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""" |
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This code is refer from: |
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https://github.com/RubanSeven/Text-Image-Augmentation-python/blob/master/warp_mls.py |
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""" |
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import numpy as np |
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class WarpMLS: |
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def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.): |
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self.src = src |
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self.src_pts = src_pts |
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self.dst_pts = dst_pts |
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self.pt_count = len(self.dst_pts) |
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self.dst_w = dst_w |
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self.dst_h = dst_h |
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self.trans_ratio = trans_ratio |
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self.grid_size = 100 |
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self.rdx = np.zeros((self.dst_h, self.dst_w)) |
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self.rdy = np.zeros((self.dst_h, self.dst_w)) |
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@staticmethod |
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def __bilinear_interp(x, y, v11, v12, v21, v22): |
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return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 * |
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(1 - y) + v22 * y) * x |
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def generate(self): |
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self.calc_delta() |
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return self.gen_img() |
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def calc_delta(self): |
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w = np.zeros(self.pt_count, dtype=np.float32) |
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if self.pt_count < 2: |
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return |
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i = 0 |
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while 1: |
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if self.dst_w <= i < self.dst_w + self.grid_size - 1: |
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i = self.dst_w - 1 |
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elif i >= self.dst_w: |
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break |
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j = 0 |
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while 1: |
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if self.dst_h <= j < self.dst_h + self.grid_size - 1: |
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j = self.dst_h - 1 |
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elif j >= self.dst_h: |
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break |
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sw = 0 |
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swp = np.zeros(2, dtype=np.float32) |
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swq = np.zeros(2, dtype=np.float32) |
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new_pt = np.zeros(2, dtype=np.float32) |
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cur_pt = np.array([i, j], dtype=np.float32) |
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k = 0 |
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for k in range(self.pt_count): |
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]: |
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break |
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w[k] = 1. / ( |
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(i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0]) + |
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(j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1])) |
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sw += w[k] |
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swp = swp + w[k] * np.array(self.dst_pts[k]) |
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swq = swq + w[k] * np.array(self.src_pts[k]) |
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if k == self.pt_count - 1: |
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pstar = 1 / sw * swp |
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qstar = 1 / sw * swq |
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miu_s = 0 |
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for k in range(self.pt_count): |
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]: |
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continue |
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pt_i = self.dst_pts[k] - pstar |
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miu_s += w[k] * np.sum(pt_i * pt_i) |
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cur_pt -= pstar |
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cur_pt_j = np.array([-cur_pt[1], cur_pt[0]]) |
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for k in range(self.pt_count): |
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if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]: |
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continue |
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pt_i = self.dst_pts[k] - pstar |
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pt_j = np.array([-pt_i[1], pt_i[0]]) |
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tmp_pt = np.zeros(2, dtype=np.float32) |
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tmp_pt[0] = np.sum(pt_i * cur_pt) * self.src_pts[k][0] - \ |
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np.sum(pt_j * cur_pt) * self.src_pts[k][1] |
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tmp_pt[1] = -np.sum(pt_i * cur_pt_j) * self.src_pts[k][0] + \ |
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np.sum(pt_j * cur_pt_j) * self.src_pts[k][1] |
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tmp_pt *= (w[k] / miu_s) |
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new_pt += tmp_pt |
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new_pt += qstar |
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else: |
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new_pt = self.src_pts[k] |
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self.rdx[j, i] = new_pt[0] - i |
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self.rdy[j, i] = new_pt[1] - j |
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j += self.grid_size |
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i += self.grid_size |
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def gen_img(self): |
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src_h, src_w = self.src.shape[:2] |
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dst = np.zeros_like(self.src, dtype=np.float32) |
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for i in np.arange(0, self.dst_h, self.grid_size): |
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for j in np.arange(0, self.dst_w, self.grid_size): |
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ni = i + self.grid_size |
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nj = j + self.grid_size |
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w = h = self.grid_size |
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if ni >= self.dst_h: |
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ni = self.dst_h - 1 |
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h = ni - i + 1 |
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if nj >= self.dst_w: |
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nj = self.dst_w - 1 |
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w = nj - j + 1 |
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di = np.reshape(np.arange(h), (-1, 1)) |
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dj = np.reshape(np.arange(w), (1, -1)) |
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delta_x = self.__bilinear_interp( |
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di / h, dj / w, self.rdx[i, j], self.rdx[i, nj], |
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self.rdx[ni, j], self.rdx[ni, nj]) |
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delta_y = self.__bilinear_interp( |
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di / h, dj / w, self.rdy[i, j], self.rdy[i, nj], |
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self.rdy[ni, j], self.rdy[ni, nj]) |
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nx = j + dj + delta_x * self.trans_ratio |
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ny = i + di + delta_y * self.trans_ratio |
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nx = np.clip(nx, 0, src_w - 1) |
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ny = np.clip(ny, 0, src_h - 1) |
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nxi = np.array(np.floor(nx), dtype=np.int32) |
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nyi = np.array(np.floor(ny), dtype=np.int32) |
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nxi1 = np.array(np.ceil(nx), dtype=np.int32) |
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nyi1 = np.array(np.ceil(ny), dtype=np.int32) |
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if len(self.src.shape) == 3: |
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x = np.tile(np.expand_dims(ny - nyi, axis=-1), (1, 1, 3)) |
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y = np.tile(np.expand_dims(nx - nxi, axis=-1), (1, 1, 3)) |
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else: |
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x = ny - nyi |
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y = nx - nxi |
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dst[i:i + h, j:j + w] = self.__bilinear_interp( |
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x, y, self.src[nyi, nxi], self.src[nyi, nxi1], |
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self.src[nyi1, nxi], self.src[nyi1, nxi1]) |
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dst = np.clip(dst, 0, 255) |
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dst = np.array(dst, dtype=np.uint8) |
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return dst |
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