 50, apertureSize=5)
                bin = cv2.dilate(bin, None)
            else:
                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
            bin, contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            for cnt in contours:
                cnt_len = cv2.arcLength(cnt, True)
                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
                    cnt = cnt.reshape(-1, 2)
                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
                    if max_cos < 0.1:
                        squares.append(cnt)
    return squares 

def findEllipses(edges):
    contours, _ = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    ellipseMask = np.zeros(edges.shape, dtype=np.uint8)
    contourMask = np.zeros(edges.shape, dtype=np.uint8)

    pi_4 = np.pi * 4

    for i, contour in enumerate(contours):
        if len(contour) < 5:
            continue

        area = cv2.contourArea(contour)
        if area <= 100:  # skip ellipses smaller then 10x10
            continue

        arclen = cv2.arcLength(contour, True)
        circularity = (pi_4 * area) / (arclen * arclen)
        ellipse = cv2.fitEllipse(contour)
        poly = cv2.ellipse2Poly((int(ellipse[0][0]), int(ellipse[0][1])), (int(ellipse[1][0] / 2), int(ellipse[1][1] / 2)), int(ellipse[2]), 0, 360, 5)

        # if contour is circular enough
        if circularity > 0.6:
            cv2.fillPoly(ellipseMask, [poly], 255)
            continue

        # if contour has enough similarity to an ellipse
        similarity = cv2.matchShapes(poly.reshape((poly.shape[0], 1, poly.shape[1])), contour, cv2.cv.CV_CONTOURS_MATCH_I2, 0)
        if similarity <= 0.2:
            cv2.fillPoly(contourMask, [poly], 255)

    return ellipseMask, contourMask 

def polygons(self):
        """
        Returns or generates :class:`Polygons` representation of mask.

        :returns: Polygons representation
        :rtype: :class:`Polygons`
        """
        if not self._c_polygons:

            # Generate polygons from mask
            mask = self.array.astype(np.uint8)
            mask = cv2.copyMakeBorder(mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=0)
            polygons = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE, offset=(-1, -1))
            polygons = polygons[0] if len(polygons) == 2 else polygons[1]
            polygons = [polygon.flatten() for polygon in polygons]

            self._c_polygons = Polygons(polygons)
            self._c_polygons._c_mask = self

        return self._c_polygons 

def findPossibleCharsInPlate(imgGrayscale, imgThresh):
    listOfPossibleChars = []                        # this will be the return value
    contours = []
    imgThreshCopy = imgThresh.copy()

            # find all contours in plate
    contours, npaHierarchy = cv2.findContours(imgThreshCopy, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

    for contour in contours:                        # for each contour
        possibleChar = PossibleChar.PossibleChar(contour)

        if checkIfPossibleChar(possibleChar):              # if contour is a possible char, note this does not compare to other chars (yet) . . .
            listOfPossibleChars.append(possibleChar)       # add to list of possible chars
        # end if
    # end if

    return listOfPossibleChars
# end function

################################################################################################### 

def crop_and_clean_mask_to_int(mask, x, y, w, h):
    mask = np.uint8(mask[y:y + h, x:x + w]) * 255

    # TODO: put this into a function (it's used above as well)
    cnts, hier = cv2.findContours(mask.copy(), cv2.RETR_LIST,
                                  cv2.CHAIN_APPROX_SIMPLE)

    max_id = 0
    max_length = len(cnts[0])
    for i in range(1, len(cnts)):
        if len(cnts[i]) > max_length:
            max_id = i
            max_length = len(cnts[i])

    tmp_mask = np.dstack((mask, mask, mask))
    for i, cnt in enumerate(cnts):
        if i != max_id:
            cv2.drawContours(tmp_mask, [cnt], 0, (0, 0, 0), -1)
    return cv2.split(tmp_mask)[0] 

def crop_image(silhs):
    res = np.asarray(silhs).any(axis=0)
    cnts, hier = cv2.findContours(
        np.uint8(res) * 255, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    """
    checks = []
    for cnt in cnts:
        kk = np.zeros((1000, 1000, 3), dtype=np.uint8)
        hull = cv2.convexHull(cnt)
        cv2.drawContours(kk, [cnt], 0, (255,255,255), -1)
        checks.append(kk)
    """

    max_id = 0
    max_length = len(cnts[0])
    for i in range(1, len(cnts)):
        if len(cnts[i]) > max_length:
            max_id = i
            max_length = len(cnts[i])

