From 7956f49b22dd7b5f1b18f660766342b9f68c9d92 Mon Sep 17 00:00:00 2001 From: zhihaolee <1090259542@qq.com> Date: Mon, 1 Aug 2022 10:53:35 +0800 Subject: [PATCH] Cliff (#93) * Create README.MD * code and data link * Update README.MD Update README.MD --- CLIFF/LICENSE | 21 + CLIFF/README.MD | 77 ++- ...HIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt | 65 +++ CLIFF/assets/im00492/im00492.png | Bin 0 -> 210973 bytes CLIFF/assets/im00492/im00492_bbox.jpg | Bin 0 -> 69990 bytes .../im00492/im00492_front_view_cliff_hr48.jpg | Bin 0 -> 69283 bytes .../im00492/im00492_side_view_cliff_hr48.jpg | Bin 0 -> 6463 bytes CLIFF/assets/im08036/im08036.png | Bin 0 -> 412562 bytes CLIFF/assets/im08036/im08036_bbox.jpg | Bin 0 -> 123631 bytes .../im08036/im08036_front_view_cliff_hr48.jpg | Bin 0 -> 117813 bytes .../im08036/im08036_side_view_cliff_hr48.jpg | Bin 0 -> 13148 bytes CLIFF/assets/teaser.gif | Bin 0 -> 2744200 bytes .../viz_cliffGT_perImg-COCO_sample.jpg | Bin 0 -> 49358 bytes .../viz_cliffGT_perSubject-MPII_sample.jpg | Bin 0 -> 32319 bytes CLIFF/common/constants.py | 23 + CLIFF/common/imutils.py | 150 +++++ CLIFF/common/mocap_dataset.py | 48 ++ CLIFF/common/renderer_pyrd.py | 90 +++ CLIFF/common/skeleton_drawer.py | 112 ++++ CLIFF/common/utils.py | 78 +++ CLIFF/data/smpl_mean_params.npz | Bin 0 -> 1310 bytes CLIFF/demo.py | 261 +++++++++ CLIFF/lib/yolov3_dataset.py | 36 ++ CLIFF/lib/yolov3_detector.py | 68 +++ CLIFF/models/backbones/hrnet/cls_hrnet.py | 518 ++++++++++++++++++ .../backbones/hrnet/hrnet_config/__init__.py | 9 + .../backbones/hrnet/hrnet_config/default.py | 136 +++++ .../backbones/hrnet/hrnet_config/models.py | 58 ++ ...hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml | 92 ++++ ...hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml | 92 ++++ CLIFF/models/backbones/resnet.py | 94 ++++ CLIFF/models/cliff_hr48/cliff.py | 105 ++++ CLIFF/models/cliff_res50/cliff.py | 97 ++++ CLIFF/requirements.txt | 11 + CLIFF/scripts/run_cliffGT_visualization.sh | 17 + CLIFF/scripts/run_demo.sh | 29 + CLIFF/viz_cliffGT_perImg.py | 122 +++++ CLIFF/viz_cliffGT_perSubject.py | 101 ++++ 38 files changed, 2496 insertions(+), 14 deletions(-) create mode 100644 CLIFF/LICENSE create mode 100644 CLIFF/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt create mode 100644 CLIFF/assets/im00492/im00492.png create mode 100644 CLIFF/assets/im00492/im00492_bbox.jpg create mode 100644 CLIFF/assets/im00492/im00492_front_view_cliff_hr48.jpg create mode 100644 CLIFF/assets/im00492/im00492_side_view_cliff_hr48.jpg create mode 100644 CLIFF/assets/im08036/im08036.png create mode 100644 CLIFF/assets/im08036/im08036_bbox.jpg create mode 100644 CLIFF/assets/im08036/im08036_front_view_cliff_hr48.jpg create mode 100644 CLIFF/assets/im08036/im08036_side_view_cliff_hr48.jpg create mode 100644 CLIFF/assets/teaser.gif create mode 100644 CLIFF/assets/visualized_cliffGT/viz_cliffGT_perImg-COCO_sample.jpg create mode 100644 CLIFF/assets/visualized_cliffGT/viz_cliffGT_perSubject-MPII_sample.jpg create mode 100644 CLIFF/common/constants.py create mode 100644 CLIFF/common/imutils.py create mode 100644 CLIFF/common/mocap_dataset.py create mode 100644 CLIFF/common/renderer_pyrd.py create mode 100644 CLIFF/common/skeleton_drawer.py create mode 100644 CLIFF/common/utils.py create mode 100644 CLIFF/data/smpl_mean_params.npz create mode 100644 CLIFF/demo.py create mode 100644 CLIFF/lib/yolov3_dataset.py create mode 100644 CLIFF/lib/yolov3_detector.py create mode 100644 CLIFF/models/backbones/hrnet/cls_hrnet.py create mode 100644 CLIFF/models/backbones/hrnet/hrnet_config/__init__.py create mode 100644 CLIFF/models/backbones/hrnet/hrnet_config/default.py create mode 100644 CLIFF/models/backbones/hrnet/hrnet_config/models.py create mode 100644 CLIFF/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml create mode 100644 CLIFF/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml create mode 100644 CLIFF/models/backbones/resnet.py create mode 100644 CLIFF/models/cliff_hr48/cliff.py create mode 100644 CLIFF/models/cliff_res50/cliff.py create mode 100644 CLIFF/requirements.txt create mode 100644 CLIFF/scripts/run_cliffGT_visualization.sh create mode 100644 CLIFF/scripts/run_demo.sh create mode 100644 CLIFF/viz_cliffGT_perImg.py create mode 100644 CLIFF/viz_cliffGT_perSubject.py diff --git a/CLIFF/LICENSE b/CLIFF/LICENSE new file mode 100644 index 0000000..d0dc4d3 --- /dev/null +++ b/CLIFF/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. \ No newline at end of file diff --git a/CLIFF/README.MD b/CLIFF/README.MD index 775c76f..e828c7c 100644 --- a/CLIFF/README.MD +++ b/CLIFF/README.MD @@ -1,11 +1,14 @@ # CLIFF [ECCV 2022 Oral] + +*(This testing video is from the 3DPW testset, and processed frame by frame without temporal smoothing.)* -This repo contains the CLIFF demo code, +## Introduction +This repo contains the demo code, and the pseudo-GT SMPL parameters used in the training for the paper: -**CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation** +**CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation**. ## Installation ```bash @@ -13,10 +16,13 @@ conda create -n cliff python=3.10 pip install -r requirements.txt ``` -1. Download [the SMPL models](https://smpl.is.tue.mpg.de/) for rendering the reconstructed meshes -2. Download [pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3), unzip it under the `lib` directory, and change the folder name to **"pytorch_yolo_v3_master"** -2. Download the pretrained checkpoints and some testing samples to run the demo [[Google Drive]()] -3. Download the CLIFF pseudo-GT for the in-the-wild datasets [[Google Drive]()] +1. Download [the SMPL models](https://smpl.is.tue.mpg.de) for rendering the reconstructed meshes +2. Download [pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3), + unzip it under the `lib` directory, and change the folder name to **"pytorch_yolo_v3_master"** +3. Download the pretrained checkpoints and some testing samples to run the demo [[Google Drive]( + https://drive.google.com/drive/folders/1EmSZwaDULhT9m1VvH7YOpCXwBWgYrgwP?usp=sharing)] +4. Download the CLIFF pseudo-GT for the in-the-wild datasets [[Google Drive]( + https://drive.google.com/drive/folders/1EmSZwaDULhT9m1VvH7YOpCXwBWgYrgwP?usp=sharing)] Finally put these data following the directory structure as below: ``` @@ -26,38 +32,81 @@ ${ROOT} |-- mpii_cliffGT.npz |-- data |-- ckpt - |-- hr48-PA43.0_MJE69.0_MVE81.2_3dpw-PA32.6_MJE47.6_MVE60.1_h36m-p2.pt - |-- res50-PA45.7_MJE72.0_MVE85.3_3dpw-PA35.7_MJE52.8_MVE63.2_h36m-p2.pt + |-- hr48-PA43.0_MJE69.0_MVE81.2_3dpw.pt + |-- res50-PA45.7_MJE72.0_MVE85.3_3dpw.pt + |-- yolov3.weights |-- smpl |-- SMPL_FEMALE.pkl |-- SMPL_MALE.pkl |-- SMPL_NEUTRAL.pkl |-- lib |-- pytorch_yolo_v3_master - |-- yolov3.weights ``` - ## Demo ```shell sh scripts/run_demo.sh ``` -Change the options in the scripts. -See the option description in the bottom lines of `demo.py` + +

+ + + + +

+ +

+ + + + +

+ +The results will be saved in the same directory as the input, including the detected bbox, +the front-view and side-view rendering of the reconstructed meshes, +and the npz file containing all the estimation information. + +One can change the demo options in the script. +Please see the option description in the bottom lines of `demo.py`. ## Pseudo-GT + +Run the script to visualize the pseudoGT, press the `<-` and `->` button to the next sample, and `esc` to exit. + ```shell sh scripts/run_cliffGT_visualization.sh ``` +

+ + +

