seathru.py

import collections
import sys
import argparse
import numpy as np
import sklearn as sk
import scipy as sp
import scipy.optimize
import scipy.stats
import math
from PIL import Image
import rawpy
import matplotlib
from matplotlib import pyplot as plt
from skimage import exposure
from skimage.restoration import denoise_bilateral, denoise_tv_chambolle, estimate_sigma
from skimage.morphology import closing, opening, erosion, dilation, disk, diamond, square

matplotlib.use('TkAgg')

'''
Finds points for which to estimate backscatter
by partitioning the image into different depth
ranges and taking the darkest RGB triplets 
from that set as estimations of the backscatter
'''
def find_backscatter_estimation_points(img, depths, num_bins=10, fraction=0.01, max_vals=20, min_depth_percent=0.0):
    z_max, z_min = np.max(depths), np.min(depths)
    min_depth = z_min + (min_depth_percent * (z_max - z_min))
    z_ranges = np.linspace(z_min, z_max, num_bins + 1)
    img_norms = np.mean(img, axis=2)
    points_r = []
    points_g = []
    points_b = []
    for i in range(len(z_ranges) - 1):
        a, b = z_ranges[i], z_ranges[i+1]
        locs = np.where(np.logical_and(depths > min_depth, np.logical_and(depths >= a, depths <= b)))
        norms_in_range, px_in_range, depths_in_range = img_norms[locs], img[locs], depths[locs]
        arr = sorted(zip(norms_in_range, px_in_range, depths_in_range), key=lambda x: x[0])
        points = arr[:min(math.ceil(fraction * len(arr)), max_vals)]
        points_r.extend([(z, p[0]) for n, p, z in points])
        points_g.extend([(z, p[1]) for n, p, z in points])
        points_b.extend([(z, p[2]) for n, p, z in points])
    return np.array(points_r), np.array(points_g), np.array(points_b)

'''
Estimates coefficients for the backscatter curve
based on the backscatter point values and their depths
'''
def find_backscatter_values(B_pts, depths, restarts=10, max_mean_loss_fraction=0.1):
    B_vals, B_depths = B_pts[:, 1], B_pts[:, 0]
    z_max, z_min = np.max(depths), np.min(depths)
    max_mean_loss = max_mean_loss_fraction * (z_max - z_min)
    coefs = None
    best_loss = np.inf
    def estimate(depths, B_inf, beta_B, J_prime, beta_D_prime):
        val = (B_inf * (1 - np.exp(-1 * beta_B * depths))) + (J_prime * np.exp(-1 * beta_D_prime * depths))
        return val
    def loss(B_inf, beta_B, J_prime, beta_D_prime):
        val = np.mean(np.abs(B_vals - estimate(B_depths, B_inf, beta_B, J_prime, beta_D_prime)))
        return val
    bounds_lower = [0,0,0,0]
    bounds_upper = [1,5,1,5]
    for _ in range(restarts):
        try:
            optp, pcov = sp.optimize.curve_fit(
                f=estimate,
                xdata=B_depths,
                ydata=B_vals,
                p0=np.random.random(4) * bounds_upper,
                bounds=(bounds_lower, bounds_upper),
            )
            l = loss(*optp)
            if l < best_loss:
                best_loss = l
                coefs = optp
        except RuntimeError as re:
            print(re, file=sys.stderr)
    if best_loss > max_mean_loss:
        print('Warning: could not find accurate reconstruction. Switching to linear model.', flush=True)
        slope, intercept, r_value, p_value, std_err = sp.stats.linregress(B_depths, B_vals)
        BD = (slope * depths) + intercept
        return BD, np.array([slope, intercept])
    return estimate(depths, *coefs), coefs

