estimator/utils/image_ops.py

import cv2
import numpy as np

def shift_2d_replace(data, dx, dy, constant=False):
    shifted_data = np.roll(data, dx, axis=1)
    if dx < 0:
        shifted_data[:, dx:] = constant
    elif dx > 0:
        shifted_data[:, 0:dx] = constant

    shifted_data = np.roll(shifted_data, dy, axis=0)
    if dy < 0:
        shifted_data[dy:, :] = constant
    elif dy > 0:
        shifted_data[0:dy, :] = constant
    return shifted_data

def soft_edge_error(pred, gt, radius=1):
    abs_diff=[]
    for i in range(-radius, radius + 1):
        for j in range(-radius, radius + 1):
            abs_diff.append(np.abs(shift_2d_replace(gt, i, j, 0) - pred))
    return np.minimum.reduce(abs_diff)

def get_boundaries(disp, th=1., dilation=10):
    edges_y = np.logical_or(np.pad(np.abs(disp[1:, :] - disp[:-1, :]) > th, ((1, 0), (0, 0))),
                            np.pad(np.abs(disp[:-1, :] - disp[1:, :]) > th, ((0, 1), (0, 0))))
    edges_x = np.logical_or(np.pad(np.abs(disp[:, 1:] - disp[:, :-1]) > th, ((0, 0), (1, 0))),
                            np.pad(np.abs(disp[:, :-1] - disp[:,1:]) > th, ((0, 0), (0, 1))))
    edges = np.logical_or(edges_y,  edges_x).astype(np.float32)

    if dilation > 0:
        kernel = np.ones((dilation, dilation), np.uint8)
        edges = cv2.dilate(edges, kernel, iterations=1)

    return edges