+Data format: +``` +'imgname': # image name, e.g., images/015601864.jpg, train2014/COCO_train2014_000000044474.jpg +'center': # bbox center, [x, y] +'scale': # bbox scale, bbox_size_in_pixel / 200. +'part': # 2D keypoint annotation, shape (24, 3), [x, y, conf], see common/skeleton_drawer.py for the order +'annot_id': # annotation ID, only available for the COCO dataset +'pose': # SMPL pose parameters in axis-angle, shape (72,) +'shape': # SMPL shape parameters, shape (10,) +'has_smpl': # whether the smpl parameters are available (true for all samples) +'global_t': # Pelvis translation in the camera coordinate system w.r.t the original full-frame image +'focal_l': # estimated focal length for the original image, np.sqrt(img_w ** 2 + img_h ** 2) +'S': # 3D joints with Pelvis aligned at (0, 0, 0), shape (24, 4), [x, y, z, conf], same order as 'part' +``` + +## Issues +When submitting an issue, please add **"[CLIFF]"** before the title, e.g., `[CLIFF] your issue title`. +This is for clarifying which subproject the issue points to. ## Citing ``` -@Inproceedings{li_2022_eccv_cliff, +@Inproceedings{li2022cliff, Title = {CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation}, Author = {Li, Zhihao and Liu, Jianzhuang and Zhang, Zhensong and Xu, Songcen and Yan, Youliang}, Booktitle = {ECCV}, Year = {2022} } -``` \ No newline at end of file +``` diff --git a/CLIFF/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt b/CLIFF/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt new file mode 100644 index 0000000..ac9b515 --- /dev/null +++ b/CLIFF/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt @@ -0,0 +1,65 @@ +Please note we provide an open source software notice for the third party open source software along with this software and/or this software component contributed by Huawei (in the following just “this SOFTWARE”). The open source software licenses are granted by the respective right holders. + +Warranty Disclaimer +THE OPEN SOURCE SOFTWARE IN THIS SOFTWARE IS DISTRIBUTED IN THE HOPE THAT IT WILL BE USEFUL, BUT WITHOUT ANY WARRANTY, WITHOUT EVEN THE IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. SEE THE APPLICABLE LICENSES FOR MORE DETAILS. + +Copyright Notice and License Texts +Software: SPIN (https://github.com/nkolot/SPIN) +Copyright notice: +Copyright (c) 2019, +University of Pennsylvania, +Max Planck Institute for Intelligent Systems +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + +3. The names of the contributors may not be used to endorse or + promote products derived from this software without specific + prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Copyright Notice and License Texts +Software: HRNet-Image-Classification (https://github.com/HRNet/HRNet-Image-Classification) +Copyright notice: +MIT License + +Copyright (c) 2019 Microsoft Corporation + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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z(Iq3!O{jsu>R{`X=W%vAvjQV=?;RQSs&l6fqA2-gxDetRh}XU93-;>cV~kT~v(;#{ zeQ{4KEM)=fY)l_PF7kPA)Rh;2+`C0IbdEc_i;u>rlq1npnGMU*A8FPjh$`QU%3{AM zDbb&mjlC zT>F+5y&wn4W6V3=69P&UXT-+VJbANW$cLR?z>gz!pE8a3_SrWY=n^%=z#eK^rB3HHOEV_!36o8$2pQR-q~E835Koa|C7^A=arTf#veRpCjQ?k!9K@UW z$KVqr^$PgdT8z|J*mMUvPTt5*0*6QaqXDeM`lu^@?j||E_8SedPi`Q`CMG7ZQV1x` zKa))JQUn6H1@Vb!4De47ZJPB3P;l)|2_Skbzo7Uu1q5Xlc|Jh^aw;u65)A@41GJf! zxW#~*}x)ep>`v`;SBGNk5Y@k(%$jpxMzQ zQhd!C*?|Uh_tbgWh28gt4^|f!fjsD z!Byp#@{6=8kjJi}PgJws0XLEv%eN)aFAp|1G;7Se*c=!@%jEdy!rbj)40~`PYziVrz_$(y;U#+s7*!|7Gs4NvF(z~i&nq}t=WNz(& z&?Ci^LO(%~e>4URLf_xqE;Ox}lT-5g6Tr9_RO$KM=b`IQ1+c;Yc!VK`Vm{tc*kn>W zY=z}NMjG8S1^|{=Ui~%lRl2VC;XgsIYyiB{AJh29?O^|KY={#m=fEB?cI7l>mnD`x zDdnLYwvHs5^Z120E`+yIf)oi4dRmmKCtrpa#4sW5Ks(_tg&R@MLK28mspqg!n zbU{tEd9wcazH3jhqxa&9_EaXJi6>+t 2: + new_shape += [img.shape[2]] + new_img = np.zeros(new_shape, dtype=np.float32) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + try: + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] + except Exception as e: + print(e) + + new_img = cv2.resize(new_img, (res[1], res[0])) # (cols, rows) + + return new_img, ul, br + + +def bbox_from_detector(bbox, rescale=1.1): + """ + Get center and scale of bounding box from bounding box. + The expected format is [min_x, min_y, max_x, max_y]. + """ + # center + center_x = (bbox[0] + bbox[2]) / 2.0 + center_y = (bbox[1] + bbox[3]) / 2.0 + center = torch.tensor([center_x, center_y]) + + # scale + bbox_w = bbox[2] - bbox[0] + bbox_h = bbox[3] - bbox[1] + bbox_size = max(bbox_w * constants.CROP_ASPECT_RATIO, bbox_h) + scale = bbox_size / 200.0 + # adjust bounding box tightness + scale *= rescale + return center, scale + + +def process_image(orig_img_rgb, bbox, + crop_height=constants.CROP_IMG_HEIGHT, + crop_width=constants.CROP_IMG_WIDTH): + """ + Read image, do preprocessing and possibly crop it according to the bounding box. + If there are bounding box annotations, use them to crop the image. + If no bounding box is specified but openpose detections are available, use them to get the bounding box. + """ + try: + center, scale = bbox_from_detector(bbox) + except Exception as e: + print("Error occurs in person detection", e) + # Assume that the person is centered in the image + height = orig_img_rgb.shape[0] + width = orig_img_rgb.shape[1] + center = np.array([width // 2, height // 2]) + scale = max(height, width * crop_height / float(crop_width)) / 200. + + img, ul, br = crop(orig_img_rgb, center, scale, (crop_height, crop_width)) + crop_img = img.copy() + + img = img / 255. + mean = np.array(constants.IMG_NORM_MEAN, dtype=np.float32) + std = np.array(constants.IMG_NORM_STD, dtype=np.float32) + norm_img = (img - mean) / std + norm_img = np.transpose(norm_img, (2, 0, 1)) + + return norm_img, center, scale, ul, br, crop_img + + +def rot6d_to_rotmat(x): + """Convert 6D rotation representation to 3x3 rotation matrix. + Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 + Input: + (B,6) Batch of 6-D rotation representations + Output: + (B,3,3) Batch of corresponding rotation matrices + """ + x = x.view(-1, 3, 2) + a1 = x[:, :, 0] + a2 = x[:, :, 1] + b1 = F.normalize(a1) + b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1) + b3 = torch.cross(b1, b2) + return torch.stack((b1, b2, b3), dim=-1) diff --git a/CLIFF/common/mocap_dataset.py b/CLIFF/common/mocap_dataset.py new file mode 100644 index 0000000..2d44319 --- /dev/null +++ b/CLIFF/common/mocap_dataset.py @@ -0,0 +1,48 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +from torch.utils.data import Dataset + +from common.imutils import process_image +from common.utils import estimate_focal_length + + +class MocapDataset(Dataset): + def __init__(self, img_bgr_list, detection_list): + self.img_bgr_list = img_bgr_list + self.detection_list = detection_list + + def __len__(self): + return len(self.detection_list) + + def __getitem__(self, idx): + """ + bbox: [batch_id, min_x, min_y, max_x, max_y, det_conf, nms_conf, category_id] + :param idx: + :return: + """ + item = {} + img_idx = int(self.detection_list[idx][0].item()) + img_bgr = self.img_bgr_list[img_idx] + img_rgb = img_bgr[:, :, ::-1] + img_h, img_w, _ = img_rgb.shape + focal_length = estimate_focal_length(img_h, img_w) + + bbox = self.detection_list[idx][1:5] + norm_img, center, scale, crop_ul, crop_br, _ = process_image(img_rgb, bbox) + + item["norm_img"] = norm_img + item["center"] = center + item["scale"] = scale + item["crop_ul"] = crop_ul + item["crop_br"] = crop_br + item["img_h"] = img_h + item["img_w"] = img_w + item["focal_length"] = focal_length + return item diff --git a/CLIFF/common/renderer_pyrd.py b/CLIFF/common/renderer_pyrd.py new file mode 100644 index 0000000..cb973be --- /dev/null +++ b/CLIFF/common/renderer_pyrd.py @@ -0,0 +1,90 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import