# %BANNER_BEGIN% # --------------------------------------------------------------------- # %COPYRIGHT_BEGIN% # # Magic Leap, Inc. ("COMPANY") CONFIDENTIAL # # Unpublished Copyright (c) 2020 # Magic Leap, Inc., All Rights Reserved. # # NOTICE: All information contained herein is, and remains the property # of COMPANY. The intellectual and technical concepts contained herein # are proprietary to COMPANY and may be covered by U.S. and Foreign # Patents, patents in process, and are protected by trade secret or # copyright law. Dissemination of this information or reproduction of # this material is strictly forbidden unless prior written permission is # obtained from COMPANY. Access to the source code contained herein is # hereby forbidden to anyone except current COMPANY employees, managers # or contractors who have executed Confidentiality and Non-disclosure # agreements explicitly covering such access. # # The copyright notice above does not evidence any actual or intended # publication or disclosure of this source code, which includes # information that is confidential and/or proprietary, and is a trade # secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION, # PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS # SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS # STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND # INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE # CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS # TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE, # USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART. # # %COPYRIGHT_END% # ---------------------------------------------------------------------- # %AUTHORS_BEGIN% # # Originating Authors: Paul-Edouard Sarlin # Daniel DeTone # Tomasz Malisiewicz # # %AUTHORS_END% # --------------------------------------------------------------------*/ # %BANNER_END% from pathlib import Path import time from collections import OrderedDict from threading import Thread import numpy as np import cv2 import torch import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') class AverageTimer: """ Class to help manage printing simple timing of code execution. """ def __init__(self, smoothing=0.3, newline=False): self.smoothing = smoothing self.newline = newline self.times = OrderedDict() self.will_print = OrderedDict() self.reset() def reset(self): now = time.time() self.start = now self.last_time = now for name in self.will_print: self.will_print[name] = False def update(self, name='default'): now = time.time() dt = now - self.last_time if name in self.times: dt = self.smoothing * dt + (1 - self.smoothing) * self.times[name] self.times[name] = dt self.will_print[name] = True self.last_time = now def print(self, text='Timer'): total = 0. print('[{}]'.format(text), end=' ') for key in self.times: val = self.times[key] if self.will_print[key]: print('%s=%.3f' % (key, val), end=' ') total += val print('total=%.3f sec {%.1f FPS}' % (total, 1./total), end=' ') if self.newline: print(flush=True) else: print(end='\r', flush=True) self.reset() class VideoStreamer: """ Class to help process image streams. Four types of possible inputs:" 1.) USB Webcam. 2.) An IP camera 3.) A directory of images (files in directory matching 'image_glob'). 4.) A video file, such as an .mp4 or .avi file. """ def __init__(self, basedir, resize, skip, image_glob, max_length=1000000): self._ip_grabbed = False self._ip_running = False self._ip_camera = False self._ip_image = None self._ip_index = 0 self.cap = [] self.camera = True self.video_file = False self.listing = [] self.resize = resize self.interp = cv2.INTER_AREA self.i = 0 self.skip = skip self.max_length = max_length if isinstance(basedir, int) or basedir.isdigit(): print('==> Processing USB webcam input: {}'.format(basedir)) self.cap = cv2.VideoCapture(int(basedir)) self.listing = range(0, self.max_length) elif basedir.startswith(('http', 'rtsp')): print('==> Processing IP camera input: {}'.format(basedir)) self.cap = cv2.VideoCapture(basedir) self.start_ip_camera_thread() self._ip_camera = True self.listing = range(0, self.max_length) elif Path(basedir).is_dir(): print('==> Processing