python类boundingRect()的实例源码

def __bound_contours(roi):
    """
        returns modified roi(non-destructive) and rectangles that founded by the algorithm.
        @roi region of interest to find contours
        @return (roi, rects)
    """

    roi_copy = roi.copy()
    roi_hsv = cv2.cvtColor(roi, cv2.COLOR_RGB2HSV)
    # filter black color
    mask1 = cv2.inRange(roi_hsv, np.array([0, 0, 0]), np.array([180, 255, 125]))
    mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    mask1 = cv2.Canny(mask1, 100, 300)
    mask1 = cv2.GaussianBlur(mask1, (1, 1), 0)
    mask1 = cv2.Canny(mask1, 100, 300)

    # mask1 = cv2.morphologyEx(mask1, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)))

    # Find contours for detected portion of the image
    im2, cnts, hierarchy = cv2.findContours(mask1.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # get largest five contour area
    rects = []
    for c in cnts:
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        x, y, w, h = cv2.boundingRect(approx)
        if h >= 15:
            # if height is enough
            # create rectangle for bounding
            rect = (x, y, w, h)
            rects.append(rect)
            cv2.rectangle(roi_copy, (x, y), (x+w, y+h), (0, 255, 0), 1);

    return (roi_copy, rects)

def crop_and_store(frame, mouth_coordinates, name):
    """
    Args:
        1. frame:               The frame which has to be cropped.
        2. mouth_coordinates:   The coordinates which help in deciding which region is to be cropped.
        3. name:                The path name to be used for storing the cropped image.
    """

    # Find bounding rectangle for mouth coordinates
    x, y, w, h = cv2.boundingRect(mouth_coordinates)

    mouth_roi = frame[y:y + h, x:x + w]

    h, w, channels = mouth_roi.shape
    # If the cropped region is very small, ignore this case.
    if h < 10 or w < 10:
        return

    resized = resize(mouth_roi, 32, 32)
    cv2.imwrite(name, resized)

def extract_corners(self, image):
        """
        Find the 4 corners of a binary image
        :param image: binary image
        :return: 4 main vertices or None
        """
        cnts, _ = cv2.findContours(image.copy(),
                                   cv2.RETR_EXTERNAL,
                                   cv2.CHAIN_APPROX_SIMPLE)[-2:]
        cnt = cnts[0]
        _, _, h, w = cv2.boundingRect(cnt)
        epsilon = min(h, w) * 0.5
        vertices = cv2.approxPolyDP(cnt, epsilon, True)
        vertices = cv2.convexHull(vertices, clockwise=True)
        vertices = self.correct_vertices(vertices)

        return vertices

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 = find_contours(bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            for cnt in contours:
                x, y, w, h = cv2.boundingRect(cnt)
                cnt_len = cv2.arcLength(cnt, True)
                cnt = cv2.approxPolyDP(cnt, 0.02*cnt_len, True)
                area = cv2.contourArea(cnt)
                if len(cnt) == 4 and 20 < area < 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:
                        if (1 - (float(w) / float(h)) <= 0.07 and 1 - (float(h) / float(w)) <= 0.07):
                            squares.append(cnt)
    return squares

def affine_skew(self, tilt, phi, img, mask=None):
        h, w = img.shape[:2]
        if mask is None:
            mask = np.zeros((h, w), np.uint8)
            mask[:] = 255
        A = np.float32([[1, 0, 0], [0, 1, 0]])
        if phi != 0.0:
            phi = np.deg2rad(phi)
            s, c = np.sin(phi), np.cos(phi)
            A = np.float32([[c, -s], [s, c]])
            corners = [[0, 0], [w, 0], [w, h], [0, h]]
            tcorners = np.int32(np.dot(corners, A.T))
            x, y, w, h = cv2.boundingRect(tcorners.reshape(1, -1, 2))
            A = np.hstack([A, [[-x], [-y]]])
            img = cv2.warpAffine(img, A, (w, h), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE)
        if tilt != 1.0:
            s = 0.8*np.sqrt(tilt * tilt - 1)
            img = cv2.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01)
            img = cv2.resize(img, (0, 0), fx=1.0 / tilt, fy=1.0, interpolation=cv2.INTER_NEAREST)
            A[0] /= tilt
        if phi != 0.0 or tilt != 1.0:
            h, w = img.shape[:2]
            mask = cv2.warpAffine(mask, A, (w, h), flags=cv2.INTER_NEAREST)
        Ai = cv2.invertAffineTransform(A)
        return img, mask, Ai

