training/dataset/utils/warp.py

import numpy as np
import cv2
# from core import randomex


def random_normal(size=(1,), trunc_val=2.5):
    len = np.array(size).prod()
    result = np.empty((len,), dtype=np.float32)

    for i in range(len):
        while True:
            x = np.random.normal()
            if x >= -trunc_val and x <= trunc_val:
                break
        result[i] = (x / trunc_val)

    return result.reshape(size)


def gen_warp_params(w, flip, rotation_range=[-10, 10], scale_range=[-0.5, 0.5], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05], rnd_state=None):
    if rnd_state is None:
        rnd_state = np.random

    rotation = rnd_state.uniform(rotation_range[0], rotation_range[1])
    scale = rnd_state.uniform(1 + scale_range[0], 1 + scale_range[1])
    tx = rnd_state.uniform(tx_range[0], tx_range[1])
    ty = rnd_state.uniform(ty_range[0], ty_range[1])
    p_flip = flip and rnd_state.randint(10) < 4

    # random warp by grid
    cell_size = [w // (2**i) for i in range(1, 4)][rnd_state.randint(3)]
    cell_count = w // cell_size + 1

    grid_points = np.linspace(0, w, cell_count)
    mapx = np.broadcast_to(grid_points, (cell_count, cell_count)).copy()
    mapy = mapx.T

    mapx[1:-1, 1:-1] = mapx[1:-1, 1:-1] + \
        random_normal(
            size=(cell_count-2, cell_count-2))*(cell_size*0.24)
    mapy[1:-1, 1:-1] = mapy[1:-1, 1:-1] + \
        random_normal(
            size=(cell_count-2, cell_count-2))*(cell_size*0.24)

    half_cell_size = cell_size // 2

    mapx = cv2.resize(mapx, (w+cell_size,)*2)[
        half_cell_size:-half_cell_size-1, half_cell_size:-half_cell_size-1].astype(np.float32)
    mapy = cv2.resize(mapy, (w+cell_size,)*2)[
        half_cell_size:-half_cell_size-1, half_cell_size:-half_cell_size-1].astype(np.float32)

    # random transform
    random_transform_mat = cv2.getRotationMatrix2D(
        (w // 2, w // 2), rotation, scale)
    random_transform_mat[:, 2] += (tx*w, ty*w)

    params = dict()
    params['mapx'] = mapx
    params['mapy'] = mapy
    params['rmat'] = random_transform_mat
    params['w'] = w
    params['flip'] = p_flip

    return params


def warp_by_params(params, img, can_warp, can_transform, can_flip, border_replicate, cv2_inter=cv2.INTER_CUBIC):
    if can_warp:
        img = cv2.remap(img, params['mapx'], params['mapy'], cv2_inter)
    if can_transform:
        img = cv2.warpAffine(img, params['rmat'], (params['w'], params['w']), borderMode=(
            cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT), flags=cv2_inter)
    if len(img.shape) == 2:
        img = img[..., None]
    if can_flip and params['flip']:
        img = img[:, ::-1, ...]
    return img

from skimage.transform import PiecewiseAffineTransform, warp
def random_deform(imageSize, nrows, ncols, mean=0, std=5):
    try:
        h, w, c = imageSize
    except:
        h, w = imageSize
        c = 1
    rows = np.linspace(0, h, nrows).astype(np.int32)
    cols = np.linspace(0, w, ncols).astype(np.int32)
    rows, cols = np.meshgrid(rows, cols)
    anchors = np.vstack([rows.flat, cols.flat]).T
    assert anchors.shape[1] == 2 and anchors.shape[0] == ncols * nrows
    deformed = anchors + np.random.normal(mean, std, size=anchors.shape)
    #print(anchors)
    #print(deformed)
    np.clip(deformed[:,0], 0, h-1, deformed[:,0])
    np.clip(deformed[:,1], 0, w-1, deformed[:,1])
    return anchors.astype(np.float32), deformed.astype(np.float32)


def piecewise_affine_transform(image, srcAnchor, tgtAnchor):
    trans = PiecewiseAffineTransform()
    trans.estimate(srcAnchor, tgtAnchor)
    # tform.estimate(from_.astype(np.float32), to_.astype(
    #     np.float32))  # tform.estimate(from_, to_)
    # M = tform.params[0:2, :]
    warped = warp(image, trans)
    return warped

def warp_mask(mask, std):
    ach, tgt_ach = random_deform(mask.shape, 4, 4, std=std)
    warped_mask = piecewise_affine_transform(mask, ach, tgt_ach)
    return (warped_mask*255).astype(np.uint8)