init fusion lcd orin config
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MobKBK
167
ALIKE/demo.py
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167
ALIKE/demo.py
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import copy
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import os
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import cv2
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import glob
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import logging
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import argparse
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import numpy as np
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from tqdm import tqdm
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from alike import ALike, configs
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class ImageLoader(object):
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def __init__(self, filepath: str):
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self.N = 3000
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if filepath.startswith('camera'):
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camera = int(filepath[6:])
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self.cap = cv2.VideoCapture(camera)
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if not self.cap.isOpened():
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raise IOError(f"Can't open camera {camera}!")
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logging.info(f'Opened camera {camera}')
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self.mode = 'camera'
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elif os.path.exists(filepath):
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if os.path.isfile(filepath):
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self.cap = cv2.VideoCapture(filepath)
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if not self.cap.isOpened():
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raise IOError(f"Can't open video {filepath}!")
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rate = self.cap.get(cv2.CAP_PROP_FPS)
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self.N = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
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duration = self.N / rate
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logging.info(f'Opened video {filepath}')
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logging.info(f'Frames: {self.N}, FPS: {rate}, Duration: {duration}s')
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self.mode = 'video'
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else:
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self.images = glob.glob(os.path.join(filepath, '*.png')) + \
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glob.glob(os.path.join(filepath, '*.jpg')) + \
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glob.glob(os.path.join(filepath, '*.ppm'))
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self.images.sort()
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self.N = len(self.images)
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logging.info(f'Loading {self.N} images')
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self.mode = 'images'
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else:
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raise IOError('Error filepath (camerax/path of images/path of videos): ', filepath)
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def __getitem__(self, item):
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if self.mode == 'camera' or self.mode == 'video':
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if item > self.N:
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return None
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ret, img = self.cap.read()
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if not ret:
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raise "Can't read image from camera"
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if self.mode == 'video':
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self.cap.set(cv2.CAP_PROP_POS_FRAMES, item)
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elif self.mode == 'images':
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filename = self.images[item]
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img = cv2.imread(filename)
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if img is None:
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raise Exception('Error reading image %s' % filename)
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return img
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def __len__(self):
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return self.N
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class SimpleTracker(object):
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def __init__(self):
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self.pts_prev = None
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self.desc_prev = None
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def update(self, img, pts, desc):
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N_matches = 0
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if self.pts_prev is None:
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self.pts_prev = pts
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self.desc_prev = desc
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out = copy.deepcopy(img)
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for pt1 in pts:
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p1 = (int(round(pt1[0])), int(round(pt1[1])))
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cv2.circle(out, p1, 1, (0, 0, 255), -1, lineType=16)
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else:
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matches = self.mnn_mather(self.desc_prev, desc)
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mpts1, mpts2 = self.pts_prev[matches[:, 0]], pts[matches[:, 1]]
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N_matches = len(matches)
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out = copy.deepcopy(img)
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for pt1, pt2 in zip(mpts1, mpts2):
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p1 = (int(round(pt1[0])), int(round(pt1[1])))
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p2 = (int(round(pt2[0])), int(round(pt2[1])))
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cv2.line(out, p1, p2, (0, 255, 0), lineType=16)
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cv2.circle(out, p2, 1, (0, 0, 255), -1, lineType=16)
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self.pts_prev = pts
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self.desc_prev = desc
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return out, N_matches
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def mnn_mather(self, desc1, desc2):
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sim = desc1 @ desc2.transpose()
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sim[sim < 0.9] = 0
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nn12 = np.argmax(sim, axis=1)
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nn21 = np.argmax(sim, axis=0)
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ids1 = np.arange(0, sim.shape[0])
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mask = (ids1 == nn21[nn12])
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matches = np.stack([ids1[mask], nn12[mask]])
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return matches.transpose()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='ALike Demo.')
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parser.add_argument('--input', type=str, default=r'E:\caodanyang\dataset\KITTI\odometry\data_odometry_color\dataset\sequences\00\image_2',
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help='Image directory or movie file or "camera0" (for webcam0).')
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parser.add_argument('--model', choices=['alike-t', 'alike-s', 'alike-n', 'alike-l'], default="alike-t",
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help="The model configuration")
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parser.add_argument('--device', type=str, default='cuda', help="Running device (default: cuda).")
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parser.add_argument('--top_k', type=int, default=-1,
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help='Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)')
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parser.add_argument('--scores_th', type=float, default=0.2,
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help='Detector score threshold (default: 0.2).')
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parser.add_argument('--n_limit', type=int, default=5000,
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help='Maximum number of keypoints to be detected (default: 5000).')
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parser.add_argument('--no_display', action='store_true',
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help='Do not display images to screen. Useful if running remotely (default: False).')
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parser.add_argument('--no_sub_pixel', action='store_true',
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help='Do not detect sub-pixel keypoints (default: False).')
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO)
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image_loader = ImageLoader(args.input)
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model = ALike(**configs[args.model],
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device=args.device,
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top_k=args.top_k,
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scores_th=args.scores_th,
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n_limit=args.n_limit)
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tracker = SimpleTracker()
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if not args.no_display:
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logging.info("Press 'q' to stop!")
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cv2.namedWindow(args.model)
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runtime = []
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progress_bar = tqdm(image_loader)
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for img in progress_bar:
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if img is None:
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break
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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pred = model(img_rgb, sub_pixel=not args.no_sub_pixel)
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kpts = pred['keypoints']
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desc = pred['descriptors']
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runtime.append(pred['time'])
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out, N_matches = tracker.update(img, kpts, desc)
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ave_fps = (1. / np.stack(runtime)).mean()
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status = f"Fps:{ave_fps:.1f}, Keypoints/Matches: {len(kpts)}/{N_matches}"
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progress_bar.set_description(status)
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if not args.no_display:
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cv2.setWindowTitle(args.model, args.model + ': ' + status)
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cv2.imshow(args.model, out)
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if cv2.waitKey(1) == ord('q'):
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break
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logging.info('Finished!')
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if not args.no_display:
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logging.info('Press any key to exit!')
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cv2.waitKey()
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