tensorlayer/prepro.py
def keypoint_random_rotate(image, annos, mask=None, rg=15.):
    """Rotate an image and corresponding keypoints.

    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.
    rg : int or float
        Degree to rotate, usually 0 ~ 180.

    Returns
    ----------
    preprocessed image, annos, mask

    """

    def _rotate_coord(shape, newxy, point, angle):
        angle = -1 * angle / 180.0 * math.pi
        ox, oy = shape
        px, py = point
        ox /= 2
        oy /= 2
        qx = math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)
        qy = math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)
        new_x, new_y = newxy
        qx += ox - new_x
        qy += oy - new_y
        return int(qx + 0.5), int(qy + 0.5)

    def _largest_rotated_rect(w, h, angle):
        """
        Get largest rectangle after rotation.
        http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
        """
        angle = angle / 180.0 * math.pi
        if w <= 0 or h <= 0:
            return 0, 0

        width_is_longer = w >= h
        side_long, side_short = (w, h) if width_is_longer else (h, w)

        # since the solutions for angle, -angle and 180-angle are all the same,
        # if suffices to look at the first quadrant and the absolute values of sin,cos:
        sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
        if side_short <= 2. * sin_a * cos_a * side_long:
            # half constrained case: two crop corners touch the longer side,
            #   the other two corners are on the mid-line parallel to the longer line
            x = 0.5 * side_short
            wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
        else:
            # fully constrained case: crop touches all 4 sides
            cos_2a = cos_a * cos_a - sin_a * sin_a
            wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
        return int(np.round(wr)), int(np.round(hr))

    img_shape = np.shape(image)
    height = img_shape[0]
    width = img_shape[1]
    deg = np.random.uniform(-rg, rg)

    img = image
    center = (img.shape[1] * 0.5, img.shape[0] * 0.5)  # x, y
    rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1)
    ret = cv2.warpAffine(img, rot_m, img.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT)
    if img.ndim == 3 and ret.ndim == 2:
        ret = ret[:, :, np.newaxis]
    neww, newh = _largest_rotated_rect(ret.shape[1], ret.shape[0], deg)
    neww = min(neww, ret.shape[1])
    newh = min(newh, ret.shape[0])
    newx = int(center[0] - neww * 0.5)
    newy = int(center[1] - newh * 0.5)
    # print(ret.shape, deg, newx, newy, neww, newh)
    img = ret[newy:newy + newh, newx:newx + neww]
    # adjust meta data
    adjust_joint_list = []
    for joint in annos:  # TODO : speed up with affine transform
        adjust_joint = []
        for point in joint:
            if point[0] < -100 or point[1] < -100:
                adjust_joint.append((-1000, -1000))
                continue

            x, y = _rotate_coord((width, height), (newx, newy), point, deg)

            if x > neww - 1 or y > newh - 1:
                adjust_joint.append((-1000, -1000))
                continue
            if x < 0 or y < 0:
                adjust_joint.append((-1000, -1000))
                continue

            adjust_joint.append((x, y))
        adjust_joint_list.append(adjust_joint)
    joint_list = adjust_joint_list

    if mask is not None:
        msk = mask
        center = (msk.shape[1] * 0.5, msk.shape[0] * 0.5)  # x, y
        rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1)
        ret = cv2.warpAffine(msk, rot_m, msk.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT)
        if msk.ndim == 3 and msk.ndim == 2:
            ret = ret[:, :, np.newaxis]
        neww, newh = _largest_rotated_rect(ret.shape[1], ret.shape[0], deg)
        neww = min(neww, ret.shape[1])
        newh = min(newh, ret.shape[0])
        newx = int(center[0] - neww * 0.5)
        newy = int(center[1] - newh * 0.5)
        # print(ret.shape, deg, newx, newy, neww, newh)
        msk = ret[newy:newy + newh, newx:newx + neww]
        return img, joint_list, msk
    else:
        return img, joint_list, None
def keypoint_random_rotate(image, annos, mask=None, rg=15.):
    """Rotate an image and corresponding keypoints.

    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.
    rg : int or float
        Degree to rotate, usually 0 ~ 180.

