├── LICENCE ├── README.md ├── dataset ├── README.md ├── depthEstimator.py ├── gsImgRectifier.py ├── gtFlowGenerator.py ├── poseHandler.py ├── pwcnet.py ├── stereoRectifier.py └── tum_process.py ├── images ├── data_gen.png ├── dso.jpg ├── networks.png ├── res_img.png └── rs_pose_net.png └── network ├── DataLoader.py ├── README.md ├── RsDepthNet.py ├── RsPoseNet.py ├── helpers.py ├── test.py ├── train_rsdepthnet.py └── train_rsposenet.py /LICENCE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IMU-Assisted Learning of Single-View Rolling Shutter Correction 2 | Follow the README in *dataset/* for dataset information and in *network/* for network training and testing. 3 | 4 | ## Samples 5 | 6 | 7 | ## DSO on resulting images 8 | -------------------------------------------------------------------------------- /dataset/README.md: -------------------------------------------------------------------------------- 1 | # Dataset Process 2 | * Overview 3 | 4 | 5 | 6 | * Usage 7 | * Option a: Process data from raw TUM dataset. 8 | * Dependencies (version we use) 9 | * [numpy(1.17.4)](https://numpy.org/) 10 | * [cv2](https://pypi.org/project/opencv-python/), [csv](https://docs.python.org/3/library/csv.html), [yaml](https://pyyaml.org/wiki/PyYAMLDocumentation) for reading TUM dataset 11 | * [Tensorflow(2.3.0)](https://www.tensorflow.org/) for pwcnet 12 | * [scipy(1.4.1)](https://www.scipy.org/) for pose interpolation 13 | * [pandas(1.2.3)](https://pandas.pydata.org/) for image post-processing (interpolation) 14 | * [tqdm](https://pypi.org/project/tqdm/) for progress visualization 15 | 16 | * Download [TUM Rolling Shutter Dataset](https://vision.in.tum.de/data/datasets/rolling-shutter-dataset) with Euroc/DSO format, modify **data_path** in **tum_process.py** accordingly. 17 | * Download [PWC-Net weights](https://drive.google.com/file/d/1hB5nCbBJf6I06dL5VX4aiAdTYUzlLCsi/view?usp=sharing) (thank Igor Slinko for shaing the weights), modify **ckpt_path** in **pwcnet.py** accordingly. 18 | * Specify **save_path** in **tum_process.py** if needed; otherwise, save to *../data/* by default. 19 | * Process the dataset. 20 | ``` 21 | python3 tum_process.py 22 | ``` 23 | * Option b: Download the [processed data](https://drive.google.com/file/d/16GJnkvVX1t6cU7lUOvpoc8L-cKy-R55-/view?usp=sharing) directly. 24 | 25 | # Dataset Format 26 | * Network input: 27 | * **seq\*/cam1/images/**: input RS images. 28 | * **seq\*/cam1/imu_cam1_v1.npy**: input row-wise IMU data rotated to cam1 frame, with a shape of [image_count, image_height, 6], the last dimension represents [gryo_x, gryo_y, gryo_z, acc_x, acc_y, acc_z]. 29 | * **seq\*/cam1/camera.npy**: global camera intrinsic parameters, [fx, fy, cx, cy]. 30 | * **seq\*/cam1/v1_lut.npy**: global lens distortion look-up table, with a shape of [image_height, image_width], the value represents the original scan-line of that pixel. 31 | 32 | * Network training ground-truth: 33 | * **seq\*/cam1/depth/**: depth maps. 34 | * **seq\*/cam1/flows_rs2gs/**: RS correction flow mapping from RS image to GS image. 35 | * **seq\*/cam1/pose_cam1_v1.npy**: row-wise pose for cam1, with a shape of [image_count, image_height, 6], the last dimension represents [t_x, t_y, t_z, r_x, r_y, r_z], the rotation uses angle-axis representation. 36 | 37 | * Other data for testing: 38 | * **seq\*/cam1/images_gs/**: ground-truth GS images. 39 | * **seq\*/cam1/pose_w_cam1.txt**: ground-truth pose of cam1 in world frame, with a shape of [image_count, 8], the last dimension represents [image_index, t_x, t_y, t_z, q_x, q_y, q_z, q_w], the rotation uses quaternion representation; we use it to benchmark visual SLAM systems on resulting images. 40 | 41 | # Code Citations 42 | * The implementation of PWC-Net in this work (**pwcnet.py**) is written by [Phil Ferriere](https://github.com/philferriere/tfoptflow/blob/master/tfoptflow/model_pwcnet.py). -------------------------------------------------------------------------------- /dataset/depthEstimator.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | import numpy as np 19 | import os 20 | import cv2 21 | from tqdm import tqdm 22 | from numpy import linalg as LA 23 | 24 | from pwcnet import ModelPWCNet 25 | 26 | pwc_net = ModelPWCNet() 27 | 28 | 29 | def getFlowBD(img0, img1, windowName=''): 30 | FLOW_THRES = 2 # threshold to accept a flow by bi-directional matching 31 | h, w = img0.shape[:2] 32 | 33 | img_pairs = [(img0, img1), (img1, img0)] 34 | flow01, flow10 = pwc_net.predict_from_img_pairs(img_pairs, batch_size=2) 35 | 36 | flow01_filtered = np.full_like(flow01, np.nan) 37 | for v0 in range(h): 38 | for u0 in range(w): 39 | fu01, fv01 = flow01[v0, u0, :] 40 | u1, v1 = u0+fu01, v0+fv01 41 | u1i, v1i = int(u1+0.5), int(v1+0.5) 42 | if 0 <= v1i < h and 0 <= u1i < w: 43 | fu10, fv10 = flow10[v1i, u1i, :] 44 | du, dv = u1+fu10-u0, v1+fv10-v0 45 | if (du*du+dv*dv) < FLOW_THRES: # bi-directional filtering 46 | flow01_filtered[v0, u0, 0] = flow01[v0, u0, 0] 47 | flow01_filtered[v0, u0, 1] = flow01[v0, u0, 1] 48 | return flow01_filtered 49 | 50 | 51 | def getRay(cam, uv): 52 | ray_uv = np.ones(3, dtype=np.float32) 53 | ray_uv[0] = (uv[0]-cam[2]) / cam[0] 54 | ray_uv[1] = (uv[1]-cam[3]) / cam[1] 55 | return np.expand_dims(ray_uv / LA.norm(ray_uv), -1) 56 | 57 | 58 | def depthFromTriangulation(cam_ref, cam_cur, T_cur_ref, uv_ref, uv_cur): 59 | R_cur_ref = T_cur_ref[0:3, 0:3] 60 | t_cur_ref = T_cur_ref[0:3, 3] 61 | ray_uv_ref = getRay(cam_ref, uv_ref) 62 | ray_uv_cur = getRay(cam_cur, uv_cur) 63 | 64 | A = np.hstack((np.matmul(R_cur_ref, ray_uv_ref), ray_uv_cur)) 65 | AtA = np.matmul(A.T, A) 66 | if LA.det(AtA) < 1e-5: 67 | return -1 68 | depth2 = - np.matmul(np.matmul(LA.inv(AtA), A.T), t_cur_ref) 69 | depth = np.fabs(depth2[0]) 70 | 71 | return depth*ray_uv_ref[-1] 72 | 73 | 74 | def calculateCurDepth(cam0, img0, cam1, img1, T_cam0_v1, v1_lut): 75 | h, w = img0.shape[:2] 76 | 77 | depth1 = np.full([h, w], np.nan, dtype=float) 78 | 79 | flow10 = getFlowBD(img1, img0, 'Match') 80 | for v1 in range(h): 81 | for u1 in range(w): 82 | fu, fv = flow10[v1, u1, :] 83 | if not np.isnan(fu): 84 | uv0 = [u1+fu, v1+fv] 85 | depth1[v1, u1] = depthFromTriangulation( 86 | cam1, cam0, T_cam0_v1[v1_lut[v1, u1]], [u1, v1], uv0) 87 | 88 | return depth1 89 | 90 | 91 | def getDepth(save_path): 92 | img0_path = save_path+"cam0/images/" 93 | img1_path = save_path+"cam1/images/" 94 | depth1_path = save_path+"cam1/depth/" 95 | if not os.path.exists(depth1_path): 96 | os.makedirs(depth1_path) 97 | 98 | # Load cameras 99 | cam0 = np.load(save_path+"cam0/camera.npy") 100 | cam1 = np.load(save_path+"cam1/camera.npy") 101 | 102 | # Load poses 103 | T_cam0_v1 = np.load(save_path+"T_cam0_v1.npy") 104 | v1_lut = np.load(save_path+"cam1/v1_lut.npy") 105 | img_count = T_cam0_v1.shape[0] 106 | 107 | # Get depth 108 | for i in tqdm(range(img_count)): 109 | img0 = cv2.imread('{}{}.png'.format(img0_path, i)) 110 | img1 = cv2.imread('{}{}.png'.format(img1_path, i)) 111 | depth1 = calculateCurDepth( 112 | cam0, img0, cam1, img1, T_cam0_v1[i], v1_lut) 113 | fname = os.path.join(depth1_path, str(i)) 114 | np.save(fname, depth1) 115 | -------------------------------------------------------------------------------- /dataset/gsImgRectifier.