    (x, y, w, h) = cv2.boundingRect(cnts[max_id])
    return (x, y, w, h) 

def find_squares(img):
    img = cv2.GaussianBlur(img, (5, 5), 0)
    squares = []
    for gray in cv2.split(img):
        for thrs in xrange(0, 255, 26):
            if thrs == 0:
                bin = cv2.Canny(gray, 0, 50, apertureSize=5)
                bin = cv2.dilate(bin, None)
            else:
                retval, bin = cv2.threshold(gray, thrs, 255, cv2.THRESH_BINARY)
            contours, hierarchy = cv2.findContours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            for cnt in contours:
                cnt_len = cv2.arcLength(cnt, True)
                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
                if len(cnt) == 4 and cv2.contourArea(cnt) > 1000 and cv2.isContourConvex(cnt):
                    cnt = cnt.reshape(-1, 2)
                    max_cos = np.max([angle_cos( cnt[i], cnt[(i+1) % 4], cnt[(i+2) % 4] ) for i in xrange(4)])
                    if max_cos < 0.1:
                        squares.append(cnt)
    return squares 

def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0)
    im2, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
    return contours 

def compute_missing_cell_contours(self, missing_cells_mask):
        contx, _ = _find_contours(missing_cells_mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_TC89_KCOS)
        return contx 

def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        '''
        _bitmap: single map with shape (H, W),
            whose values are binarized as {0, 1}
        '''

        assert len(_bitmap.shape) == 2
        bitmap = _bitmap.cpu().numpy()  # The first channel
        pred = pred.cpu().detach().numpy()
        height, width = bitmap.shape
        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        num_contours = min(len(contours), self.max_candidates)
        boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
        scores = np.zeros((num_contours,), dtype=np.float32)

        for index in range(num_contours):
            contour = contours[index].squeeze(1)
            points, sside = self.get_mini_boxes(contour)
            if sside < self.min_size:
                continue
            points = np.array(points)
            score = self.box_score_fast(pred, contour)
            if self.box_thresh > score:
                continue

            box = self.unclip(points, unclip_ratio=self.unclip_ratio).reshape(-1, 1, 2)
            box, sside = self.get_mini_boxes(box)
            if sside < self.min_size + 2:
                continue
            box = np.array(box)
            if not isinstance(dest_width, int):
                dest_width = dest_width.item()
                dest_height = dest_height.item()

            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
            box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height)
            boxes[index, :, :] = box.astype(np.int16)
            scores[index] = score
        return boxes, scores 

def find_marker(image):
    # convert the image to grayscale, blur it, and detect edges
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    edged = cv2.Canny(gray, 35, 125)

    # find the contours in the edged image and keep the largest one;
    # we'll assume that this is our piece of paper in the image
    cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]
    c = max(cnts, key = cv2.contourArea)

    # compute the bounding box of the of the paper region and return it
    return cv2.minAreaRect(c) 

def __init__(self, retrieval_mode=cv2.RETR_LIST, approximation_method=cv2.CHAIN_APPROX_SIMPLE, *args, **kwargs):
        self.__retrieval_mode = retrieval_mode
        self.__approximation_method = approximation_method
        self.__min_arc_length = 0
        self.__max_arc_length = 9223372036854775807
        self.__min_contour_area = 0
        self.__max_contour_area = 9223372036854775807
        self.__discard_convex = False
        self.__discard_non_convex = False
        self.__min_roundness = 0.0
        self.__max_roundness = 1.0
        super(OpencvFindContours, self).__init__("", *args, **kwargs)
        self.on_new_contours_result.do = self.do_contours_result 

def find_mostlikely_ROI(mask):
    contours,hierarchy=cv2.findContours(mask, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
    if len(contours)>0:
        areas = []
        for contour in contours:
            areas.append(cv2.contourArea(contour))
        index = areas.index(max(areas))
        mask = np.zeros_like(mask)
        mask = cv2.fillPoly(mask,[contours[index]],(255))
    return mask 

def wordSegmentation(img, kernelSize=25, sigma=11, theta=7, minArea=0):
	"""Scale space technique for word segmentation proposed by R. Manmatha: http://ciir.cs.umass.edu/pubfiles/mm-27.pdf
	
