Python transform.SimilarityTransform方法代码示例

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def estimate_norm(lmk, image_size = 112, mode='arcface'):
  assert lmk.shape==(5,2)
  tform = trans.SimilarityTransform()
  lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
  min_M = []
  min_index = []
  min_error = float('inf') 
  if mode=='arcface':
    assert image_size==112
    src = arcface_src
  else:
    src = src_map[image_size]
  for i in np.arange(src.shape[0]):
    tform.estimate(lmk, src[i])
    M = tform.params[0:2,:]
    results = np.dot(M, lmk_tran.T)
    results = results.T
    error = np.sum(np.sqrt(np.sum((results - src[i]) ** 2,axis=1)))
#         print(error)
    if error< min_error:
        min_error = error
        min_M = M
        min_index = i
  return min_M, min_index 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def distort_affine_skimage(image, rotation=10.0, shear=5.0, random_state=None):
    if random_state is None:
        random_state = np.random.RandomState(None)

    rot = np.deg2rad(np.random.uniform(-rotation, rotation))
    sheer = np.deg2rad(np.random.uniform(-shear, shear))

    shape = image.shape
    shape_size = shape[:2]
    center = np.float32(shape_size) / 2. - 0.5

    pre = transform.SimilarityTransform(translation=-center)
    affine = transform.AffineTransform(rotation=rot, shear=sheer, translation=center)
    tform = pre + affine

    distorted_image = transform.warp(image, tform.params, mode='reflect')

    return distorted_image.astype(np.float32) 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def function(self, x, y):

        signal2D = self.signal.data
        order = self.order
        d11 = self.d11.value
        d12 = self.d12.value
        d21 = self.d21.value
        d22 = self.d22.value
        t1 = self.t1.value
        t2 = self.t2.value

        D = np.array([[d11, d12, t1], [d21, d22, t2], [0.0, 0.0, 1.0]])

        shifty, shiftx = np.array(signal2D.shape[:2]) / 2

        shift = tf.SimilarityTransform(translation=[-shiftx, -shifty])
        tform = tf.AffineTransform(matrix=D)
        shift_inv = tf.SimilarityTransform(translation=[shiftx, shifty])

        transformed = tf.warp(
            signal2D, (shift + (tform + shift_inv)).inverse, order=order
        )

        return transformed 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def scale_mask(mask, factor=1.05):
  nzy, nzx, _ = mask.nonzero()
  if nzy.size == 0:
    return mask
  #center_y, center_x = nzy.mean(), nzx.mean()
  #print center_y, center_x
  center_y, center_x = (nzy.max() + nzy.min()) / 2, (nzx.max() + nzx.min()) / 2
  #print center_y, center_x

  shift_ = SimilarityTransform(translation=[-center_x, -center_y])
  shift_inv = SimilarityTransform(translation=[center_x, center_y])

  A = SimilarityTransform(scale=(factor, factor))
  mask_out = warp(mask, (shift_ + (A + shift_inv)).inverse)
  mask_out = (mask_out > 0.5).astype("float32")
  #import matplotlib.pyplot as plt
  #im = numpy.concatenate([mask, mask, mask_out],axis=2)
  #plt.imshow(im)
  #plt.show()
  return mask_out 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def estimate_norm(self, lmk):
        assert lmk.shape == (5, 2)
        tform = trans.SimilarityTransform()
        lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
        min_M = []
        min_index = []
        min_error = float('inf')
        src = self.arcface_src

        for i in np.arange(src.shape[0]):
            tform.estimate(lmk, src[i])
            M = tform.params[0:2, :]
            results = np.dot(M, lmk_tran.T)
            results = results.T
            error = np.sum(np.sqrt(np.sum((results - src[i]) ** 2, axis=1)))
            #         print(error)
            if error < min_error:
                min_error = error
                min_M = M
                min_index = i
        return min_M, min_index 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def _scale_mask(mask, scale_amount=0.025):
  """Damages a mask for a single object by randomly scaling it in numpy.

  Args:
    mask: Boolean numpy array of shape(height, width, 1).
    scale_amount: Float scalar, the maximum factor for random scaling.

  Returns:
    The scaled version of mask.
  """
  nzy, nzx, _ = mask.nonzero()
  cy = 0.5 * (nzy.max() - nzy.min())
  cx = 0.5 * (nzx.max() - nzx.min())
  scale_factor = np.random.uniform(1.0 - scale_amount, 1.0 + scale_amount)
  shift = transform.SimilarityTransform(translation=[-cx, -cy])
  inv_shift = transform.SimilarityTransform(translation=[cx, cy])
  s = transform.SimilarityTransform(scale=[scale_factor, scale_factor])
  m = (shift + (s + inv_shift)).inverse
  scaled_mask = transform.warp(mask, m) > 0.5
  return scaled_mask 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def _rotate_mask(mask, max_rot_degrees=3.0):
  """Damages a mask for a single object by randomly rotating it in numpy.

  Args:
    mask: Boolean numpy array of shape(height, width, 1).
    max_rot_degrees: Float scalar, the maximum number of degrees to rotate.

