tensorlayer/prepro.py
def keypoint_random_crop(image, annos, mask=None, size=(368, 368)):
    """Randomly crop an image and corresponding keypoints without influence scales, given by ``keypoint_random_resize_shortestedge``.

    Parameters
    -----------
    image : 3 channel image
        The given image for augmentation.
    annos : list of list of floats
        The keypoints annotation of people.
    mask : single channel image or None
        The mask if available.
    size : tuple of int
        The size of returned image.

    Returns
    ----------
    preprocessed image, annotation, mask

    """

    _target_height = size[0]
    _target_width = size[1]
    target_size = (_target_width, _target_height)

    if len(np.shape(image)) == 2:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    height, width, _ = np.shape(image)

    for _ in range(50):
        x = random.randrange(0, width - target_size[0]) if width > target_size[0] else 0
        y = random.randrange(0, height - target_size[1]) if height > target_size[1] else 0

        # check whether any face is inside the box to generate a reasonably-balanced datasets
        for joint in annos:
            if x <= joint[0][0] < x + target_size[0] and y <= joint[0][1] < y + target_size[1]:
                break

    def pose_crop(image, annos, mask, x, y, w, h):  # TODO : speed up with affine transform
        # adjust image
        target_size = (w, h)

        img = image
        resized = img[y:y + target_size[1], x:x + target_size[0], :]
        resized_mask = mask[y:y + target_size[1], x:x + target_size[0]]
        # adjust meta data
        adjust_joint_list = []
        for joint in annos:
            adjust_joint = []
            for point in joint:
                if point[0] < -10 or point[1] < -10:
                    adjust_joint.append((-1000, -1000))
                    continue
                new_x, new_y = point[0] - x, point[1] - y
                # should not crop outside the image
                if new_x > w - 1 or new_y > h - 1:
                    adjust_joint.append((-1000, -1000))
                    continue
                adjust_joint.append((new_x, new_y))
            adjust_joint_list.append(adjust_joint)

        return resized, adjust_joint_list, resized_mask

    return pose_crop(image, annos, mask, x, y, target_size[0], target_size[1])
def keypoint_random_crop(image, annos, mask=None, size=(368, 368)):
    """Randomly crop an image and corresponding keypoints without influence scales, given by ``keypoint_random_resize_shortestedge``.

    Parameters
    -----------
    image : 3 channel image
        The given image for augmentation.
    annos : list of list of floats
        The keypoints annotation of people.
    mask : single channel image or None
        The mask if available.
    size : tuple of int
        The size of returned image.

    Returns
    ----------
    preprocessed image, annotation, mask

    """

    _target_height = size[0]
    _target_width = size[1]
    target_size = (_target_width, _target_height)

    if len(np.shape(image)) == 2:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    height, width, _ = np.shape(image)

    for _ in range(50):
        x = random.randrange(0, width - target_size[0]) if width > target_size[0] else 0
        y = random.randrange(0, height - target_size[1]) if height > target_size[1] else 0

        # check whether any face is inside the box to generate a reasonably-balanced datasets
        for joint in annos:
            if x <= joint[0][0] < x + target_size[0] and y <= joint[0][1] < y + target_size[1]:
                break

    def pose_crop(image, annos, mask, x, y, w, h):  # TODO : speed up with affine transform
        # adjust image
        target_size = (w, h)

        img = image
        resized = img[y:y + target_size[1], x:x + target_size[0], :]
        resized_mask = mask[y:y + target_size[1], x:x + target_size[0]]
        # adjust meta data
        adjust_joint_list = []
        for joint in annos:
            adjust_joint = []
            for point in joint:
                if point[0] < -10 or point[1] < -10:
                    adjust_joint.append((-1000, -1000))
                    continue
                new_x, new_y = point[0] - x, point[1] - y
                # should not crop outside the image
                if new_x > w - 1 or new_y > h - 1:
                    adjust_joint.append((-1000, -1000))
                    continue
                adjust_joint.append((new_x, new_y))
            adjust_joint_list.append(adjust_joint)

        return resized, adjust_joint_list, resized_mask

    return pose_crop(image, annos, mask, x, y, target_size[0], target_size[1])
def keypoint_random_crop ( image , annos , mask = None , size = ( 368 , 368 ) ) : _target_height = size [ 0 ] _target_width = size [ 1 ] target_size = ( _target_width , _target_height ) if len ( np . shape ( image ) ) == 2 : image = cv2 . cvtColor ( image , cv2 . COLOR_GRAY2RGB ) height , width , _ = np . shape ( image ) for _ in range ( 50 ) : x = random . randrange ( 0 , width - target_size [ 0 ] ) if width > target_size [ 0 ] else 0 y = random . randrange ( 0 , height - target_size [ 1 ] ) if height > target_size [ 1 ] else 0 # check whether any face is inside the box to generate a reasonably-balanced datasets for joint in annos : if x <= joint [ 0 ] [ 0 ] < x + target_size [ 0 ] and y <= joint [ 0 ] [ 1 ] < y + target_size [ 1 ] : break def pose_crop ( image , annos , mask , x , y , w , h ) : # TODO : speed up with affine transform # adjust image target_size = ( w , h ) img = image resized = img [ y : y + target_size [ 1 ] , x : x + target_size [ 0 ] , : ] resized_mask = mask [ y : y + target_size [ 1 ] , x : x + target_size [ 0 ] ] # adjust meta data adjust_joint_list = [ ] for joint in annos : adjust_joint = [ ] for point in joint : if point [ 0 ] < - 10 or point [ 1 ] < - 10 : adjust_joint . append ( ( - 1000 , - 1000 ) ) continue new_x , new_y = point [ 0 ] - x , point [ 1 ] - y # should not crop outside the image if new_x > w - 1 or new_y > h - 1 : adjust_joint . append ( ( - 1000 , - 1000 ) ) continue adjust_joint . append ( ( new_x , new_y ) ) adjust_joint_list . append ( adjust_joint ) return resized , adjust_joint_list , resized_mask return pose_crop ( image , annos , mask , x , y , target_size [ 0 ] , target_size [ 1 ] )
Randomly crop an image and corresponding keypoints without influence scales, given by ``keypoint_random_resize_shortestedge``.

    Parameters
    -----------
    image : 3 channel image
        The given image for augmentation.
    annos : list of list of floats
        The keypoints annotation of people.
    mask : single channel image or None
        The mask if available.
    size : tuple of int
        The size of returned image.

    Returns
    ----------
    preprocessed image, annotation, mask
Randomly crop an image and corresponding keypoints without influence scales given by keypoint_random_resize_shortestedge .