def test_empty_selem():
    # check that min, max and mean returns zeros if structuring element is
    # empty

    image = np.zeros((5, 5), dtype=np.uint16)
    out = np.zeros_like(image)
    mask = np.ones_like(image, dtype=np.uint8)
    res = np.zeros_like(image)
    image[2, 2] = 255
    image[2, 3] = 128
    image[1, 2] = 16

    elem = np.array([[0, 0, 0], [0, 0, 0]], dtype=np.uint8)

    rank.mean(image=image, selem=elem, out=out, mask=mask,
              shift_x=0, shift_y=0)
    assert_equal(res, out)
    rank.geometric_mean(image=image, selem=elem, out=out, mask=mask,
                        shift_x=0, shift_y=0)
    assert_equal(res, out)
    rank.minimum(image=image, selem=elem, out=out, mask=mask,
                 shift_x=0, shift_y=0)
    assert_equal(res, out)
    rank.maximum(image=image, selem=elem, out=out, mask=mask,
                 shift_x=0, shift_y=0)
    assert_equal(res, out)

def test_compare_with_grey_erosion():
    # compare the result of maximum filter with erode

    image = (np.random.rand(100, 100) * 256).astype(np.uint8)
    out = np.empty_like(image)
    mask = np.ones(image.shape, dtype=np.uint8)

    for r in range(3, 20, 2):
        elem = np.ones((r, r), dtype=np.uint8)
        rank.minimum(image=image, selem=elem, out=out, mask=mask)
        cm = grey.erosion(image=image, selem=elem)
        assert_equal(out, cm)

def test_percentile_min():
    # check that percentile p0 = 0 is identical to local min
    img = data.camera()
    img16 = img.astype(np.uint16)
    selem = disk(15)
    # check for 8bit
    img_p0 = rank.percentile(img, selem=selem, p0=0)
    img_min = rank.minimum(img, selem=selem)
    assert_equal(img_p0, img_min)
    # check for 16bit
    img_p0 = rank.percentile(img16, selem=selem, p0=0)
    img_min = rank.minimum(img16, selem=selem)
    assert_equal(img_p0, img_min)

def test_smallest_selem16():
    # check that min, max and mean returns identity if structuring element
    # contains only central pixel

    image = np.zeros((5, 5), dtype=np.uint16)
    out = np.zeros_like(image)
    mask = np.ones_like(image, dtype=np.uint8)
    image[2, 2] = 255
    image[2, 3] = 128
    image[1, 2] = 16

    elem = np.array([[1]], dtype=np.uint8)
    rank.mean(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0)
    assert_equal(image, out)
    rank.minimum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0)
    assert_equal(image, out)
    rank.maximum(image=image, selem=elem, out=out, mask=mask, shift_x=0, shift_y=0)
    assert_equal(image, out)

def test_16bit():
    image = np.zeros((21, 21), dtype=np.uint16)
    selem = np.ones((3, 3), dtype=np.uint8)

    for bitdepth in range(17):
        value = 2 ** bitdepth - 1
        image[10, 10] = value
        assert rank.minimum(image, selem)[10, 10] == 0
        assert rank.maximum(image, selem)[10, 10] == value
        assert rank.mean(image, selem)[10, 10] == int(value / selem.size)

def test_16bit():
    image = np.zeros((21, 21), dtype=np.uint16)
    selem = np.ones((3, 3), dtype=np.uint8)

    for bitdepth in range(17):
        value = 2 ** bitdepth - 1
        image[10, 10] = value
        if bitdepth > 11:
            expected = ['Bitdepth of %s' % (bitdepth - 1)]
        else:
            expected = []
        with expected_warnings(expected):
            assert rank.minimum(image, selem)[10, 10] == 0
            assert rank.maximum(image, selem)[10, 10] == value
            assert rank.mean(image, selem)[10, 10] == int(value / selem.size)

def check_all():
    np.random.seed(0)
    image = np.random.rand(25, 25)
    selem = morphology.disk(1)
    refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))

    assert_equal(refs["autolevel"], rank.autolevel(image, selem))
    assert_equal(refs["autolevel_percentile"], rank.autolevel_percentile(image, selem))
    assert_equal(refs["bottomhat"], rank.bottomhat(image, selem))
    assert_equal(refs["equalize"], rank.equalize(image, selem))
    assert_equal(refs["gradient"], rank.gradient(image, selem))
    assert_equal(refs["gradient_percentile"], rank.gradient_percentile(image, selem))
    assert_equal(refs["maximum"], rank.maximum(image, selem))
    assert_equal(refs["mean"], rank.mean(image, selem))
    assert_equal(refs["mean_percentile"], rank.mean_percentile(image, selem))
    assert_equal(refs["mean_bilateral"], rank.mean_bilateral(image, selem))
    assert_equal(refs["subtract_mean"], rank.subtract_mean(image, selem))
    assert_equal(refs["subtract_mean_percentile"], rank.subtract_mean_percentile(image, selem))
    assert_equal(refs["median"], rank.median(image, selem))
    assert_equal(refs["minimum"], rank.minimum(image, selem))
    assert_equal(refs["modal"], rank.modal(image, selem))
    assert_equal(refs["enhance_contrast"], rank.enhance_contrast(image, selem))
    assert_equal(refs["enhance_contrast_percentile"], rank.enhance_contrast_percentile(image, selem))
    assert_equal(refs["pop"], rank.pop(image, selem))
    assert_equal(refs["pop_percentile"], rank.pop_percentile(image, selem))
    assert_equal(refs["pop_bilateral"], rank.pop_bilateral(image, selem))
    assert_equal(refs["sum"], rank.sum(image, selem))
    assert_equal(refs["sum_bilateral"], rank.sum_bilateral(image, selem))
    assert_equal(refs["sum_percentile"], rank.sum_percentile(image, selem))
    assert_equal(refs["threshold"], rank.threshold(image, selem))
    assert_equal(refs["threshold_percentile"], rank.threshold_percentile(image, selem))
    assert_equal(refs["tophat"], rank.tophat(image, selem))
    assert_equal(refs["noise_filter"], rank.noise_filter(image, selem))
    assert_equal(refs["entropy"], rank.entropy(image, selem))
    assert_equal(refs["otsu"], rank.otsu(image, selem))
    assert_equal(refs["percentile"], rank.percentile(image, selem))
    assert_equal(refs["windowed_histogram"], rank.windowed_histogram(image, selem))