    Returns
    ----------
    preprocessed image, annos, mask

    """

    def _rotate_coord(shape, newxy, point, angle):
        angle = -1 * angle / 180.0 * math.pi
        ox, oy = shape
        px, py = point
        ox /= 2
        oy /= 2
        qx = math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)
        qy = math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)
        new_x, new_y = newxy
        qx += ox - new_x
        qy += oy - new_y
        return int(qx + 0.5), int(qy + 0.5)

    def _largest_rotated_rect(w, h, angle):
        """
        Get largest rectangle after rotation.
        http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
        """
        angle = angle / 180.0 * math.pi
        if w <= 0 or h <= 0:
            return 0, 0

        width_is_longer = w >= h
        side_long, side_short = (w, h) if width_is_longer else (h, w)

        # since the solutions for angle, -angle and 180-angle are all the same,
        # if suffices to look at the first quadrant and the absolute values of sin,cos:
        sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
        if side_short <= 2. * sin_a * cos_a * side_long:
            # half constrained case: two crop corners touch the longer side,
            #   the other two corners are on the mid-line parallel to the longer line
            x = 0.5 * side_short
            wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
        else:
            # fully constrained case: crop touches all 4 sides
            cos_2a = cos_a * cos_a - sin_a * sin_a
            wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
        return int(np.round(wr)), int(np.round(hr))

    img_shape = np.shape(image)
    height = img_shape[0]
    width = img_shape[1]
    deg = np.random.uniform(-rg, rg)

    img = image
    center = (img.shape[1] * 0.5, img.shape[0] * 0.5)  # x, y
    rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1)
    ret = cv2.warpAffine(img, rot_m, img.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT)
    if img.ndim == 3 and ret.ndim == 2:
        ret = ret[:, :, np.newaxis]
    neww, newh = _largest_rotated_rect(ret.shape[1], ret.shape[0], deg)
    neww = min(neww, ret.shape[1])
    newh = min(newh, ret.shape[0])
    newx = int(center[0] - neww * 0.5)
    newy = int(center[1] - newh * 0.5)
    # print(ret.shape, deg, newx, newy, neww, newh)
    img = ret[newy:newy + newh, newx:newx + neww]
    # adjust meta data
    adjust_joint_list = []
    for joint in annos:  # TODO : speed up with affine transform
        adjust_joint = []
        for point in joint:
            if point[0] < -100 or point[1] < -100:
                adjust_joint.append((-1000, -1000))
                continue

            x, y = _rotate_coord((width, height), (newx, newy), point, deg)

            if x > neww - 1 or y > newh - 1:
                adjust_joint.append((-1000, -1000))
                continue
            if x < 0 or y < 0:
                adjust_joint.append((-1000, -1000))
                continue

            adjust_joint.append((x, y))
        adjust_joint_list.append(adjust_joint)
    joint_list = adjust_joint_list