'''
Estimate illumination map from local color space averaging
'''
def estimate_illumination(img, B, neighborhood_map, num_neighborhoods, p=0.5, f=2.0, max_iters=100, tol=1E-5):
    D = img - B
    avg_cs = np.zeros_like(img)
    avg_cs_prime = np.copy(avg_cs)
    sizes = np.zeros(num_neighborhoods)
    locs_list = [None] * num_neighborhoods
    for label in range(1, num_neighborhoods + 1):
        locs_list[label - 1] = np.where(neighborhood_map == label)
        sizes[label - 1] = np.size(locs_list[label - 1][0])
    for _ in range(max_iters):
        for label in range(1, num_neighborhoods + 1):
            locs = locs_list[label - 1]
            size = sizes[label - 1] - 1
            avg_cs_prime[locs] = (1 / size) * (np.sum(avg_cs[locs]) - avg_cs[locs])
        new_avg_cs = (D * p) + (avg_cs_prime * (1 - p))
        if(np.max(np.abs(avg_cs - new_avg_cs)) < tol):
            break
        avg_cs = new_avg_cs
    return f * denoise_bilateral(np.maximum(0, avg_cs))

'''
Estimate values for beta_D
'''
def estimate_wideband_attentuation(depths, illum, radius = 6, max_val = 10.0):
    eps = 1E-8
    BD = np.minimum(max_val, -np.log(illum + eps) / (np.maximum(0, depths) + eps))
    mask = np.where(np.logical_and(depths > eps, illum > eps), 1, 0)
    refined_attenuations = denoise_bilateral(closing(np.maximum(0, BD * mask), disk(radius)))
    return refined_attenuations, []

'''
Calculate the values of beta_D for an image from the depths, illuminations, and constants
'''
def calculate_beta_D(depths, a, b, c, d):
    return (a * np.exp(b * depths)) + (c * np.exp(d * depths))

'''
Filter the data such that only one point is selected per "bin", defined using a radius.
The median value is selected per bin.
This prevents the regression from being overwhelmed due to the
large amount of junk data at certain points in the range.
'''
def filter_data(X, Y, radius_fraction=0.01):
    idxs = np.argsort(X)
    X_s = X[idxs]
    Y_s = Y[idxs]
    x_max, x_min = np.max(X), np.min(X)
    radius = (radius_fraction * (x_max - x_min))
    ds = np.cumsum(X_s - np.roll(X_s, (1,)))
    dX = [X_s[0]]
    dY = [Y_s[0]]
    tempX = []
    tempY = []
    pos = 0
    for i in range(1, ds.shape[0]):
        if ds[i] - ds[pos] >= radius:
            tempX.append(X_s[i])
            tempY.append(Y_s[i])
            idxs = np.argsort(tempY)
            med_idx = len(idxs) // 2
            dX.append(tempX[med_idx])
            dY.append(tempY[med_idx])
            pos = i
        else:
            tempX.append(X_s[i])
            tempY.append(Y_s[i])
    return np.array(dX), np.array(dY)

'''
Estimate coefficients for the 2-term exponential
describing the wideband attenuation
'''
def refine_wideband_attentuation(depths, illum, estimation, restarts=10, min_depth_fraction = 0.1, max_mean_loss_fraction=np.inf, l=1.0, radius_fraction=0.01):
    eps = 1E-8
    z_max, z_min = np.max(depths), np.min(depths)
    min_depth = z_min + (min_depth_fraction * (z_max - z_min))
    max_mean_loss = max_mean_loss_fraction * (z_max - z_min)
    coefs = None
    best_loss = np.inf
    locs = np.where(np.logical_and(illum > 0, np.logical_and(depths > min_depth, estimation > eps)))
    def calculate_reconstructed_depths(depths, illum, a, b, c, d):
        eps = 1E-5
        res = -np.log(illum + eps) / (calculate_beta_D(depths, a, b, c, d) + eps)
        return res
    def loss(a, b, c, d):
        return np.mean(np.abs(depths[locs] - calculate_reconstructed_depths(depths[locs], illum[locs], a, b, c, d)))
    dX, dY = filter_data(depths[locs], estimation[locs], radius_fraction)
    for _ in range(restarts):
        try:
            optp, pcov = sp.optimize.curve_fit(
                f=calculate_beta_D,
                xdata=dX,
                ydata=dY,
                p0=np.abs(np.random.random(4)) * np.array([1., -1., 1., -1.]),
                bounds=([0, -100, 0, -100], [100, 0, 100, 0]))
            L = loss(*optp)
            if L < best_loss:
                best_loss = L
                coefs = optp
        except RuntimeError as re:
            print(re, file=sys.stderr)
    # Uncomment to see the regression
    # plt.clf()
    # plt.scatter(depths[locs], estimation[locs])
    # plt.plot(np.sort(depths[locs]), calculate_beta_D(np.sort(depths[locs]), *coefs))
    # plt.show()
    if best_loss > max_mean_loss:
        print('Warning: could not find accurate reconstruction. Switching to linear model.', flush=True)
        slope, intercept, r_value, p_value, std_err = sp.stats.linregress(depths[locs], estimation[locs])
        BD = (slope * depths + intercept)
        return l * BD, np.array([slope, intercept])
    print(f'Found best loss {best_loss}', flush=True)
    BD = l * calculate_beta_D(depths, *coefs)
    return BD, coefs