  Returns:
    The scaled version of mask.
  """
  cy = 0.5 * mask.shape[0]
  cx = 0.5 * mask.shape[1]
  rot_degrees = np.random.uniform(-max_rot_degrees, max_rot_degrees)
  shift = transform.SimilarityTransform(translation=[-cx, -cy])
  inv_shift = transform.SimilarityTransform(translation=[cx, cy])
  r = transform.SimilarityTransform(rotation=np.deg2rad(rot_degrees))
  m = (shift + (r + inv_shift)).inverse
  scaled_mask = transform.warp(mask, m) > 0.5
  return scaled_mask 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def transform(data, center, output_size, scale, rotation):
    scale_ratio = float(output_size)/scale
    rot = float(rotation)*np.pi/180.0
    #translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
    t1 = stf.SimilarityTransform(scale=scale_ratio)
    cx = center[0]*scale_ratio
    cy = center[1]*scale_ratio
    t2 = stf.SimilarityTransform(translation=(-1*cx, -1*cy))
    t3 = stf.SimilarityTransform(rotation=rot)
    t4 = stf.SimilarityTransform(translation=(output_size/2, output_size/2))
    t = t1+t2+t3+t4
    trans = t.params[0:2]
    #print('M', scale, rotation, trans)
    cropped = cv2.warpAffine(data,trans,(output_size, output_size), borderValue = 0.0)
    return cropped, trans 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def test_register_nddata(self):
        from astropy.nddata import NDData
        from skimage.transform import SimilarityTransform

        transf = SimilarityTransform(rotation=np.pi / 2.0, translation=(1, 0))

        nd = NDData(
            [[0.0, 1.0], [2.0, 3.0]], mask=[[True, False], [False, False]]
        )
        registered_img, footp = aa.apply_transform(
            transf, nd, nd, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, True], [False, False]])
        self.assertTrue(all(err_mask.flatten()))

        # Test now if there is no assigned mask during creation
        nd = NDData([[0.0, 1.0], [2.0, 3.0]])
        registered_img, footp = aa.apply_transform(
            transf, nd, nd, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, False], [False, False]])
        self.assertTrue(all(err_mask.flatten())) 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def test_register_ccddata(self):
        from ccdproc import CCDData
        from skimage.transform import SimilarityTransform

        transf = SimilarityTransform(rotation=np.pi / 2.0, translation=(1, 0))

        cd = CCDData(
            [[0.0, 1.0], [2.0, 3.0]],
            mask=[[True, False], [False, False]],
            unit="adu",
        )
        registered_img, footp = aa.apply_transform(
            transf, cd, cd, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, True], [False, False]])
        self.assertTrue(all(err_mask.flatten()))

        cd = CCDData([[0.0, 1.0], [2.0, 3.0]], unit="adu")
        registered_img, footp = aa.apply_transform(
            transf, cd, cd, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, False], [False, False]])
        self.assertTrue(all(err_mask.flatten())) 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def test_register_npma(self):
        from skimage.transform import SimilarityTransform

        transf = SimilarityTransform(rotation=np.pi / 2.0, translation=(1, 0))
        nparr = np.array([[0.0, 1.0], [2.0, 3.0]])
        mask = [[True, False], [False, False]]

        ma = np.ma.array(nparr, mask=mask)
        registered_img, footp = aa.apply_transform(
            transf, ma, ma, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, True], [False, False]])
        self.assertTrue(all(err_mask.flatten()))

        ma = np.ma.array(nparr)
        registered_img, footp = aa.apply_transform(
            transf, ma, ma, propagate_mask=True
        )
        err = np.linalg.norm(
            registered_img - np.array([[2.0, 0.0], [3.0, 1.0]])
        )
        self.assertLess(err, 1e-6)
        err_mask = footp == np.array([[False, False], [False, False]])
        self.assertTrue(all(err_mask.flatten())) 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def applyGeometricTransformation(startXs, startYs, newXs, newYs, bbox):
    n_object = bbox.shape[0]
    newbbox = np.zeros_like(bbox)
    Xs = newXs.copy()
    Ys = newYs.copy()
    for obj_idx in range(n_object):
        startXs_obj = startXs[:,[obj_idx]]
        startYs_obj = startYs[:,[obj_idx]]
        newXs_obj = newXs[:,[obj_idx]]
        newYs_obj = newYs[:,[obj_idx]]
        desired_points = np.hstack((startXs_obj,startYs_obj))
        actual_points = np.hstack((newXs_obj,newYs_obj))
        t = tf.SimilarityTransform()
        t.estimate(dst=actual_points, src=desired_points)
        mat = t.params

        # estimate the new bounding box with all the feature points
        # coords = np.vstack((bbox[obj_idx,:,:].T,np.array([1,1,1,1])))
        # new_coords = mat.dot(coords)
        # newbbox[obj_idx,:,:] = new_coords[0:2,:].T

