Spaces:
Sleeping
Sleeping
File size: 4,995 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
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 |
from pathlib import Path
import argparse
import cv2
import matplotlib.cm as cm
import torch
import numpy as np
from utils.nnmatching import NNMatching
from utils.misc import (
AverageTimer,
VideoStreamer,
make_matching_plot_fast,
frame2tensor,
)
torch.set_grad_enabled(False)
def compute_essential(matched_kp1, matched_kp2, K):
pts1 = cv2.undistortPoints(
matched_kp1,
cameraMatrix=K,
distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
)
pts2 = cv2.undistortPoints(
matched_kp2,
cameraMatrix=K,
distCoeffs=(-0.117918271740560, 0.075246403574314, 0, 0),
)
K_1 = np.eye(3)
# Estimate the homography between the matches using RANSAC
ransac_model, ransac_inliers = cv2.findEssentialMat(
pts1, pts2, K_1, method=cv2.RANSAC, prob=0.999, threshold=0.001, maxIters=10000
)
if ransac_inliers is None or ransac_model.shape != (3, 3):
ransac_inliers = np.array([])
ransac_model = None
return ransac_model, ransac_inliers, pts1, pts2
sizer = (960, 640)
focallength_x = 4.504986436499113e03 / (6744 / sizer[0])
focallength_y = 4.513311442889859e03 / (4502 / sizer[1])
K = np.eye(3)
K[0, 0] = focallength_x
K[1, 1] = focallength_y
K[0, 2] = 3.363322177533149e03 / (6744 / sizer[0]) # * 0.5
K[1, 2] = 2.291824660547715e03 / (4502 / sizer[1]) # * 0.5
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="DarkFeat demo",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--input", type=str, help="path to an image directory")
parser.add_argument(
"--output_dir",
type=str,
default=None,
help="Directory where to write output frames (If None, no output)",
)
parser.add_argument(
"--image_glob",
type=str,
nargs="+",
default=["*.ARW"],
help="Glob if a directory of images is specified",
)
parser.add_argument(
"--resize",
type=int,
nargs="+",
default=[640, 480],
help="Resize the input image before running inference. If two numbers, "
"resize to the exact dimensions, if one number, resize the max "
"dimension, if -1, do not resize",
)
parser.add_argument(
"--force_cpu", action="store_true", help="Force pytorch to run in CPU mode."
)
parser.add_argument("--model_path", type=str, help="Path to the pretrained model")
opt = parser.parse_args()
print(opt)
assert len(opt.resize) == 2
print("Will resize to {}x{} (WxH)".format(opt.resize[0], opt.resize[1]))
device = "cuda" if torch.cuda.is_available() and not opt.force_cpu else "cpu"
print('Running inference on device "{}"'.format(device))
matching = NNMatching(opt.model_path).eval().to(device)
keys = ["keypoints", "scores", "descriptors"]
vs = VideoStreamer(opt.input, opt.resize, opt.image_glob)
frame, ret = vs.next_frame()
assert ret, "Error when reading the first frame (try different --input?)"
frame_tensor = frame2tensor(frame, device)
last_data = matching.darkfeat({"image": frame_tensor})
last_data = {k + "0": [last_data[k]] for k in keys}
last_data["image0"] = frame_tensor
last_frame = frame
last_image_id = 0
if opt.output_dir is not None:
print("==> Will write outputs to {}".format(opt.output_dir))
Path(opt.output_dir).mkdir(exist_ok=True)
timer = AverageTimer()
while True:
frame, ret = vs.next_frame()
if not ret:
print("Finished demo_darkfeat.py")
break
timer.update("data")
stem0, stem1 = last_image_id, vs.i - 1
frame_tensor = frame2tensor(frame, device)
pred = matching({**last_data, "image1": frame_tensor})
kpts0 = last_data["keypoints0"][0].cpu().numpy()
kpts1 = pred["keypoints1"][0].cpu().numpy()
matches = pred["matches0"][0].cpu().numpy()
confidence = pred["matching_scores0"][0].cpu().numpy()
timer.update("forward")
valid = matches > -1
mkpts0 = kpts0[valid]
mkpts1 = kpts1[matches[valid]]
E, inliers, pts1, pts2 = compute_essential(mkpts0, mkpts1, K)
color = cm.jet(
np.clip(confidence[valid][inliers[:, 0].astype("bool")] * 2 - 1, -1, 1)
)
text = ["DarkFeat", "Matches: {}".format(inliers.sum())]
out = make_matching_plot_fast(
last_frame,
frame,
mkpts0[inliers[:, 0].astype("bool")],
mkpts1[inliers[:, 0].astype("bool")],
color,
text,
path=None,
small_text=" ",
)
if opt.output_dir is not None:
stem = "matches_{:06}_{:06}".format(stem0, stem1)
out_file = str(Path(opt.output_dir, stem + ".png"))
print("Writing image to {}".format(out_file))
cv2.imwrite(out_file, out)
|