metrics_onefig.py

"""
This scripts plots examples of the images that get best and worse metrics
"""
print("Imports...", end="")
import os
import sys
from argparse import ArgumentParser
from pathlib import Path

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import yaml
from imageio import imread
from matplotlib.gridspec import GridSpec
from skimage.color import rgba2rgb
from sklearn.metrics.pairwise import euclidean_distances

sys.path.append("../")

from climategan.data import encode_mask_label
from climategan.eval_metrics import edges_coherence_std_min
from eval_masker import crop_and_resize

# -----------------------
# -----  Constants  -----
# -----------------------

# Metrics
metrics = ["error", "f05", "edge_coherence"]

dict_metrics = {
    "names": {
        "tpr": "TPR, Recall, Sensitivity",
        "tnr": "TNR, Specificity, Selectivity",
        "fpr": "FPR",
        "fpt": "False positives relative to image size",
        "fnr": "FNR, Miss rate",
        "fnt": "False negatives relative to image size",
        "mpr": "May positive rate (MPR)",
        "mnr": "May negative rate (MNR)",
        "accuracy": "Accuracy (ignoring may)",
        "error": "Error",
        "f05": "F05 score",
        "precision": "Precision",
        "edge_coherence": "Edge coherence",
        "accuracy_must_may": "Accuracy (ignoring cannot)",
    },
    "key_metrics": ["error", "f05", "edge_coherence"],
}


# Colors
colorblind_palette = sns.color_palette("colorblind")
color_cannot = colorblind_palette[1]
color_must = colorblind_palette[2]
color_may = colorblind_palette[7]
color_pred = colorblind_palette[4]

icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
color_tp = icefire[0]
color_tn = icefire[1]
color_fp = icefire[4]
color_fn = icefire[3]


def parsed_args():
    """
    Parse and returns command-line args

    Returns:
        argparse.Namespace: the parsed arguments
    """
    parser = ArgumentParser()
    parser.add_argument(
        "--input_csv",
        default="ablations_metrics_20210311.csv",
        type=str,
        help="CSV containing the results of the ablation study",
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        help="Output directory",
    )
    parser.add_argument(
        "--models_log_path",
        default=None,
        type=str,
        help="Path containing the log files of the models",
    )
    parser.add_argument(
        "--masker_test_set_dir",
        default=None,
        type=str,
        help="Directory containing the test images",
    )
    parser.add_argument(
        "--best_model",
        default="dada, msd_spade, pseudo",
        type=str,
        help="The string identifier of the best model",
    )
    parser.add_argument(
        "--dpi",
        default=200,
        type=int,
        help="DPI for the output images",
    )
    parser.add_argument(
        "--alpha",
        default=0.5,
        type=float,
        help="Transparency of labels shade",
    )
    parser.add_argument(
        "--percentile",
        default=0.05,
        type=float,
        help="Transparency of labels shade",
    )
    parser.add_argument(
        "--seed",
        default=None,
        type=int,
        help="Bootstrap random seed, for reproducibility",
    )
    parser.add_argument(
        "--no_images",
        action="store_true",
        default=False,
        help="Do not generate images",
    )

    return parser.parse_args()


def map_color(arr, input_color, output_color, rtol=1e-09):
    """
    Maps one color to another
    """
    input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
    output = arr.copy()
    output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
    return output


def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
    label_colmap = label.astype(float)
    label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
    label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
    label_colmap = map_color(label_colmap, (0, 0, 0), color_may)

    ax.imshow(img)
    ax.imshow(label_colmap, alpha=0.5)
    ax.axis("off")

    if n_ in [1, 3, 5]:
        color_ = "green"
    else:
        color_ = "red"

    ax.text(
        -0.15,
        0.5,
        img_id,
        color=color_,
        fontweight="roman",
        fontsize="x-large",
        horizontalalignment="left",
        verticalalignment="center",
        transform=ax.transAxes,
    )

    if add_title:
        ax.set_title("Labels", rotation=0, fontsize="x-large")


def plot_pred(ax, img, pred, img_id, add_title, do_legend):
    pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))

    pred_colmap = pred.astype(float)
    pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
    pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
    pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma

    ax.imshow(img)
    ax.imshow(pred_colmap_ma, alpha=0.5)
    ax.axis("off")

    if add_title:
        ax.set_title("Prediction", rotation=0, fontsize="x-large")


def plot_correct_incorrect(
    ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
):
    # FP
    fp_map = imread(
        model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
    )
    fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))

    fp_map_colmap = fp_map.astype(float)
    fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)