os +import trimesh +import pyrender +import numpy as np +import colorsys +import cv2 + + +class Renderer(object): + + def __init__(self, focal_length=600, img_w=512, img_h=512, faces=None, + same_mesh_color=False): + os.environ['PYOPENGL_PLATFORM'] = 'egl' + self.renderer = pyrender.OffscreenRenderer(viewport_width=img_w, + viewport_height=img_h, + point_size=1.0) + self.camera_center = [img_w // 2, img_h // 2] + self.focal_length = focal_length + self.faces = faces + self.same_mesh_color = same_mesh_color + + def render_front_view(self, verts, bg_img_rgb=None, bg_color=(0, 0, 0, 0)): + # Create a scene for each image and render all meshes + scene = pyrender.Scene(bg_color=bg_color, ambient_light=np.ones(3) * 0) + # Create camera. Camera will always be at [0,0,0] + camera = pyrender.camera.IntrinsicsCamera(fx=self.focal_length, fy=self.focal_length, + cx=self.camera_center[0], cy=self.camera_center[1]) + scene.add(camera, pose=np.eye(4)) + + # Create light source + light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=3.0) + # for DirectionalLight, only rotation matters + light_pose = trimesh.transformations.rotation_matrix(np.radians(-45), [1, 0, 0]) + scene.add(light, pose=light_pose) + light_pose = trimesh.transformations.rotation_matrix(np.radians(45), [0, 1, 0]) + scene.add(light, pose=light_pose) + + # Need to flip x-axis + rot = trimesh.transformations.rotation_matrix(np.radians(180), [1, 0, 0]) + # multiple person + num_people = len(verts) + # for every person in the scene + for n in range(num_people): + mesh = trimesh.Trimesh(verts[n], self.faces) + mesh.apply_transform(rot) + if self.same_mesh_color: + mesh_color = colorsys.hsv_to_rgb(0.6, 0.5, 1.0) + else: + mesh_color = colorsys.hsv_to_rgb(float(n) / num_people, 0.5, 1.0) + material = pyrender.MetallicRoughnessMaterial( + metallicFactor=0.2, + alphaMode='OPAQUE', + baseColorFactor=mesh_color) + mesh = pyrender.Mesh.from_trimesh(mesh, material=material, wireframe=False) + scene.add(mesh, 'mesh') + + # Alpha channel was not working previously, need to check again + # Until this is fixed use hack with depth image to get the opacity + color_rgba, depth_map = self.renderer.render(scene, flags=pyrender.RenderFlags.RGBA) + color_rgb = color_rgba[:, :, :3] + if bg_img_rgb is None: + return color_rgb + else: + mask = depth_map > 0 + bg_img_rgb[mask] = color_rgb[mask] + return bg_img_rgb + + def render_side_view(self, verts): + centroid = verts.mean(axis=(0, 1)) # n*6890*3 -> 3 + # make the centroid at the image center (the X and Y coordinates are zeros) + centroid[:2] = 0 + aroundy = cv2.Rodrigues(np.array([0, np.radians(90.), 0]))[0][np.newaxis, ...] # 1*3*3 + pred_vert_arr_side = np.matmul((verts - centroid), aroundy) + centroid + side_view = self.render_front_view(pred_vert_arr_side) + return side_view + + def delete(self): + """ + Need to delete before creating the renderer next time + """ + self.renderer.delete() diff --git a/CLIFF/common/skeleton_drawer.py b/CLIFF/common/skeleton_drawer.py new file mode 100644 index 0000000..4ec8f34 --- /dev/null +++ b/CLIFF/common/skeleton_drawer.py @@ -0,0 +1,112 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import numpy as np +import cv2 + +""" +Joint Order + +0: 'Right Ankle' +1: 'Right Knee' +2: 'Right Hip' +3: 'Left Hip' +4: 'Left Knee' +5: 'Left Ankle' +6: 'Right Wrist' +7: 'Right Elbow' +8: 'Right Shoulder' +9: 'Left Shoulder' +10: 'Left Elbow' +11: 'Left Wrist' +12: 'Neck (LSP)' +13: 'Top of Head (LSP)' +14: 'Pelvis (MPII)' +15: 'Thorax (MPII)' +16: 'Spine (H36M)' +17: 'Jaw (H36M)' +18: 'Head (H36M)' +19: 'Nose' +20: 'Left Eye' +21: 'Right Eye' +22: 'Left Ear' +23: 'Right Ear' +""" + +palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], + [230, 230, 0], [255, 153, 255], [153, 204, 255], + [255, 102, 255], [255, 51, 255], [102, 178, 255], + [51, 153, 255], [255, 153, 153], [255, 102, 102], + [255, 51, 51], [153, 255, 153], [102, 255, 102], + [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], + [255, 255, 255]]) # BGR + +# --------------- COCO --------------- +skeleton_coco = [[9, 8], [8, 2], [2, 3], [3, 9], # torso + [9, 10], [10, 11], # left arm + [8, 7], [7, 6], # right arm + [2, 1], [1, 0], # right leg + [3, 4], [4, 5], # left leg + [23, 21], [21, 19], [19, 20], [20, 22], [20, 21], # face + [9, 22], [8, 23], ] # ear and shoulder +skeleton_coco_color = palette[[9, 9, 9, 9, # torso, orange + 7, 7, 0, 0, 0, 0, 7, 7, # limb, blue(0), pink(7) + 16, 16, 16, 16, 16, # face, green + 16, 16]] # ear and shoulder, green +joint_coco_color = palette[[0, 0, 0, 7, 7, 7, 0, 0, 0, 7, 7, 7, + 9, 9, 9, 9, 9, 9, 9, + 16, 16, 16, 16, 16]] + +# --------------- MPII --------------- +skeleton_mpii = [[14, 15], [15, 12], [12, 13], # torso + [15, 9], [9, 10], [10, 11], # left arm + [15, 8], [8, 7], [7, 6], # right arm + [14, 2], [2, 1], [1, 0], # right leg + [14, 3], [3, 4], [4, 5]] # left leg +skeleton_mpii_color = palette[[9, 9, 9, # torso, orange + 7, 7, 7, + 0, 0, 0, + 0, 0, 0, + 7, 7, 7]] # limb, blue(0), pink(7) +joint_mpii_color = palette[[0, 0, 0, 7, 7, 7, 0, 0, 0, 7, 7, 7, + 9, 9, 9, 9, 9, 9, 9, + 16, 16, 16, 16, 16]] + + +def draw_skeleton(img, kp_24joints): + # -------- check the format -------- + # COCO format + skeleton = skeleton_coco + skeleton_color = skeleton_coco_color + joint_color = joint_coco_color + if kp_24joints[13, 2] > 0: + # MPII format + skeleton = skeleton_mpii + skeleton_color = skeleton_mpii_color + joint_color = joint_mpii_color + + # -------- draw lines -------- + for k in np.arange(len(skeleton)): + parent = skeleton[k][0] + child = skeleton[k][1] + # there are missing joint in this bone + if kp_24joints[parent][2] * kp_24joints[child][2] <= 0: + continue + parent_pos = (int(kp_24joints[parent][0]), int(kp_24joints[parent][1])) + child_pos = (int(kp_24joints[child][0]), int(kp_24joints[child][1])) + color = tuple([int(x) for x in skeleton_color[k]]) + cv2.line(img, parent_pos, child_pos, color, 3) + + # -------- draw joints -------- + for index, (px, py, conf) in enumerate(kp_24joints): + if conf > 0: + color = tuple([int(x) for x in joint_color[index]]) + cv2.circle(img, (int(px), int(py)), 5, color, -1) + + return img diff --git a/CLIFF/common/utils.py b/CLIFF/common/utils.py new file mode 100644 index 0000000..e8d7b11 --- /dev/null +++ b/CLIFF/common/utils.py @@ -0,0 +1,78 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import torch +from collections import OrderedDict +import os +import glob +from tqdm import tqdm +import cv2 +import subprocess + + +def strip_prefix_if_present(state_dict, prefix): + keys = sorted(state_dict.keys()) + if not any(key.startswith(prefix) for key in keys): + return state_dict + stripped_state_dict = OrderedDict() + for key, value in state_dict.items(): + stripped_state_dict[key.replace(prefix, "")] = value + return stripped_state_dict + + +def cam_crop2full(crop_cam, center, scale, full_img_shape, focal_length): + """ + convert the camera parameters from the crop camera to the full camera + :param crop_cam: shape=(N, 3) weak perspective camera in cropped img coordinates (s, tx, ty) + :param center: shape=(N, 2) bbox coordinates (c_x, c_y) + :param scale: shape=(N, 1) square bbox resolution (b / 200) + :param full_img_shape: shape=(N, 2) original image height and width + :param focal_length: shape=(N,) + :return: + """ + img_h, img_w = full_img_shape[:, 0], full_img_shape[:, 1] + cx, cy, b = center[:, 0], center[:, 1], scale * 200 + w_2, h_2 = img_w / 2., img_h / 2. + bs = b * crop_cam[:, 0] + 1e-9 + tz = 2 * focal_length / bs + tx = (2 * (cx - w_2) / bs) + crop_cam[:, 1] + ty = (2 * (cy - h_2) / bs) + crop_cam[:, 2] + full_cam = torch.stack([tx, ty, tz], dim=-1) + return full_cam + + +def video_to_images(vid_file, img_folder=None): + command = ['ffmpeg', + '-i', vid_file, + '-f', 'image2', + '-v', 'error', + f'{img_folder}/%06d.png'] + print(f'Running \"{" ".join(command)}\"') + subprocess.call(command) + print(f'Images saved to \"{img_folder}\"') + + +def images_to_video(img_dir, video_path, frame_rate=30): + img_list = glob.glob(os.path.join(img_dir, '*.jpg')) + img_list.extend(glob.glob(os.path.join(img_dir, '*.png'))) + img_list.sort() + + img_exp = cv2.imread(img_list[0]) + rows, cols, ch = img_exp.shape + video = cv2.VideoWriter(video_path, + cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), + frame_rate, (cols, rows)) + + for img_path in tqdm(img_list): + video.write(cv2.imread(img_path)) + video.release() + + +def estimate_focal_length(img_h, img_w): + return (img_w * img_w + img_h * img_h) ** 0.5 # fov: 55 degree diff --git a/CLIFF/data/smpl_mean_params.npz b/CLIFF/data/smpl_mean_params.npz new file mode 100644 index 0000000000000000000000000000000000000000..b59913714b1cef7728994b6898348762b2179dae GIT binary patch literal 1310 zcmWIWW@Zs#00GXmGX5=Vw0l+n*&xgb#Kjqj1*v*@1(lQiLVW`w85znLs?}3ci<67g ztrXO4(oEEK6x7r5i%N~Tr9(*^SPxqq&=cZ##$Q<7D0^Ix&d{zswfp5x1U z?8L+C?9X3ZWV?RZ&An{S>GtsJDN=GJEg;#YMK2Yvb(&6CG@hd3?9~ zb+_K$>ek8KLjN{e&wg8PpDD^@TbR1rW@~z_{af(_+szR%d#mQw+xJXo+c)z=v~~0T zO8asvmVJK@@Y$JI)Y^An(6x&=)4%ukpL+XgcLeu5;S;oJ{a$asp{m$AvOU_y(W=^h z?#6$6Q+syV-hNqOztvlJpY;T$?gyPT;N_9?1<`?&o!?RmGX-rnRV_uh3c8EkKQ)!V0OYT3?~YqvdcR72H1iFf>V5i7;)(;hYLvtf|2 z`&3(QU*qg;e_&dZwUeH(J(H8aU4&o$J~^2Rdy73D`#CQ0@0;&YXTS8I-CouUQMM}- z>g^T2UbL;r@7^o!S7-m`ZjY^-@RYr>+V%DV*SGBznWepVM^3eU5~H=w zENe_*5?4^*N=r+FC-MMqMkWzv+~o|=)nL%j2%_Mn4!Sn<{0z|q%uS8wflOHLM%RTN bJ_ub~nc<-o;LXYgl3)hHML;@@1;hgYpoW`g literal 0 HcmV?d00001 diff --git a/CLIFF/demo.py b/CLIFF/demo.py new file mode 100644 index 0000000..ad2bbbd --- /dev/null +++ b/CLIFF/demo.py @@ -0,0 +1,261 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import os +import os.path as osp +import cv2 +import glob +import torch +import argparse +import numpy as np +from tqdm import tqdm +import smplx +from torch.utils.data import DataLoader +import torchgeometry as tgm + +from models.cliff_hr48.cliff import CLIFF as cliff_hr48 +from models.cliff_res50.cliff import CLIFF as cliff_res50 +from common import constants +from common.utils import strip_prefix_if_present, cam_crop2full, video_to_images +from common.utils import estimate_focal_length +from common.renderer_pyrd import Renderer +from lib.yolov3_detector import HumanDetector +from common.mocap_dataset import MocapDataset +from lib.yolov3_dataset import DetectionDataset + + +def main(args): + device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + + print("Input path:", args.input_path) + print("Input type:", args.input_type) + if args.input_type == "image": + img_path_list = [args.input_path] + base_dir = osp.dirname(osp.abspath(args.input_path)) + front_view_dir = side_view_dir = bbox_dir = base_dir + result_filepath = f"{args.input_path[:-4]}_cliff_{args.backbone}.npz" + else: + if args.input_type == "video": + basename = osp.basename(args.input_path).split('.')[0] + base_dir = osp.join(osp.dirname(osp.abspath(args.input_path)), basename) + img_dir = osp.join(base_dir, "imgs") + front_view_dir = osp.join(base_dir, "front_view_%s" % args.backbone) + side_view_dir = osp.join(base_dir, "side_view_%s" % args.backbone) + bbox_dir = osp.join(base_dir, "bbox") + result_filepath = osp.join(base_dir, f"{basename}_cliff_{args.backbone}.npz") + if osp.exists(img_dir): + print(f"Skip extracting images from video, because \"{img_dir}\" already exists") + else: + os.makedirs(img_dir, exist_ok=True) + video_to_images(args.input_path, img_folder=img_dir) + + elif args.input_type == "folder": + img_dir = osp.join(args.input_path, "imgs") + front_view_dir = osp.join(args.input_path, "front_view_%s" % args.backbone) + side_view_dir = osp.join(args.input_path, "side_view_%s" % args.backbone) + bbox_dir = osp.join(args.input_path, "bbox") + basename = args.input_path.split('/')[-1] + result_filepath = osp.join(args.input_path, f"{basename}_cliff_{args.backbone}.npz") + + # get all image paths + img_path_list = glob.glob(osp.join(img_dir, '*.jpg')) + img_path_list.extend(glob.glob(osp.join(img_dir, '*.png'))) + img_path_list.sort() + + # load all images + print("Loading images ...") + orig_img_bgr_all = [cv2.imread(img_path) for img_path in tqdm(img_path_list)] + print("Image number:", len(img_path_list)) + + print("--------------------------- Detection ---------------------------") + # Setup human detector + human_detector = HumanDetector() + det_batch_size = min(args.batch_size, len(orig_img_bgr_all)) + detection_dataset = DetectionDataset(orig_img_bgr_all, human_detector.in_dim) + detection_data_loader = DataLoader(detection_dataset, batch_size=det_batch_size, num_workers=0) + detection_all = [] + for batch_idx, batch in enumerate(tqdm(detection_data_loader)): + norm_img = batch["norm_img"].to(device).float() + dim = batch["dim"].to(device).float() + + detection_result = human_detector.detect_batch(norm_img, dim) + detection_result[:, 0] += batch_idx * det_batch_size + detection_all.extend(detection_result.cpu().numpy()) + detection_all = np.array(detection_all) + + print("--------------------------- 3D HPS estimation ---------------------------") + # Create the model instance + cliff = eval("cliff_" + args.backbone) + cliff_model = cliff(constants.SMPL_MEAN_PARAMS).to(device) + # Load the pretrained model + print("Load the CLIFF checkpoint from path:", args.ckpt) + state_dict = torch.load(args.ckpt)['model'] + state_dict = strip_prefix_if_present(state_dict, prefix="module.") + cliff_model.load_state_dict(state_dict, strict=True) + cliff_model.eval() + + # Setup the SMPL model + smpl_model = smplx.create(constants.SMPL_MODEL_DIR, "smpl").to(device) + + pred_vert_arr = [] + if args.save_results: + smpl_pose = [] + smpl_betas = [] + smpl_trans = [] + smpl_joints = [] + cam_focal_l = [] + + mocap_db = MocapDataset(orig_img_bgr_all, detection_all) + mocap_data_loader = DataLoader(mocap_db, batch_size=min(args.batch_size, len(detection_all)), num_workers=0) + for batch in tqdm(mocap_data_loader): + norm_img = batch["norm_img"].to(device).float() + center = batch["center"].to(device).float() + scale = batch["scale"].to(device).float() + img_h = batch["img_h"].to(device).float() + img_w = batch["img_w"].to(device).float() + focal_length = batch["focal_length"].to(device).float() + + cx, cy, b = center[:, 0], center[:, 1], scale * 200 + bbox_info = torch.stack([cx - img_w / 2., cy - img_h / 2., b], dim=-1) + bbox_info[:, :2] = bbox_info[:, :2] / focal_length.unsqueeze(-1) * 2.8 # [-1, 1] + bbox_info[:, 2] = (bbox_info[:, 2] - 0.24 * focal_length) / (0.06 * focal_length) # [-1, 1] + + with torch.no_grad(): + pred_rotmat, pred_betas, pred_cam_crop = cliff_model(norm_img, bbox_info) + + # convert the camera parameters from the crop camera to the full camera + full_img_shape = torch.stack((img_h, img_w), dim=-1) + pred_cam_full = cam_crop2full(pred_cam_crop, center, scale, full_img_shape, focal_length) + + pred_output = smpl_model(betas=pred_betas, + body_pose=pred_rotmat[:, 1:], + global_orient=pred_rotmat[:, [0]], + pose2rot=False, + transl=pred_cam_full) + pred_vertices = pred_output.vertices + pred_vert_arr.extend(pred_vertices.cpu().numpy()) + + if args.save_results: + if args.pose_format == "aa": + rot_pad = torch.tensor([0, 0, 1], dtype=torch.float32, device=device).view(1, 3, 1) + rot_pad = rot_pad.expand(pred_rotmat.shape[0] * 24, -1, -1) + rotmat = torch.cat((pred_rotmat.view(-1, 3, 3), rot_pad), dim=-1) + pred_pose = tgm.rotation_matrix_to_angle_axis(rotmat).contiguous().view(-1, 72) # N*72 + else: + pred_pose = pred_rotmat # N*24*3*3 + + smpl_pose.extend(pred_pose.cpu().numpy()) + smpl_betas.extend(pred_betas.cpu().numpy()) + smpl_trans.extend(pred_cam_full.cpu().numpy()) + smpl_joints.extend(pred_output.joints.cpu().numpy()) + cam_focal_l.extend(focal_length.cpu().numpy()) + + if args.save_results: + print(f"Save results to \"{result_filepath}\"") + np.savez(result_filepath, imgname=img_path_list, + pose=smpl_pose, shape=smpl_betas, global_t=smpl_trans, + pred_joints=smpl_joints, focal_l=cam_focal_l, + detection_all=detection_all) + + print("--------------------------- Visualization ---------------------------") + # make the output directory + os.makedirs(front_view_dir, exist_ok=True) + print("Front view directory:", front_view_dir) + if args.show_sideView: + os.makedirs(side_view_dir, exist_ok=True) + print("Side view directory:", side_view_dir) + if args.show_bbox: + os.makedirs(bbox_dir, exist_ok=True) + print("Bounding box directory:", bbox_dir) + + pred_vert_arr = np.array(pred_vert_arr) + for img_idx, orig_img_bgr in enumerate(tqdm(orig_img_bgr_all)): + chosen_mask = detection_all[:, 0] == img_idx + chosen_vert_arr = pred_vert_arr[chosen_mask] + + # setup renderer for visualization + img_h, img_w, _ = orig_img_bgr.shape + focal_length = estimate_focal_length(img_h, img_w) + renderer = Renderer(focal_length=focal_length, img_w=img_w, img_h=img_h, + faces=smpl_model.faces, + same_mesh_color=(args.input_type == "video")) + front_view = renderer.render_front_view(chosen_vert_arr, + bg_img_rgb=orig_img_bgr[:, :, ::-1].copy()) + + # save rendering results + basename = osp.basename(img_path_list[img_idx]).split(".")[0] + filename = basename + "_front_view_cliff_%s.jpg" % args.backbone + front_view_path = osp.join(front_view_dir, filename) + cv2.imwrite(front_view_path, front_view[:, :, ::-1]) + + if args.show_sideView: + side_view_img = renderer.render_side_view(chosen_vert_arr) + filename = basename + "_side_view_cliff_%s.jpg" % args.backbone + side_view_path = osp.join(side_view_dir, filename) + cv2.imwrite(side_view_path, side_view_img[:, :, ::-1]) + + # delete the renderer for preparing a new one + renderer.delete() + + # draw the detection bounding boxes + if args.show_bbox: + chosen_detection = detection_all[chosen_mask] + bbox_info = chosen_detection[:, 1:6] + + bbox_img_bgr = orig_img_bgr.copy() + for min_x, min_y, max_x, max_y, conf in bbox_info: + ul = (int(min_x), int(min_y)) + br = (int(max_x), int(max_y)) + cv2.rectangle(bbox_img_bgr, ul, br, color=(0, 255, 0), thickness=2) + cv2.putText(bbox_img_bgr, "%.1f" % conf, ul, + cv2.FONT_HERSHEY_COMPLEX_SMALL, fontScale=1.0, color=(0, 0, 255), thickness=1) + filename = basename + "_bbox.jpg" + bbox_path = osp.join(bbox_dir, filename) + cv2.imwrite(bbox_path, bbox_img_bgr) + + # make videos + if args.make_video: + print("--------------------------- Making videos ---------------------------") + from common.utils import images_to_video + images_to_video(front_view_dir, video_path=front_view_dir + ".mp4", frame_rate=args.frame_rate) + images_to_video(side_view_dir, video_path=side_view_dir + ".mp4", frame_rate=args.frame_rate) + images_to_video(bbox_dir, video_path=bbox_dir + ".mp4", frame_rate=args.frame_rate) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument('--input_type', default='image', choices=['image', 'folder', 'video'], + help='input type') + parser.add_argument('--input_path', default='test_samples/nba.jpg', help='path to the input data') + + parser.add_argument('--ckpt', + default="data/ckpt/hr48-PA43.0_MJE69.0_MVE81.2_3dpw.pt", + help='path to the pretrained checkpoint') + parser.add_argument("--backbone", default="hr48", choices=['res50', 'hr48'], + help="the backbone architecture") + parser.add_argument('--batch_size', type=int, default=32, + help='batch size for detection and motion capture') + + parser.add_argument('--save_results', action='store_true', + help='save the results as a npz file') + parser.add_argument('--pose_format', default='aa', choices=['aa', 'rotmat'], + help='aa for axis angle, rotmat for rotation matrix') + + parser.add_argument('--show_bbox', action='store_true', + help='show the detection bounding boxes') + parser.add_argument('--show_sideView', action='store_true', + help='show the result from the side view') + + parser.add_argument('--make_video', action='store_true', + help='make a video of the rendering results') + parser.add_argument('--frame_rate', type=int, default=30, help='frame rate') + + args = parser.parse_args() + main(args) diff --git a/CLIFF/lib/yolov3_dataset.py b/CLIFF/lib/yolov3_dataset.py new file mode 100644 index 0000000..c8a48cb --- /dev/null +++ b/CLIFF/lib/yolov3_dataset.py @@ -0,0 +1,36 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import numpy as np +from torch.utils.data import Dataset +from lib.pytorch_yolo_v3_master.preprocess import letterbox_image + + +class DetectionDataset(Dataset): + def __init__(self, img_bgr_list, inp_dim): + self.img_bgr_list = img_bgr_list + self.inp_dim = inp_dim + + def __len__(self): + return len(self.img_bgr_list) + + def __getitem__(self, idx): + item = {} + + img_bgr = self.img_bgr_list[idx] + norm_img = (letterbox_image(img_bgr, (self.inp_dim, self.inp_dim))) + norm_img = norm_img[:, :, ::-1].transpose((2, 0, 1)).copy() + norm_img = norm_img / 255.0 + + dim = np.array([img_bgr.shape[1], img_bgr.shape[0]]) + + item["norm_img"] = norm_img + item["dim"] = dim + + return item diff --git a/CLIFF/lib/yolov3_detector.py b/CLIFF/lib/yolov3_detector.py new file mode 100644 index 0000000..c4b35b3 --- /dev/null +++ b/CLIFF/lib/yolov3_detector.py @@ -0,0 +1,68 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import os +from os.path import join +import sys +import torch + +curr_dir = os.path.dirname(os.path.abspath(__file__)) +yolo_path = join(curr_dir, "pytorch_yolo_v3_master") +sys.path.append(yolo_path) +from util import write_results +from darknet import Darknet + + +class HumanDetector: + def __init__(self): + # Set up the neural network + cfgfile = join(yolo_path, "cfg/yolov3.cfg") + weightsfile = join(curr_dir, "../data/ckpt/yolov3.weights") + # Input resolution of the network. Increase to increase accuracy. Decrease to increase speed + reso = 416 + self.num_classes = 80 + self.confidence = 0.5 + self.nms_thesh = 0.4 + + print("Load the yolo_v3 checkpoint from path:", weightsfile) + self.model = Darknet(cfgfile) + self.model.load_weights(weightsfile) + + self.model.net_info["height"] = reso + self.in_dim = reso + assert self.in_dim % 32 == 0 + assert self.in_dim > 32 + + # If there's a GPU availible, put the model on GPU + self.model.cuda() + # Set the model in evaluation mode + self.model.eval() + + @torch.no_grad() + def detect_batch(self, img_bgr_batch, img_dim_batch): + output = self.model(img_bgr_batch, True) # GPU available + output = write_results(output, self.confidence, self.num_classes, nms=True, nms_conf=self.nms_thesh) + + img_dim_batch = img_dim_batch.repeat(1, 2) + img_dim_batch = torch.index_select(img_dim_batch, 0, output[:, 0].long()) + scale = torch.min(self.in_dim / img_dim_batch, 1)[0].view(-1, 1) + + output[:, [1, 3]] -= (self.in_dim - scale * img_dim_batch[:, 0].view(-1, 1)) / 2 + output[:, [2, 4]] -= (self.in_dim - scale * img_dim_batch[:, 1].view(-1, 1)) / 2 + + output[:, 1:5] /= scale + for i in range(output.shape[0]): + output[i, [1, 3]] = torch.clamp(output[i, [1, 3]], 0.0, img_dim_batch[i, 