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import numpy as np
import torch
import torchvision.transforms.functional as F
from types import SimpleNamespace
from .extract_features import read_image, resize_image
import cv2
device = "cuda" if torch.cuda.is_available() else "cpu"
confs = {
# Best quality but loads of points. Only use for small scenes
"loftr": {
"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, # max error for assigned keypoints (in px)
"cell_size": 1, # size of quantization patch (max 1 kp/patch)
},
# Semi-scalable loftr which limits detected keypoints
"loftr_aachen": {
"output": "matches-loftr_aachen",
"model": {
"name": "loftr",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {"grayscale": True, "resize_max": 1024, "dfactor": 8},
"max_error": 2, # max error for assigned keypoints (in px)
"cell_size": 8, # size of quantization patch (max 1 kp/patch)
},
# Use for matching superpoint feats with loftr
"loftr_superpoint": {
"output": "matches-loftr_aachen",
"model": {
"name": "loftr",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {"grayscale": True, "resize_max": 1024, "dfactor": 8},
"max_error": 4, # max error for assigned keypoints (in px)
"cell_size": 4, # size of quantization patch (max 1 kp/patch)
},
# Use topicfm for matching feats
"topicfm": {
"output": "matches-topicfm",
"model": {
"name": "topicfm",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1024,
"dfactor": 8,
"width": 640,
"height": 480,
},
},
# Use aspanformer for matching feats
"aspanformer": {
"output": "matches-aspanformer",
"model": {
"name": "aspanformer",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1024,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
"xfeat_dense": {
"output": "matches-xfeat_dense",
"model": {
"name": "xfeat_dense",
"max_keypoints": 8000,
},
"preprocessing": {
"grayscale": False,
"force_resize": False,
"resize_max": 1024,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
"dkm": {
"output": "matches-dkm",
"model": {
"name": "dkm",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": False,
"force_resize": True,
"resize_max": 1024,
"width": 80,
"height": 60,
"dfactor": 8,
},
},
"roma": {
"output": "matches-roma",
"model": {
"name": "roma",
"weights": "outdoor",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": False,
"force_resize": True,
"resize_max": 1024,
"width": 320,
"height": 240,
"dfactor": 8,
},
},
"dedode_sparse": {
"output": "matches-dedode",
"model": {
"name": "dedode",
"max_keypoints": 2000,
"match_threshold": 0.2,
"dense": False,
},
"preprocessing": {
"grayscale": False,
"force_resize": True,
"resize_max": 1024,
"width": 768,
"height": 768,
"dfactor": 8,
},
},
"sold2": {
"output": "matches-sold2",
"model": {
"name": "sold2",
"max_keypoints": 2000,
"match_threshold": 0.2,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1024,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
"gluestick": {
"output": "matches-gluestick",
"model": {
"name": "gluestick",
"use_lines": True,
"max_keypoints": 1000,
"max_lines": 300,
"force_num_keypoints": False,
},
"preprocessing": {
"grayscale": True,
"force_resize": True,
"resize_max": 1024,
"width": 640,
"height": 480,
"dfactor": 8,
},
},
}
def scale_keypoints(kpts, scale):
if np.any(scale != 1.0):
kpts *= kpts.new_tensor(scale)
return kpts
def scale_lines(lines, scale):
if np.any(scale != 1.0):
lines *= lines.new_tensor(scale)
return lines
def match(model, path_0, path_1, conf):
default_conf = {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"cache_images": False,
"force_resize": False,
"width": 320,
"height": 240,
}
def preprocess(image: np.ndarray):
image = image.astype(np.float32, copy=False)
size = image.shape[:2][::-1]
scale = np.array([1.0, 1.0])
if conf.resize_max:
scale = conf.resize_max / max(size)
if scale < 1.0:
size_new = tuple(int(round(x * scale)) for x in size)
image = resize_image(image, size_new, "cv2_area")
scale = np.array(size) / np.array(size_new)
if conf.force_resize:
size = image.shape[:2][::-1]
image = resize_image(image, (conf.width, conf.height), "cv2_area")
size_new = (conf.width, conf.height)
scale = np.array(size) / np.array(size_new)
if conf.grayscale:
assert image.ndim == 2, image.shape
image = image[None]
else:
image = image.transpose((2, 0, 1)) # HxWxC to CxHxW
image = torch.from_numpy(image / 255.0).float()
# assure that the size is divisible by dfactor
size_new = tuple(
map(
lambda x: int(x // conf.dfactor * conf.dfactor),
image.shape[-2:],
)
)
image = F.resize(image, size=size_new, antialias=True)
scale = np.array(size) / np.array(size_new)[::-1]
return image, scale
conf = SimpleNamespace(**{**default_conf, **conf})
image0 = read_image(path_0, conf.grayscale)
image1 = read_image(path_1, conf.grayscale)
image0, scale0 = preprocess(image0)
image1, scale1 = preprocess(image1)
image0 = image0.to(device)[None]
image1 = image1.to(device)[None]
pred = model({"image0": image0, "image1": image1})
