Python util.img_as_ubyte方法代碼示例

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def chooseFrame(self):
        ret, frame = self.vid.read()
        fname = Path(self.filename)
        output_path = self.config_path.parents[0] / "labeled-data" / fname.stem

        if output_path.exists():
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frame = img_as_ubyte(frame)
            img_name = (
                str(output_path)
                + "/img"
                + str(self.currFrame).zfill(int(np.ceil(np.log10(self.numberFrames))))
                + ".png"
            )
            if self.cropping:
                crop_img = frame[self.y1 : self.y2, self.x1 : self.x2]
                cv2.imwrite(img_name, cv2.cvtColor(crop_img, cv2.COLOR_RGB2BGR))
            else:
                cv2.imwrite(img_name, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
        else:
            print(
                "%s path not found. Please make sure that the video was added to the config file using the function 'deeplabcut.add_new_videos'."
                % output_path
            ) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def execute(b64):
    b64 = base64.b64decode(b64)
    b64 = BytesIO(b64)
    img = Image.open(b64)
    img = np.array(img)
    img = util.img_as_ubyte(img)

    res_leaf = []
    cor_size = (0, 0, img.shape[1], img.shape[0])
    quadtree(img, res_leaf, cor_size, 0)
    #    fig, ax = plt.subplots(1)
    #    for rect in res_leaf:
    #        rect = patches.Rectangle((rect[0], rect[1]), rect[2], rect[3], linewidth=0.1, edgecolor='b', facecolor='none')
    #        ax.add_patch(rect)

    #   ax.imshow(img)
    #   plt.show()
    b = balance(res_leaf, img.shape[1], img.shape[0])
    s = symmetry(res_leaf, img.shape[1], img.shape[0])
    e = equilibrium(res_leaf, img.shape[1], img.shape[0])
    n = len(res_leaf)

    return [b, s, e, n] 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def execute(b64):
    b64 = base64.b64decode(b64)
    b64 = BytesIO(b64)
    img = Image.open(b64)
    img= np.array(img)
    img_la = color.rgb2gray(img)
    img_la = util.img_as_ubyte(img_la)

    # 0.11 and 0.27, sigma = 1,     from Measuring visual clutter
    # See sigma here: https://dsp.stackexchange.com/questions/4716/differences-between-opencv-canny-and-matlab-canny
    img_la = cv2.GaussianBlur(img_la, (7, 7), 1)
    cd = cv2.Canny(img_la, 0.11, 0.27)
    total = cd.shape[0] * cd.shape[1] # Total number of pixels
    number_edges = np.count_nonzero(cd) # Number of edge pixels
    contour_density = float(number_edges) / float(total) # Ratio

    result = [contour_density]

    return result 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def execute(b64):
    b64 = base64.b64decode(b64)
    b64 = BytesIO(b64)
    img = Image.open(b64)
    img = np.array(img)
    img = util.img_as_ubyte(img)
    img = img.reshape(-1, 3)
    img = [tuple(l) for l in img]

    lum = []
    for pixel in img:
        # Based on: https://en.wikipedia.org/wiki/Luma_(video)
        y = 0.2126 * pixel[0] + 0.7152 * pixel[1] + 0.0722 * pixel[2]
        lum.append(y)

    result = np.std(lum)

    return [result] 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def execute(b64):
    b64 = base64.b64decode(b64)
    b64 = BytesIO(b64)
    img = Image.open(b64)
    img= np.array(img)
    img = util.img_as_ubyte(img)

    # Convert the LAB space
    lab = color.rgb2lab(img)

    L = lab[:, :, 0]
    A = lab[:, :, 1]
    B = lab[:, :, 2]