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import print_function, division 18 | import os 19 | from tqdm import tqdm 20 | import numpy as np 21 | import pandas as pd 22 | import cv2 23 | 24 | 25 | def rectify_gs_imgs(save_path, resolution): 26 | gs_img_path = save_path+"cam1/images_gs/" 27 | if not os.path.exists(gs_img_path): 28 | os.makedirs(gs_img_path) 29 | 30 | cols, rows = resolution 31 | rs_img_path = save_path+"cam1/images/" 32 | flows_rs2gs_path = save_path+"cam1/flows_rs2gs/" 33 | count = len(os.listdir(flows_rs2gs_path)) 34 | for i in tqdm(range(count)): 35 | fi = str(i) 36 | flow_rs2gs = np.load(flows_rs2gs_path + fi + '.npy') 37 | size_1d = rows*cols 38 | ind_v, ind_u = np.indices((rows, cols), dtype='float32') 39 | 40 | # map: rs to gs 41 | uv_rs = np.stack((ind_u, ind_v), axis=-1) 42 | uv_gs = np.array(uv_rs + flow_rs2gs + 0.5, dtype='int') 43 | u_gs = np.clip(uv_gs[:, :, 0], 0, cols-1) 44 | v_gs = np.clip(uv_gs[:, :, 1], 0, rows-1) 45 | uv_gs_1d = v_gs * cols + u_gs 46 | uv_gs_1d = np.reshape(uv_gs_1d, (size_1d)) 47 | 48 | # reverse the map: gs to rs 49 | flow_gs2rs_1d_u = np.full((size_1d), np.nan, dtype='float32') 50 | flow_gs2rs_1d_v = np.full((size_1d), np.nan, dtype='float32') 51 | flow_gs2rs_1d_u[uv_gs_1d] = np.reshape(-flow_rs2gs[:, :, 0], (size_1d)) 52 | flow_gs2rs_1d_v[uv_gs_1d] = np.reshape(-flow_rs2gs[:, :, 1], (size_1d)) 53 | flow_gs2rs = np.stack([np.reshape(flow_gs2rs_1d_u, (rows, cols)), 54 | np.reshape(flow_gs2rs_1d_v, (rows, cols))], axis=-1) 55 | 56 | # 2d interpolation 57 | flow_gs2rs[:, :, 0] = pd.DataFrame(flow_gs2rs[:, :, 0]).interpolate() 58 | flow_gs2rs[:, :, 1] = pd.DataFrame(flow_gs2rs[:, :, 1]).interpolate() 59 | 60 | # reconstruct gs image 61 | map_u = ind_u + flow_gs2rs[:, :, 0] 62 | map_v = ind_v + flow_gs2rs[:, :, 1] 63 | img_rs = cv2.imread(rs_img_path + fi + '.png') 64 | img_gs = cv2.remap(img_rs, map_u, map_v, cv2.INTER_LINEAR) 65 | cv2.imwrite('{}{}.png'.format(gs_img_path, i), img_gs) 66 | -------------------------------------------------------------------------------- /dataset/gtFlowGenerator.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | import numpy as np 19 | import os 20 | from tqdm import tqdm 21 | from numpy import linalg as LA 22 | 23 | 24 | def projectPoint(ua, va, da, cam_a, T_b_a, cam_b): 25 | Xa = np.ones([3, 1], dtype=np.float32) 26 | Xa[0] = (ua-cam_a[2]) / cam_a[0] 27 | Xa[1] = (va-cam_a[3]) / cam_a[1] 28 | Xa = Xa*da 29 | Xb = np.matmul(T_b_a[0:3, 0:3], Xa)+np.expand_dims(T_b_a[0:3, 3], -1) 30 | ub = Xb[0, 0]/Xb[2, 0]*cam_b[0] + cam_b[2] 31 | vb = Xb[1, 0]/Xb[2, 0]*cam_b[1] + cam_b[3] 32 | return [ub, vb] 33 | 34 | 35 | def getRS2GSFlow(depth_rs, cam1, T_cam0_v1, v1_lut): 36 | h, w = depth_rs.shape[:2] 37 | flow_rs2gs = np.full([h, w, 2], np.nan, dtype=np.float32) 38 | 39 | T_gs_rs = np.empty_like(T_cam0_v1) 40 | for v1 in range(h): 41 | T_gs_rs[v1] = np.matmul(LA.inv(T_cam0_v1[0]), T_cam0_v1[v1]) 42 | 43 | # Project from rs to gs 44 | for v1rs in range(h): 45 | for u1rs in range(w): 46 | if np.isnan(depth_rs[v1rs, u1rs]): 47 | continue 48 | 49 | [u1gs, v1gs] = projectPoint( 50 | u1rs, v1rs, depth_rs[v1rs, u1rs], cam1, T_gs_rs[v1_lut[v1rs, u1rs]], cam1) 51 | if not np.isnan(u1gs): 52 | flow_rs2gs[v1rs, u1rs, 0] = u1gs - u1rs 53 | flow_rs2gs[v1rs, u1rs, 1] = v1gs - v1rs 54 | 55 | return flow_rs2gs 56 | 57 | 58 | def getRS2GSFlows(save_path, ns_per_v): 59 | depth1_path = save_path+"cam1/depth/" 60 | cam1 = np.load(save_path+"cam1/camera.npy") 61 | flows_rs2gs_path = save_path+"cam1/flows_rs2gs/" 62 | if not os.path.exists(flows_rs2gs_path): 63 | os.makedirs(flows_rs2gs_path) 64 | 65 | # Load poses 66 | T_cam0_v1 = np.load(save_path+"T_cam0_v1.npy") 67 | v1_lut = np.load(save_path+"cam1/v1_lut.npy") 68 | 69 | img_count = T_cam0_v1.shape[0] 70 | for i in tqdm(range(img_count)): 71 | depth1 = np.load('{}{}.npy'.format(depth1_path, i)) 72 | flow_rs2gs = getRS2GSFlow(depth1, cam1, T_cam0_v1[i], v1_lut) 73 | rs2gs_name = os.path.join(flows_rs2gs_path, str(i)) 74 | np.save(rs2gs_name, flow_rs2gs) 75 | -------------------------------------------------------------------------------- /dataset/poseHandler.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | from scipy.spatial.transform import Rotation, RotationSpline 19 | from scipy.interpolate import interp1d, CubicSpline 20 | import numpy as np 21 | import os 22 | import csv 23 | from tqdm import tqdm 24 | from numpy import linalg as LA 25 | 26 | 27 | class Imu: 28 | def __init__(self, imu_file, R_cam1_imu): 29 | imu_reader = csv.reader(open(imu_file), delimiter=" ") 30 | next(imu_reader) # skip the header line 31 | imu_ns_w_a = np.array(list(imu_reader)) 32 | imu_ns = np.array(imu_ns_w_a[:, 0], dtype=np.long) 33 | imu_w_a = np.array(imu_ns_w_a[:, 1:], dtype=np.float) 34 | self.first_ns = imu_ns[1] 35 | self.last_ns = imu_ns[-2] 36 | 37 | cam1_w_a = np.zeros((len(imu_ns), 6)) 38 | for i in range(len(imu_ns)): 39 | cam1_w_a[i, :3] = np.matmul(R_cam1_imu, imu_w_a[i, :3]) 40 | cam1_w_a[i, 3:] = np.matmul(R_cam1_imu, imu_w_a[i, 3:]) 41 | 42 | self.imu_interp = interp1d(imu_ns, cam1_w_a, axis=0) 43 | 44 | def isValidNs(self, query_ns): 45 | return self.first_ns <= query_ns <= self.last_ns 46 | 47 | def getImuAt(self, query_ns): 48 | return self.imu_interp(query_ns) 49 | 50 | 51 | class Pose: 52 | def __init__(self, gt_file, T_imu_cam1): 53 | gt_reader = csv.reader(open(gt_file), delimiter=",") 54 | next(gt_reader) # skip the header line 55 | gt_ns_t_q = np.array(list(gt_reader)) 56 | gt_ns = np.array(gt_ns_t_q[:, 0], dtype=np.long) 57 | gt_t_q = np.array(gt_ns_t_q[:, 1:], dtype=np.float) 58 | self.first_ns = gt_ns[1] 59 | self.last_ns = gt_ns[-2] 60 | 61 | # transfer to target coordinate 62 | ts, Rs = [], [] 63 | for i in range(len(gt_ns)): 64 | T_w_gt = np.identity(4) 65 | T_w_gt[0:3, 3] = gt_t_q[i, :3] 66 | # convert to qx qy qz qw for from_quat 67 | gt_q = gt_t_q[i, np.ix_([4, 5, 6, 3])] 68 | T_w_gt[0:3, 0:3] = Rotation.from_quat(gt_q).as_matrix() 69 | T_w_cam1 = np.matmul(T_w_gt, T_imu_cam1) 70 | ts.append(T_w_cam1[0:3, 3]) 71 | Rs.append(T_w_cam1[0:3, 0:3]) 72 | 73 | # splines 74 | self.t_spline = CubicSpline(gt_ns, ts) 75 | self.R_spline = RotationSpline(gt_ns, Rotation.from_matrix(Rs)) 76 | 77 | def isValidNs(self, query_ns): 78 | return self.first_ns <= query_ns <= self.last_ns 79 | 80 | def getPoseAt(self, query_ns): 81 | T = np.identity(4) 82 | T[0:3, 0:3] = self.R_spline(query_ns).as_matrix() 83 | T[0:3, 3] = self.t_spline(query_ns) 84 | return T 85 | 86 | 87 | def getPoses(data_path, save_path, img_h, ns_per_v): 88 | # image name/time_ns 89 | img_ns = [] 90 | times = open(data_path+'cam0/times.txt').read().splitlines() 91 | for i in range(1, len(times)): 92 | cur_ns = times[i].split(' ')[0] 93 | img_ns.append(int(cur_ns)) 94 | 95 | # pose ground truth 96 | cam1 = np.load(save_path+"cam1/camera.npy") 97 | T_cam0_cam1 = np.identity(4) 98 | T_cam0_cam1[0, 3] = -cam1[4] 99 | T_imu_cam0 = np.load(save_path+"cam0/T_imu_cam0.npy") 100 | T_imu_cam1 = np.matmul(T_imu_cam0, T_cam0_cam1) 101 | gt_pose_cam1 = Pose(data_path+'gt_imu.csv', T_imu_cam1) 102 | imu_cam1 = Imu(data_path+'imu.txt', np.transpose(T_imu_cam1[:3, :3])) 103 | 104 | valid_ns, T_cam0_v1, pose_w_cam1, pose_cam1_v1, imu_cam1_v1 = [], [], [], [], [] 105 | for i in tqdm(range(len(img_ns))): 106 | if not (gt_pose_cam1.isValidNs(img_ns[i]+ns_per_v*(img_h-1)) 107 | and gt_pose_cam1.isValidNs(img_ns[i]) 108 | and imu_cam1.isValidNs(img_ns[i]+ns_per_v*(img_h-1)) 109 | and imu_cam1.isValidNs(img_ns[i])): 110 | continue 111 | valid_ns.append(img_ns[i]) 112 | 113 | T_w_cam1 = gt_pose_cam1.getPoseAt(img_ns[i]) 114 | t = T_w_cam1[0:3, 3] 115 | q = Rotation.from_matrix(T_w_cam1[0:3, 0:3]).as_quat() 116 | pose_w_cam1.append( 117 | np.array([i, t[0], t[1], t[2], q[0], q[1], q[2], q[3]])) 118 | 119 | T_cam1_w = LA.inv(T_w_cam1) 120 | T_cam0_w = np.matmul(T_cam0_cam1, T_cam1_w) 121 | # get pose for each scan line 122 | T_cam0_v1_i = [] 123 | pose_cam1_v1_i = np.zeros((img_h, 6)) 124 | imu_cam1_v1_i = np.zeros((img_h, 6)) 125 | for v in range(img_h): 126 | # row-wise imu 127 | imu_cam1_v1_i[v] = imu_cam1.getImuAt(img_ns[i]+ns_per_v*v) 128 | # row-wise pose 129 | T_w_v1 = gt_pose_cam1.getPoseAt(img_ns[i]+ns_per_v*v) 130 | T_cam0_v1_i.append(np.matmul(T_cam0_w, T_w_v1)) 131 | 132 | T_cam1_v1_i = np.matmul(T_cam1_w, T_w_v1) 133 | t = T_cam1_v1_i[0:3, 3] 134 | r = Rotation.from_matrix(T_cam1_v1_i[0:3, 0:3]).as_rotvec() 135 | pose_cam1_v1_i[v] = [t[0], t[1], t[2], r[0], r[1], r[2]] 136 | T_cam0_v1.append(T_cam0_v1_i) 137 | pose_cam1_v1.append(pose_cam1_v1_i) 138 | imu_cam1_v1.append(imu_cam1_v1_i) 139 | 140 | ns_path = os.path.join(save_path, "valid_ns.npy") 141 | np.save(ns_path, np.array(valid_ns)) 142 | 143 | pose0_path = os.path.join(save_path, "T_cam0_v1.npy") 144 | np.save(pose0_path, np.array(T_cam0_v1)) 145 | 146 | pose1w_path = os.path.join(save_path, "cam1/pose_w_cam1.txt") 147 | np.savetxt(pose1w_path, np.array(pose_w_cam1), delimiter=',') 148 | 149 | posev1_path = os.path.join(save_path, "cam1/pose_cam1_v1.npy") 150 | np.save(posev1_path, np.array(pose_cam1_v1)) 151 | 152 | imu_cam1_path = os.path.join(save_path, "cam1/imu_cam1_v1.npy") 153 | np.save(imu_cam1_path, np.array(imu_cam1_v1)) 154 | -------------------------------------------------------------------------------- /dataset/pwcnet.py: -------------------------------------------------------------------------------- 1 | ################################## Third-party Code ################################## 2 | """ 3 | PWC-Net model class written by Phil Ferriere 4 | https://github.com/philferriere/tfoptflow/blob/master/tfoptflow/model_pwcnet.py 5 | 6 | Licensed under the MIT License 7 | (see https://github.com/philferriere/tfoptflow/blob/master/LICENSE for details) 8 | """ 9 | ###################################################################################### 10 | 11 | from __future__ import absolute_import, division, print_function 12 | import numpy as np 13 | import tensorflow.compat.v1 as tf 14 | import pathlib 15 | import os 16 | 17 | from tensorflow.python.framework import constant_op 18 | from tensorflow.python.framework import dtypes 19 | from tensorflow.python.framework import ops 20 | from tensorflow.python.ops import array_ops 21 | from tensorflow.python.ops import math_ops 22 | 23 | _DEFAULT_PWCNET_TEST_OPTIONS = { 24 | 'ckpt_path': '/mnt/data2/jiawei/unrolling/sintel_gray_weights/pwcnet.sintel_gray.ckpt-54000', 25 | 'controller': '/device:CPU:0', 26 | 'batch_size': 1, 27 | 'pyr_lvls': 6, 28 | 'flow_pred_lvl': 2, 29 | 'search_range': 4 30 | } 31 | 32 | # from ref_model import PWCNet 33 | 34 | 35 | def cost_volume(c1, warp, search_range, name): 36 | """Build cost volume for associating a pixel from Image1 with its corresponding pixels in Image2. 37 | Args: 38 | c1: Level of the feature pyramid of Image1 39 | warp: Warped level of the feature pyramid of image22 40 | search_range: Search range (maximum displacement) 41 | """ 42 | padded_lvl = tf.pad(warp, [[0, 0], [search_range, search_range], [ 43 | search_range, search_range], [0, 0]]) 44 | _, h, w, _ = tf.unstack(tf.shape(c1)) 45 | max_offset = search_range * 2 + 1 46 | 47 | cost_vol = [] 48 | for y in range(0, max_offset): 49 | for x in range(0, max_offset): 50 | slice = tf.slice(padded_lvl, [0, y, x, 0], [-1, h, w, -1]) 51 | cost = tf.reduce_mean(c1 * slice, axis=3, keepdims=True) 52 | cost_vol.append(cost) 53 | cost_vol = tf.concat(cost_vol, axis=3) 54 | cost_vol = tf.nn.leaky_relu(cost_vol, alpha=0.1, name=name) 55 | 56 | return cost_vol 57 | 58 | 59 | def _interpolate_bilinear(grid, 60 | query_points, 61 | name='interpolate_bilinear', 62 | indexing='ij'): 63 | if indexing != 'ij' and indexing != 'xy': 64 | raise ValueError('Indexing mode must be \'ij\' or \'xy\'') 65 | 66 | with ops.name_scope(name): 67 | grid = ops.convert_to_tensor(grid) 68 | query_points = ops.convert_to_tensor(query_points) 69 | shape = array_ops.unstack(array_ops.shape(grid)) 70 | if len(shape) != 4: 71 | msg = 'Grid must be 4 dimensional. Received: ' 72 | raise ValueError(msg + str(shape)) 73 | 74 | batch_size, height, width, channels = shape 75 | query_type = query_points.dtype 76 | query_shape = array_ops.unstack(array_ops.shape(query_points)) 77 | grid_type = grid.dtype 78 | 79 | if len(query_shape) != 3: 80 | msg = ('Query points must be 3 dimensional. Received: ') 81 | raise ValueError(msg + str(query_shape)) 82 | 83 | _, num_queries, _ = query_shape 84 | 85 | alphas = [] 86 | floors = [] 87 | ceils = [] 88 | 89 | index_order = [0, 1] if indexing == 'ij' else [1, 0] 90 | unstacked_query_points = array_ops.unstack(query_points, axis=2) 91 | 92 | for dim in index_order: 93 | with ops.name_scope('dim-' + str(dim)): 94 | queries = unstacked_query_points[dim] 95 | 96 | size_in_indexing_dimension = shape[dim + 1] 97 | 98 | # max_floor is size_in_indexing_dimension - 2 so that max_floor + 1 99 | # is still a valid index into the grid. 100 | max_floor = math_ops.cast( 101 | size_in_indexing_dimension - 2, query_type) 102 | min_floor = constant_op.constant(0.0, dtype=query_type) 103 | floor = math_ops.minimum( 104 | math_ops.maximum(min_floor, math_ops.floor(queries)), max_floor) 105 | int_floor = math_ops.cast(floor, dtypes.int32) 106 | floors.append(int_floor) 107 | ceil = int_floor + 1 108 | ceils.append(ceil) 109 | 110 | # alpha has the same type as the grid, as we will directly use alpha 111 | # when taking linear combinations of pixel values from the image. 112 | alpha = math_ops.cast(queries - floor, grid_type) 113 | min_alpha = constant_op.constant(0.0, dtype=grid_type) 114 | max_alpha = constant_op.constant(1.0, dtype=grid_type) 115 | alpha = math_ops.minimum( 116 | math_ops.maximum(min_alpha, alpha), max_alpha) 117 | 118 | # Expand alpha to [b, n, 1] so we can use broadcasting 119 | # (since the alpha values don't depend on the channel). 120 | alpha = array_ops.expand_dims(alpha, 2) 121 | alphas.append(alpha) 122 | 123 | flattened_grid = array_ops.reshape(grid, 124 | [batch_size * height * width, channels]) 125 | batch_offsets = array_ops.reshape( 126 | math_ops.range(batch_size) * height * width, [batch_size, 1]) 127 | 128 | # This wraps array_ops.gather. We reshape the image data such that the 129 | # batch, y, and x coordinates are pulled into the first dimension. 130 | # Then we gather. Finally, we reshape the output back. It's possible this 131 | # code would be made simpler by using array_ops.gather_nd. 132 | def gather(y_coords, x_coords, name): 133 | with ops.name_scope('gather-' + name): 134 | linear_coordinates = batch_offsets + y_coords * width + x_coords 135 | gathered_values = array_ops.gather( 136 | flattened_grid, linear_coordinates) 137 | return array_ops.reshape(gathered_values, 138 | [batch_size, num_queries, channels]) 139 | 140 | # grab the pixel values in the 4 corners around each query point 141 | top_left = gather(floors[0], floors[1], 'top_left') 142 | top_right = gather(floors[0], ceils[1], 'top_right') 143 | bottom_left = gather(ceils[0], floors[1], 'bottom_left') 144 | bottom_right = gather(ceils[0], ceils[1], 'bottom_right') 145 | 146 | # now, do the actual interpolation 147 | with ops.name_scope('interpolate'): 148 | interp_top = alphas[1] * (top_right - top_left) + top_left 149 | interp_bottom = alphas[1] * \ 150 | (bottom_right - bottom_left) + bottom_left 151 | interp = alphas[0] * (interp_bottom - interp_top) + interp_top 152 | 153 | return interp 154 | 155 | 156 | def dense_image_warp(image, flow, name='dense_image_warp'): 157 | with ops.name_scope(name): 158 | batch_size, height, width, channels = array_ops.unstack( 159 | array_ops.shape(image)) 160 | # The flow is defined on the image grid. Turn the flow into a list of query 161 | # points in the grid space. 162 | grid_x, grid_y = array_ops.meshgrid( 163 | math_ops.range(width), math_ops.range(height)) 164 | stacked_grid = math_ops.cast( 165 | array_ops.stack([grid_y, grid_x], axis=2), flow.dtype) 166 | batched_grid = array_ops.expand_dims(stacked_grid, axis=0) 167 | query_points_on_grid = batched_grid - flow 168 | query_points_flattened = array_ops.reshape(query_points_on_grid, 169 | [batch_size, height * width, 2]) 170 | # Compute values at the query points, then reshape the result back to the 171 | # image grid. 172 | interpolated = _interpolate_bilinear(image, query_points_flattened) 173 | interpolated = array_ops.reshape(interpolated, 174 | [batch_size, height, width, channels]) 175 | return interpolated 176 | 177 | 178 | class ModelPWCNet: 179 | def __init__(self, name='pwcnet', session=None, options=_DEFAULT_PWCNET_TEST_OPTIONS): 180 | self.opts = options 181 | self.y_hat_train_tnsr = self.y_hat_val_tnsr = self.y_hat_test_tnsr = None 182 | self.name = name 183 | 184 | tf.reset_default_graph() 185 | self.graph = tf.Graph() 186 | with self.graph.as_default(): 187 | # Configure a TF session, if one doesn't already exist 188 | if session is None: 189 | config = tf.ConfigProto() 190 | config.gpu_options.allow_growth = True 191 | config.allow_soft_placement = True 192 | self.sess = tf.Session(config=config) 193 | else: 194 | self.sess = session 195 | 196 | # Build the TF graph 197 | batch_size = self.opts['batch_size'] 198 | self.x_tnsr = tf.placeholder( 199 | tf.float32, [batch_size] + [2, None, None, 3], 'x_tnsr') 200 | self.y_tnsr = tf.placeholder( 201 | tf.float32, [batch_size] + [None, None, 2], 'y_tnsr') 202 | 203 | # Build the backbone neural nets and collect the output tensors 204 | with tf.device(self.opts['controller']): 205 | self.flow_pred_tnsr, self.flow_pyr_tnsr = self.nn(self.x_tnsr) 206 | 207 | # Set output tensors 208 | self.y_hat_test_tnsr = [self.flow_pred_tnsr, self.flow_pyr_tnsr] 209 | 210 | # Init saver (override if you wish) and load checkpoint if it exists 211 | self.saver = tf.train.Saver() 212 | 213 | # Initialize the graph with the content of the checkpoint 214 | self.last_ckpt = os.path.join(os.getcwd(), self.opts['ckpt_path']) 215 | assert(self.last_ckpt is not None) 216 | self.saver.restore(self.sess, self.last_ckpt) 217 | 218 | ### 219 | # Sample mgmt 220 | ### 221 | def adapt_x(self, x): 222 | # Ensure we're dealing with RGB image pairs 223 | assert (isinstance(x, np.ndarray) or isinstance(x, list)) 224 | if isinstance(x, np.ndarray): 225 | assert (len(x.shape) == 5) 226 | assert (x.shape[1] == 2 and x.shape[4] == 3) 227 | else: 228 | assert (len(x[0].shape) == 4) 229 | assert (x[0].shape[0] == 2 or x[0].shape[3] == 3) 230 | 231 | # Bring image range from 0..255 to 0..1 and use floats (also, list[(2,H,W,3)] -> (batch_size,2,H,W,3)) 232 | x_adapt = np.array(x, dtype=np.float32) if isinstance( 233 | x, list) else x.astype(np.float32) 234 | x_adapt /= 255. 