def sizeFiltering(contours):
    """
    this function filters out the smaller retroreflector (as well as any noise) by size
    """

    if len(contours) == 0:
        print "sizeFiltering: Error, no contours found"
        return 0

    big = contours[0]

    for c in contours:
        if type(c) and type(big) == numpy.ndarray:
            if cv2.contourArea(c) > cv2.contourArea(big):
                big = c
        else:
            print type(c) and type(big)
            return 0
    x,y,w,h = cv2.boundingRect(big)
    return big

def profile_score(contour, binary):
    """
    Calculate a score based on the "profile" of the target, basically how closely its geometry matches with the expected
    geometry of the goal
    :param contour:
    :param binary:
    :return:
    """
    bounding = cv2.boundingRect(contour)
    pixels = np.zeros((binary.shape[0], binary.shape[1]))
    cv2.drawContours(pixels, [contour], -1, 255, -1)
    col_averages = np.mean(pixels, axis=0)[bounding[0]:bounding[0] + bounding[2]]
    row_averages = np.mean(pixels, axis=1)[bounding[1]:bounding[1] + bounding[3]]
    # normalize to between 0 and 1
    col_averages *= 1.0 / col_averages.max()
    row_averages *= 1.0 / row_averages.max()

    col_diff = np.subtract(col_averages, col_profile(col_averages.shape[0], bounding[2]))
    row_diff = np.subtract(row_averages, row_profile(row_averages.shape[0], bounding[3]))

    # average difference should be close to 0
    avg_diff = np.mean([np.mean(col_diff), np.mean(row_diff)])
    return 100 - (avg_diff * 50)

def diff_rect(img1, img2, pos=None):
    """find counters include pos in differences between img1 & img2 (cv2 images)"""
    diff = cv2.absdiff(img1, img2)
    diff = cv2.GaussianBlur(diff, (3, 3), 0)
    edges = cv2.Canny(diff, 100, 200)
    _, thresh = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    if not contours:
        return None
    contours.sort(key=lambda c: len(c))
    # no pos provide, just return the largest different area rect
    if pos is None:
        cnt = contours[-1]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        return (x0, y0, x1, y1)
    # else the rect should contain the pos
    x, y = pos
    for i in range(len(contours)):
        cnt = contours[-1-i]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        if x0 <= x <= x1 and y0 <= y <= y1:
            return (x0, y0, x1, y1)

def sort_contours(cnts, method="left-to-right"):
    # initialize the reverse flag and sort index
    reverse = False
    i = 0

    # handle if we need to sort in reverse
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True

    # handle if we are sorting against the y-coordinate rather than
    # the x-coordinate of the bounding box
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1

    # construct the list of bounding boxes and sort them from top to
    # bottom
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
        key=lambda b:b[1][i], reverse=reverse))

    # return the list of sorted contours and bounding boxes
    return (cnts, boundingBoxes)

def find_contours(self, img):

        thresh_img = self.threshold(img)

        _, contours, _ = cv2.findContours(thresh_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        result = []
        for cnt in contours:
            approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)

            if self.draw_approx:
                cv2.drawContours(self.out, [approx], -1, self.BLUE, 2, lineType=8)

            if len(approx) > 3 and len(approx) < 15:
                _, _, w, h = cv2.boundingRect(approx)
                if h > self.min_height and w > self.min_width:
                        hull = cv2.convexHull(cnt)
                        approx2 = cv2.approxPolyDP(hull,0.01*cv2.arcLength(hull,True),True)

                        if self.draw_approx2:
                            cv2.drawContours(self.out, [approx2], -1, self.GREEN, 2, lineType=8)