	Args:
		img: grayscale uint8 image of the text-line to be segmented.
		kernelSize: size of filter kernel, must be an odd integer.
		sigma: standard deviation of Gaussian function used for filter kernel.
		theta: approximated width/height ratio of words, filter function is distorted by this factor.
		minArea: ignore word candidates smaller than specified area.
		
	Returns:
		List of tuples. Each tuple contains the bounding box and the image of the segmented word.
	"""

	# apply filter kernel
	kernel = createKernel(kernelSize, sigma, theta)
	imgFiltered = cv2.filter2D(img, -1, kernel, borderType=cv2.BORDER_REPLICATE).astype(np.uint8)
	(_, imgThres) = cv2.threshold(imgFiltered, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
	imgThres = 255 - imgThres

	# find connected components. OpenCV: return type differs between OpenCV2 and 3
	if cv2.__version__.startswith('3.'):
		(_, components, _) = cv2.findContours(imgThres, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
	else:
		(components, _) = cv2.findContours(imgThres, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

	# append components to result
	res = []
	for c in components:
		# skip small word candidates
		if cv2.contourArea(c) < minArea:
			continue
		# append bounding box and image of word to result list
		currBox = cv2.boundingRect(c) # returns (x, y, w, h)
		(x, y, w, h) = currBox
		currImg = img[y:y+h, x:x+w]
		res.append((currBox, currImg))

	# return list of words, sorted by x-coordinate
	return sorted(res, key=lambda entry:entry[0][0]) 

def plot_mask(self, img: np.array, mask: np.array):
        if len(img.shape) == 2:
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        img = img.astype(np.uint8)
        mask = mask.astype(np.uint8)

        for i, c in enumerate(mask):
            contours, _ = cv2.findContours(
                c, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE
            )
            color = self.colors[i] if self.colors else (0, 255, 0)
            for i in range(0, len(contours)):
                cv2.polylines(img, contours[i], True, color, 2)

        return img 

def contouring(binary_img=None):

    # return two values: contours, hierarchy
    # cv2.RETR_EXTERNAL

    return cv2.findContours(binary_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) 

def find_components_list(self):
        return self._find_components(cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) 

def remove_borders(image):
    image = cv2.imread(image)
    orig = image.copy()
    ratio = image.shape[0] / 500.0
    image = resize(image, height=500)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray,(7,7),0)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    contrasted = clahe.apply(blur)
    im = Image.fromarray(contrasted)
    
    edged = cv2.Canny(blur, 20, 170)
    _, cnts, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    largest_area = 0
    for c in cnts:
        r = cv2.minAreaRect(c)
        area = r[1][0]*r[1][1]
        if area > largest_area:
            largest_area = area
            rect = r

    screenCnt = np.int0(cv2.boxPoints(rect))
    im = Image.fromarray(edged)

    cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
    im = Image.fromarray(image)

    if screenCnt is not None and len(screenCnt) > 0:
        return four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
    return orig 

def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0)
    im2, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
    return contours 

def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0)
    im2, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
    return contours 

def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0) 
    # Find all the contours in the thresholded image. The values 
    # for the second and third parameters are restricted to a 
    # certain number of possible values. You can learn more 'findContours' function here: 
    # http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html#findcontours
    im2, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
    return contours