235 | 236 | # Make sure the image dimensions are multiples of 2**pyramid_levels, pad them if they're not 237 | _, pad_h = divmod(x_adapt.shape[2], 2**self.opts['pyr_lvls']) 238 | if pad_h != 0: 239 | pad_h = 2 ** self.opts['pyr_lvls'] - pad_h 240 | _, pad_w = divmod(x_adapt.shape[3], 2**self.opts['pyr_lvls']) 241 | if pad_w != 0: 242 | pad_w = 2 ** self.opts['pyr_lvls'] - pad_w 243 | x_adapt_info = None 244 | if pad_h != 0 or pad_w != 0: 245 | padding = [(0, 0), (0, 0), (0, pad_h), (0, pad_w), (0, 0)] 246 | x_adapt_info = x_adapt.shape # Save original shape 247 | x_adapt = np.pad(x_adapt, padding, 248 | mode='constant', constant_values=0.) 249 | 250 | return x_adapt, x_adapt_info 251 | 252 | def postproc_y_hat_test(self, y_hat, adapt_info=None): 253 | assert (isinstance(y_hat, list) and len(y_hat) == 2) 254 | 255 | # Have the samples been padded to fit the network's requirements? If so, crop flows back to original size. 256 | pred_flows = y_hat[0] 257 | if adapt_info is not None: 258 | pred_flows = pred_flows[:, 0:adapt_info[1], 0:adapt_info[2], :] 259 | 260 | # Individuate flows of the flow pyramid (at this point, they are still batched) 261 | pyramids = y_hat[1] 262 | pred_flows_pyramid = [] 263 | for idx in range(len(pred_flows)): 264 | pyramid = [] 265 | for lvl in range(self.opts['pyr_lvls'] - self.opts['flow_pred_lvl'] + 1): 266 | pyramid.append(pyramids[lvl][idx]) 267 | pred_flows_pyramid.append(pyramid) 268 | 269 | return pred_flows, pred_flows_pyramid 270 | 271 | def predict_from_img_pairs(self, img_pairs, batch_size=1): 272 | with self.graph.as_default(): 273 | # Chunk image pair list 274 | batch_size = self.opts['batch_size'] 275 | test_size = len(img_pairs) 276 | rounds, rounds_left = divmod(test_size, batch_size) 277 | if rounds_left: 278 | rounds += 1 279 | 280 | # Loop through input samples and run inference on them 281 | preds, test_ptr = [], 0 282 | for _round in range(rounds): 283 | # In batch mode, make sure to wrap around if there aren't enough input samples to process 284 | if test_ptr + batch_size < test_size: 285 | new_ptr = test_ptr + batch_size 286 | indices = list(range(test_ptr, test_ptr + batch_size)) 287 | else: 288 | new_ptr = (test_ptr + batch_size) % test_size 289 | indices = list(range(test_ptr, test_size)) + \ 290 | list(range(0, new_ptr)) 291 | test_ptr = new_ptr 292 | 293 | # Repackage input image pairs as np.ndarray 294 | x = np.array([img_pairs[idx] for idx in indices]) 295 | 296 | # Make input samples conform to the network's requirements 297 | # x: [batch_size,2,H,W,3] uint8; x_adapt: [batch_size,2,H,W,3] float32 298 | x_adapt, x_adapt_info = self.adapt_x(x) 299 | if x_adapt_info is not None: 300 | y_adapt_info = ( 301 | x_adapt_info[0], x_adapt_info[2], x_adapt_info[3], 2) 302 | else: 303 | y_adapt_info = None 304 | 305 | # Run the adapted samples through the network 306 | feed_dict = {self.x_tnsr: x_adapt} 307 | y_hat = self.sess.run( 308 | self.y_hat_test_tnsr, feed_dict=feed_dict) 309 | y_hats, _ = self.postproc_y_hat_test(y_hat, y_adapt_info) 310 | 311 | # Return flat list of predicted labels 312 | for y_hat in y_hats: 313 | preds.append(y_hat) 314 | 315 | return preds[0:test_size] 316 | 317 | ### 318 | # PWC-Net pyramid helpers 319 | ### 320 | def extract_features(self, x_tnsr, name='featpyr'): 321 | assert(1 <= self.opts['pyr_lvls'] <= 6) 322 | # Make the feature pyramids 1-based for better readability down the line 323 | num_chann = [None, 16, 32, 64, 96, 128, 196] 324 | c1, c2 = [None], [None] 325 | init = tf.keras.initializers.he_normal() 326 | with tf.variable_scope(name): 327 | for pyr, x, reuse, name in zip([c1, c2], [x_tnsr[:, 0], x_tnsr[:, 1]], [None, True], ['c1', 'c2']): 328 | for lvl in range(1, self.opts['pyr_lvls'] + 1): 329 | # tf.layers.conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', ... , name, reuse) 330 | # reuse is set to True because we want to learn a single set of weights for the pyramid 331 | # kernel_initializer = 'he_normal' or tf.keras.initializers.he_normal(seed=None) 332 | f = num_chann[lvl] 333 | x = tf.layers.conv2d( 334 | x, f, 3, 2, 'same', kernel_initializer=init, name='conv{}a'.format(lvl), reuse=reuse) 335 | x = tf.nn.leaky_relu(x, alpha=0.1) 336 | x = tf.layers.conv2d( 337 | x, f, 3, 1, 'same', kernel_initializer=init, name='conv{}aa'.format(lvl), reuse=reuse) 338 | x = tf.nn.leaky_relu(x, alpha=0.1) 339 | x = tf.layers.conv2d( 340 | x, f, 3, 1, 'same', kernel_initializer=init, name='conv{}b'.format(lvl), reuse=reuse) 341 | x = tf.nn.leaky_relu(x, alpha=0.1, name=str(name)+str(lvl)) 342 | pyr.append(x) 343 | return c1, c2 344 | 345 | ### 346 | # PWC-Net warping helpers 347 | ### 348 | def warp(self, c2, sc_up_flow, lvl, name='warp'): 349 | op_name = str(name)+str(lvl) 350 | with tf.name_scope(name): 351 | return dense_image_warp(c2, sc_up_flow, name=op_name) 352 | 353 | def deconv(self, x, lvl, name='up_flow'): 354 | op_name = str(name)+str(lvl) 355 | with tf.variable_scope('upsample'): 356 | # tf.layers.conv2d_transpose(inputs, filters, kernel_size, strides=(1, 1), padding='valid', ... , name) 357 | return tf.layers.conv2d_transpose(x, 2, 4, 2, 'same', name=op_name) 358 | 359 | ### 360 | # Cost Volume helpers 361 | ### 362 | def corr(self, c1, warp, lvl, name='corr'): 363 | op_name = 'corr'+str(lvl) 364 | with tf.name_scope(name): 365 | return cost_volume(c1, warp, self.opts['search_range'], op_name) 366 | 367 | ### 368 | # Optical flow estimator helpers 369 | ### 370 | def predict_flow(self, corr, c1, up_flow, up_feat, lvl, name='predict_flow'): 371 | op_name = 'flow'+str(lvl) 372 | init = tf.keras.initializers.he_normal() 373 | with tf.variable_scope(name): 374 | if c1 is None and up_flow is None and up_feat is None: 375 | x = corr 376 | else: 377 | x = tf.concat([corr, c1, up_flow, up_feat], axis=3) 378 | 379 | conv = tf.layers.conv2d( 380 | x, 128, 3, 1, 'same', kernel_initializer=init, name='conv{}_0'.format(lvl)) 381 | # default alpha is 0.2 for TF 382 | act = tf.nn.leaky_relu(conv, alpha=0.1) 383 | x = tf.concat([act, x], axis=3) 384 | 385 | conv = tf.layers.conv2d( 386 | x, 128, 3, 1, 'same', kernel_initializer=init, name='conv{}_1'.format(lvl)) 387 | act = tf.nn.leaky_relu(conv, alpha=0.1) 388 | x = tf.concat([act, x], axis=3) 389 | 390 | conv = tf.layers.conv2d( 391 | x, 96, 3, 1, 'same', kernel_initializer=init, name='conv{}_2'.format(lvl)) 392 | act = tf.nn.leaky_relu(conv, alpha=0.1) 393 | x = tf.concat([act, x], axis=3) 394 | 395 | conv = tf.layers.conv2d( 396 | x, 64, 3, 1, 'same', kernel_initializer=init, name='conv{}_3'.format(lvl)) 397 | act = tf.nn.leaky_relu(conv, alpha=0.1) 398 | x = tf.concat([act, x], axis=3) 399 | 400 | conv = tf.layers.conv2d( 401 | x, 32, 3, 1, 'same', kernel_initializer=init, name='conv{}_4'.format(lvl)) 402 | # will also be used as an input by the context network 403 | act = tf.nn.leaky_relu(conv, alpha=0.1) 404 | upfeat = tf.concat([act, x], axis=3, name='upfeat'+str(lvl)) 405 | 406 | flow = tf.layers.conv2d(upfeat, 2, 3, 1, 'same', name=op_name) 407 | 408 | return upfeat, flow 409 | 410 | ### 411 | # PWC-Net