                        result.append(approx2)
        return result

def mark_hand_center(frame_in,cont):    
    max_d=0
    pt=(0,0)
    x,y,w,h = cv2.boundingRect(cont)
    for ind_y in xrange(int(y+0.3*h),int(y+0.8*h)): #around 0.25 to 0.6 region of height (Faster calculation with ok results)
        for ind_x in xrange(int(x+0.3*w),int(x+0.6*w)): #around 0.3 to 0.6 region of width (Faster calculation with ok results)
            dist= cv2.pointPolygonTest(cont,(ind_x,ind_y),True)
            if(dist>max_d):
                max_d=dist
                pt=(ind_x,ind_y)
    if(max_d>radius_thresh*frame_in.shape[1]):
        thresh_score=True
        cv2.circle(frame_in,pt,int(max_d),(255,0,0),2)
    else:
        thresh_score=False
    return frame_in,pt,max_d,thresh_score

# 6. Find and display gesture

def diagContour(image):
     #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them
    _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black
    image.fill(0)
    boundingRect = []
    firstFail = []
    #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing
    for j in [i for i in contours if cv2.contourArea(i) > minArea]:
        #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner
        br = cv2.boundingRect(j)
        if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol):
            boundingRect.append(br)
        else:
            firstFail.append([br, br[2]/br[3], cv2.contourArea(j)/(br[2]*br[3])])
    secondRound = []
    for x in range(0, len(boundingRect)):
        for y in range(x+1, len(boundingRect)):
            i = boundingRect[x]
            j = boundingRect[y]
            secondRound.append([(x,y,i,j), (abs(i[1]-j[1]), i[3]/2), abs(i[0]-j[0])/i[1]])
    for x in secondRound:
        if(x[1][0] < x[1][1] and x[2] - GRPASPECT < grpAspectTol):
            return [x[0][2], x[0][3]]
        return None;

def get_contours(image, polydb=0.1, contour_range=7):
    # find the contours in the edged image, keeping only the largest ones, and initialize the screen contour
    contours = _findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:contour_range]
    # loop over the contours
    screenCnt = None
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True) #finds the Contour Perimeter
        approx = cv2.approxPolyDP(c, polydb * peri, True)
        # if our approximated contour has four points, then we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break
    if screenCnt is None:
        raise EdgeNotFound()
    # sometimes the algorythm finds a strange non-convex shape. The shape conforms to the card but its not complete, so then just complete the shape into a convex form
    if not cv2.isContourConvex(screenCnt):
        screenCnt = cv2.convexHull(screenCnt)
        x,y,w,h = cv2.boundingRect(screenCnt)
        screenCnt = numpy.array([[[x, y]], [[x+w, y]], [[x+w, y+h]], [[x, y+h]]])
    return screenCnt

def GetImageContour(self):
        thresholdImage = self.__convertImagetoBlackWhite()  #B & W with adaptive threshold
        thresholdImage = cv.Canny(thresholdImage, 100, 200) #Edges by canny edge detection
        thresholdImage, contours, hierarchy = cv.findContours(
            thresholdImage, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
        self.Contours = contours
        # uncomment this to see the contours on the image
        # cv2.drawContours(thresholdImage, contours, -1, (0,255,0), 3)
        # patternFindingObj=PatternFinding()
        # areas= [cv.contourArea(contour) for contour in contours]
        # for index in xrange(len(contours)):
        #     IsPattern=self.IsPossibleQRContour(index)
        #     if IsPattern is True:
        #         x,y,w,h=cv.boundingRect(contours[index])
        #         cv.rectangle(self.imageOriginal,(x,y),(x+w,y+h),(0,0,255),2)
        #         cv.imshow("hello",self.imageOriginal)
        # maxAreaIndex=np.argmax(areas)
        # x,y,w,h=cv.boundingRect(contours[maxAreaIndex])
        # cv.rectangle(self.image2,(x,y),(x+w,y+h),(0,255,0),2)
        # cv.imshow("hello",self.imageOriginal)
        # cv.waitKey(0)
        #cv.destroyAllWindows()
        contour_group = (thresholdImage, contours, hierarchy)
        return contour_group

def findSquare( self,frame ):
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (7, 7), 0)
        edged = cv2.Canny(blurred, 60, 60)
        # find contours in the edge map
        (cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        # loop over our contours to find hexagon
        cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:50]
        screenCnt = None
        for c in cnts:
            # approximate the contour
            peri = cv2.arcLength(c, True)
            approx = cv2.approxPolyDP(c, 0.004 * peri, True)
            # if our approximated contour has four points, then
            # we can assume that we have found our squeare

