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import cv2
import warnings
import numpy as np
from pathlib import Path
from hloc import logger
from common.utils import (
get_matcher_zoo,
load_config,
DEVICE,
ROOT,
)
from common.api import ImageMatchingAPI
def test_all(config: dict = None):
img_path1 = ROOT / "datasets/sacre_coeur/mapping/02928139_3448003521.jpg"
img_path2 = ROOT / "datasets/sacre_coeur/mapping/17295357_9106075285.jpg"
image0 = cv2.imread(str(img_path1))[:, :, ::-1] # RGB
image1 = cv2.imread(str(img_path2))[:, :, ::-1] # RGB
matcher_zoo_restored = get_matcher_zoo(config["matcher_zoo"])
for k, v in matcher_zoo_restored.items():
if image0 is None or image1 is None:
logger.error("Error: No images found! Please upload two images.")
enable = config["matcher_zoo"][k].get("enable", True)
skip_ci = config["matcher_zoo"][k].get("skip_ci", False)
if enable and not skip_ci:
logger.info(f"Testing {k} ...")
api = ImageMatchingAPI(conf=v, device=DEVICE)
api(image0, image1)
log_path = ROOT / "experiments" / "all"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
else:
logger.info(f"Skipping {k} ...")
return 0
def test_one():
img_path1 = ROOT / "datasets/sacre_coeur/mapping/02928139_3448003521.jpg"
img_path2 = ROOT / "datasets/sacre_coeur/mapping/17295357_9106075285.jpg"
image0 = cv2.imread(str(img_path1))[:, :, ::-1] # RGB
image1 = cv2.imread(str(img_path2))[:, :, ::-1] # RGB
# sparse
conf = {
"feature": {
"output": "feats-superpoint-n4096-rmax1600",
"model": {
"name": "superpoint",
"nms_radius": 3,
"max_keypoints": 4096,
"keypoint_threshold": 0.005,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1600,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
"matcher": {
"output": "matches-NN-mutual",
"model": {
"name": "nearest_neighbor",
"do_mutual_check": True,
"match_threshold": 0.2,
},
},
"dense": False,
}
api = ImageMatchingAPI(conf=conf, device=DEVICE)
api(image0, image1)
log_path = ROOT / "experiments" / "one"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
# dense
conf = {
"matcher": {
"output": "matches-loftr",
"model": {
"name": "loftr",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"width": 640,
"height": 480,
"force_resize": True,
},
"max_error": 1,
"cell_size": 1,
},
"dense": True,
}
api = ImageMatchingAPI(conf=conf, device=DEVICE)
api(image0, image1)
log_path = ROOT / "experiments" / "one"
log_path.mkdir(exist_ok=True, parents=True)
api.visualize(log_path=log_path)
return 0
if __name__ == "__main__":
config = load_config(ROOT / "common/config.yaml")
test_one()
test_all(config)
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