context network helpers 412 | ### 413 | def refine_flow(self, feat, flow, lvl, name='ctxt'): 414 | op_name = 'refined_flow'+str(lvl) 415 | init = tf.keras.initializers.he_normal() 416 | with tf.variable_scope(name): 417 | x = tf.layers.conv2d(feat, 128, 3, 1, 'same', dilation_rate=1, 418 | kernel_initializer=init, name='dc_conv{}1'.format(lvl)) 419 | x = tf.nn.leaky_relu(x, alpha=0.1) # default alpha is 0.2 for TF 420 | x = tf.layers.conv2d(x, 128, 3, 1, 'same', dilation_rate=2, 421 | kernel_initializer=init, name='dc_conv{}2'.format(lvl)) 422 | x = tf.nn.leaky_relu(x, alpha=0.1) 423 | x = tf.layers.conv2d(x, 128, 3, 1, 'same', dilation_rate=4, 424 | kernel_initializer=init, name='dc_conv{}3'.format(lvl)) 425 | x = tf.nn.leaky_relu(x, alpha=0.1) 426 | x = tf.layers.conv2d(x, 96, 3, 1, 'same', dilation_rate=8, 427 | kernel_initializer=init, name='dc_conv{}4'.format(lvl)) 428 | x = tf.nn.leaky_relu(x, alpha=0.1) 429 | x = tf.layers.conv2d(x, 64, 3, 1, 'same', dilation_rate=16, 430 | kernel_initializer=init, name='dc_conv{}5'.format(lvl)) 431 | x = tf.nn.leaky_relu(x, alpha=0.1) 432 | x = tf.layers.conv2d(x, 32, 3, 1, 'same', dilation_rate=1, 433 | kernel_initializer=init, name='dc_conv{}6'.format(lvl)) 434 | x = tf.nn.leaky_relu(x, alpha=0.1) 435 | x = tf.layers.conv2d(x, 2, 3, 1, 'same', dilation_rate=1, 436 | kernel_initializer=init, name='dc_conv{}7'.format(lvl)) 437 | 438 | return tf.add(flow, x, name=op_name) 439 | 440 | ### 441 | # PWC-Net nn builder 442 | ### 443 | def nn(self, x_tnsr, name='pwcnet'): 444 | with tf.variable_scope(name): 445 | 446 | # Extract pyramids of CNN features from both input images (1-based lists)) 447 | c1, c2 = self.extract_features(x_tnsr) 448 | 449 | flow_pyr = [] 450 | 451 | for lvl in range(self.opts['pyr_lvls'], self.opts['flow_pred_lvl'] - 1, -1): 452 | 453 | if lvl == self.opts['pyr_lvls']: 454 | # Compute the cost volume 455 | corr = self.corr(c1[lvl], c2[lvl], lvl) 456 | 457 | # Estimate the optical flow 458 | upfeat, flow = self.predict_flow( 459 | corr, None, None, None, lvl) 460 | else: 461 | # Warp level of Image1's using the upsampled flow 462 | scaler = 20. / 2**lvl # scaler values are 0.625, 1.25, 2.5, 5.0 463 | warp = self.warp(c2[lvl], up_flow * scaler, lvl) 464 | 465 | # Compute the cost volume 466 | corr = self.corr(c1[lvl], warp, lvl) 467 | 468 | # Estimate the optical flow 469 | upfeat, flow = self.predict_flow( 470 | corr, c1[lvl], up_flow, up_feat, lvl) 471 | 472 | _, lvl_height, lvl_width, _ = tf.unstack(tf.shape(c1[lvl])) 473 | 474 | if lvl != self.opts['flow_pred_lvl']: 475 | flow = self.refine_flow(upfeat, flow, lvl) 476 | 477 | # Upsample predicted flow and the features used to compute predicted flow 478 | flow_pyr.append(flow) 479 | 480 | up_flow = self.deconv(flow, lvl, 'up_flow') 481 | up_feat = self.deconv(upfeat, lvl, 'up_feat') 482 | else: 483 | # Refine the final predicted flow 484 | flow = self.refine_flow(upfeat, flow, lvl) 485 | flow_pyr.append(flow) 486 | 487 | # Upsample the predicted flow (final output) to match the size of the images 488 | scaler = 2**self.opts['flow_pred_lvl'] 489 | size = (lvl_height * scaler, lvl_width * scaler) 490 | flow_pred = tf.image.resize_bilinear( 491 | flow, size, name="flow_pred") * scaler 492 | break 493 | 494 | return flow_pred, flow_pyr 495 | -------------------------------------------------------------------------------- /dataset/stereoRectifier.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | import numpy as np 19 | import cv2 20 | import os 21 | import yaml 22 | from numpy import linalg as LA 23 | from tqdm import tqdm 24 | 25 | 26 | def stereoRectify(data_path, save_path, resolution): 27 | img0_path = save_path+"cam0/images/" 28 | img1_path = save_path+"cam1/images/" 29 | if not os.path.exists(img0_path): 30 | os.makedirs(img0_path) 31 | if not os.path.exists(img1_path): 32 | os.makedirs(img1_path) 33 | 34 | # Read original calibration file 35 | with open(data_path+"camchain.yaml") as file: 36 | camchain = yaml.load(file, Loader=yaml.FullLoader) 37 | 38 | imageSize = tuple(camchain['cam0']['resolution']) 39 | cam0_intrinsics = camchain['cam0']['intrinsics'] 40 | K0 = np.matrix([[cam0_intrinsics[0], 0, cam0_intrinsics[2]], 41 | [0, cam0_intrinsics[1], cam0_intrinsics[3]], 42 | [0, 0, 1]]) 43 | D0 = np.array(camchain['cam0']['distortion_coeffs']) 44 | 45 | cam1_intrinsics = camchain['cam1']['intrinsics'] 46 | K1 = np.matrix([[cam1_intrinsics[0], 0, cam1_intrinsics[2]], 47 | [0, cam1_intrinsics[1], cam1_intrinsics[3]], 48 | [0, 0, 1]]) 49 | D1 = np.array(camchain['cam1']['distortion_coeffs']) 50 | 51 | T01 = np.matrix(camchain['cam1']['T_cn_cnm1']) 52 | R = T01[np.ix_([0, 1, 2], [0, 1, 2])] 53 | tvec = T01[np.ix_([0, 1, 2], [3])] 54 | 55 | # Fisheye stere0 rectify 56 | R0, R1, P0, P1, Q = cv2.fisheye.stereoRectify( 57 | K0, D0, K1, D1, imageSize, R, tvec, 0, newImageSize=resolution) 58 | map0 = cv2.fisheye.initUndistortRectifyMap( 59 | K0, D0, R0, P0, resolution, cv2.CV_32F) 60 | map1 = cv2.fisheye.initUndistortRectifyMap( 61 | K1, D1, R1, P1, resolution, cv2.CV_32F) 62 | np.save(save_path+"cam0/stereo_map.npy", np.array(map0)) 63 | np.save(save_path+"cam1/stereo_map.npy", np.array(map1)) 64 | 65 | # Loopup table for rolling shutter time query 66 | cols_idx, _ = np.indices((imageSize[1], imageSize[0]), dtype=np.float32) 67 | cols_idx = cols_idx / imageSize[1] * resolution[1] 68 | v1_lut = cv2.remap(cols_idx, map1[0], map1[1], cv2.INTER_NEAREST) 69 | v1_lut = np.array(v1_lut, dtype=int) 70 | np.save(save_path+"cam1/v1_lut.npy", v1_lut) 71 | 72 | fxfycxcytx0 = np.array( 73 | [P0[0, 0], P0[1, 1], P0[0, 2], P0[1, 2], P0[0, 3]/P0[0, 0]]) 74 | fxfycxcytx1 = np.array( 75 | [P1[0, 0], P1[1, 1], P1[0, 2], P1[1, 2], P1[0, 3]/P1[0, 0]]) 76 | np.save(save_path+"cam0/camera.npy", fxfycxcytx0) 77 | np.save(save_path+"cam1/camera.npy", fxfycxcytx1) 78 | 79 | T_cam0_imu = np.matrix(camchain['cam0']['T_cam_imu']) 80 | T2rectified = np.identity(4) 81 | T2rectified[0:3, 0:3] = R0 82 | T_imu_cam0 = LA.inv(T2rectified*T_cam0_imu) 83 | np.save(save_path+"cam0/T_imu_cam0.npy", T_imu_cam0) 84 | 85 | 86 | def stereoRemap(data_path, save_path): 87 | valid_ns = np.load(save_path+"valid_ns.npy") 88 | map0 = np.load(save_path+"cam0/stereo_map.npy") 89 | map1 = np.load(save_path+"cam1/stereo_map.npy") 90 | maps = [map0, map1] 91 | # Remap images 92 | for i in tqdm(range(valid_ns.shape[0])): 93 | for cam_i in [0, 1]: 94 | img = cv2.imread( 95 | '{}cam{}/images/{}.png'.format(data_path, cam_i, valid_ns[i])) 96 | img_rect = cv2.remap( 97 | img, maps[cam_i][0], maps[cam_i][1], cv2.INTER_LINEAR) 98 | save_file = '{}cam{}/images/{}.png'.format(save_path, cam_i, i) 99 | cv2.imwrite(save_file, img_rect) 100 | -------------------------------------------------------------------------------- /dataset/tum_process.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | import os 19 | import shutil 20 | import numpy as np 21 | 22 | from stereoRectifier import stereoRectify, stereoRemap 23 | from poseHandler import getPoses 24 | from depthEstimator import getDepth 25 | from gtFlowGenerator import getRS2GSFlows 26 | from gsImgRectifier import rectify_gs_imgs 27 | 28 | data_path = '/mnt/data2/jiawei/unrolling/tum_data/' 29 | save_path = os.getcwd()+'/../data/' 30 | seqs = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'] 31 | output_resolution = (320, 256) 32 | ns_per_v = 29.4737*1023/(output_resolution[1]-1)*1000 33 | 34 | for seq in seqs: 35 | print('\n\n\nProcessing Sequence '+str(seq)) 36 | seq_path = os.path.join(data_path, 'dataset-seq{}/dso/'.format(seq)) 37 | seq_save_path = os.path.join(save_path, 'seq{}/'.format(seq)) 38 | 39 | print(seq_save_path) 40 | if not os.path.exists(seq_save_path): 41 | os.makedirs(seq_save_path) 42 | 43 | stereoRectify(seq_path, seq_save_path, output_resolution) 44 | 45 | print('Getting Pose...') 46 | getPoses(seq_path, seq_save_path, output_resolution[1], ns_per_v) 47 | 48 | print('Stereo Rectifying...') 49 | stereoRemap(seq_path, seq_save_path) 50 | 51 | print('Getting Depth...') 52 | getDepth(seq_save_path) 53 | 54 | print('Getting Unrolling Flow...') 55 | getRS2GSFlows(seq_save_path, ns_per_v) 56 | 57 | print('Getting GS Image...') 