            if len(approx) >= 4:
                screenCnt = approx
                x,y,w,h = cv2.boundingRect(c)
                cv2.drawContours(image, [approx], -1, (0, 0, 255), 1)
                #cv2.imshow("Screen", image)
                #create the mask and remove rest of the background
                mask = np.zeros(image.shape[:2], dtype = "uint8")
                cv2.drawContours(mask, [screenCnt], -1, 255, -1)
                masked = cv2.bitwise_and(image, image, mask = mask)
                #cv2.imshow("Masked",masked  )
                #crop the masked image to to be compared to referance image
                cropped = masked[y:y+h,x:x+w]
                #scale the image so it is fixed size as referance image
                cropped = cv2.resize(cropped, (200,200), interpolation =cv2.INTER_AREA)

                return cropped

def extract_corners(self, image):
        """
        Find the 4 corners of a binary image
        :param image: binary image
        :return: 4 main vertices or None
        """
        cnts, _ = cv2.findContours(image.copy(),
                                   cv2.RETR_EXTERNAL,
                                   cv2.CHAIN_APPROX_SIMPLE)[-2:]
        cnt = cnts[0]
        _, _, h, w = cv2.boundingRect(cnt)
        epsilon = min(h, w) * 0.5
        o_vertices = cv2.approxPolyDP(cnt, epsilon, True)
        vertices = cv2.convexHull(o_vertices, clockwise=True)
        vertices = self.correct_vertices(vertices)

        if self.debug:
            temp = cv2.cvtColor(image.copy(), cv2.COLOR_GRAY2BGR)
            cv2.drawContours(temp, cnts, -1, (0, 255, 0), 10)
            cv2.drawContours(temp, o_vertices, -1, (255, 0, 0), 30)
            cv2.drawContours(temp, vertices, -1, (0, 0, 255), 20)
            self.save2image(temp)

        return vertices

def diff_rect(img1, img2, pos=None):
    """find counters include pos in differences between img1 & img2 (cv2 images)"""
    diff = cv2.absdiff(img1, img2)
    diff = cv2.GaussianBlur(diff, (3, 3), 0)
    edges = cv2.Canny(diff, 100, 200)
    _, thresh = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
    if not contours:
        return None
    contours.sort(key=lambda c: len(c))
    # no pos provide, just return the largest different area rect
    if pos is None:
        cnt = contours[-1]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        return (x0, y0, x1, y1)
    # else the rect should contain the pos
    x, y = pos
    for i in range(len(contours)):
        cnt = contours[-1-i]
        x0, y0, w, h = cv2.boundingRect(cnt)
        x1, y1 = x0+w, y0+h
        if x0 <= x <= x1 and y0 <= y <= y1:
            return (x0, y0, x1, y1)

def find_chars(img):
    gray = np.array(img.convert("L"))
    ret, mask = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
    image_final = cv2.bitwise_and(gray, gray, mask=mask)
    ret, new_img = cv2.threshold(image_final, 180, 255, cv2.THRESH_BINARY_INV)
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
    dilated = cv2.dilate(new_img, kernel, iterations=1)
    # Image.fromarray(dilated).save('out.png') # for debugging
    _, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

    coords = []
    for contour in contours:
        # get rectangle bounding contour
        [x, y, w, h] = cv2.boundingRect(contour)
        # ignore large chars (probably not chars)
        if w > 70 and h > 70:
            continue
        coords.append((x, y, w, h))
    return coords


# find list of eye coordinates in image

def keep_box(contour):
    xx, yy, w_, h_ = cv2.boundingRect(contour)

    # width and height need to be floats
    w_ *= 1.0
    h_ *= 1.0

    # Test it's shape - if it's too oblong or tall it's
    # probably not a real character
    if w_ / h_ < 0.1 or w_ / h_ > 10:
        if DEBUG:
            print "\t Rejected because of shape: (" + str(xx) + "," + str(yy) + "," + str(w_) + "," + str(h_) + ")" + \
                  str(w_ / h_)
        return False