    # FN
    fn_map = imread(
        model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
    )
    fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))

    fn_map_colmap = fn_map.astype(float)
    fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)

    # TP
    tp_map = imread(
        model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
    )
    tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))

    tp_map_colmap = tp_map.astype(float)
    tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)

    # TN
    tn_map = imread(
        model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
    )
    tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))

    tn_map_colmap = tn_map.astype(float)
    tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)

    label_colmap = label.astype(float)
    label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
    label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
    label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma

    # Combine masks
    maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
    maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
    maps_ma = maps_ma.mask * img + maps_ma

    ax.imshow(img)
    ax.imshow(label_colmap_ma, alpha=0.5)
    ax.imshow(maps_ma, alpha=0.5)
    ax.axis("off")

    if add_title:
        ax.set_title("Metric", rotation=0, fontsize="x-large")


def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
    pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))

    ec, pred_ec, label_ec = edges_coherence_std_min(
        np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
    )

    ##################
    # Edge distances #
    ##################

    # Location of edges
    pred_ec_coord = np.argwhere(pred_ec > 0)
    label_ec_coord = np.argwhere(label_ec > 0)

    # Normalized pairwise distances between pred and label
    dist_mat = np.divide(
        euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
    )

    # Standard deviation of the minimum distance from pred to label
    min_dist = np.min(dist_mat, axis=1)  # noqa: F841

    #############
    # Make plot #
    #############

    pred_ec = np.tile(
        np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
    )
    pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
    pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred)  # noqa: F841

    label_ec = np.tile(
        np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
    )
    label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
    label_ec_colmap_ma = np.ma.masked_not_equal(  # noqa: F841
        label_ec_colmap, color_must
    )

    # Combined pred and label edges
    combined_ec = pred_ec_colmap + label_ec_colmap
    combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
    combined_ec_img = combined_ec_ma.mask * img + combined_ec

    # Pred
    pred_colmap = pred.astype(float)
    pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
    pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)

    # Must
    label_colmap = label.astype(float)
    label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
    label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)

    # TP
    tp_map = imread(
        model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
    )
    tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
    tp_map_colmap = tp_map.astype(float)
    tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
    tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)

    # Combination
    comb_pred = (
        (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
        & tp_map_colmap_ma.mask
        & combined_ec_ma.mask
    ) * pred_colmap
    comb_label = (
        (label_colmap_ma.mask ^ pred_colmap_ma.mask)
        & pred_colmap_ma.mask
        & combined_ec_ma.mask
    ) * label_colmap
    comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
    combined = comb_tp + comb_label + comb_pred
    combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
    combined_ma = combined_ma.mask * combined_ec_img + combined_ma

    ax.imshow(combined_ec_img, alpha=1)
    ax.imshow(combined_ma, alpha=0.5)
    ax.axis("off")

    # Plot lines
    idx_sort_x = np.argsort(pred_ec_coord[:, 1])
    offset = 100
    for idx in range(offset, pred_ec_coord.shape[0], offset):
        y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
        argmin = np.argmin(dist_mat[idx_sort_x[idx]])
        y1, x1 = label_ec_coord[argmin, :]
        ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)

    if add_title:
        ax.set_title("Metric", rotation=0, fontsize="x-large")


def plot_images_metric(
    axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
):

    # Read images
    img_path = imgs_orig_path / img_filename
    label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
    img, label = crop_and_resize(img_path, label_path)
    img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0

    pred = imread(
        model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
    )