58 | rectify_gs_imgs(seq_save_path, output_resolution) 59 | 60 | # clean up 61 | cam0_folder = os.path.join(seq_save_path, 'cam0/') 62 | shutil.rmtree(cam0_folder) if os.path.exists(cam0_folder) else None 63 | tmp_files = ['T_cam0_v1.npy', 'valid_ns.npy', 'cam1/stereo_map.npy'] 64 | for file in tmp_files: 65 | file_path = os.path.join(seq_save_path, file) 66 | os.remove(file_path) if os.path.exists(file_path) else None 67 | -------------------------------------------------------------------------------- /images/data_gen.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IRVLab/unrolling/b670eacddc07c9c956c32354786a443ad18529e5/images/data_gen.png -------------------------------------------------------------------------------- /images/dso.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IRVLab/unrolling/b670eacddc07c9c956c32354786a443ad18529e5/images/dso.jpg -------------------------------------------------------------------------------- /images/networks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IRVLab/unrolling/b670eacddc07c9c956c32354786a443ad18529e5/images/networks.png -------------------------------------------------------------------------------- /images/res_img.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IRVLab/unrolling/b670eacddc07c9c956c32354786a443ad18529e5/images/res_img.png -------------------------------------------------------------------------------- /images/rs_pose_net.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IRVLab/unrolling/b670eacddc07c9c956c32354786a443ad18529e5/images/rs_pose_net.png -------------------------------------------------------------------------------- /network/DataLoader.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import division, absolute_import 18 | import os 19 | import numpy as np 20 | import keras 21 | import cv2 22 | 23 | 24 | class TumDataSet(): 25 | def __init__(self, gyro=False, acc=False, test_seq=[2, 7], data_path='../data/'): 26 | num_seqs = 10 27 | test_seq = np.array(test_seq, dtype='int') 28 | 29 | # parameters 30 | lut = np.load(os.path.join(data_path, 'seq1/cam1/v1_lut.npy')) 31 | cam = np.load(os.path.join(data_path, 'seq1/cam1/camera.npy')) 32 | cam = np.array([[cam[0], 0, cam[2]], 33 | [0, cam[1], cam[3]], 34 | [0, 0, 1]], dtype='float32') 35 | self.params = {'lut': lut, 36 | 'cam': cam, 37 | 'img_shape': [256, 320]} 38 | 39 | # get entire dataset and split data for training/testing 40 | self.data = {} 41 | self.data['train'] = {'size': 0, 'image_paths': [], 'imus': [], 'poses': [], 42 | 'vels': [], 'depth_paths': [], 'flow_paths': []} 43 | self.data['val'] = {'size': 0, 'image_paths': [], 'imus': [], 'poses': [], 44 | 'vels': [], 'depth_paths': [], 'flow_paths': []} 45 | self.data['test'] = {} 46 | for seq in range(1, num_seqs+1): 47 | seq_path = os.path.join(data_path, 'seq'+str(seq)+'/cam1/') 48 | poses = np.load(seq_path+'pose_cam1_v1.npy') 49 | vels = poses[:, -1, :] # velocity = last row pose / 1.0 50 | imus_raw = np.load(seq_path+'imu_cam1_v1.npy') 51 | imus = np.empty((imus_raw.shape[0], imus_raw.shape[1], 0)) 52 | if gyro: 53 | imus = np.append(imus, imus_raw[:, :, :3], axis=-1) 54 | if acc: 55 | imus = np.append(imus, imus_raw[:, :, 3:], axis=-1) 56 | count = len(os.listdir(seq_path + 'flows_rs2gs/')) 57 | image_paths, depth_paths, flow_paths = [], [], [] 58 | for i in range(count): 59 | fi = str(i) 60 | image_paths.append(seq_path + 'images/' + fi + '.png') 61 | depth_paths.append(seq_path + 'depth/' + fi + '.npy') 62 | flow_paths.append(seq_path + 'flows_rs2gs/' + str(fi) + '.npy') 63 | 64 | # split data 65 | if seq in test_seq: 66 | self.data['test'][seq] = {'size': len(image_paths), 'image_paths': image_paths, 67 | 'imus': imus, 'poses': poses, 'vels': vels, 68 | 'depth_paths': depth_paths, 'flow_paths': flow_paths} 69 | else: 70 | # use the middle 10% as validation 71 | val_start = int(0.45*count) 72 | val_end = int(0.55*count) 73 | self.appendElementByIdx('train', image_paths, imus, depth_paths, flow_paths, 74 | poses, vels, [i for i in range(val_start)]) 75 | self.appendElementByIdx('val', image_paths, imus, depth_paths, flow_paths, 76 | poses, vels, [i for i in range(val_start, val_end)]) 77 | self.appendElementByIdx('train', image_paths, imus, depth_paths, flow_paths, 78 | poses, vels, [i for i in range(val_end, count)]) 79 | 80 | self.data['train']['size'] = len(self.data['train']['image_paths']) 81 | self.data['val']['size'] = len(self.data['val']['image_paths']) 82 | 83 | def appendElementByIdx(self, split, image_paths, imus, depth_paths, flow_paths, poses, vels, indices): 84 | for i in indices: 85 | self.data[split]['image_paths'].append(image_paths[i]) 86 | self.data[split]['imus'].append(imus[i]) 87 | self.data[split]['poses'].append(poses[i]) 88 | self.data[split]['vels'].append(vels[i]) 89 | self.data[split]['depth_paths'].append(depth_paths[i]) 90 | self.data[split]['flow_paths'].append(flow_paths[i]) 91 | 92 | 93 | class DataGenerator(keras.utils.Sequence): 94 | 95 | def __init__(self, data, batch_size, dtype): 96 | self.data = data 97 | self.batch_size = batch_size 98 | self.dtype = dtype 99 | self.on_epoch_end() 100 | 101 | def __len__(self): 102 | return int(np.ceil(self.data['size'] / self.batch_size)) 103 | 104 | def __getitem__(self, idx): 105 | indexes = self.indexes[idx*self.batch_size:(idx+1)*self.batch_size] 106 | batch_image = np.array( 107 | [cv2.imread(self.data['image_paths'][i]) for i in indexes]) / 255.0 108 | batch_depth = np.expand_dims( 109 | np.array([np.load(self.data['depth_paths'][i]) for i in indexes]), axis=-1) 110 | batch_flow = np.array( 111 | [np.load(self.data['flow_paths'][i]) for i in indexes]) 112 | batch_depth_flow = np.concatenate((batch_depth, batch_flow), axis=-1) 113 | 114 | if self.dtype == 'depth': 115 | inputs = batch_image 116 | outputs = batch_depth 117 | elif self.dtype == 'vel': 118 | batch_vel = np.array([self.data['vels'][i] for i in indexes]) 119 | inputs = batch_image 120 | outputs = {'vel': batch_vel, 'flow': batch_depth_flow} 121 | else: 122 | assert(self.dtype == 'pose') 123 | batch_imu = np.array([self.data['imus'][i] for i in indexes]) 124 | batch_pose = np.array([self.data['poses'][i] for i in indexes]) 125 | inputs = [batch_image, batch_imu] 126 | outputs = {'pose': batch_pose, 'flow': batch_depth_flow} 127 | 128 | return inputs, outputs 129 | 130 | def on_epoch_end(self): 131 | self.indexes = np.arange(self.data['size']) 132 | np.random.shuffle(self.indexes) 133 | -------------------------------------------------------------------------------- /network/README.md: -------------------------------------------------------------------------------- 1 | # Network 2 | * Overview 3 | 4 | 5 | * **RsPoseNet** 6 | 7 | 8 | 9 | # Files 10 | * **DataLoader.py** loads the dataset, remember to modify the **data_path** in this file to the processed dataset. 11 | * **RsDepthNet.py** and **train_rsdepthnet.py** is the implementation of RsDepthNet. 12 | * **RsPoseNet.py** and **train_rsposenet.py** is the implementation of the proposed RsPoseNet. 13 | * **test.py** calculates the EPE and Improvement Ratio, as well as rectifies images. 14 | 15 | # Usage 16 | * Dependencies (version we use) 17 | * [numpy(1.17.4)](https://numpy.org/) 18 | * [cv2](https://pypi.org/project/opencv-python/) for reading images 19 | * [Tensorflow(2.3.0)](https://www.tensorflow.org/), [Keras(2.4.3)](https://keras.io/), [classification-models(*ResNet34*)](https://github.com/qubvel/classification_models) for network structure 20 | * [pandas(1.2.3)](https://pandas.pydata.org/) for image post-processing (interpolation) 21 | 22 | * Training 23 | ``` 24 | python3 train_rsdepthnet.py 25 | python3 train_rsposenet.py 26 | ``` 27 | or download the our trained [checkpoints](https://drive.google.com/file/d/1CMi2j5-aU4YCWay3ebMWkr3ClFqPsSSs/view?usp=sharing). 28 | 29 | * Testing 30 | ``` 31 | python3 test.py 32 | ``` 33 | the recitified images are saved to *./results/*. -------------------------------------------------------------------------------- /network/RsDepthNet.