image directory input: {}'.format(basedir)) self.listing = list(Path(basedir).glob(image_glob[0])) for j in range(1, len(image_glob)): image_path = list(Path(basedir).glob(image_glob[j])) self.listing = self.listing + image_path self.listing.sort() self.listing = self.listing[::self.skip] self.max_length = np.min([self.max_length, len(self.listing)]) if self.max_length == 0: raise IOError('No images found (maybe bad \'image_glob\' ?)') self.listing = self.listing[:self.max_length] self.camera = False elif Path(basedir).exists(): print('==> Processing video input: {}'.format(basedir)) self.cap = cv2.VideoCapture(basedir) self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) num_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) self.listing = range(0, num_frames) self.listing = self.listing[::self.skip] self.video_file = True self.max_length = np.min([self.max_length, len(self.listing)]) self.listing = self.listing[:self.max_length] else: raise ValueError('VideoStreamer input \"{}\" not recognized.'.format(basedir)) if self.camera and not self.cap.isOpened(): raise IOError('Could not read camera') def load_image(self, impath): """ Read image as grayscale and resize to img_size. Inputs impath: Path to input image. Returns grayim: uint8 numpy array sized H x W. """ grayim = cv2.imread(impath, 0) if grayim is None: raise Exception('Error reading image %s' % impath) w, h = grayim.shape[1], grayim.shape[0] w_new, h_new = process_resize(w, h, self.resize) grayim = cv2.resize( grayim, (w_new, h_new), interpolation=self.interp) return grayim def next_frame(self): """ Return the next frame, and increment internal counter. Returns image: Next H x W image. status: True or False depending whether image was loaded. """ if self.i == self.max_length: return (None, False) if self.camera: if self._ip_camera: #Wait for first image, making sure we haven't exited while self._ip_grabbed is False and self._ip_exited is False: time.sleep(.001) ret, image = self._ip_grabbed, self._ip_image.copy() if ret is False: self._ip_running = False else: ret, image = self.cap.read() if ret is False: print('VideoStreamer: Cannot get image from camera') return (None, False) w, h = image.shape[1], image.shape[0] if self.video_file: self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.listing[self.i]) w_new, h_new = process_resize(w, h, self.resize) image = cv2.resize(image, (w_new, h_new), interpolation=self.interp) image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) else: image_file = str(self.listing[self.i]) image = self.load_image(image_file) self.i = self.i + 1 return (image, True) def start_ip_camera_thread(self): self._ip_thread = Thread(target=self.update_ip_camera, args=()) self._ip_running = True self._ip_thread.start() self._ip_exited = False return self def update_ip_camera(self): while self._ip_running: ret, img = self.cap.read() if ret is False: self._ip_running = False self._ip_exited = True self._ip_grabbed = False return self._ip_image = img self._ip_grabbed = ret self._ip_index += 1 #print('IPCAMERA THREAD got frame {}'.format(self._ip_index)) def cleanup(self): self._ip_running = False # --- PREPROCESSING --- def process_resize(w, h, resize): assert(len(resize) > 0 and len(resize) <= 2) if len(resize) == 1 and resize[0] > -1: scale = resize[0] / max(h, w) w_new, h_new = int(round(w*scale)), int(round(h*scale)) elif len(resize) == 1 and resize[0] == -1: w_new, h_new = w, h else: # len(resize) == 2: w_new, h_new = resize[0], resize[1] # Issue warning if resolution is too small or too large. if max(w_new, h_new) < 160: print('Warning: input resolution is very small, results may vary') elif max(w_new, h_new) > 2000: print('Warning: input resolution is very large, results may vary') return w_new, h_new def frame2tensor(frame, device): return torch.from_numpy(frame/255.).float()[None, None].to(device) def read_image(path, device, resize, rotation, resize_float): image = cv2.imread(str(path), cv2.IMREAD_GRAYSCALE) if image is None: return None, None, None w, h = image.shape[1], image.shape[0] w_new, h_new = process_resize(w, h, resize) scales = (float(w) / float(w_new), float(h) / float(h_new)) if