Spaces:
Running
Running
File size: 4,874 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 |
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 |
import os
from torch.multiprocessing import Process, Manager, set_start_method, Pool
import functools
import argparse
import yaml
import numpy as np
import sys
import cv2
from tqdm import trange
set_start_method("spawn", force=True)
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
sys.path.insert(0, ROOT_DIR)
from components import load_component
from utils import evaluation_utils, metrics
parser = argparse.ArgumentParser(description="dump eval data.")
parser.add_argument(
"--config_path", type=str, default="configs/eval/scannet_eval_sgm.yaml"
)
parser.add_argument("--num_process_match", type=int, default=4)
parser.add_argument("--num_process_eval", type=int, default=4)
parser.add_argument("--vis_folder", type=str, default=None)
args = parser.parse_args()
def feed_match(info, matcher):
x1, x2, desc1, desc2, size1, size2 = (
info["x1"],
info["x2"],
info["desc1"],
info["desc2"],
info["img1"].shape[:2],
info["img2"].shape[:2],
)
test_data = {
"x1": x1,
"x2": x2,
"desc1": desc1,
"desc2": desc2,
"size1": np.flip(np.asarray(size1)),
"size2": np.flip(np.asarray(size2)),
}
corr1, corr2 = matcher.run(test_data)
return [corr1, corr2]
def reader_handler(config, read_que):
reader = load_component("reader", config["name"], config)
for index in range(len(reader)):
index += 0
info = reader.run(index)
read_que.put(info)
read_que.put("over")
def match_handler(config, read_que, match_que):
matcher = load_component("matcher", config["name"], config)
match_func = functools.partial(feed_match, matcher=matcher)
pool = Pool(args.num_process_match)
cache = []
while True:
item = read_que.get()
# clear cache
if item == "over":
if len(cache) != 0:
results = pool.map(match_func, cache)
for cur_item, cur_result in zip(cache, results):
cur_item["corr1"], cur_item["corr2"] = cur_result[0], cur_result[1]
match_que.put(cur_item)
match_que.put("over")
break
cache.append(item)
# print(len(cache))
if len(cache) == args.num_process_match:
# matching in parallel
results = pool.map(match_func, cache)
for cur_item, cur_result in zip(cache, results):
cur_item["corr1"], cur_item["corr2"] = cur_result[0], cur_result[1]
match_que.put(cur_item)
cache = []
pool.close()
pool.join()
def evaluate_handler(config, match_que):
evaluator = load_component("evaluator", config["name"], config)
pool = Pool(args.num_process_eval)
cache = []
for _ in trange(config["num_pair"]):
item = match_que.get()
if item == "over":
if len(cache) != 0:
results = pool.map(evaluator.run, cache)
for cur_res in results:
evaluator.res_inqueue(cur_res)
break
cache.append(item)
if len(cache) == args.num_process_eval:
results = pool.map(evaluator.run, cache)
for cur_res in results:
evaluator.res_inqueue(cur_res)
cache = []
if args.vis_folder is not None:
# dump visualization
corr1_norm, corr2_norm = evaluation_utils.normalize_intrinsic(
item["corr1"], item["K1"]
), evaluation_utils.normalize_intrinsic(item["corr2"], item["K2"])
inlier_mask = metrics.compute_epi_inlier(
corr1_norm, corr2_norm, item["e"], config["inlier_th"]
)
display = evaluation_utils.draw_match(
item["img1"], item["img2"], item["corr1"], item["corr2"], inlier_mask
)
cv2.imwrite(
os.path.join(args.vis_folder, str(item["index"]) + ".png"), display
)
evaluator.parse()
if __name__ == "__main__":
with open(args.config_path, "r") as f:
config = yaml.load(f)
if args.vis_folder is not None and not os.path.exists(args.vis_folder):
os.mkdir(args.vis_folder)
read_que, match_que, estimate_que = (
Manager().Queue(maxsize=100),
Manager().Queue(maxsize=100),
Manager().Queue(maxsize=100),
)
read_process = Process(target=reader_handler, args=(config["reader"], read_que))
match_process = Process(
target=match_handler, args=(config["matcher"], read_que, match_que)
)
evaluate_process = Process(
target=evaluate_handler, args=(config["evaluator"], match_que)
)
read_process.start()
match_process.start()
evaluate_process.start()
read_process.join()
match_process.join()
evaluate_process.join()
|