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | # fmt: off 18 | from __future__ import absolute_import, division, print_function 19 | import os 20 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 21 | import tensorflow as tf 22 | from keras.models import Input, Model 23 | from keras.layers import Conv2D, Conv2DTranspose, Activation 24 | from keras.layers import BatchNormalization, UpSampling2D, Concatenate 25 | 26 | # fmt: on 27 | 28 | 29 | def iconv_pr(iconv_prv, pr_prv, conv_encoder, filters, lvl): 30 | upconv = Activation('relu')(BatchNormalization(name='upconv{}bn'.format(lvl))( 31 | Conv2DTranspose(filters, 4, 2, 'same', name='upconv'+str(lvl))(iconv_prv))) 32 | upsampled = UpSampling2D(interpolation='bilinear', 33 | name='upsampled'+str(lvl))(pr_prv) 34 | ctnt = Concatenate()([upconv, upsampled, conv_encoder]) 35 | iconv = Conv2D(filters, 3, 1, 'same', activation='relu', 36 | name='iconv'+str(lvl))(ctnt) 37 | pr = Conv2D(1, 3, 1, 'same', activation='relu', 38 | name='pr'+str(lvl))(iconv) 39 | return [iconv, pr] 40 | 41 | 42 | class RsDepthNet(): 43 | def __init__(self, params): 44 | rows, cols = params['img_shape'] 45 | input_img = Input((rows, cols, 3)) 46 | depth = self.depthNet(input_img) 47 | self.model = Model(inputs=input_img, outputs=depth) 48 | 49 | def depthNet(self, img): 50 | # encoder 51 | c1 = Conv2D(64, 7, 2, 'same', activation='relu', name='conv1')(img) 52 | c2 = Conv2D(128, 5, 2, 'same', activation='relu', name='conv2')(c1) 53 | c3a = Conv2D(256, 5, 2, 'same', activation='relu', name='conv3a')(c2) 54 | c3b = Conv2D(256, 3, 1, 'same', activation='relu', name='conv3b')(c3a) 55 | c4a = Conv2D(512, 3, 2, 'same', activation='relu', name='conv4a')(c3b) 56 | c4b = Conv2D(512, 3, 1, 'same', activation='relu', name='conv4b')(c4a) 57 | c5a = Conv2D(512, 3, 2, 'same', activation='relu', name='conv5a')(c4b) 58 | c5b = Conv2D(512, 3, 1, 'same', activation='relu', name='conv5b')(c5a) 59 | c6a = Conv2D(1024, 3, 2, 'same', activation='relu', name='conv6a')(c5b) 60 | c6b = Conv2D(1024, 3, 1, 'same', activation='relu', name='conv6b')(c6a) 61 | 62 | pr6 = Conv2D(1, 3, 1, 'same', activation='relu', name='pr6')(c6a) 63 | 64 | # decoder 65 | [ic5, pr5] = iconv_pr(c6b, pr6, c5b, 512, 5) 66 | [ic4, pr4] = iconv_pr(ic5, pr5, c4b, 256, 4) 67 | [ic3, pr3] = iconv_pr(ic4, pr4, c3b, 128, 3) 68 | [ic2, pr2] = iconv_pr(ic3, pr3, c2, 64, 2) 69 | [ic1, pr1] = iconv_pr(ic2, pr2, c1, 32, 1) 70 | 71 | d1 = UpSampling2D(interpolation='bilinear', name='depth')(pr1) 72 | 73 | return d1 74 | 75 | def depthLoss(self, y_true, y_pred): 76 | diff = tf.where(tf.math.is_nan(y_true), 77 | tf.zeros_like(y_true), y_true-y_pred) 78 | return tf.reduce_mean(tf.abs(diff)) 79 | -------------------------------------------------------------------------------- /network/RsPoseNet.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | # fmt: off 18 | from __future__ import absolute_import, division, print_function 19 | import os 20 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 21 | import tensorflow as tf 22 | from keras.models import Input, Model 23 | from keras.layers import Conv2D, Conv2DTranspose, Activation, LSTM 24 | from keras.layers import BatchNormalization, Concatenate, Reshape 25 | import numpy as np 26 | 27 | from helpers import baseNet, flowLossByPose 28 | # fmt: on 29 | 30 | 31 | def poseConv(x, filters, kernel_size, lvl): 32 | x = Conv2D(filters, kernel_size, (1, 2), 'same', 33 | activation='relu', name='conv'+str(lvl))(x) 34 | x = Conv2DTranspose(filters, kernel_size, (2, 1), 35 | 'same', name='upconv'+str(lvl))(x) 36 | x = Activation('relu')(BatchNormalization(name='bn'+str(lvl))(x)) 37 | return x 38 | 39 | 40 | class RsPoseNet(): 41 | def __init__(self, params, gyro=True, acc=True): 42 | rows, cols = params['img_shape'] 43 | self.params = params 44 | self.pose_scale = 10 45 | 46 | # inputs 47 | input_img = Input((rows, cols, 3)) 48 | input_imu = Input((rows, 3*gyro+3*acc)) 49 | pose_scaled = self.poseNet(input_img, input_imu) # for poseLoss 50 | pose_mat = self.convertToPoseMat(pose_scaled) # for flowLoss 51 | 52 | self.model = Model(inputs=[input_img, input_imu], outputs={ 53 | 'pose': pose_scaled, 'flow': pose_mat}) 54 | 55 | def poseNet(self, input_img, input_imu): 56 | features = baseNet(input_img) 57 | 58 | x = poseConv(features, 512, 3, 5) 59 | x = poseConv(x, 256, 3, 4) 60 | x = poseConv(x, 128, 3, 3) 61 | x = poseConv(x, 64, 3, 2) 62 | x = poseConv(x, 32, 3, 1) 63 | 64 | img_pose = Conv2D(6, 1, 1, 'same', activation='tanh', 65 | name='pose_conv')(x) 66 | 67 | img_pose = Reshape( 68 | (self.params['img_shape'][0], 6), name='img_pose')(img_pose) 69 | 70 | # extend pose from image by IMU 71 | img_imu = Concatenate()([img_pose, input_imu]) 72 | h = LSTM(6, return_sequences=True, name='imu_lstm1')(img_imu) 73 | pose = LSTM(6, return_sequences=True, name='pose')(h) 74 | 75 | return pose 76 | 77 | def convertToPoseMat(self, pose_scaled): 78 | # -1 x 256 x 6 -> -1 x 256 x 320 x 6 79 | pose_mat = tf.gather(pose_scaled / self.pose_scale, 80 | self.params['lut'], axis=1, name='flow') 81 | return pose_mat 82 | 83 | def poseLoss(self, y_true, y_pred): 84 | return tf.reduce_mean(tf.norm(self.pose_scale*y_true - y_pred, axis=-1)) 85 | 86 | def flowLoss(self, y_true, y_pred): 87 | return flowLossByPose(y_true, y_pred, self.params) 88 | -------------------------------------------------------------------------------- /network/helpers.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from __future__ import absolute_import, division, print_function 18 | import tensorflow as tf 19 | from classification_models.keras import Classifiers 20 | from keras.applications.vgg16 import VGG16 21 | import numpy as np 22 | 23 | 24 | def getFlow(depth, pose, params): 25 | rows, cols = params['img_shape'] 26 | 27 | # split pose to translation and angel-axis representation rotation 28 | trans, rot = tf.split(pose, [3, 3], -1) 29 | angle = tf.expand_dims(tf.norm(rot, axis=-1), -1) 30 | cos_angle = tf.cos(angle) 31 | axis = rot / (angle + 1e-20) 32 | 33 | # recover 3D point in RS frame 34 | v_rs, u_rs = np.indices((rows, cols), dtype='float32') 35 | Kiu_rs = np.matmul(np.stack([u_rs, v_rs, np.ones_like( 36 | u_rs)], axis=-1), np.linalg.inv(params['cam']).T) 37 | depth = tf.stack([tf.squeeze(depth, axis=-1)]*3, axis=-1) 38 | xyz_rs = depth * Kiu_rs 39 | 40 | # project to GS frame 41 | xyz_gs = xyz_rs * cos_angle + tf.linalg.cross(axis, xyz_rs) * tf.sin( 42 | angle) + axis * tf.reduce_sum(axis * xyz_rs, -1, True) * (1-cos_angle) + trans 43 | uvd_gs = tf.matmul(xyz_gs, params['cam'].T) 44 | uv_gs, d_gs = tf.split(uvd_gs, [2, 1], -1) 45 | uv_gs = uv_gs / (d_gs + 1e-20) 46 | 47 | uv_rs = np.stack([u_rs, v_rs], axis=-1) 48 | flow = tf.identity(uv_gs - uv_rs, name='flow') 49 | return flow 50 | 51 | 52 | def flowLossByPose(df_true, p_pred, params): 53 | d_true, f_true = tf.split(df_true, [1, 2], -1) 54 | d_true = tf.where(tf.math.is_nan(d_true), 55 | tf.ones_like(d_true), d_true) 56 | f_pred = getFlow(d_true, p_pred, params) 57 | diff = tf.where(tf.math.is_nan(f_true), 58 | tf.zeros_like(f_true), f_true-f_pred) 59 | return tf.reduce_mean(tf.norm(diff, axis=-1)) 60 | 61 | 62 | def baseNet(img_input, base='ResNet34'): 63 | _, rows, cols, _ = img_input.shape 64 | if base == 'ResNet34': 65 | ResNet34, _ = Classifiers.get('resnet34') 66 | features = ResNet34(input_shape=(rows, cols, 3), 67 | weights='imagenet', include_top=False)(img_input) 68 | elif base == 'ResNet50': 69 | ResNet50, _ = Classifiers.get('resnet50') 70 | features = ResNet50(input_shape=(rows, cols, 3), 71 | weights='imagenet', include_top=False)(img_input) 72 | elif base == 'VGG16': 73 | vgg = VGG16(input_shape=(rows, cols, 3), 74 | weights='imagenet', include_top=False)(img_input) 75 | features = vgg.get_layer('block5_pool') 76 | return features 77 | -------------------------------------------------------------------------------- /network/test.