    # Get average and standard deviation for each value separately
    meanL = np.mean(L)
    stdL = np.std(L)
    meanA = np.mean(A)
    stdA = np.std(A)
    meanB = np.mean(B)
    stdB = np.std(B)

    result = [meanL, stdL, meanA, stdA, meanB, stdB]

    return result 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def img_data_to_tifs(data, times, crappify, max_scale=1.05):
    np.warnings.filterwarnings('ignore')
    lr_imgs = {}
    lr_up_imgs = {}
    hr_imgs = {}
    for time_col in range(times):
        try:
            img = data[time_col].astype(np.float).copy()
            img_max = img.max() * max_scale
            if img_max == 0: continue  #do not save images with no contents.
            img /= img_max
            down_img, down_up_img = crappify(img)
        except:
            continue

        tag = (0, 0, time_col)
        img = img_as_ubyte(img)
        pimg = PIL.Image.fromarray(img, mode='L')
        small_img = PIL.Image.fromarray(img_as_ubyte(down_img))
        big_img = PIL.Image.fromarray(img_as_ubyte(down_up_img))
        hr_imgs[tag] = pimg
        lr_imgs[tag] = small_img
        lr_up_imgs[tag] = big_img

    np.warnings.filterwarnings('default')
    return hr_imgs, lr_imgs, lr_up_imgs 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def save_img(fn, img):
    if len(img.shape) == 2:
        np.warnings.filterwarnings('ignore')
        PIL.Image.fromarray(img_as_ubyte(img), mode='L').save(f'{fn}.tif')
        np.warnings.filterwarnings('default')
    else:
        img8 = (img * 255.).astype(np.uint8)
        np.save(fn.with_suffix('.npy'), img8, allow_pickle=False) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def test_uint(dtype):
    data = np.arange(50, dtype=dtype)
    data_scaled = data * 256 ** (data.dtype.itemsize - 1)
    assert convert_to_uint8(data_scaled).dtype == np.uint8
    assert np.all(data == convert_to_uint8(data_scaled))
    assert np.all(img_as_ubyte(data) == convert_to_uint8(data))
    assert np.all(img_as_ubyte(data_scaled) == convert_to_uint8(data_scaled)) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def test_int(dtype):
    data = np.arange(50, dtype=dtype)
    data_scaled = data * 256 ** (data.dtype.itemsize - 1)
    assert convert_to_uint8(data).dtype == np.uint8
    assert convert_to_uint8(data_scaled).dtype == np.uint8
    assert np.all(img_as_ubyte(data) == convert_to_uint8(data))
    assert np.all(2 * data == convert_to_uint8(data_scaled))
    assert np.all(img_as_ubyte(data_scaled) == convert_to_uint8(data_scaled))
    assert np.all(img_as_ubyte(data - 10) == convert_to_uint8(data - 10))
    assert np.all(
        img_as_ubyte(data_scaled - 10) == convert_to_uint8(data_scaled - 10)
    ) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def test_float(dtype):
    data = np.linspace(0, 0.5, 128, dtype=dtype, endpoint=False)
    res = np.arange(128, dtype=np.uint8)
    assert convert_to_uint8(data).dtype == np.uint8
    assert np.all(convert_to_uint8(data) == res)
    data = np.linspace(0, 1, 256, dtype=dtype)
    res = np.arange(256, dtype=np.uint8)
    assert np.all(convert_to_uint8(data) == res)
    assert np.all(img_as_ubyte(data) == convert_to_uint8(data))
    assert np.all(img_as_ubyte(data - 0.5) == convert_to_uint8(data - 0.5)) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def test_bool():
    data = np.zeros((10, 10), dtype=np.bool)
    data[2:-2, 2:-2] = 1
    converted = convert_to_uint8(data)
    assert converted.dtype == np.uint8
    assert np.all(img_as_ubyte(data) == converted) 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def face_rectangle(img, n_upsamples=1):
    """Find a face rectangle.

    Parameters
    ----------
    img : np.ndarray
        Image of any dtype and number of channels.

    Returns
    -------
    corners : list
        List of tuples where each tuple represents the top left and bottom right coordinates of
        the face rectangle. Note that these coordinates use the `(row, column)` convention. The
        length of the list is equal to the number of detected faces.

    faces : list
        Instance of ``dlib.rectagles`` that can be used in other algorithm.

    n_upsamples : int
        Upsample factor to apply to the image before detection. Allows to recognize
        more faces.