'''
Reconstruct the scene and globally white balance
based the Gray World Hypothesis
'''
def recover_image(img, depths, B, beta_D, nmap):
    res = (img - B) * np.exp(beta_D * np.expand_dims(depths, axis=2))
    res = np.maximum(0.0, np.minimum(1.0, res))
    res[nmap == 0] = 0
    res = scale(wbalance_gw(res))
    res[nmap == 0] = img[nmap == 0]
    return res


'''
Reconstruct the scene and globally white balance
'''
def recover_image_S4(img, B, illum, nmap):
    eps = 1E-8
    res = (img - B) / (illum + eps)
    res = np.maximum(0.0, np.minimum(1.0, res))
    res[nmap == 0] = img[nmap == 0]
    return scale(wbalance_no_red_gw(res))


'''
Constructs a neighborhood map from depths and 
epsilon
'''
def construct_neighborhood_map(depths, epsilon=0.05):
    eps = (np.max(depths) - np.min(depths)) * epsilon
    nmap = np.zeros_like(depths).astype(np.int32)
    n_neighborhoods = 1
    while np.any(nmap == 0):
        locs_x, locs_y = np.where(nmap == 0)
        start_index = np.random.randint(0, len(locs_x))
        start_x, start_y = locs_x[start_index], locs_y[start_index]
        q = collections.deque()
        q.append((start_x, start_y))
        while not len(q) == 0:
            x, y = q.pop()
            if np.abs(depths[x, y] - depths[start_x, start_y]) <= eps:
                nmap[x, y] = n_neighborhoods
                if 0 <= x < depths.shape[0] - 1:
                    x2, y2 = x + 1, y
                    if nmap[x2, y2] == 0:
                        q.append((x2, y2))
                if 1 <= x < depths.shape[0]:
                    x2, y2 = x - 1, y
                    if nmap[x2, y2] == 0:
                        q.append((x2, y2))
                if 0 <= y < depths.shape[1] - 1:
                    x2, y2 = x, y + 1
                    if nmap[x2, y2] == 0:
                        q.append((x2, y2))
                if 1 <= y < depths.shape[1]:
                    x2, y2 = x, y - 1
                    if nmap[x2, y2] == 0:
                        q.append((x2, y2))
        n_neighborhoods += 1
    zeros_size_arr = sorted(zip(*np.unique(nmap[depths == 0], return_counts=True)), key=lambda x: x[1], reverse=True)
    if len(zeros_size_arr) > 0:
        nmap[nmap == zeros_size_arr[0][0]] = 0 #reset largest background to 0
    return nmap, n_neighborhoods - 1

'''
Finds the closest nonzero label to a location
'''
def find_closest_label(nmap, start_x, start_y):
    mask = np.zeros_like(nmap).astype(np.bool)
    q = collections.deque()
    q.append((start_x, start_y))
    while not len(q) == 0:
        x, y = q.pop()
        if 0 <= x < nmap.shape[0] and 0 <= y < nmap.shape[1]:
            if nmap[x, y] != 0:
                return nmap[x, y]
            mask[x, y] = True
            if 0 <= x < nmap.shape[0] - 1:
                x2, y2 = x + 1, y
                if not mask[x2, y2]:
                    q.append((x2, y2))
            if 1 <= x < nmap.shape[0]:
                x2, y2 = x - 1, y
                if not mask[x2, y2]:
                    q.append((x2, y2))
            if 0 <= y < nmap.shape[1] - 1:
                x2, y2 = x, y + 1
                if not mask[x2, y2]:
                    q.append((x2, y2))
            if 1 <= y < nmap.shape[1]:
                x2, y2 = x, y - 1
                if not mask[x2, y2]:
                    q.append((x2, y2))