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | # fmt: off 18 | from __future__ import print_function, division 19 | import os 20 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 21 | import time 22 | import shutil 23 | import numpy as np 24 | import pandas as pd 25 | import cv2 26 | 27 | from DataLoader import TumDataSet 28 | from RsDepthNet import RsDepthNet 29 | from helpers import getFlow 30 | from RsPoseNet import RsPoseNet 31 | # fmt: on 32 | 33 | 34 | def get_flows_pred(data, batch_size = 32): 35 | flows_pred = np.empty((0, rows, cols, 2)) 36 | for i in range(0, data['size'], batch_size): 37 | rg = range(i, min(i+batch_size, data['size'])) 38 | img = np.array([cv2.imread(data['image_paths'][j]) 39 | for j in rg]) / 255.0 40 | imu = np.array([data['imus'][j] for j in rg]) 41 | pose_mat = model_rspose.model.predict([img, imu])['flow'] 42 | depth_pred = model_depth.model.predict(img) 43 | flow_pred = getFlow(depth_pred, pose_mat, dataset.params).numpy() 44 | flows_pred = np.append(flows_pred, flow_pred, axis=0) 45 | 46 | return flows_pred 47 | 48 | 49 | def rectify_imgs(data, flow, img_path): 50 | os.makedirs(img_path) 51 | for i in range(data['size']): 52 | flow_rs2gs = flow[i] 53 | size_1d = rows*cols 54 | ind_v, ind_u = np.indices((rows, cols), dtype='float32') 55 | 56 | # map: rs to gs 57 | uv_rs = np.stack((ind_u, ind_v), axis=-1) 58 | uv_gs = np.array(uv_rs + flow_rs2gs + 0.5, dtype='int') 59 | u_gs = np.clip(uv_gs[:, :, 0], 0, cols-1) 60 | v_gs = np.clip(uv_gs[:, :, 1], 0, rows-1) 61 | uv_gs_1d = v_gs * cols + u_gs 62 | uv_gs_1d = np.reshape(uv_gs_1d, (size_1d)) 63 | 64 | # reverse the map: gs to rs 65 | flow_gs2rs_1d_u = np.full((size_1d), np.nan, dtype='float32') 66 | flow_gs2rs_1d_v = np.full((size_1d), np.nan, dtype='float32') 67 | flow_gs2rs_1d_u[uv_gs_1d] = np.reshape(-flow_rs2gs[:, :, 0], (size_1d)) 68 | flow_gs2rs_1d_v[uv_gs_1d] = np.reshape(-flow_rs2gs[:, :, 1], (size_1d)) 69 | flow_gs2rs = np.stack([np.reshape(flow_gs2rs_1d_u, (rows, cols)), 70 | np.reshape(flow_gs2rs_1d_v, (rows, cols))], axis=-1) 71 | 72 | # 2d interpolation 73 | flow_gs2rs[:, :, 0] = pd.DataFrame(flow_gs2rs[:, :, 0]).interpolate() 74 | flow_gs2rs[:, :, 1] = pd.DataFrame(flow_gs2rs[:, :, 1]).interpolate() 75 | 76 | # reconstruct gs image 77 | map_u = ind_u + flow_gs2rs[:, :, 0] 78 | map_v = ind_v + flow_gs2rs[:, :, 1] 79 | img_rs = cv2.imread(data['image_paths'][i]) 80 | img_gs = cv2.remap(img_rs, map_u, map_v, cv2.INTER_LINEAR) 81 | cv2.imwrite('{}{}.png'.format(img_path, i), img_gs) 82 | 83 | 84 | if __name__ == "__main__": 85 | # load data 86 | dataset = TumDataSet(True, True) 87 | rows, cols = dataset.params['img_shape'] 88 | 89 | # load depth model 90 | model_depth = RsDepthNet(dataset.params) 91 | model_depth.model.load_weights(os.path.join( 92 | os.getcwd(), 'checkpoints/model_depth.hdf5')) 93 | 94 | # load pose model 95 | model_rspose = RsPoseNet(dataset.params) 96 | model_rspose.model.load_weights(os.path.join( 97 | os.getcwd(), 'checkpoints/model_rspose_yy.hdf5')) 98 | 99 | save_path = 'results/' 100 | if os.path.exists(save_path): 101 | shutil.rmtree(save_path) 102 | 103 | seq_data = dataset.data['test'] 104 | for seq, data in seq_data.items(): 105 | print('\n\n\n******************** Sequence {} *********************'.format(seq)) 106 | flows_gt = np.array([np.load(data['flow_paths'][j]) 107 | for j in range(data['size'])]) 108 | errs_input = np.nanmean( 109 | np.sqrt(np.sum(np.square(flows_gt), axis=-1)), axis=(1, 2)) 110 | print('Input EPE: {:.3f}'.format(np.mean(errs_input))) 111 | 112 | # predict correction flow 113 | t0 = time.time() 114 | flows_pred = get_flows_pred(data) 115 | fps = data['size'] / (time.time() - t0) 116 | errs_pred = np.nanmean( 117 | np.sqrt(np.sum(np.square(flows_gt-flows_pred), axis=-1)), axis=(1, 2)) 118 | print('Prediction: {:.3f} x {:.1f}% @ {:.1f}FPS'.format(np.mean( 119 | errs_pred), 100*(np.count_nonzero(errs_input-errs_pred > 0))/data['size'], fps)) 120 | 121 | # rectify images 122 | t0 = time.time() 123 | rectify_imgs(data, flows_pred, '{}{}/'.format(save_path, seq)) 124 | fps = data['size'] / (time.time() - t0) 125 | print('Rectify Images: {:.1f}FPS'.format(fps)) 126 | -------------------------------------------------------------------------------- /network/train_rsdepthnet.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | # fmt: off 18 | from __future__ import print_function, division 19 | import os 20 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 21 | import shutil 22 | 23 | from keras.optimizers import Adam 24 | from keras.callbacks import ModelCheckpoint, TensorBoard 25 | 26 | from DataLoader import TumDataSet, DataGenerator 27 | from RsDepthNet import RsDepthNet 28 | # fmt: on 29 | 30 | # dataset 31 | batch_size = 32 32 | tum = TumDataSet() 33 | train_dg = DataGenerator(tum.data['train'], batch_size, dtype='depth') 34 | val_dg = DataGenerator(tum.data['val'], batch_size, dtype='depth') 35 | 36 | # weight saving directory 37 | checkpoint_path = os.path.join(os.getcwd(), "checkpoints/") 38 | if not os.path.exists(checkpoint_path): 39 | os.makedirs(checkpoint_path) 40 | checkpoint_file = os.path.join(checkpoint_path, 'model_depth.hdf5') 41 | 42 | # tensorboard 43 | log_path = os.path.join(checkpoint_path, '.logs/depth') 44 | if os.path.exists(log_path): 45 | shutil.rmtree(log_path) 46 | 47 | # load model 48 | model = RsDepthNet(tum.params) 49 | 50 | # training 51 | model.model.compile(optimizer=Adam(learning_rate=1e-3), loss=model.depthLoss) 52 | model.model.fit(train_dg, validation_data=val_dg, epochs=100, callbacks=[ 53 | ModelCheckpoint(checkpoint_file, 54 | save_weights_only=True, save_best_only=True), 55 | TensorBoard(log_dir=log_path)]) 56 | -------------------------------------------------------------------------------- /network/train_rsposenet.py: -------------------------------------------------------------------------------- 1 | # Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption 2 | # Copyright (C) <2021> 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published by 6 | # the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | # fmt: off 18 | from __future__ import print_function, division 19 | import sys 20 | import os 21 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 22 | import shutil 23 | import argparse 24 | 25 | from keras.optimizers import Adam 26 | from keras.callbacks import ModelCheckpoint, TensorBoard 27 | 28 | from DataLoader import TumDataSet, DataGenerator 29 | from RsPoseNet import RsPoseNet 30 | # fmt: on 31 | 32 | # whether to use IMU data 33 | parser = argparse.ArgumentParser() 34 | parser.add_argument('--ng', action='store_const', default=False, 35 | const=True, help='whether to disable gyroscope data') 36 | parser.add_argument('--na', action='store_const', default=False, 37 | const=True, help='whether to disable accelerator data') 38 | args = parser.parse_args() 39 | gyro = not args.ng 40 | acc = not args.na 41 | suffix = ('y' if gyro else 'n') + ('y' if acc else 'n') 42 | print('gyroscope: ' + ('y' if gyro else 'n') + '; ' + 43 | 'accelerator: ' + ('y' if acc else 'n')) 44 | 45 | # dataset 46 | batch_size = 32 47 | tum = TumDataSet(gyro, acc) 48 | train_dg = DataGenerator(tum.data['train'], batch_size, dtype='pose') 49 | val_dg = DataGenerator(tum.data['val'], batch_size, dtype='pose') 50 | 51 | # weight saving directory 52 | checkpoint_path = os.path.join(os.getcwd(), "checkpoints/") 53 | if not os.path.exists(checkpoint_path): 54 | os.makedirs(checkpoint_path) 55 | 56 | # tensorboard 57 | log_path = os.path.join(checkpoint_path, '.logs/rspose_'+suffix) 58 | if os.path.exists(log_path): 59 | shutil.rmtree(log_path) 60 | 61 | # checkpoint file 62 | checkpoint_file = os.path.join( 63 | checkpoint_path, 'model_rspose_{}.hdf5'.format(suffix)) 64 | 65 | # load model 66 | model = RsPoseNet(tum.params, gyro, acc) 67 | 68 | # training 69 | print('Training with pose loss') 70 | model.model.compile(optimizer=Adam(learning_rate=1e-3), 71 | loss={'pose': model.poseLoss, 'flow': model.flowLoss}, 72 | loss_weights={'pose': 1, 'flow': 0}) # flowLoss is included just for monitoring 73 | model.model.fit(train_dg, validation_data=val_dg, epochs=50, callbacks=[ 74 | ModelCheckpoint( 75 | checkpoint_file, save_weights_only=True, save_best_only=True), 76 | TensorBoard(log_dir=log_path)]) 77 | 78 | print('Training with flow loss') 79 | model.model.compile(optimizer=Adam(learning_rate=1e-4), 80 | loss={'pose': model.poseLoss, 'flow': model.flowLoss}, 81 | loss_weights={'pose': 0, 'flow': 1}) 82 | model.model.load_weights(checkpoint_file) 83 | model.model.fit(train_dg, validation_data=val_dg, epochs=50, callbacks=[ 84 | ModelCheckpoint( 85 | checkpoint_file, save_weights_only=True, save_best_only=True), 86 | TensorBoard(log_dir=log_path)]) 87 | --------------------------------------------------------------------------------