ImageProvider

plantimager.controller.ImageProvider

ImageProvider

ImageProvider()

Bases: QQuickImageProvider

Provides images from memory to QML

Source code in plantimager/controller/ImageProvider.py

def __init__(self):
    super().__init__(QQmlImageProviderBase.ImageType.Image)
    self.images: dict[str, QImage] = {}

compute_focus

compute_focus(image)

Computes the focus measure of an image using the Sobel filter.

This function calculates the gradient magnitude of an input image in the x and y directions, combining the results to estimate focus. The computed focus measure highlights edges and areas of high intensity change, which are often indicative of regions in focus.

see https://blog.roboflow.com/computer-vision-camera-focus-guide/#tenengrad-function

Parameters:

Name	Type	Description	Default
image	ndarray	A 2D array representing the input grayscale image whose focus measure is to be computed.	required

Returns:

Type	Description
ndarray	A 2D array representing the gradient magnitude of the input image, where higher values correspond to more focused regions.

Source code in plantimager/controller/ImageProvider.py

def compute_focus(image: np.ndarray, /,):
    """
    Computes the focus measure of an image using the Sobel filter.

    This function calculates the gradient magnitude of an input image in the
    x and y directions, combining the results to estimate focus. The computed
    focus measure highlights edges and areas of high intensity change, which
    are often indicative of regions in focus.

    see https://blog.roboflow.com/computer-vision-camera-focus-guide/#tenengrad-function

    Parameters
    ----------
    image : np.ndarray
        A 2D array representing the input grayscale image whose focus measure is to be
        computed.

    Returns
    -------
    np.ndarray
        A 2D array representing the gradient magnitude of the input image,
        where higher values correspond to more focused regions.
    """
    downscaled = skimage.transform.downscale_local_mean(image, 2).astype(np.uint8)
    grad_x = skimage.filters.sobel(downscaled, axis=0)
    grad_y = skimage.filters.sobel(downscaled, axis=1)
    #plt_imshow(downscaled, title="downscaled")
    #plt_imshow(grad_x, title="grad_x", vmin=grad_x.min(), vmax=grad_x.max())
    #plt_imshow(grad_y, title="grad_y", vmin=grad_y.min(), vmax=grad_y.max())
    magnitude = np.sqrt(grad_x**2 + grad_y**2)
    magnitude = np.astype((magnitude-magnitude.min())/magnitude.max()*255, np.uint8)
    #smoothed = scipy.ndimage.gaussian_filter(magnitude, sigma=1)
    #plt_imshow(smoothed, title="smoothed")
    #magnitude = scipy.ndimage.grey_opening(magnitude, size=(3,3))
    #plt_imshow(magnitude, title="magnitude after opening")
    #plt_imshow(np.floor(magnitude/magnitude.max()*255))
    return skimage.transform.resize(magnitude, image.shape, anti_aliasing=False, mode='constant')

focus_highlight

focus_highlight(image, percentile)

Enhances the focus areas of an image by adding a red highlight to regions of the image that have a focus value greater than a specified percentile.

Parameters:

Name	Type	Description	Default
image	QImage	The input image to be processed. Must be in the QImage format.	required
percentile	float	The percentile threshold for selecting focus areas. Focus values above this percentile will be highlighted.	required

Returns:

Type	Description
QImage	A new image with highlighted focus areas in red.

Source code in plantimager/controller/ImageProvider.py

def focus_highlight(image: QImage, percentile: float) -> QImage:
    """
    Enhances the focus areas of an image by adding a red highlight to regions of
    the image that have a focus value greater than a specified percentile.

    Parameters
    ----------
    image : QImage
        The input image to be processed. Must be in the QImage format.
    percentile : float
        The percentile threshold for selecting focus areas. Focus values above
        this percentile will be highlighted.

    Returns
    -------
    QImage
        A new image with highlighted focus areas in red.
    """
    image_array = qimage_to_ndarray_rgb(image)
    image_grayscale = qimage_to_ndarray_gray(image)
    focus_array = compute_focus(image_grayscale)
    focus_array = np.astype((focus_array-focus_array.min())/focus_array.max()*255, np.uint8)
    #plt_imshow(focus_array, vmin=focus_array.min(), vmax=focus_array.max())
    highlight_mask = focus_array > np.percentile(focus_array.astype(np.float32), percentile, overwrite_input=True)
    highlight_image = image_array.copy()
    highlight_image[:, :, :] = focus_array[:, :, None]
    new_mask = np.zeros_like(highlight_image, dtype=bool)
    #new_mask[:,:,1] = highlight_mask
    new_mask[:,:,0] = highlight_mask
    #highlight_image[highlight_mask] = 255 - highlight_image[highlight_mask]
    #highlight_image[new_mask] = 255
    #plt_imshow(255*highlight_mask.astype(np.uint8))
    #plt_imshow(highlight_image)
    return ndarray_to_qimage(
        highlight_image,
        QImage.Format.Format_RGB888,
    )