'''
Refines the neighborhood map to remove artifacts
'''
def refine_neighborhood_map(nmap, min_size = 10, radius = 3):
    refined_nmap = np.zeros_like(nmap)
    vals, counts = np.unique(nmap, return_counts=True)
    neighborhood_sizes = sorted(zip(vals, counts), key=lambda x: x[1], reverse=True)
    num_labels = 1
    for label, size in neighborhood_sizes:
        if size >= min_size and label != 0:
            refined_nmap[nmap == label] = num_labels
            num_labels += 1
    for label, size in neighborhood_sizes:
        if size < min_size and label != 0:
            for x, y in zip(*np.where(nmap == label)):
                refined_nmap[x, y] = find_closest_label(refined_nmap, x, y)
    refined_nmap = closing(refined_nmap, square(radius))
    return refined_nmap, num_labels - 1


def load_image_and_depth_map(img_fname, depths_fname, size_limit = 1024):
    depths = Image.open(depths_fname)
    try:
        img = Image.fromarray(rawpy.imread(img_fname).postprocess())
    except rawpy.LibRawFileUnsupportedError:
        img = Image.open(img_fname)
    img.thumbnail((size_limit, size_limit), Image.ANTIALIAS)
    depths = depths.resize(img.size, Image.ANTIALIAS)
    return np.float32(img) / 255.0, np.array(depths)

'''
White balance with 'grey world' hypothesis
'''
def wbalance_gw(img):
    r = img[:,:,0]
    g = img[:,:,1]
    b = img[:,:,2]
    dr = 1.0 / np.mean(r[r != 0])
    dg = 1.0 / np.mean(g[g != 0])
    db = 1.0 / np.mean(b[b != 0])
    dsum = dr + dg + db
    dr = dr / dsum * 3.
    dg = dg / dsum * 3.
    db = db / dsum * 3.

    img[:, :, 0] *= dr
    img[:, :, 1] *= dg
    img[:, :, 2] *= db
    return img


'''
White balance based on top 10% average values of each channel
'''
def wbalance_10p(img):
    dr = 1.0 / np.mean(np.sort(img[:, :, 0], axis=None)[int(round(-1 * np.size(img[:, :, 0]) * 0.1)):])
    dg = 1.0 / np.mean(np.sort(img[:, :, 1], axis=None)[int(round(-1 * np.size(img[:, :, 0]) * 0.1)):])
    db = 1.0 / np.mean(np.sort(img[:, :, 2], axis=None)[int(round(-1 * np.size(img[:, :, 0]) * 0.1)):])
    dsum = dr + dg + db
    dr = dr / dsum * 3.
    dg = dg / dsum * 3.
    db = db / dsum * 3.

    img[:, :, 0] *= dr
    img[:, :, 1] *= dg
    img[:, :, 2] *= db
    return img

'''
White balance based on top 10% average values of blue and green channel
'''
def wbalance_no_red_10p(img):
    dg = 1.0 / np.mean(np.sort(img[:, :, 1], axis=None)[int(round(-1 * np.size(img[:, :, 0]) * 0.1)):])
    db = 1.0 / np.mean(np.sort(img[:, :, 2], axis=None)[int(round(-1 * np.size(img[:, :, 0]) * 0.1)):])
    dsum = dg + db
    dg = dg / dsum * 2.
    db = db / dsum * 2.
    img[:, :, 0] *= (db + dg) / 2
    img[:, :, 1] *= dg
    img[:, :, 2] *= db
    return img