    # check size of the box
    if ((w_ * h_) > ((img_x * img_y) / 5)) or ((w_ * h_) < 15):
        if DEBUG:
            print "\t Rejected because of size"
        return False

    return True

def test_initial_pass_through_compare(self):
        original = cv2.imread(os.path.join(self.provider.assets, "start_screen.png"))
        against = self.provider.get_img_from_screen_shot()
        wrong = cv2.imread(os.path.join(self.provider.assets, "battle.png"))

        # convert the images to grayscale
        original = mask_image([127], [255], cv2.cvtColor(original, cv2.COLOR_BGR2GRAY), True)
        against = mask_image([127], [255], cv2.cvtColor(against, cv2.COLOR_BGR2GRAY), True)
        wrong = mask_image([127], [255], cv2.cvtColor(wrong, cv2.COLOR_BGR2GRAY), True)
        # initialize the figure
        (score, diff) = compare_ssim(original, against, full=True)
        diff = (diff * 255).astype("uint8")
        self.assertTrue(score > .90, 'If this is less then .90 the initial compare of the app will fail')
        (score, nothing) = compare_ssim(original, wrong, full=True)
        self.assertTrue(score < .90)
        if self.__debug_pictures__:
            # threshold the difference image, followed by finding contours to
            # obtain the regions of the two input images that differ
            thresh = cv2.threshold(diff, 0, 255,
                                   cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
            cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
                                    cv2.CHAIN_APPROX_SIMPLE)
            cnts = cnts[0]
            # loop over the contours
            for c in cnts:
                # compute the bounding box of the contour and then draw the
                # bounding box on both input images to represent where the two
                # images differ
                (x, y, w, h) = cv2.boundingRect(c)
                cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2)
                cv2.rectangle(against, (x, y), (x + w, y + h), (0, 0, 255), 2)
            # show the output images
            diffs = ("Original", original), ("Modified", against), ("Diff", diff), ("Thresh", thresh)
            images = ("Original", original), ("Against", against), ("Wrong", wrong)
            self.setup_compare_images(diffs)
            self.setup_compare_images(images)

def draw_contours(self):
        """"""
        # contours all the objects found
        # (findContours changes the source image,
        #  hence copy)
        contours, _ = cv2.findContours(self.mask.copy(),
                                       cv2.RETR_TREE,
                                       cv2.CHAIN_APPROX_SIMPLE)
        # rectangles
        for contour in contours:
            size = cv2.contourArea(contour)
            if size > self.threshold:  # only larger objects
                ret_x, ret_y, ret_w, ret_h = cv2.boundingRect(contour)
                cv2.rectangle(self.display, (ret_x, ret_y),
                              (ret_x+ret_w,
                               ret_y+ret_h),
                              (0, 255, 255), 2)

def remove_border(contour, ary):
    """Remove everything outside a border contour."""
    # Use a rotated rectangle (should be a good approximation of a border).
    # If it's far from a right angle, it's probably two sides of a border and
    # we should use the bounding box instead.
    c_im = np.zeros(ary.shape)
    r = cv2.minAreaRect(contour)
    degs = r[2]
    if angle_from_right(degs) <= 10.0:
        box = cv2.boxPoints(r)
        box = np.int0(box)
        cv2.drawContours(c_im, [box], 0, 255, -1)
        cv2.drawContours(c_im, [box], 0, 0, 4)
    else:
        x1, y1, x2, y2 = cv2.boundingRect(contour)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4)

    return np.minimum(c_im, ary)

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 keep_box(contour):
    xx, yy, w_, h_ = cv2.boundingRect(contour)

    # width and height need to be floats
    w_ *= 1.0
    h_ *= 1.0

    # Test it's shape - if it's too oblong or tall it's
    # probably not a real character
    if w_ / h_ < 0.1 or w_ / h_ > 10:
        if DEBUG:
            print "\t Rejected because of shape: (" + str(xx) + "," + str(yy) + "," + str(w_) + "," + str(h_) + ")" + \
                  str(w_ / h_)
        return False