    # Label
    plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)

    # Prediction
    plot_pred(axes[1], img, pred, img_id, add_title, do_legend)

    # Correct / incorrect
    if metric in ["error", "f05"]:
        plot_correct_incorrect(
            axes[2],
            img_filename,
            img,
            metric,
            label,
            img_id,
            n_,
            add_title,
            do_legend=False,
        )
        handles = []
        lw = 1.0
        handles.append(
            mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(
                facecolor=color_may,
                label="May-be-flooded",
                linewidth=lw,
                alpha=0.66,
            )
        )
        labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
        if metric == "error":
            if n_ in [1, 3, 5]:
                title = "Low error rate"
            else:
                title = "High error rate"
        else:
            if n_ in [1, 3, 5]:
                title = "High F05 score"
            else:
                title = "Low F05 score"
    # Edge coherence
    elif metric == "edge_coherence":
        plot_edge_coherence(
            axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
        )
        handles = []
        lw = 1.0
        handles.append(
            mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
        )
        handles.append(
            mpatches.Patch(
                facecolor=color_must,
                label="Must-be-flooded",
                linewidth=lw,
                alpha=0.66,
            )
        )
        labels = ["TP", "Prediction", "Must-be-flooded"]
        if n_ in [1, 3, 5]:
            title = "High edge coherence"
        else:
            title = "Low edge coherence"

    else:
        raise ValueError

    labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
        srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
    )

    plot_legend(axes[3], img, handles, labels, labels_values_title, title)


def plot_legend(ax, img, handles, labels, labels_values_title, title):
    img_ = np.zeros_like(img, dtype=np.uint8)
    img_.fill(255)
    ax.imshow(img_)
    ax.axis("off")

    leg1 = ax.legend(
        handles=handles,
        labels=labels,
        title=title,
        title_fontsize="medium",
        labelspacing=0.6,
        loc="upper left",
        fontsize="x-small",
        frameon=False,
    )
    leg1._legend_box.align = "left"

    leg2 = ax.legend(
        title=labels_values_title,
        title_fontsize="small",
        loc="lower left",
        frameon=False,
    )
    leg2._legend_box.align = "left"

    ax.add_artist(leg1)


def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):

    sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)

    # Set X-label
    ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")

    # Set Y-label
    ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")

    # Change spines
    sns.despine(ax=ax, left=True, bottom=True)

    annotate_scatterplot(ax, dict_images, x_metric, y_metric)


def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):

    # Other
    if plot_all:
        sns.scatterplot(
                data=df_all.loc[df_all.ground == True], 
                x="error", y="f05", hue="edge_coherence", ax=ax,
                marker='+', alpha=0.25)
        sns.scatterplot(
                data=df_all.loc[df_all.instagan == True], 
                x="error", y="f05", hue="edge_coherence", ax=ax,
                marker='x', alpha=0.25)
        sns.scatterplot(
                data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
                    (df_all.model_feats != args.best_model)], 
                x="error", y="f05", hue="edge_coherence", ax=ax,
                marker='s', alpha=0.25)

    # Best model
    cmap_ = sns.cubehelix_palette(as_cmap=True)
    sns.scatterplot(
        data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
    )

    norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
    sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
    sm.set_array([])

    # Remove the legend and add a colorbar
    ax.get_legend().remove()
    ax_cbar = ax.figure.colorbar(sm)
    ax_cbar.set_label("Edge coherence", labelpad=8)

    # Set X-label
    ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")

    # Set Y-label
    ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")

    annotate_scatterplot(ax, dict_images, "error", "f05")

    # Change spines
    sns.despine(ax=ax, left=True, bottom=True)

    # Set XY limits
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    ax.set_xlim([0.0, xlim[1]])
    ax.set_ylim([ylim[0], 1.0])


def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
    xlim = ax.get_xlim()
    ylim = ax.get_ylim()
    x_len = xlim[1] - xlim[0]
    y_len = ylim[1] - ylim[0]
    x_th = xlim[1] - x_len / 2.0
    y_th = ylim[1] - y_len / 2.0
    for text, d in dict_images.items():
        if text in ["B", "D", "F"]:
            x = d[x_metric]
            y = d[y_metric]

            x_text = x + x_len * offset if x < x_th else x - x_len * offset
            y_text = y + y_len * offset if y < y_th else y - y_len * offset

            ax.annotate(
                xy=(x, y),
                xycoords="data",
                xytext=(x_text, y_text),
                textcoords="data",
                text=text,
                arrowprops=dict(facecolor="black", shrink=0.05),
                fontsize="medium",
                color="black",
            )
        elif text == "A":
            x = (
                dict_images["A"][x_metric]
                + dict_images["C"][x_metric]
                + dict_images["E"][x_metric]
            ) / 3
            y = (
                dict_images["A"][y_metric]
                + dict_images["C"][y_metric]
                + dict_images["E"][y_metric]
            ) / 3

            x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
            y_text = (
                y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
            )

            ax.annotate(
                xy=(x, y),
                xycoords="data",
                xytext=(x_text, y_text),
                textcoords="data",
                text="A, C, E",
                arrowprops=dict(facecolor="black", shrink=0.05),
                fontsize="medium",
                color="black",
            )


if __name__ == "__main__":
    # -----------------------------
    # -----  Parse arguments  -----
    # -----------------------------
    args = parsed_args()
    print("Args:\n" + "\n".join([f"    {k:20}: {v}" for k, v in vars(args).items()]))