'''
White balance with 'grey world' hypothesis
'''
def wbalance_no_red_gw(img):
    r = img[:,:,0]
    g = img[:,:,1]
    b = img[:,:,2]
    dr = 1.0 / np.mean(r[r != 0])
    dg = 1.0 / np.mean(g[g != 0])
    db = 1.0 / np.mean(b[b != 0])
    dsum = dg + db
    dg = dg / dsum * 2.
    db = db / dsum * 2.

    img[:, :, 0] *= (db + dg) / 2
    img[:, :, 1] *= dg
    img[:, :, 2] *= db
    return img

def scale(img):
    return (img - np.min(img)) / (np.max(img) - np.min(img))

def run_pipeline(img, depths, args):
    if 'output_graphs' not in args:
        args.output_graphs = False
    if args.output_graphs:
        plt.imshow(depths)
        plt.title('Depth Map')
        plt.show()

    print('Estimating backscatter...', flush=True)
    ptsR, ptsG, ptsB = find_backscatter_estimation_points(img, depths, fraction=0.01, min_depth_percent=args.min_depth)

    print('Finding backscatter coefficients...', flush=True)
    Br, coefsR = find_backscatter_values(ptsR, depths, restarts=25)
    Bg, coefsG = find_backscatter_values(ptsG, depths, restarts=25)
    Bb, coefsB = find_backscatter_values(ptsB, depths, restarts=25)

    if args.output_graphs:
        print('Coefficients: \n{}\n{}\n{}'.format(coefsR, coefsG, coefsB), flush=True)
        def eval_xs(xs, coefs):
            if len(coefs) == 2:
                return xs * coefs[0] + coefs[1]
            else:
                return ((coefs[0] * (1 - np.exp(-coefs[1] * xs))) + (coefs[2] * np.exp(-coefs[3] * xs)))
        # check optimization for B channel
        plt.clf()
        plt.scatter(ptsB[:, 0].ravel(), ptsB[:, 1].ravel(), c='b')
        xs = np.linspace(np.min(ptsB[:, 0]), np.max(ptsB[:, 0]), 1000)
        ys = eval_xs(xs, coefsB)
        # ys = find_backscatter_values(ptsB, xs)
        plt.plot(xs.ravel(), ys.ravel(), c='b')
        plt.scatter(ptsG[:, 0].ravel(), ptsG[:, 1].ravel(), c='g')
        xs = np.linspace(np.min(ptsG[:, 0]), np.max(ptsG[:, 0]), 1000)
        ys = eval_xs(xs, coefsG)
        # ys = find_backscatter_values(ptsG, xs)
        plt.plot(xs.ravel(), ys.ravel(), c='g')
        plt.scatter(ptsR[:, 0].ravel(), ptsR[:, 1].ravel(), c='r')
        xs = np.linspace(np.min(ptsR[:, 0]), np.max(ptsR[:, 0]), 1000)
        ys = eval_xs(xs, coefsR)
        # ys = find_backscatter_values(ptsR, xs)
        plt.plot(xs.ravel(), ys.ravel(), c='r')
        plt.xlabel('Depth (m)')
        plt.ylabel('Color value')
        plt.title('Modelled $B_c$ values')
        plt.savefig('Bc_values.png')
        plt.show()

    print('Constructing neighborhood map...', flush=True)
    nmap, _ = construct_neighborhood_map(depths, 0.1)

    print('Refining neighborhood map...', flush=True)
    nmap, n = refine_neighborhood_map(nmap, 50)
    if args.output_graphs:
        plt.imshow(nmap)
        plt.title('Neighborhood map')
        plt.show()

    print('Estimating illumination...', flush=True)
    illR = estimate_illumination(img[:, :, 0], Br, nmap, n, p=args.p, max_iters=100, tol=1E-5, f=args.f)
    illG = estimate_illumination(img[:, :, 1], Bg, nmap, n, p=args.p, max_iters=100, tol=1E-5, f=args.f)
    illB = estimate_illumination(img[:, :, 2], Bb, nmap, n, p=args.p, max_iters=100, tol=1E-5, f=args.f)
    ill = np.stack([illR, illG, illB], axis=2)
    if args.output_graphs:
        plt.imshow(ill)
        plt.title('Illuminant map')
        plt.show()