    if mask is not None:
        msk = mask
        center = (msk.shape[1] * 0.5, msk.shape[0] * 0.5)  # x, y
        rot_m = cv2.getRotationMatrix2D((int(center[0]), int(center[1])), deg, 1)
        ret = cv2.warpAffine(msk, rot_m, msk.shape[1::-1], flags=cv2.INTER_AREA, borderMode=cv2.BORDER_CONSTANT)
        if msk.ndim == 3 and msk.ndim == 2:
            ret = ret[:, :, np.newaxis]
        neww, newh = _largest_rotated_rect(ret.shape[1], ret.shape[0], deg)
        neww = min(neww, ret.shape[1])
        newh = min(newh, ret.shape[0])
        newx = int(center[0] - neww * 0.5)
        newy = int(center[1] - newh * 0.5)
        # print(ret.shape, deg, newx, newy, neww, newh)
        msk = ret[newy:newy + newh, newx:newx + neww]
        return img, joint_list, msk
    else:
        return img, joint_list, None
def keypoint_random_rotate ( image , annos , mask = None , rg = 15. ) : def _rotate_coord ( shape , newxy , point , angle ) : angle = - 1 * angle / 180.0 * math . pi ox , oy = shape px , py = point ox /= 2 oy /= 2 qx = math . cos ( angle ) * ( px - ox ) - math . sin ( angle ) * ( py - oy ) qy = math . sin ( angle ) * ( px - ox ) + math . cos ( angle ) * ( py - oy ) new_x , new_y = newxy qx += ox - new_x qy += oy - new_y return int ( qx + 0.5 ) , int ( qy + 0.5 ) def _largest_rotated_rect ( w , h , angle ) : """
        Get largest rectangle after rotation.
        http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
        """ angle = angle / 180.0 * math . pi if w <= 0 or h <= 0 : return 0 , 0 width_is_longer = w >= h side_long , side_short = ( w , h ) if width_is_longer else ( h , w ) # since the solutions for angle, -angle and 180-angle are all the same, # if suffices to look at the first quadrant and the absolute values of sin,cos: sin_a , cos_a = abs ( math . sin ( angle ) ) , abs ( math . cos ( angle ) ) if side_short <= 2. * sin_a * cos_a * side_long : # half constrained case: two crop corners touch the longer side, #   the other two corners are on the mid-line parallel to the longer line x = 0.5 * side_short wr , hr = ( x / sin_a , x / cos_a ) if width_is_longer else ( x / cos_a , x / sin_a ) else : # fully constrained case: crop touches all 4 sides cos_2a = cos_a * cos_a - sin_a * sin_a wr , hr = ( w * cos_a - h * sin_a ) / cos_2a , ( h * cos_a - w * sin_a ) / cos_2a return int ( np . round ( wr ) ) , int ( np . round ( hr ) ) img_shape = np . shape ( image ) height = img_shape [ 0 ] width = img_shape [ 1 ] deg = np . random . uniform ( - rg , rg ) img = image center = ( img . shape [ 1 ] * 0.5 , img . shape [ 0 ] * 0.5 ) # x, y rot_m = cv2 . getRotationMatrix2D ( ( int ( center [ 0 ] ) , int ( center [ 1 ] ) ) , deg , 1 ) ret = cv2 . warpAffine ( img , rot_m , img . shape [ 1 : : - 1 ] , flags = cv2 . INTER_AREA , borderMode = cv2 . BORDER_CONSTANT ) if img . ndim == 3 and ret . ndim == 2 : ret = ret [ : , : , np . newaxis ] neww , newh = _largest_rotated_rect ( ret . shape [ 1 ] , ret . shape [ 0 ] , deg ) neww = min ( neww , ret . shape [ 1 ] ) newh = min ( newh , ret . shape [ 0 ] ) newx = int ( center [ 0 ] - neww * 0.5 ) newy = int ( center [ 1 ] - newh * 0.5 ) # print(ret.shape, deg, newx, newy, neww, newh) img = ret [ newy : newy + newh , newx : newx + neww ] # adjust meta data adjust_joint_list = [ ] for joint in annos : # TODO : speed up with affine transform adjust_joint = [ ] for point in joint : if point [ 0 ] < - 100 or point [ 1 ] < - 100 : adjust_joint . append ( ( - 1000 , - 1000 ) ) continue x , y = _rotate_coord ( ( width , height ) , ( newx , newy ) , point , deg ) if x > neww - 1 or y > newh - 1 : adjust_joint . append ( ( - 1000 , - 1000 ) ) continue if x < 0 or y < 0 : adjust_joint . append ( ( - 1000 , - 1000 ) ) continue adjust_joint . append ( ( x , y ) ) adjust_joint_list . append ( adjust_joint ) joint_list = adjust_joint_list if mask is not None : msk = mask center = ( msk . shape [ 1 ] * 0.5 , msk . shape [ 0 ] * 0.5 ) # x, y rot_m = cv2 . getRotationMatrix2D ( ( int ( center [ 0 ] ) , int ( center [ 1 ] ) ) , deg , 1 ) ret = cv2 . warpAffine ( msk , rot_m , msk . shape [ 1 : : - 1 ] , flags = cv2 . INTER_AREA , borderMode = cv2 . BORDER_CONSTANT ) if msk . ndim == 3 and msk . ndim == 2 : ret = ret [ : , : , np . newaxis ] neww , newh = _largest_rotated_rect ( ret . shape [ 1 ] , ret . shape [ 0 ] , deg ) neww = min ( neww , ret . shape [ 1 ] ) newh = min ( newh , ret . shape [ 0 ] ) newx = int ( center [ 0 ] - neww * 0.5 ) newy = int ( center [ 1 ] - newh * 0.5 ) # print(ret.shape, deg, newx, newy, neww, newh) msk = ret [ newy : newy + newh , newx : newx + neww ] return img , joint_list , msk else : return img , joint_list , None
Rotate an image and corresponding keypoints.

    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.
    rg : int or float
        Degree to rotate, usually 0 ~ 180.

    Returns
    ----------
    preprocessed image, annos, mask
Rotate an image and corresponding keypoints .