    # Determine output dir
    if args.output_dir is None:
        output_dir = Path(os.environ["SLURM_TMPDIR"])
    else:
        output_dir = Path(args.output_dir)
    if not output_dir.exists():
        output_dir.mkdir(parents=True, exist_ok=False)

    # Store args
    output_yml = output_dir / "labels.yml"
    with open(output_yml, "w") as f:
        yaml.dump(vars(args), f)

    # Data dirs
    imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
    labels_path = Path(args.masker_test_set_dir) / "labels"

    # Read CSV
    df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")

    # Select best model
    df = df_all.loc[df_all.model_feats == args.best_model]
    v_key, model_dir = df.model.unique()[0].split("/")
    model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir

    # Set up plot
    sns.reset_orig()
    sns.set(style="whitegrid")
    plt.rcParams.update({"font.family": "serif"})
    plt.rcParams.update(
        {
            "font.serif": [
                "Computer Modern Roman",
                "Times New Roman",
                "Utopia",
                "New Century Schoolbook",
                "Century Schoolbook L",
                "ITC Bookman",
                "Bookman",
                "Times",
                "Palatino",
                "Charter",
                "serif" "Bitstream Vera Serif",
                "DejaVu Serif",
            ]
        }
    )

    if args.seed:
        np.random.seed(args.seed)
    img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
    dict_images = {}
    idx = 0

    # Define grid of subplots
    grid_vmargin = 0.03  # Extent of the vertical margin between metric grids
    ax_hspace = 0.04  # Extent of the vertical space between axes of same grid
    ax_wspace = 0.05  # Extent of the horizontal space between axes of same grid
    n_grids = len(metrics)
    n_cols = 4
    n_rows = 2
    h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids

    fig1 = plt.figure(dpi=200, figsize=(11, 13))

    n_ = 0
    add_title = False
    for metric_id, metric in enumerate(metrics):

        # Create grid
        top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
        bottom_grid = top_grid - h_grid
        gridspec = GridSpec(
            n_rows,
            n_cols,
            wspace=ax_wspace,
            hspace=ax_hspace,
            bottom=bottom_grid,
            top=top_grid,
        )

        # Select best
        if metric == "error":
            ascending = True
        else:
            ascending = False
        idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
        srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
        img_id = img_ids[idx]
        dict_images.update({img_id: srs_sel})
        # Read images
        img_filename = srs_sel.filename

        axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
        if not args.no_images:
            n_ += 1
            if metric_id == 0:
                add_title = True
            plot_images_metric(
                axes_row,
                metric,
                img_filename,
                img_id,
                n_,
                srs_sel,
                add_title=add_title,
                do_legend=False,
            )
            add_title = False

        idx += 1
        print("1 more row done.")
        # Select worst
        if metric == "error":
            ascending = False
        else:
            ascending = True
        idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
        srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
        img_id = img_ids[idx]
        dict_images.update({img_id: srs_sel})
        # Read images
        img_filename = srs_sel.filename

        axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
        if not args.no_images:
            n_ += 1
            plot_images_metric(
                axes_row,
                metric,
                img_filename,
                img_id,
                n_,
                srs_sel,
                add_title=add_title,
                do_legend=False,
            )

        idx += 1
        print("1 more row done.")

    output_fig = output_dir / "all_metrics.png"

    fig1.tight_layout()  # (pad=1.5)  #
    fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")

    # Scatter plot
    fig2 = plt.figure(dpi=200)

    scatterplot_metrics(fig2.gca(), df, df_all, dict_images)

    #     fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
    #
    #     scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
    #     scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
    #     scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)

    output_fig = output_dir / "scatterplots.png"
    fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")