# Extract reference contour from the image 

def get_all_contours(img): 
    ref_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
    ret, thresh = cv2.threshold(ref_gray, 127, 255, 0)
    im2, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE )
    return contours 

def detect_biggest_polygon(binary_img):
    binary_img_to_process = binary_img.copy()  # contour detection will mess up the image
    contours, _ = cv2.findContours(binary_img_to_process, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    biggest_contour = None
    if contours != []:
        biggest_contour_index = 0
        for i in range(1, len(contours)):
            if cv2.contourArea(contours[i]) > cv2.contourArea(contours[biggest_contour_index]):
                biggest_contour_index = i
        
        biggest_contour = contours[biggest_contour_index]
    return biggest_contour 

def biggest_contours_finder(img, nb_contours_max=3):
    """
    Fonction to find the biggest contour in an binary image
    :param img: Binary Image
    :param nb_contours_max: maximal number of contours which will be returned
    :return: biggest contours found
    """
    contours = cv2.findContours(img, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[-2]
    if not contours:
        return None
    contours_area = list()
    for cnt in contours:
        contours_area.append(cv2.contourArea(cnt))
    biggest_contours = []
    le = len(contours_area)
    if nb_contours_max > le:
        nb_contours = le
        id_contours_sorted_init = list(range(nb_contours))
    else:
        nb_contours = nb_contours_max
        id_contours_sorted_init = np.argpartition(contours_area, -nb_contours)[-nb_contours:]
    id_contours_sorted = [x for x in sorted(id_contours_sorted_init, key=lambda idi: -contours_area[idi])]

    for i in range(nb_contours):
        id_used = id_contours_sorted[i]

        if contours_area[id_used] < 400:
            break

        biggest_contours.append(contours[id_used])
    return biggest_contours 

def find_markers_from_img_thresh(img_thresh, max_dist_between_centers=3, min_radius_circle=4,
                                 max_radius_circle=35, min_radius_marker=7):
    contours = cv2.findContours(img_thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]
    list_potential_markers = []
    for cnt in contours:
        (x, y), radius = cv2.minEnclosingCircle(cnt)
        if not min_radius_circle < radius < max_radius_circle:
            continue
        center = (int(round(x)), int(round(y)))
        radius = int(radius)
        list_potential_markers.append(PotentialMarker(center, radius, cnt))

    list_potential_markers = sorted(list_potential_markers, key=lambda m: m.x)
    list_good_candidates = []

    for i, potential_marker in enumerate(list_potential_markers):
        if potential_marker.is_merged:
            continue
        marker1 = Marker(potential_marker)
        center_marker = marker1.get_center()

        for potential_marker2 in list_potential_markers[i + 1:]:
            if potential_marker.is_merged:
                continue
            center_potential = potential_marker2.get_center()
            if center_potential[0] - center_marker[0] > max_dist_between_centers:
                break
            dist = euclidean_dist_2_pts(center_marker, center_potential)
            if dist <= max_dist_between_centers:
                marker1.add_circle(potential_marker2)
                center_marker = marker1.get_center()

        if marker1.nb_circles() > 2 and marker1.radius >= min_radius_marker:
            list_good_candidates.append(marker1)
            marker1.get_id_from_slice(img_thresh)

    return list_good_candidates 

def detect_edge(self, image, enabled_transform = False):
        dst = None
        orig = image.copy()

        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
        edged = cv2.Canny(blurred, 0, 20)
        _, contours, _ = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

        contours = sorted(contours, key=cv2.contourArea, reverse=True)

        for cnt in contours:
            epsilon = 0.051 * cv2.arcLength(cnt, True)
            approx = cv2.approxPolyDP(cnt, epsilon, True)

            if len(approx) == 4:
                target = approx
                cv2.drawContours(image, [target], -1, (0, 255, 0), 2)

                if enabled_transform:
                    approx = rect.rectify(target)
                    # pts2 = np.float32([[0,0],[800,0],[800,800],[0,800]])
                    # M = cv2.getPerspectiveTransform(approx,pts2)
                    # dst = cv2.warpPerspective(orig,M,(800,800))
                    dst = self.four_point_transform(orig, approx)
                break

        return image, dst 

def findPossibleCharsInScene(imgThresh):
    listOfPossibleChars = []                # this will be the return value

    intCountOfPossibleChars = 0

    imgThreshCopy = imgThresh.copy()

    contours, npaHierarchy = cv2.findContours(imgThreshCopy, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)   # find all contours
    height, width = imgThresh.shape
    imgContours = np.zeros((height, width, 3), np.uint8)