    """
    if not isinstance(img, np.ndarray):
        raise TypeError("The input needs to be a np.ndarray")

    dlib_detector = dlib.get_frontal_face_detector()

    faces = dlib_detector(img_as_ubyte(img), n_upsamples)

    corners = []
    for face in faces:
        x1, y1, x2, y2 = face.left(), face.top(), face.right(), face.bottom()
        top_left = (y1, x1)
        bottom_right = (y2, x2)
        corners.append((top_left, bottom_right))

    return corners, faces 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def landmarks_68(img, rectangle, model_path=None):
    """Predict 68 face landmarks.

    Parameters
    ----------
    img : np.ndarray
        Image of any dtype and number of channels.

    rectangle : dlib.rectangle
        Rectangle that represents the bounding box around a single face.

    model_path : str or pathlib.Path, default=None
        Path to where the pretrained model is located. If None then using the `CACHE_FOLDER` model.

    Returns
    -------
    lm_points : np.ndarray
        Array of shape `(68, 2)` where rows are different landmark points and the columns
        are x and y coordinates.

    original : dlib.full_object_detection
        Instance of ``dlib.full_object_detection``.

    """
    if model_path is None:
        model_path = CACHE_FOLDER / "shape_predictor_68_face_landmarks.dat"
        get_pretrained_68(model_path.parent)

    else:
        model_path = pathlib.Path(str(model_path))

    if not model_path.is_file():
        raise IOError("Invalid landmark model, {}".format(str(model_path)))

    lm_predictor = dlib.shape_predictor(str(model_path))

    original = lm_predictor(img_as_ubyte(img), rectangle)

    lm_points = np.array([[p.x, p.y] for p in original.parts()])

    return lm_points, original 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def GetPoseS(cfg, dlc_cfg, sess, inputs, outputs, cap, nframes):
    """ Non batch wise pose estimation for video cap."""
    if cfg["cropping"]:
        ny, nx = checkcropping(cfg, cap)

    PredictedData = np.zeros(
        (nframes, dlc_cfg["num_outputs"] * 3 * len(dlc_cfg["all_joints_names"]))
    )
    pbar = tqdm(total=nframes)
    counter = 0
    step = max(10, int(nframes / 100))
    while cap.isOpened():
        if counter % step == 0:
            pbar.update(step)

        ret, frame = cap.read()
        if ret:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            if cfg["cropping"]:
                frame = img_as_ubyte(
                    frame[cfg["y1"] : cfg["y2"], cfg["x1"] : cfg["x2"]]
                )
            else:
                frame = img_as_ubyte(frame)
            pose = predict.getpose(frame, dlc_cfg, sess, inputs, outputs)
            PredictedData[
                counter, :
            ] = (
                pose.flatten()
            )  # NOTE: thereby cfg['all_joints_names'] should be same order as bodyparts!
        else:
            nframes = counter
            break
        counter += 1

    pbar.close()
    return PredictedData, nframes 

# 需要導入模塊: from skimage import util [as 別名]
# 或者: from skimage.util import img_as_ubyte [as 別名]
def update(self):
        """
        Updates the image with the current slider index
        """
        self.grab.Enable(True)
        self.grab.Bind(wx.EVT_BUTTON, self.grabFrame)
        self.figure, self.axes, self.canvas = self.image_panel.getfigure()
        self.vid.set(1, self.currFrame)
        ret, frame = self.vid.read()
        frame = img_as_ubyte(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        if ret:
            if self.cropping:
                self.coords = (
                    self.cfg["x1"],
                    self.cfg["x2"],
                    self.cfg["y1"],
                    self.cfg["y2"],
                )
                frame = frame[
                    int(self.coords[2]) : int(self.coords[3]),
                    int(self.coords[0]) : int(self.coords[1]),
                    :,
                ]
            else:
                self.coords = None
            self.ax = self.axes.imshow(frame, cmap=self.colormap)
            self.axes.set_title(
                str(
                    str(self.currFrame)
                    + "/"
                    + str(self.numberFrames - 1)
                    + " "
                    + self.filename
                )
            )
            self.figure.canvas.draw()
        else:
            print("Invalid frame")