# Rescale keypoints and move to cpu
kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"]
kpts0 = scale_keypoints(kpts0 + 0.5, scale0) - 0.5
kpts1 = scale_keypoints(kpts1 + 0.5, scale1) - 0.5
ret = {
"image0": image0.squeeze().cpu().numpy(),
"image1": image1.squeeze().cpu().numpy(),
"keypoints0": kpts0.cpu().numpy(),
"keypoints1": kpts1.cpu().numpy(),
}
if "mconf" in pred.keys():
ret["mconf"] = pred["mconf"].cpu().numpy()
return ret
@torch.no_grad()
def match_images(model, image_0, image_1, conf, device="cpu"):
default_conf = {
"grayscale": True,
"resize_max": 1024,
"dfactor": 8,
"cache_images": False,
"force_resize": False,
"width": 320,
"height": 240,
}
def preprocess(image: np.ndarray):
image = image.astype(np.float32, copy=False)
size = image.shape[:2][::-1]
scale = np.array([1.0, 1.0])
if conf.resize_max:
scale = conf.resize_max / max(size)
if scale < 1.0:
size_new = tuple(int(round(x * scale)) for x in size)
image = resize_image(image, size_new, "cv2_area")
scale = np.array(size) / np.array(size_new)
if conf.force_resize:
size = image.shape[:2][::-1]
image = resize_image(image, (conf.width, conf.height), "cv2_area")
size_new = (conf.width, conf.height)
scale = np.array(size) / np.array(size_new)
if conf.grayscale:
assert image.ndim == 2, image.shape
image = image[None]
else:
image = image.transpose((2, 0, 1)) # HxWxC to CxHxW
image = torch.from_numpy(image / 255.0).float()
# assure that the size is divisible by dfactor
size_new = tuple(
map(
lambda x: int(x // conf.dfactor * conf.dfactor),
image.shape[-2:],
)
)
image = F.resize(image, size=size_new)
scale = np.array(size) / np.array(size_new)[::-1]
return image, scale
conf = SimpleNamespace(**{**default_conf, **conf})
if len(image_0.shape) == 3 and conf.grayscale:
image0 = cv2.cvtColor(image_0, cv2.COLOR_RGB2GRAY)
else:
image0 = image_0
if len(image_0.shape) == 3 and conf.grayscale:
image1 = cv2.cvtColor(image_1, cv2.COLOR_RGB2GRAY)
else:
image1 = image_1
# comment following lines, image is always RGB mode
# if not conf.grayscale and len(image0.shape) == 3:
# image0 = image0[:, :, ::-1] # BGR to RGB
# if not conf.grayscale and len(image1.shape) == 3:
# image1 = image1[:, :, ::-1] # BGR to RGB
image0, scale0 = preprocess(image0)
image1, scale1 = preprocess(image1)
image0 = image0.to(device)[None]
image1 = image1.to(device)[None]
pred = model({"image0": image0, "image1": image1})
s0 = np.array(image_0.shape[:2][::-1]) / np.array(image0.shape[-2:][::-1])
s1 = np.array(image_1.shape[:2][::-1]) / np.array(image1.shape[-2:][::-1])
# Rescale keypoints and move to cpu
if "keypoints0" in pred.keys() and "keypoints1" in pred.keys():
kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"]
kpts0_origin = scale_keypoints(kpts0 + 0.5, s0) - 0.5
kpts1_origin = scale_keypoints(kpts1 + 0.5, s1) - 0.5
ret = {
"image0": image0.squeeze().cpu().numpy(),
"image1": image1.squeeze().cpu().numpy(),
"image0_orig": image_0,
"image1_orig": image_1,
"keypoints0": kpts0_origin.cpu().numpy(),
"keypoints1": kpts1_origin.cpu().numpy(),
"keypoints0_orig": kpts0_origin.cpu().numpy(),
"keypoints1_orig": kpts1_origin.cpu().numpy(),
"original_size0": np.array(image_0.shape[:2][::-1]),
"original_size1": np.array(image_1.shape[:2][::-1]),
"new_size0": np.array(image0.shape[-2:][::-1]),
"new_size1": np.array(image1.shape[-2:][::-1]),
"scale0": s0,
"scale1": s1,
}
if "mconf" in pred.keys():
ret["mconf"] = pred["mconf"].cpu().numpy()
elif "scores" in pred.keys(): #adapting loftr
ret["mconf"] = pred["scores"].cpu().numpy()
else:
ret["mconf"] = np.ones_like(kpts0.cpu().numpy()[:, 0])
if "lines0" in pred.keys() and "lines1" in pred.keys():
if "keypoints0" in pred.keys() and "keypoints1" in pred.keys():
kpts0, kpts1 = pred["keypoints0"], pred["keypoints1"]
kpts0_origin = scale_keypoints(kpts0 + 0.5, s0) - 0.5
kpts1_origin = scale_keypoints(kpts1 + 0.5, s1) - 0.5
kpts0_origin = kpts0_origin.cpu().numpy()
kpts1_origin = kpts1_origin.cpu().numpy()
else:
kpts0_origin, kpts1_origin = (
None,
None,
) # np.zeros([0]), np.zeros([0])
lines0, lines1 = pred["lines0"], pred["lines1"]
lines0_raw, lines1_raw = pred["raw_lines0"], pred["raw_lines1"]
lines0_raw = torch.from_numpy(lines0_raw.copy())
lines1_raw = torch.from_numpy(lines1_raw.copy())
lines0_raw = scale_lines(lines0_raw + 0.5, s0) - 0.5
lines1_raw = scale_lines(lines1_raw + 0.5, s1) - 0.5
lines0 = torch.from_numpy(lines0.copy())
lines1 = torch.from_numpy(lines1.copy())
lines0 = scale_lines(lines0 + 0.5, s0) - 0.5
lines1 = scale_lines(lines1 + 0.5, s1) - 0.5
ret = {
"image0_orig": image_0,
"image1_orig": image_1,
"line0": lines0_raw.cpu().numpy(),
"line1": lines1_raw.cpu().numpy(),
"line0_orig": lines0.cpu().numpy(),
"line1_orig": lines1.cpu().numpy(),
"line_keypoints0_orig": kpts0_origin,
"line_keypoints1_orig": kpts1_origin,
}
del pred
torch.cuda.empty_cache()
return ret
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