    # check size of the box
    if ((w_ * h_) > ((img_x * img_y) / 5)) or ((w_ * h_) < 15):
        if DEBUG:
            print "\t Rejected because of size"
        return False

    return True

def keepBox(contour):
    xx, yy, w_, h_ = cv2.boundingRect(contour)

    # width and height need to be floats
    w_ *= 1.0
    h_ *= 1.0

    # Test it's shape - if it's too oblong or tall it's
    # probably not a real character
    if w_ / h_ < 0.1 or w_ / h_ > 10:
        if DEBUG:
            print "\t Rejected because of shape: (" + str(xx) + "," + str(yy) + "," + str(w_) + "," + str(h_) + ")" + \
                  str(w_ / h_)
        return False

    # check size of the box
    if ((w_ * h_) > ((img_x * img_y) / 5)) or ((w_ * h_) < 15):
        if DEBUG:
            print "\t Rejected because of size"
        return False

    return True


# whether contour is a child

def findIDcnt(countours):
    #????????
    widths = []
    for idx, cnt in enumerate(countours):
        x, y, width, height = cv2.boundingRect(cnt)
        widths.insert(idx, width)

    #???????????
    IDList = heapq.nlargest(3, widths)
    #???????????????????
    IDcnts = []
    for idx, item in enumerate(IDList):
        index = widths.index(item)
        IDcnts.insert(idx, countours[index])
    # print IDcnts

    return IDcnts

# ????

def contourImg(image):
    #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them
    _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black
    image.fill(0)
    boundingRect = []
    #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing
    for j in [i for i in contours if cv2.contourArea(i) > minArea]:
        #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner
        br = cv2.boundingRect(j)
        if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol):
            boundingRect.append(br)
    for x in range(0, len(boundingRect)):
        for y in range(x+1, len(boundingRect)):
            i = boundingRect[x]
            j = boundingRect[y]
            if(abs(i[1]-j[1]) < i[3]/2) and abs(abs(i[0]-j[0])/i[1] - GRPASPECT) < grpAspectTol:
                return [createRectCnt(i), createRectCnt(j)]
    return None

def diagContour(image):
     #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them
    _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black
    image.fill(0)
    boundingRect = []
    firstFail = []
    #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing
    for j in [i for i in contours if cv2.contourArea(i) > minArea]:
        #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner
        br = cv2.boundingRect(j)
        if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol):
            boundingRect.append(br)
        else:
            firstFail.append([br, br[2]/br[3], cv2.contourArea(j)/(br[2]*br[3])])
    secondRound = []
    for x in range(0, len(boundingRect)):
        for y in range(x+1, len(boundingRect)):
            i = boundingRect[x]
            j = boundingRect[y]
            secondRound.append([(x,y,i,j), (abs(i[1]-j[1]), i[3]/2), abs(i[0]-j[0])/i[1]])
    for x in secondRound:
        if(x[1][0] < x[1][1] and x[2] - GRPASPECT < grpAspectTol):
            return firstFail, secondRound, [createRectCnt(x[0][2]), createRectCnt(x[0][3])]
    return firstFail, secondRound, None

def boundingRects(scale, contours):
    for contour in contours:
        epsilon = 0.1 * cv2.arcLength(contour, True)
        approx = cv2.approxPolyDP(contour, epsilon, True)
        x, y, w, h = cv2.boundingRect(approx)

        yield [x * scale, y * scale, w * scale, h * scale]

def plateDetect(img,img2):
    '''?????????????????'''
    im2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for con in contours:
        x,y,w,h=cv2.boundingRect(con)
        area=w*h
        ratio=w/h
        if ratio>2 and ratio<4 and area>=2000 and area<=25000:
            logo_y1=max(0,int(y-h*3.0))
            logo_y2=y
            logo_x1=x
            logo_x2=x+w
            img_logo=img2.copy()
            logo=img_logo[logo_y1:logo_y2,logo_x1:logo_x2]
            cv2.imwrite('./logo1.jpg',logo)
            cv2.rectangle(img2,(x,y),(x+w,y+h),(255,0,0),2)
            cv2.rectangle(img2,(logo_x1,logo_y1),(logo_x2,logo_y2),(0,255,0),2)
            global plate
            plate=[x,y,w,h]
            #?????????
            return logo