resize_float: image = cv2.resize(image.astype('float32'), (w_new, h_new)) else: image = cv2.resize(image, (w_new, h_new)).astype('float32') if rotation != 0: image = np.rot90(image, k=rotation) if rotation % 2: scales = scales[::-1] inp = frame2tensor(image, device) return image, inp, scales def process_image(image, device, resize, rotation, resize_float): image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if image is None: return None, None, None w, h = image.shape[1], image.shape[0] w_new, h_new = process_resize(w, h, resize) scales = (float(w) / float(w_new), float(h) / float(h_new)) if resize_float: image = cv2.resize(image.astype('float32'), (w_new, h_new)) else: image = cv2.resize(image, (w_new, h_new)).astype('float32') if rotation != 0: image = np.rot90(image, k=rotation) if rotation % 2: scales = scales[::-1] inp = frame2tensor(image, device) return image, inp, scales # --- GEOMETRY --- def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999): if len(kpts0) < 5: return None f_mean = np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]]) norm_thresh = thresh / f_mean kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] E, mask = cv2.findEssentialMat( kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf, method=cv2.RANSAC) assert E is not None best_num_inliers = 0 ret = None for _E in np.split(E, len(E) / 3): n, R, t, _ = cv2.recoverPose( _E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) if n > best_num_inliers: best_num_inliers = n ret = (R, t[:, 0], mask.ravel() > 0) return ret def rotate_intrinsics(K, image_shape, rot): """image_shape is the shape of the image after rotation""" assert rot <= 3 h, w = image_shape[:2][::-1 if (rot % 2) else 1] fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2] rot = rot % 4 if rot == 1: return np.array([[fy, 0., cy], [0., fx, w-1-cx], [0., 0., 1.]], dtype=K.dtype) elif rot == 2: return np.array([[fx, 0., w-1-cx], [0., fy, h-1-cy], [0., 0., 1.]], dtype=K.dtype) else: # if rot == 3: return np.array([[fy, 0., h-1-cy], [0., fx, cx], [0., 0., 1.]], dtype=K.dtype) def rotate_pose_inplane(i_T_w, rot): rotation_matrices = [ np.array([[np.cos(r), -np.sin(r), 0., 0.], [np.sin(r), np.cos(r), 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]], dtype=np.float32) for r in [np.deg2rad(d) for d in (0, 270, 180, 90)] ] return np.dot(rotation_matrices[rot], i_T_w) def scale_intrinsics(K, scales): scales = np.diag([1./scales[0], 1./scales[1], 1.]) return np.dot(scales, K) def to_homogeneous(points): return np.concatenate([points, np.ones_like(points[:, :1])], axis=-1) def compute_epipolar_error(kpts0, kpts1, T_0to1, K0, K1): kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] kpts0 = to_homogeneous(kpts0) kpts1 = to_homogeneous(kpts1) t0, t1, t2 = T_0to1[:3, 3] t_skew = np.array([ [0, -t2, t1], [t2, 0, -t0], [-t1, t0, 0] ]) E = t_skew @ T_0to1[:3, :3] Ep0 = kpts0 @ E.T # N x 3 p1Ep0 = np.sum(kpts1 * Ep0, -1) # N Etp1 = kpts1 @ E # N x 3 d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2) + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2)) return d def angle_error_mat(R1, R2): cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2 cos = np.clip(cos, -1., 1.) # numercial errors can make it out of bounds return np.rad2deg(np.abs(np.arccos(cos))) def angle_error_vec(v1, v2): n = np.linalg.norm(v1) * np.linalg.norm(v2) return np.rad2deg(np.arccos(np.clip(np.dot(v1, v2) / n, -1.0, 1.0))) def compute_pose_error(T_0to1, R, t): R_gt = T_0to1[:3, :3] t_gt = T_0to1[:3, 3] error_t = angle_error_vec(t, t_gt) error_t = np.minimum(error_t, 180 - error_t) # ambiguity of E estimation error_R = angle_error_mat(R, R_gt) return error_t, error_R def pose_auc(errors, thresholds): sort_idx = np.argsort(errors) errors = np.array(errors.copy())[sort_idx] recall = (np.arange(len(errors)) + 1) / len(errors) errors = np.r_[0., errors] recall = np.r_[0., recall] aucs = [] for t in thresholds: last_index = np.searchsorted(errors, t) r = np.r_[recall[:last_index], recall[last_index-1]] e = np.r_[errors[:last_index], t] aucs.append(np.trapz(r, x=e)/t) return aucs # --- VISUALIZATION --- def plot_image_pair(imgs, dpi=100, size=6, pad=.5): n = len(imgs) assert n == 