0]) + output[i, [2, 4]] = torch.clamp(output[i, [2, 4]], 0.0, img_dim_batch[i, 1]) + + # get the human category + human_idx = output[:, -1] == 0 + human_result = output[human_idx, :] + # batch_id, min_x, min_y, max_x, max_y, det_conf, nms_conf, category_id + return human_result diff --git a/CLIFF/models/backbones/hrnet/cls_hrnet.py b/CLIFF/models/backbones/hrnet/cls_hrnet.py new file mode 100644 index 0000000..6957d35 --- /dev/null +++ b/CLIFF/models/backbones/hrnet/cls_hrnet.py @@ -0,0 +1,518 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import torch +import torch.nn as nn +import torch._utils +import torch.nn.functional as F + +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +ch = logging.StreamHandler() +ch.setLevel(logging.INFO) +logger.addHandler(ch) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(False) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index], stride, downsample)) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index])) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_inchannels[i], + 1, + 1, + 0, + bias=False), + nn.BatchNorm2d(num_inchannels[i], + momentum=BN_MOMENTUM), + nn.Upsample(scale_factor=2 ** (j - i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i - j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM))) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM), + nn.ReLU(False))) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class HighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + super(HighResolutionNet, self).__init__() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + + self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1'] + num_channels = self.stage1_cfg['NUM_CHANNELS'][0] + block = blocks_dict[self.stage1_cfg['BLOCK']] + num_blocks = self.stage1_cfg['NUM_BLOCKS'][0] + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + stage1_out_channel = block.expansion * num_channels + + self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition1 = self._make_transition_layer( + [stage1_out_channel], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True) + + # Classification Head + self.incre_modules, self.downsamp_modules, \ + self.final_layer = self._make_head(pre_stage_channels) + + # self.classifier = nn.Linear(2048, 1000) + + def _make_head(self, pre_stage_channels): + head_block = Bottleneck + head_channels = [32, 64, 128, 256] + + # Increasing the #channels on each resolution + # from C, 2C, 4C, 8C to 128, 256, 512, 1024 + incre_modules = [] + for i, channels in enumerate(pre_stage_channels): + incre_module = self._make_layer(head_block, + channels, + head_channels[i], + 1, + stride=1) + incre_modules.append(incre_module) + incre_modules = nn.ModuleList(incre_modules) + + # downsampling modules + downsamp_modules = [] + for i in range(len(pre_stage_channels) - 1): + in_channels = head_channels[i] * head_block.expansion + out_channels = head_channels[i + 1] * head_block.expansion + + downsamp_module = nn.Sequential( + nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=2, + padding=1), + nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + downsamp_modules.append(downsamp_module) + downsamp_modules = nn.ModuleList(downsamp_modules) + + final_layer = nn.Sequential( + nn.Conv2d( + in_channels=head_channels[3] * head_block.expansion, + out_channels=2048, + kernel_size=1, + stride=1, + padding=0 + ), + nn.BatchNorm2d(2048, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + return incre_modules, downsamp_modules, final_layer + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append(nn.Sequential( + nn.Conv2d(num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, + 1, + 1, + bias=False), + nn.BatchNorm2d( + num_channels_cur_layer[i], momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i + 1 - num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i - num_branches_pre else inchannels + conv3x3s.append(nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False), + nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(inplanes, planes, stride, downsample)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule(num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + # Classification Head + y = self.incre_modules[0](y_list[0]) + for i in range(len(self.downsamp_modules)): + y = self.incre_modules[i + 1](y_list[i + 1]) + \ + self.downsamp_modules[i](y) + + y = self.final_layer(y) + + if torch._C._get_tracing_state(): + y = y.flatten(start_dim=2).mean(dim=2) + else: + y = F.avg_pool2d(y, kernel_size=y.size()[2:]).view(y.size(0), -1) + + # y = self.classifier(y) + + return y + + def init_weights(self, pretrained='', ): + logger.info('=> pretrained: ' + pretrained) + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained) + # logger.info('=> loading pretrained model {}'.format(pretrained)) + # model_dict = self.state_dict() + # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()} + # for k, _ in pretrained_dict.items(): + # logger.info('=> loading {} pretrained model {}'.format(k, pretrained)) + # model_dict.update(pretrained_dict) + # self.load_state_dict(model_dict) + self.load_state_dict(pretrained_dict, strict=True) + else: + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +def get_cls_net(config, **kwargs): + model = HighResolutionNet(config, **kwargs) + model.init_weights(**kwargs) + return model diff --git a/CLIFF/models/backbones/hrnet/hrnet_config/__init__.py b/CLIFF/models/backbones/hrnet/hrnet_config/__init__.py new file mode 100644 index 0000000..a44e926 --- /dev/null +++ b/CLIFF/models/backbones/hrnet/hrnet_config/__init__.py @@ -0,0 +1,9 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from .default import _C as cfg +from .default import update_config +from .models import MODEL_EXTRAS diff --git a/CLIFF/models/backbones/hrnet/hrnet_config/default.py b/CLIFF/models/backbones/hrnet/hrnet_config/default.py new file mode 100644 index 0000000..e22a154 --- /dev/null +++ b/CLIFF/models/backbones/hrnet/hrnet_config/default.py @@ -0,0 +1,136 @@ + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Hanbin Dai (daihanbin.ac@gmail.com) and Feng Zhang (zhangfengwcy@gmail.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from yacs.config import CfgNode as CN + + +_C = CN() + +_C.OUTPUT_DIR = '' +_C.LOG_DIR = '' +_C.DATA_DIR = '' +_C.GPUS = (0,) +_C.WORKERS = 4 +_C.PRINT_FREQ = 20 +_C.AUTO_RESUME = False +_C.PIN_MEMORY = True +_C.RANK = 0 + +# Cudnn related params +_C.CUDNN = CN() +_C.CUDNN.BENCHMARK = True +_C.CUDNN.DETERMINISTIC = False +_C.CUDNN.ENABLED = True + +# common params for NETWORK +_C.MODEL = CN() +_C.MODEL.NAME = 'pose_hrnet' +_C.MODEL.INIT_WEIGHTS = True +_C.MODEL.PRETRAINED = '' +_C.MODEL.NUM_JOINTS = 17 +_C.MODEL.TAG_PER_JOINT = True +_C.MODEL.TARGET_TYPE = 'gaussian' +_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 +_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 +_C.MODEL.SIGMA = 2 +_C.MODEL.EXTRA = CN(new_allowed=True) + +_C.LOSS = CN() +_C.LOSS.USE_OHKM = False +_C.LOSS.TOPK = 8 +_C.LOSS.USE_TARGET_WEIGHT = True +_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False + +# DATASET related params +_C.DATASET = CN() +_C.DATASET.ROOT = '' +_C.DATASET.DATASET = 'mpii' +_C.DATASET.TRAIN_SET = 'train' +_C.DATASET.TEST_SET = 'valid' +_C.DATASET.DATA_FORMAT = 'jpg' +_C.DATASET.HYBRID_JOINTS_TYPE = '' +_C.DATASET.SELECT_DATA = False + +# training data augmentation +_C.DATASET.FLIP = True +_C.DATASET.SCALE_FACTOR = 0.25 +_C.DATASET.ROT_FACTOR = 30 +_C.DATASET.PROB_HALF_BODY = 0.0 +_C.DATASET.NUM_JOINTS_HALF_BODY = 8 +_C.DATASET.COLOR_RGB = False + +# train +_C.TRAIN = CN() + +_C.TRAIN.LR_FACTOR = 0.1 +_C.TRAIN.LR_STEP = [90, 