        # estimate the new bounding box with only the inliners (Added by Yongyi Wang)
        THRES = 1
        projected = mat.dot(np.vstack((desired_points.T.astype(float),np.ones([1,np.shape(desired_points)[0]]))))
        distance = np.square(projected[0:2,:].T - actual_points).sum(axis = 1)
        actual_inliers = actual_points[distance < THRES]
        desired_inliers = desired_points[distance < THRES]
        if np.shape(desired_inliers)[0]<4:
            print('too few points')
            actual_inliers = actual_points
            desired_inliers = desired_points
        t.estimate(dst=actual_inliers, src=desired_inliers)
        mat = t.params
        coords = np.vstack((bbox[obj_idx,:,:].T,np.array([1,1,1,1])))
        new_coords = mat.dot(coords)
        newbbox[obj_idx,:,:] = new_coords[0:2,:].T
        Xs[distance >= THRES, obj_idx] = -1
        Ys[distance >= THRES, obj_idx] = -1

    return Xs, Ys, newbbox 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def _compensate_rotation_shift(self, img, scale):
        """This is an auxiliary method used by extract_from_image.
        It is needed due to particular specifics of the skimage.transform.rotate implementation.
        Namely, when you use rotate(... , resize=True), the rotated image is rotated and shifted by certain amount.
        Thus when we need to cut out the box from the image, we need to account for this shift.
        We do this by repeating the computation from skimage.transform.rotate here.

        TODO: This makes the code uncomfortably coupled to SKImage (e.g. this logic is appropriate for skimage 0.12.1, but not for 0.11,
        and no one knows what happens in later versions). A solution would be to use skimage.transform.warp with custom settings, but we can think of it later.
        """
        ctr = np.asarray([self.center[1]*scale, self.center[0]*scale])
        tform1 = transform.SimilarityTransform(translation=ctr)
        tform2 = transform.SimilarityTransform(rotation=np.pi/2 - self.angle)
        tform3 = transform.SimilarityTransform(translation=-ctr)
        tform = tform3 + tform2 + tform1

        rows, cols = img.shape[0], img.shape[1]
        corners = np.array([
            [0, 0],
            [0, rows - 1],
            [cols - 1, rows - 1],
            [cols - 1, 0]
        ])
        corners = tform.inverse(corners)
        minc = corners[:, 0].min()
        minr = corners[:, 1].min()
        maxc = corners[:, 0].max()
        maxr = corners[:, 1].max()

        # SKImage 0.11 version
        out_rows = maxr - minr + 1
        out_cols = maxc - minc + 1

        # fit output image in new shape
        return ((cols - out_cols) / 2., (rows - out_rows) / 2.) 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def get_head_crop(img, pt1, pt2):
    im = img.copy()
    minh = 10
    minw = 20

    x = pt1[0] - pt2[0]
    y = pt1[1] - pt2[1]
    dist = math.hypot(x, y)
    croph = int((im.shape[0] - 1.0 * dist) // 2)
    cropw = int((im.shape[1] - 2.0 * dist) // 2)
    newh = im.shape[0] - 2 * croph
    neww = im.shape[1] - 2 * cropw

    if croph <= 0 or cropw <= 0 or newh < minh or neww < minw:
        return im
    else:
        angle = math.atan2(y, x) * 180 / math.pi
        centery = 0.4 * pt1[1] + 0.6 * pt2[1]
        centerx = 0.4 * pt1[0] + 0.6 * pt2[0]
        center = (centerx, centery)
        im = rotate(im, angle, resize=False, center=center)
        imcenter = (im.shape[1] / 2, im.shape[0] / 2)
        trans = (center[0] - imcenter[0], center[1] - imcenter[1])
        tform = SimilarityTransform(translation=trans)
        im = warp(im, tform)
        im = im[croph:-croph, cropw:-cropw]
        return im 

# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import SimilarityTransform [as 别名]
def img_augment(img, translation=0.0, scale=1.0, rotation=0.0, gamma=1.0,
                contrast=1.0, hue=0.0, border_mode='constant'):
    if not (np.all(np.isclose(translation, [0.0, 0.0])) and
            np.isclose(scale, 1.0) and
            np.isclose(rotation, 0.0)):
        img_center = np.array(img.shape[:2]) / 2.0
        scale = (scale, scale)
        transf = transform.SimilarityTransform(translation=-img_center)
        transf += transform.SimilarityTransform(scale=scale, rotation=rotation)
        translation = img_center + translation
        transf += transform.SimilarityTransform(translation=translation)
        img = transform.warp(img, transf, order=3, mode=border_mode)
    if not np.isclose(gamma, 1.0):
        img **= gamma
    colorspace = 'rgb'
    if not np.isclose(contrast, 1.0):
        img = color.convert_colorspace(img, colorspace, 'hsv')
        colorspace = 'hsv'
        img[..., 1:] **= contrast
    if not np.isclose(hue, 0.0):
        img = color.convert_colorspace(img, colorspace, 'hsv')
        colorspace = 'hsv'
        img[..., 0] += hue
        img[img[..., 0] > 1.0, 0] -= 1.0
        img[img[..., 0] < 0.0, 0] += 1.0
    img = color.convert_colorspace(img, colorspace, 'rgb')
    if np.min(img) < 0.0 or np.max(img) > 1.0:
        raise ValueError('Invalid values in output image.')
    return img