    for i in range(0, len(contours)):                       # for each contour

        possibleChar = PossibleChar.PossibleChar(contours[i])

        if DetectChars.checkIfPossibleChar(possibleChar):                   # if contour is a possible char, note this does not compare to other chars (yet) . . .
            intCountOfPossibleChars = intCountOfPossibleChars + 1           # increment count of possible chars
            listOfPossibleChars.append(possibleChar)                        # and add to list of possible chars
        # end if
    # end for

    return listOfPossibleChars
# end function


################################################################################################### 

def find_items(maze_image):
    # Preprocessing to find the contour of the shapes
    h, w = maze_image.shape[0], maze_image.shape[1]
    dim = (h+w)//2
    b_and_w = cv2.cvtColor(maze_image, cv2.COLOR_BGR2GRAY)
    edges = cv2.GaussianBlur(b_and_w, (11, 11), 0)
    edges = cv2.adaptiveThreshold(edges, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 9, 2)

    cv2.rectangle(edges,(0, 0),(w-1,h-1),(255,255,255),16)
    contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

    # cv2.imshow('d', edges)

    items = []

    if contours:
        item_mask = np.zeros(edges.shape, np.uint8)
        conts = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=False)

        for cnt in conts:

            if cv2.contourArea(cnt) > 0.35*dim:
                return items, item_mask

            elif cv2.contourArea(cnt) > 0.05*dim:
                d = np.mean(cnt, axis=0)
                d[0][0], d[0][1] = int(round(d[0][0])), int(round(d[0][1]))

                # TODO adjust the size here?
                if cv2.contourArea(cnt) < 0.1*dim:
                    items.append((d, 'smol'))
                    cv2.drawContours(item_mask, [cnt], -1, (255,255,255), -1)
                else:
                    items.append((d, 'big'))
                    cv2.drawContours(item_mask, [cnt], -1, (255,255,255), -1)

    return items, item_mask 

def find_bbox(img, img_closed):  # 寻找身份证正反面区域
    """
    根据二值化结果判定并裁剪出身份证正反面区域
    :param img: 原始RGB图片
    :param img_closed: 二值化后的图片
    :return: 身份证正反面区域
    """
    (contours, _) = cv2.findContours(img_closed.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)  # 求出框的个数
    # 这里opencv如果版本不对（4.0或以上）会报错，只需把(contours, _)改成 (_, contours, _)

    contours = sorted(contours, key=cv2.contourArea, reverse=True)  # 按照面积大小排序

    countours_res = []
    for i in range(0, len(contours)):
        area = cv2.contourArea(contours[i])  # 计算面积

        if (area <= 0.4 * img.shape[0] * img.shape[1]) and (area >= 0.05 * img.shape[0] * img.shape[1]):
            # 人为设定,身份证正反面框的大小不会超过整张图片大小的0.4,不会小于0.05(这个参数随便设置的)
            rect = cv2.minAreaRect(contours[i])  # 最小外接矩,返回值有中心点坐标,矩形宽高,倾斜角度三个参数
            box = cv2.boxPoints(rect)
            left_down, right_down, left_up, right_up = point_judge([int(rect[0][0]), int(rect[0][1])], box)
            src = np.float32([left_down, right_down, left_up, right_up])  # 这里注意必须对应

            dst = np.float32([[0, 0], [int(max(rect[1][0], rect[1][1])), 0], [0, int(min(rect[1][0], rect[1][1]))],
                              [int(max(rect[1][0], rect[1][1])),
                               int(min(rect[1][0], rect[1][1]))]])  # rect中的宽高不清楚是个怎么机制,但是对于身份证,肯定是宽大于高,因此加个判定
            m = cv2.getPerspectiveTransform(src, dst)  # 得到投影变换矩阵
            result = cv2.warpPerspective(img, m, (int(max(rect[1][0], rect[1][1])), int(min(rect[1][0], rect[1][1]))),
                                         flags=cv2.INTER_CUBIC)  # 投影变换
            countours_res.append(result)
    return countours_res  # 返回身份证区域