# Local maximum and local minimum are the base operators for gray-level
# morphology.
#
# .. note::
#
#     `skimage.dilate` and `skimage.erode` are equivalent filters (see below
#     for comparison).
#
# Here is an example of the classical morphological gray-level filters:
# opening, closing and morphological gradient.

from skimage.filters.rank import maximum, minimum, gradient

noisy_image = img_as_ubyte(data.camera())

closing = maximum(minimum(noisy_image, disk(5)), disk(5))
opening = minimum(maximum(noisy_image, disk(5)), disk(5))
grad = gradient(noisy_image, disk(5))

# display results
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = ax.ravel()

ax1.imshow(noisy_image, cmap=plt.cm.gray)
ax1.set_title('Original')

ax2.imshow(closing, cmap=plt.cm.gray)
ax2.set_title('Gray-level closing')

ax3.imshow(opening, cmap=plt.cm.gray)
ax3.set_title('Gray-level opening')

    def TestSample(self):  # Run pump, take samples and analyse
        global currentPhoto
        self.im1Count["text"] = "-"  # Reset text fields
        self.im2Count["text"] = "-"
        self.im3Count["text"] = "-"

        self.im1Average["text"] = "-"
        self.im2Average["text"] = "-"
        self.im3Average["text"] = "-"

        self.imFinalCount["text"] = "-"
        self.imFinalAverage["text"] = "-"
        self.sizeAct["text"] = "-"
        self.Confidence["text"] = "-"
        self.ConfDisp["bg"] = "grey"

        ##'''
        global camera
        camera.stop_preview()  # Quit preview if open

        ###########################     Run pump and take Pictures       ###############################

        self.pump_On()  # Turn on pump
        self.update_idletasks()  # Refresh Gui

        for x in range(0, 25):  # Wait 25 seconds
            self.labelCurrentAction["text"] = "Pumping Liquid - %d" % (25 - x)
            self.update_idletasks()
            time.sleep(1)
        self.pump_Off()  # Turn off pump

        for x in range(1, 4):  # Take 3 images
            self.pump_Off()
            self.labelCurrentAction["text"] = "Powder Settle Time"
            self.update_idletasks()
            time.sleep(2)
            self.labelCurrentAction["text"] = "Capturing Image %d" % x
            camera.hflip = True  # Flip camera orientation appropriately
            camera.vflip = True
            camera.capture("/home/pi/PythonTempFolder/OrigPic" + str(x) + ".jpg")  # Save image to default directory

            self.update_idletasks()
            time.sleep(2)

            if x < 3:
                self.pump_On()  # Turn on pump
                for y in range(0, 6):  # Wait 6 seconds
                    self.labelCurrentAction["text"] = "Pumping Liquid - %d" % (6 - y)
                    self.update_idletasks()
                    time.sleep(1)

                self.pump_Off()  # Turn off pump
        ##'''
        ################################################################################################

        ###########################              Analyse Pictures        ###############################
        for x in range(1, 4):
            self.labelCurrentAction["text"] = "Loading image as greyscale - im %d" % x
            self.update_idletasks()

            image1 = io.imread(
                "/home/pi/PythonTempFolder/OrigPic" + str(x) + ".jpg", as_grey=True
            )  # Load image as greyscale

            ##
            ##image1 = io.imread('/home/pi/SDP Project/PowderTests/PPIM169041/169041Pic' + str(x) + '.jpg', as_grey=True)   ##Comment Out
            ##

            self.labelCurrentAction["text"] = "Cropping"  # Crop image
            self.update_idletasks()
            fromFile = np.asarray(image1, dtype=np.float32)
            orig = fromFile[0:1080, 420:1500]
            currentPhoto = orig
            self.showCurrent()
            self.update_idletasks()
            time.sleep(2)

            self.labelCurrentAction["text"] = "Applying minimum filter"  # Apply minimum filter
            self.update_idletasks()
            image2 = minimum(orig, disk(6))
            currentPhoto = image2
            self.t.destroy()
            self.update_idletasks()
            self.showCurrent()
            self.update_idletasks()

            self.labelCurrentAction["text"] = "Applying mean filter"  # Apply mean filter
            self.update_idletasks()
            image3 = mean(image2, disk(22))
            currentPhoto = image3
            self.t.destroy()
            self.update_idletasks()
            self.showCurrent()
            self.update_idletasks()

            self.labelCurrentAction["text"] = "Applying maximum filter"  # Apply maximum filter
            self.update_idletasks()
            image4 = maximum(image3, disk(6))
            currentPhoto = image4
            self.t.destroy()
#.........这里部分代码省略.........