optical_flow_benchmark.py 10.4 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268
#!/usr/bin/env python
from __future__ import print_function
import os, sys, shutil
import argparse
import json, re
from subprocess import check_output
import datetime
import matplotlib.pyplot as plt


def load_json(path):
    f = open(path, "r")
    data = json.load(f)
    return data


def save_json(obj, path):
    tmp_file = path + ".bak"
    f = open(tmp_file, "w")
    json.dump(obj, f, indent=2)
    f.flush()
    os.fsync(f.fileno())
    f.close()
    try:
        os.rename(tmp_file, path)
    except:
        os.remove(path)
        os.rename(tmp_file, path)


def parse_evaluation_result(input_str, i):
    res = {}
    res['frame_number'] = i + 1
    res['error'] = {}
    regex = "([A-Za-z. \\[\\].0-9]+):[ ]*([0-9]*\.[0-9]+|[0-9]+)"
    for elem in re.findall(regex,input_str):
        if "Time" in elem[0]:
            res['time'] = float(elem[1])
        elif "Average" in elem[0]:
            res['error']['average'] = float(elem[1])
        elif "deviation" in elem[0]:
            res['error']['std'] = float(elem[1])
        else:
            res['error'][elem[0]] = float(elem[1])
    return res


def evaluate_sequence(sequence, algorithm, dataset, executable, img_files, gt_files,
                      state, state_path):
    if "eval_results" not in state[dataset][algorithm][-1].keys():
        state[dataset][algorithm][-1]["eval_results"] = {}
    elif sequence in state[dataset][algorithm][-1]["eval_results"].keys():
        return

    res = []
    for i in range(len(img_files) - 1):
        sys.stdout.write("Algorithm: %-20s Sequence: %-10s Done: [%3d/%3d]\r" %
                         (algorithm, sequence, i, len(img_files) - 1)),
        sys.stdout.flush()

        res_string = check_output([executable, img_files[i], img_files[i + 1],
                                   algorithm, gt_files[i]])
        res.append(parse_evaluation_result(res_string, i))
    state[dataset][algorithm][-1]["eval_results"][sequence] = res
    save_json(state, state_path)

#############################DATSET DEFINITIONS################################

def evaluate_mpi_sintel(source_dir, algorithm, evaluation_executable, state, state_path):
    evaluation_result = {}
    img_dir = os.path.join(source_dir, 'mpi_sintel', 'training', 'final')
    gt_dir = os.path.join(source_dir, 'mpi_sintel', 'training', 'flow')
    sequences = [f for f in os.listdir(img_dir)
                 if os.path.isdir(os.path.join(img_dir, f))]
    for seq in sequences:
        img_files = sorted([os.path.join(img_dir, seq, f)
                            for f in os.listdir(os.path.join(img_dir, seq))
                            if f.endswith(".png")])
        gt_files = sorted([os.path.join(gt_dir, seq, f)
                           for f in os.listdir(os.path.join(gt_dir, seq))
                           if f.endswith(".flo")])
        evaluation_result[seq] = evaluate_sequence(seq, algorithm, 'mpi_sintel',
            evaluation_executable, img_files, gt_files, state, state_path)
    return evaluation_result


def evaluate_middlebury(source_dir, algorithm, evaluation_executable, state, state_path):
    evaluation_result = {}
    img_dir = os.path.join(source_dir, 'middlebury', 'other-data')
    gt_dir = os.path.join(source_dir, 'middlebury', 'other-gt-flow')
    sequences = [f for f in os.listdir(gt_dir)
                 if os.path.isdir(os.path.join(gt_dir, f))]
    for seq in sequences:
        img_files = sorted([os.path.join(img_dir, seq, f)
                            for f in os.listdir(os.path.join(img_dir, seq))
                            if f.endswith(".png")])
        gt_files = sorted([os.path.join(gt_dir, seq, f)
                           for f in os.listdir(os.path.join(gt_dir, seq))
                           if f.endswith(".flo")])
        evaluation_result[seq] = evaluate_sequence(seq, algorithm, 'middlebury',
            evaluation_executable, img_files, gt_files, state, state_path)
    return evaluation_result


dataset_eval_functions = {
    "mpi_sintel": evaluate_mpi_sintel,
    "middlebury": evaluate_middlebury
}

###############################################################################

def create_dir(dir):
    if not os.path.exists(dir):
        os.makedirs(dir)


def parse_sequence(input_str):
    if len(input_str) == 0:
        return []
    else:
        return [o.strip() for o in input_str.split(",") if o]


def build_chart(dst_folder, state, dataset):
    fig = plt.figure(figsize=(16, 10))
    markers = ["o", "s", "h", "^", "D"]
    marker_idx = 0
    colors = ["b", "g", "r"]
    color_idx = 0
    for algo in state[dataset].keys():
        for eval_instance in state[dataset][algo]:
            name = algo + "--" + eval_instance["timestamp"]
            average_time = 0.0
            average_error = 0.0
            num_elem = 0
            for seq in eval_instance["eval_results"].keys():
                for frame in eval_instance["eval_results"][seq]:
                    average_time += frame["time"]
                    average_error += frame["error"]["average"]
                    num_elem += 1
            average_time /= num_elem
            average_error /= num_elem