2, 'number of images must be two' figsize = (size*n, size*3/4) if size is not None else None _, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi) for i in range(n): ax[i].imshow(imgs[i], cmap=plt.get_cmap('gray'), vmin=0, vmax=255) ax[i].get_yaxis().set_ticks([]) ax[i].get_xaxis().set_ticks([]) for spine in ax[i].spines.values(): # remove frame spine.set_visible(False) plt.tight_layout(pad=pad) def plot_keypoints(kpts0, kpts1, color='w', ps=2): ax = plt.gcf().axes ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps) ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps) def plot_matches(kpts0, kpts1, color, lw=1.5, ps=4): fig = plt.gcf() ax = fig.axes fig.canvas.draw() transFigure = fig.transFigure.inverted() fkpts0 = transFigure.transform(ax[0].transData.transform(kpts0)) fkpts1 = transFigure.transform(ax[1].transData.transform(kpts1)) fig.lines = [matplotlib.lines.Line2D( (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw) for i in range(len(kpts0))] ax[0].scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps) ax[1].scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps) def make_matching_plot(image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, text, path, show_keypoints=False, fast_viz=False, opencv_display=False, opencv_title='matches', small_text=[]): if fast_viz: make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, text, path, show_keypoints, 10, opencv_display, opencv_title, small_text) return plot_image_pair([image0, image1]) if show_keypoints: plot_keypoints(kpts0, kpts1, color='k', ps=4) plot_keypoints(kpts0, kpts1, color='w', ps=2) plot_matches(mkpts0, mkpts1, color) fig = plt.gcf() txt_color = 'k' if image0[:100, :150].mean() > 200 else 'w' fig.text( 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes, fontsize=15, va='top', ha='left', color=txt_color) txt_color = 'k' if image0[-100:, :150].mean() > 200 else 'w' fig.text( 0.01, 0.01, '\n'.join(small_text), transform=fig.axes[0].transAxes, fontsize=5, va='bottom', ha='left', color=txt_color) plt.savefig(str(path), bbox_inches='tight', pad_inches=0) plt.close() def make_matching_plot_fast(image0, image1, kpts0, kpts1, mkpts0, mkpts1, color, text, path=None, show_keypoints=False, margin=10, opencv_display=False, opencv_title='', small_text=[]): H0, W0 = image0.shape H1, W1 = image1.shape H, W = max(H0, H1), W0 + W1 + margin out = 255*np.ones((H, W), np.uint8) out[:H0, :W0] = image0 out[:H1, W0+margin:] = image1 out = np.stack([out]*3, -1) if show_keypoints: kpts0, kpts1 = np.round(kpts0).astype(int), np.round(kpts1).astype(int) white = (255, 255, 255) black = (0, 0, 0) for x, y in kpts0: cv2.circle(out, (x, y), 2, black, -1, lineType=cv2.LINE_AA) cv2.circle(out, (x, y), 1, white, -1, lineType=cv2.LINE_AA) for x, y in kpts1: cv2.circle(out, (x + margin + W0, y), 2, black, -1, lineType=cv2.LINE_AA) cv2.circle(out, (x + margin + W0, y), 1, white, -1, lineType=cv2.LINE_AA) mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int) color = (np.array(color[:, :3])*255).astype(int)[:, ::-1] for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, color): c = c.tolist() cv2.line(out, (x0, y0), (x1 + margin + W0, y1), color=c, thickness=1, lineType=cv2.LINE_AA) # display line end-points as circles cv2.circle(out, (x0, y0), 2, c, -1, lineType=cv2.LINE_AA) cv2.circle(out, (x1 + margin + W0, y1), 2, c, -1, lineType=cv2.LINE_AA) # Scale factor for consistent visualization across scales. sc = min(H / 640., 2.0) # Big text. Ht = int(30 * sc) # text height txt_color_fg = (255, 255, 255) txt_color_bg = (0, 0, 0) for i, t in enumerate(text): cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX, 1.0*sc, txt_color_bg, 2, cv2.LINE_AA) cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX, 1.0*sc, txt_color_fg, 1, cv2.LINE_AA) # Small text. Ht = int(18 * sc) # text height for i, t in enumerate(reversed(small_text)): cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX, 0.5*sc, txt_color_bg, 2, cv2.LINE_AA) cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX, 0.5*sc, txt_color_fg, 1, cv2.LINE_AA) return out def error_colormap(x): return np.clip( np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)], -1), 0, 1)