    print('Estimating wideband attenuation...', flush=True)
    beta_D_r, _ = estimate_wideband_attentuation(depths, illR)
    refined_beta_D_r, coefsR = refine_wideband_attentuation(depths, illR, beta_D_r, radius_fraction=args.spread_data_fraction, l=args.l)
    beta_D_g, _ = estimate_wideband_attentuation(depths, illG)
    refined_beta_D_g, coefsG = refine_wideband_attentuation(depths, illG, beta_D_g, radius_fraction=args.spread_data_fraction, l=args.l)
    beta_D_b, _ = estimate_wideband_attentuation(depths, illB)
    refined_beta_D_b, coefsB = refine_wideband_attentuation(depths, illB, beta_D_b, radius_fraction=args.spread_data_fraction, l=args.l)

    if args.output_graphs:
        print('Coefficients: \n{}\n{}\n{}'.format(coefsR, coefsG, coefsB), flush=True)
        # plot the wideband attenuation values
        plt.clf()
        plt.imshow(np.stack([scale(refined_beta_D_r), np.zeros_like(beta_D_r), np.zeros_like(beta_D_r)], axis=2))
        plt.show()
        plt.clf()
        plt.imshow(np.stack([np.zeros_like(beta_D_r), scale(refined_beta_D_g), np.zeros_like(beta_D_r)], axis=2))
        plt.show()
        plt.clf()
        plt.imshow(np.stack([np.zeros_like(beta_D_r), np.zeros_like(beta_D_r), scale(refined_beta_D_b)], axis=2))
        plt.show()

    # check optimization for beta_D channel
    if args.output_graphs:
        eps = 1E-5
        def eval_xs(xs, coefs):
            if len(coefs) == 2:
                return xs * coefs[0] + coefs[1]
            else:
                return (coefs[0] * np.exp(coefs[1] * xs)) + (coefs[2] * np.exp(coefs[3] * xs))
        locs = np.where(
            np.logical_and(beta_D_r > eps, np.logical_and(beta_D_g > eps, np.logical_and(depths > eps, beta_D_b > eps))))
        plt.scatter(depths[locs].ravel(), beta_D_b[locs].ravel(), c='b', alpha=0.1, edgecolors='none')
        xs = np.linspace(np.min(depths[locs]), np.max(depths[locs]), 1000)
        ys = eval_xs(xs, coefsB)
        plt.plot(xs.ravel(), ys.ravel(), c='b')
        plt.scatter(depths[locs].ravel(), beta_D_g[locs].ravel(), c='g', alpha=0.1, edgecolors='none')
        ys = eval_xs(xs, coefsG)
        plt.plot(xs.ravel(), ys.ravel(), c='g')
        plt.scatter(depths[locs].ravel(), beta_D_r[locs].ravel(), c='r', alpha=0.1, edgecolors='none')
        ys = eval_xs(xs, coefsR)
        plt.plot(xs.ravel(), ys.ravel(), c='r')
        plt.xlabel('Depth (m)')
        plt.ylabel('$\\beta^D$')
        plt.title('Modelled $\\beta^D$ values')
        plt.savefig('betaD_values.png')
        plt.show()

    print('Reconstructing image...', flush=True)
    B = np.stack([Br, Bg, Bb], axis=2)
    beta_D = np.stack([refined_beta_D_r, refined_beta_D_g, refined_beta_D_b], axis=2)
    recovered = recover_image(img, depths, B, beta_D, nmap)


    if args.output_graphs:
        beta_D = (beta_D - np.min(beta_D)) / (np.max(beta_D) - np.min(beta_D))
        fig = plt.figure(figsize=(50, 20))
        fig.add_subplot(2, 3, 1)
        plt.imshow(img)
        plt.title('Original Image')
        fig.add_subplot(2, 3, 2)
        plt.imshow(nmap)
        plt.title('Neighborhood Map')
        fig.add_subplot(2, 3, 3)
        plt.imshow(B)
        plt.title('Backscatter Estimation')
        fig.add_subplot(2, 3, 4)
        plt.imshow(ill)
        plt.title('Illumination Map')
        fig.add_subplot(2, 3, 5)
        plt.imshow(beta_D)
        plt.title('Attenuation Coefficients')
        fig.add_subplot(2, 3, 6)
        plt.imshow(recovered)
        plt.title('Recovered Image')
        plt.tight_layout(True)
        plt.savefig('components.png')
        plt.show()