            marker_style = colors[color_idx] + markers[marker_idx]
            color_idx += 1
            if color_idx >= len(colors):
                color_idx = 0
            marker_idx += 1
            if marker_idx >= len(markers):
                marker_idx = 0
            plt.gca().plot([average_time], [average_error],
                           marker_style,
                           markersize=14,
                           label=name)

    plt.gca().set_ylabel('Average Endpoint Error (EPE)', fontsize=20)
    plt.gca().set_xlabel('Average Runtime (seconds per frame)', fontsize=20)
    plt.gca().set_xscale("log")
    plt.gca().set_title('Evaluation on ' + dataset, fontsize=20)

    plt.gca().legend()
    fig.savefig(os.path.join(dst_folder, "evaluation_results_" + dataset + ".png"),
                bbox_inches='tight')
    plt.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Optical flow benchmarking script',
        formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument(
        "bin_path",
        default="./optflow-example-optical_flow_evaluation",
        help="Path to the optical flow evaluation executable")
    parser.add_argument(
        "-a",
        "--algorithms",
        metavar="ALGORITHMS",
        default="",
        help=("Comma-separated list of optical-flow algorithms to evaluate "
              "(example: -a farneback,tvl1,deepflow). Note that previously "
              "evaluated algorithms are also included in the output charts"))
    parser.add_argument(
        "-d",
        "--datasets",
        metavar="DATASETS",
        default="mpi_sintel",
        help=("Comma-separated list of datasets for evaluation (currently only "
              "'mpi_sintel' and 'middlebury' are supported)"))
    parser.add_argument(
        "-f",
        "--dataset_folder",
        metavar="DATASET_FOLDER",
        default="./OF_datasets",
        help=("Path to a folder containing datasets. To enable evaluation on "
              "MPI Sintel dataset, please download it using the following links: "
              "http://files.is.tue.mpg.de/sintel/MPI-Sintel-training_images.zip and "
              "http://files.is.tue.mpg.de/sintel/MPI-Sintel-training_extras.zip and "
              "unzip these archives into the 'mpi_sintel' folder. To enable evaluation "
              "on the Middlebury dataset use the following links: "
              "http://vision.middlebury.edu/flow/data/comp/zip/other-color-twoframes.zip, "
              "http://vision.middlebury.edu/flow/data/comp/zip/other-gt-flow.zip. "
              "These should be unzipped into 'middlebury' folder"))
    parser.add_argument(
        "-o",
        "--out",
        metavar="OUT_DIR",
        default="./OF_evaluation_results",
        help="Output directory where to store benchmark results")
    parser.add_argument(
        "-s",
        "--state",
        metavar="STATE_JSON",
        default="./OF_evaluation_state.json",
        help=("Path to a json file that stores the current evaluation state and "
              "previous evaluation results"))
    args, other_args = parser.parse_known_args()

    if not os.path.isfile(args.bin_path):
        print("Error: " + args.bin_path + " does not exist")
        sys.exit(1)

    if not os.path.exists(args.dataset_folder):
        print("Error: " + args.dataset_folder + (" does not exist. Please, correctly "
                                                 "specify the -f parameter"))
        sys.exit(1)

    state = {}
    if os.path.isfile(args.state):
        state = load_json(args.state)

    algorithm_list = parse_sequence(args.algorithms)
    dataset_list = parse_sequence(args.datasets)
    for dataset in dataset_list:
        if dataset not in dataset_eval_functions.keys():
            print("Error: unsupported dataset " + dataset)
            sys.exit(1)
        if dataset not in os.listdir(args.dataset_folder):
            print("Error: " + os.path.join(args.dataset_folder, dataset) + (" does not exist. "
                              "Please, download the dataset and follow the naming conventions "
                              "(use -h for more information)"))
            sys.exit(1)

    for dataset in dataset_list:
        if dataset not in state.keys():
            state[dataset] = {}
        for algorithm in algorithm_list:
            if algorithm in state[dataset].keys():
                last_eval_instance = state[dataset][algorithm][-1]
                if "finished" not in last_eval_instance.keys():
                    print(("Continuing an unfinished evaluation of " +
                          algorithm + " started at " + last_eval_instance["timestamp"]))
                else:
                    state[dataset][algorithm].append({"timestamp":
                        datetime.datetime.now().strftime("%Y-%m-%d--%H-%M")})
            else:
                state[dataset][algorithm] = [{"timestamp":
                    datetime.datetime.now().strftime("%Y-%m-%d--%H-%M")}]
            save_json(state, args.state)
            dataset_eval_functions[dataset](args.dataset_folder, algorithm, args.bin_path,
                                            state, args.state)
            state[dataset][algorithm][-1]["finished"] = True
            save_json(state, args.state)
    save_json(state, args.state)

    create_dir(args.out)
    for dataset in dataset_list:
        build_chart(args.out, state, dataset)