    return recovered

def preprocess_for_monodepth(img_fname, output_fname, size_limit=1024):
    img = Image.fromarray(rawpy.imread(img_fname).postprocess())
    img.thumbnail((size_limit, size_limit), Image.ANTIALIAS)
    img_adapteq = exposure.equalize_adapthist(np.array(img), clip_limit=0.03)
    Image.fromarray((np.round(img_adapteq * 255.0)).astype(np.uint8)).save(output_fname)

def preprocess_sfm_depth_map(depths, min_depth, max_depth):
    z_min = np.min(depths) + (min_depth * (np.max(depths) - np.min(depths)))
    z_max = np.min(depths) + (max_depth * (np.max(depths) - np.min(depths)))
    if max_depth != 0:
        depths[depths == 0] = z_max
    depths[depths < z_min] = 0
    return depths

def preprocess_monodepth_depth_map(depths, additive_depth, multiply_depth):
    depths = ((depths - np.min(depths)) / (
                np.max(depths) - np.min(depths))).astype(np.float32)
    depths = (multiply_depth * (1.0 - depths)) + additive_depth
    return depths

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--image', required=True, help='Input image')
    parser.add_argument('--depth-map', required=True, help='Input depth map')
    parser.add_argument('--output', default='output.png', help='Output filename')
    parser.add_argument('--f', type=float, default=2.0, help='f value (controls brightness)')
    parser.add_argument('--l', type=float, default=0.5, help='l value (controls balance of attenuation constants)')
    parser.add_argument('--p', type=float, default=0.01, help='p value (controls locality of illuminant map)')
    parser.add_argument('--min-depth', type=float, default=0.1, help='Minimum depth value to use in estimations (range 0-1)')
    parser.add_argument('--max-depth', type=float, default=1.0, help='Replacement depth percentile value for invalid depths (range 0-1)')
    parser.add_argument('--spread-data-fraction', type=float, default=0.01, help='Require data to be this fraction of depth range away from each other in attenuation estimations')
    parser.add_argument('--size', type=int, default=320, help='Size to output')
    parser.add_argument('--output-graphs', action='store_true', help='Output graphs')
    parser.add_argument('--preprocess-for-monodepth', action='store_true', help='Preprocess for monodepth depth maps')
    parser.add_argument('--monodepth', action='store_true', help='Preprocess for monodepth')
    parser.add_argument('--monodepth-add-depth', type=float, default=2.0, help='Additive value for monodepth map')
    parser.add_argument('--monodepth-multiply-depth', type=float, default=10.0, help='Multiplicative value for monodepth map')
    parser.add_argument('--equalize-image', action='store_true', help='Histogram equalization for final output')
    args = parser.parse_args()

    if args.preprocess_for_monodepth:
        preprocess_for_monodepth(args.image, args.output, args.size)
    else:
        print('Loading image...', flush=True)
        img, depths = load_image_and_depth_map(args.image, args.depth_map, args.size)
        if args.monodepth:
            depths = preprocess_monodepth_depth_map(depths, args.monodepth_add_depth, args.monodepth_multiply_depth)
        else:
            depths = preprocess_sfm_depth_map(depths, args.min_depth, args.max_depth)
        recovered = run_pipeline(img, depths, args)
        if args.equalize_image:
            recovered = exposure.equalize_adapthist(np.array(recovered), clip_limit=0.03)
            sigma_est = estimate_sigma(recovered, multichannel=True, average_sigmas=True)
            recovered = denoise_tv_chambolle(recovered, sigma_est, multichannel=True)
        plt.imsave(args.output, recovered)
        print('Done.')