110] +_C.TRAIN.LR = 0.001 + +_C.TRAIN.OPTIMIZER = 'adam' +_C.TRAIN.MOMENTUM = 0.9 +_C.TRAIN.WD = 0.0001 +_C.TRAIN.NESTEROV = False +_C.TRAIN.GAMMA1 = 0.99 +_C.TRAIN.GAMMA2 = 0.0 + +_C.TRAIN.BEGIN_EPOCH = 0 +_C.TRAIN.END_EPOCH = 140 + +_C.TRAIN.RESUME = False +_C.TRAIN.CHECKPOINT = '' + +_C.TRAIN.BATCH_SIZE_PER_GPU = 32 +_C.TRAIN.SHUFFLE = True + +# testing +_C.TEST = CN() + +# size of images for each device +_C.TEST.BATCH_SIZE_PER_GPU = 32 +# Test Model Epoch +_C.TEST.FLIP_TEST = False +_C.TEST.POST_PROCESS = False +_C.TEST.BLUR_KERNEL = 11 + +_C.TEST.USE_GT_BBOX = False + +# nms +_C.TEST.IMAGE_THRE = 0.1 +_C.TEST.NMS_THRE = 0.6 +_C.TEST.SOFT_NMS = False +_C.TEST.OKS_THRE = 0.5 +_C.TEST.IN_VIS_THRE = 0.0 +_C.TEST.COCO_BBOX_FILE = '' +_C.TEST.BBOX_THRE = 1.0 +_C.TEST.MODEL_FILE = '' + +# debug +_C.DEBUG = CN() +_C.DEBUG.DEBUG = False +_C.DEBUG.SAVE_BATCH_IMAGES_GT = False +_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False +_C.DEBUG.SAVE_HEATMAPS_GT = False +_C.DEBUG.SAVE_HEATMAPS_PRED = False + + +def update_config(cfg, config_file): + cfg.defrost() + cfg.merge_from_file(config_file) + cfg.freeze() + + +if __name__ == '__main__': + import sys + with open(sys.argv[1], 'w') as f: + print(_C, file=f) diff --git a/CLIFF/models/backbones/hrnet/hrnet_config/models.py b/CLIFF/models/backbones/hrnet/hrnet_config/models.py new file mode 100644 index 0000000..8e04c4f --- /dev/null +++ b/CLIFF/models/backbones/hrnet/hrnet_config/models.py @@ -0,0 +1,58 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from yacs.config import CfgNode as CN + + +# pose_resnet related params +POSE_RESNET = CN() +POSE_RESNET.NUM_LAYERS = 50 +POSE_RESNET.DECONV_WITH_BIAS = False +POSE_RESNET.NUM_DECONV_LAYERS = 3 +POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256] +POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4] +POSE_RESNET.FINAL_CONV_KERNEL = 1 +POSE_RESNET.PRETRAINED_LAYERS = ['*'] + +# pose_multi_resoluton_net related params +POSE_HIGH_RESOLUTION_NET = CN() +POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] +POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 +POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 + +POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] +POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE3 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128] +POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE4 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] +POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM' + + +MODEL_EXTRAS = { + 'pose_resnet': POSE_RESNET, + 'pose_high_resolution_net': POSE_HIGH_RESOLUTION_NET, +} diff --git a/CLIFF/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml b/CLIFF/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml new file mode 100644 index 0000000..b28cae6 --- /dev/null +++ b/CLIFF/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml @@ -0,0 +1,92 @@ +GPUS: (0,1,2,3) +LOG_DIR: 'log/' +DATA_DIR: '' +OUTPUT_DIR: 'output/' +WORKERS: 4 +PRINT_FREQ: 1000 + +MODEL: + NAME: cls_hrnet + IMAGE_SIZE: + - 224 + - 224 + EXTRA: + STAGE1: + NUM_MODULES: 1 + NUM_RANCHES: 1 + BLOCK: BOTTLENECK + NUM_BLOCKS: + - 4 + NUM_CHANNELS: + - 64 + FUSE_METHOD: SUM + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + - 384 + FUSE_METHOD: SUM +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATASET: + DATASET: 'imagenet' + DATA_FORMAT: 'jpg' + ROOT: 'data/imagenet/' + TEST_SET: 'val' + TRAIN_SET: 'train' +TEST: + BATCH_SIZE_PER_GPU: 32 + MODEL_FILE: '' +TRAIN: + BATCH_SIZE_PER_GPU: 32 + BEGIN_EPOCH: 0 + END_EPOCH: 100 + RESUME: true + LR_FACTOR: 0.1 + LR_STEP: + - 30 + - 60 + - 90 + OPTIMIZER: sgd + LR: 0.05 + WD: 0.0001 + MOMENTUM: 0.9 + NESTEROV: true + SHUFFLE: true +DEBUG: + DEBUG: false diff --git a/CLIFF/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml b/CLIFF/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml new file mode 100644 index 0000000..65d7103 --- /dev/null +++ b/CLIFF/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml @@ -0,0 +1,92 @@ +GPUS: (0,1,2,3) +LOG_DIR: 'log/' +DATA_DIR: '' +OUTPUT_DIR: 'output/' +WORKERS: 4 +PRINT_FREQ: 1000 + +MODEL: + NAME: cls_hrnet + IMAGE_SIZE: + - 224 + - 224 + EXTRA: + STAGE1: + NUM_MODULES: 1 + NUM_RANCHES: 1 + BLOCK: BOTTLENECK + NUM_BLOCKS: + - 4 + NUM_CHANNELS: + - 64 + FUSE_METHOD: SUM + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + - 512 + FUSE_METHOD: SUM +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATASET: + DATASET: 'imagenet' + DATA_FORMAT: 'jpg' + ROOT: 'data/imagenet/' + TEST_SET: 'val' + TRAIN_SET: 'train' +TEST: + BATCH_SIZE_PER_GPU: 32 + MODEL_FILE: '' +TRAIN: + BATCH_SIZE_PER_GPU: 32 + BEGIN_EPOCH: 0 + END_EPOCH: 100 + RESUME: true + LR_FACTOR: 0.1 + LR_STEP: + - 30 + - 60 + - 90 + OPTIMIZER: sgd + LR: 0.05 + WD: 0.0001 + MOMENTUM: 0.9 + NESTEROV: true + SHUFFLE: true +DEBUG: + DEBUG: false diff --git a/CLIFF/models/backbones/resnet.py b/CLIFF/models/backbones/resnet.py new file mode 100644 index 0000000..1c72616 --- /dev/null +++ b/CLIFF/models/backbones/resnet.py @@ -0,0 +1,94 @@ +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems +# This script is borrowed and extended from SPIN + +import torch.nn as nn + + +class Bottleneck(nn.Module): + """ Redefinition of Bottleneck residual block + Adapted from the official PyTorch implementation + """ + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__(self, layers, block=Bottleneck): + super().__init__() + self.inplanes = 64 + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d((8, 6), stride=1) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) diff --git a/CLIFF/models/cliff_hr48/cliff.py b/CLIFF/models/cliff_hr48/cliff.py new file mode 100644 index 0000000..e431a6e --- /dev/null +++ b/CLIFF/models/cliff_hr48/cliff.py @@ -0,0 +1,105 @@ +# 2022.07.19 - Changed for CLIFF +# Huawei Technologies Co., Ltd. + +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +# This script is borrowed and extended from SPIN + +import torch +import torch.nn as nn +import numpy as np +import math +import os.path as osp + +from common.imutils import rot6d_to_rotmat +from models.backbones.hrnet.cls_hrnet import HighResolutionNet +from models.backbones.hrnet.hrnet_config import cfg +from models.backbones.hrnet.hrnet_config import update_config + + +class CLIFF(nn.Module): + """ SMPL Iterative Regressor with ResNet50 backbone""" + + def __init__(self, smpl_mean_params, img_feat_num=2048): + super(CLIFF, self).__init__() + curr_dir = osp.dirname(osp.abspath(__file__)) + config_file = osp.join(curr_dir, "../backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml") + update_config(cfg, config_file) + self.encoder = HighResolutionNet(cfg) + + npose = 24 * 6 + nshape = 10 + ncam = 3 + nbbox = 3 + + fc1_feat_num = 1024 + fc2_feat_num = 1024 + final_feat_num = fc2_feat_num + reg_in_feat_num = img_feat_num + nbbox + npose + nshape + ncam + # CUDA Error: an illegal memory access was encountered + # the above error will occur, if use mobilenet v3 with BN, so don't use BN + self.fc1 = nn.Linear(reg_in_feat_num, fc1_feat_num) + self.drop1 = nn.Dropout() + self.fc2 = nn.Linear(fc1_feat_num, fc2_feat_num) + self.drop2 = nn.Dropout() + + self.decpose = nn.Linear(final_feat_num, npose) + self.decshape = nn.Linear(final_feat_num, nshape) + self.deccam = nn.Linear(final_feat_num, ncam) + + nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) + nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) + nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + mean_params = np.load(smpl_mean_params) + init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0) + init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0) + self.register_buffer('init_pose', init_pose) + self.register_buffer('init_shape', init_shape) + self.register_buffer('init_cam', init_cam) + + def forward(self, x, bbox, init_pose=None, init_shape=None, init_cam=None, n_iter=3): + batch_size = x.shape[0] + if init_pose is None: + init_pose = self.init_pose.expand(batch_size, -1) + if init_shape is None: + init_shape = self.init_shape.expand(batch_size, -1) + if init_cam is None: + init_cam = self.init_cam.expand(batch_size, -1) + + xf = self.encoder(x) + + pred_pose = init_pose + pred_shape = init_shape + pred_cam = init_cam + for i in range(n_iter): + xc = torch.cat([xf, bbox, pred_pose, pred_shape, pred_cam], 1) + + xc = self.fc1(xc) + xc = self.drop1(xc) + xc = self.fc2(xc) + xc = self.drop2(xc) + + pred_pose = self.decpose(xc) + pred_pose + pred_shape = self.decshape(xc) + pred_shape + pred_cam = self.deccam(xc) + pred_cam + + pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3) + + return pred_rotmat, pred_shape, pred_cam diff --git a/CLIFF/models/cliff_res50/cliff.py b/CLIFF/models/cliff_res50/cliff.py new file mode 100644 index 0000000..ef30e0f --- /dev/null +++ b/CLIFF/models/cliff_res50/cliff.py @@ -0,0 +1,97 @@ +# 2022.07.19 - Changed for CLIFF +# Huawei Technologies Co., Ltd. + +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +# This script is borrowed and extended from SPIN + +import torch +import torch.nn as nn +import numpy as np +import math + +from common.imutils import rot6d_to_rotmat +from models.backbones.resnet import ResNet + + +class CLIFF(nn.Module): + """ SMPL Iterative Regressor with ResNet50 backbone""" + + def __init__(self, smpl_mean_params, img_feat_num=2048): + super(CLIFF, self).__init__() + self.encoder = ResNet(layers=[3, 4, 6, 3]) + + npose = 24 * 6 + nshape = 10 + ncam = 3 + nbbox = 3 + + fc1_feat_num = 1024 + fc2_feat_num = 1024 + final_feat_num = fc2_feat_num + reg_in_feat_num = img_feat_num + nbbox + npose + nshape + ncam + self.fc1 = nn.Linear(reg_in_feat_num, fc1_feat_num) + self.drop1 = nn.Dropout() + self.fc2 = nn.Linear(fc1_feat_num, fc2_feat_num) + self.drop2 = nn.Dropout() + + self.decpose = nn.Linear(final_feat_num, npose) + self.decshape = nn.Linear(final_feat_num, nshape) + self.deccam = nn.Linear(final_feat_num, ncam) + + nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) + nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) + nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + mean_params = np.load(smpl_mean_params) + init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0) + init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0) + self.register_buffer('init_pose', init_pose) + self.register_buffer('init_shape', init_shape) + self.register_buffer('init_cam', init_cam) + + def forward(self, x, bbox, init_pose=None, init_shape=None, init_cam=None, n_iter=3): + batch_size = x.shape[0] + if init_pose is None: + init_pose = self.init_pose.expand(batch_size, -1) + if init_shape is None: + init_shape = self.init_shape.expand(batch_size, -1) + if init_cam is None: + init_cam = self.init_cam.expand(batch_size, -1) + + xf = self.encoder(x) + + pred_pose = init_pose + pred_shape = init_shape + pred_cam = init_cam + for i in range(n_iter): + xc = torch.cat([xf, bbox, pred_pose, pred_shape, pred_cam], 1) + + xc = self.fc1(xc) + xc = self.drop1(xc) + xc = self.fc2(xc) + xc = self.drop2(xc) + + pred_pose = self.decpose(xc) + pred_pose + pred_shape = self.decshape(xc) + pred_shape + pred_cam = self.deccam(xc) + pred_cam + + pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3) + + return pred_rotmat, pred_shape, pred_cam diff --git a/CLIFF/requirements.txt b/CLIFF/requirements.txt new file mode 100644 index 0000000..6261670 --- /dev/null +++ b/CLIFF/requirements.txt @@ -0,0 +1,11 @@ +torch>=1.11.0 +numpy>=1.22.4 +opencv-python>=4.5.5.64 +matplotlib>=3.5.2 +pillow>=9.1.1 +tqdm>=4.64.0 +trimesh>=3.12.3 +pyrender>=0.1.45 +yacs>=0.1.8 +smplx>=0.1.28 +torchgeometry>=0.1.2 \ No newline at end of file diff --git a/CLIFF/scripts/run_cliffGT_visualization.sh b/CLIFF/scripts/run_cliffGT_visualization.sh new file mode 100644 index 0000000..2c03a80 --- /dev/null +++ b/CLIFF/scripts/run_cliffGT_visualization.sh @@ -0,0 +1,17 @@ +export CUDA_VISIBLE_DEVICES=0 + +if false; then +# MPII +IMG_DIR="path to the MPII images" +CLIFF_GT_PATH="cliffGT_v1/mpii_cliffGT.npz" +fi + +if true; then +# COCO 2014 part +IMG_DIR="path to the COCO images" +CLIFF_GT_PATH="cliffGT_v1/coco2014part_cliffGT.npz" +fi + + +#python viz_cliffGT_perImg.py --img_dir ${IMG_DIR} --cliffGT_path ${CLIFF_GT_PATH} +python viz_cliffGT_perSubject.py --img_dir ${IMG_DIR} --cliffGT_path ${CLIFF_GT_PATH} \ No newline at end of file diff --git a/CLIFF/scripts/run_demo.sh b/CLIFF/scripts/run_demo.sh new file mode 100644 index 0000000..f87ecdb --- /dev/null +++ b/CLIFF/scripts/run_demo.sh @@ -0,0 +1,29 @@ +export CUDA_VISIBLE_DEVICES=0 +export EGL_DEVICE_ID=0 + +if true; then +CKPT_PATH=data/ckpt/hr48-PA43.0_MJE69.0_MVE81.2_3dpw.pt +BACKBONE=hr48 +else +CKPT_PATH=data/ckpt/res50-PA45.7_MJE72.0_MVE85.3_3dpw.pt +BACKBONE=res50 +fi + +if false; then +python demo.py --ckpt ${CKPT_PATH} --backbone ${BACKBONE} \ + --input_path data/test_samples/crowdpose_100024.jpg \ + --input_type image \ + --show_bbox --show_sideView --save_results +fi + +if false; then +python demo.py --ckpt ${CKPT_PATH} --backbone ${BACKBONE} \ + --input_path data/test_samples/downtown_runForBus_00 --input_type video \ + --show_bbox --show_sideView --save_results --make_video --frame_rate 30 +fi + +if true; then +python demo.py --ckpt ${CKPT_PATH} --backbone ${BACKBONE} \ + --input_path data/test_samples/lspet_samples --input_type folder \ + --show_bbox --show_sideView --save_results +fi diff --git a/CLIFF/viz_cliffGT_perImg.py b/CLIFF/viz_cliffGT_perImg.py new file mode 100644 index 0000000..fdd9b70 --- /dev/null +++ b/CLIFF/viz_cliffGT_perImg.py @@ -0,0 +1,122 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import cv2 +import smplx +import torch +import argparse +import numpy as np +import random +from os.path import join as osp +import matplotlib.pyplot as plt + +from common.renderer_pyrd import Renderer +from common.skeleton_drawer import draw_skeleton +from common.constants import SMPL_MODEL_DIR + + +def main(img_dir, cliffGT_path, smpl_path): + # setup the SMPL model + smpl_model = smplx.create(smpl_path, "smpl") + + cliff_gt = np.load(cliffGT_path) + print("cliff_gt keys:", list(cliff_gt.keys())) + + imgname_arr = cliff_gt["imgname"] + imgname_arr_unique = np.unique(imgname_arr) + imgname_arr_unique = sorted(imgname_arr_unique) + # imgname_arr_unique = sorted(imgname_arr_unique, reverse=True) + # random.shuffle(imgname_arr_unique) + + global target_index + target_index = 0 + while True: + # iterate each image + target_imgname = imgname_arr_unique[target_index] + # specify image name + # target_imgname = "train2014/COCO_train2014_000000003077.jpg" + # target_imgname = "images/000845150.jpg" + # target_imgname = "images/000460055.jpg" + print("image name:", target_imgname) + + # load the image + img_path = osp(img_dir, target_imgname) + img = cv2.imread(img_path) + img_h, img_w, _ = np.shape(img) + + smpl_vertices_list = [] + target_focal_l = -1 + skeleton2d_img = img.copy() + for imgname, pose, shape, global_t, focal_l, part in \ + zip(cliff_gt["imgname"], cliff_gt["pose"], cliff_gt["shape"], + cliff_gt["global_t"], cliff_gt["focal_l"], cliff_gt["part"]): + if imgname != target_imgname: + continue + + target_focal_l = focal_l + # get mesh vertices using the SMPL model + pose = torch.FloatTensor(pose).view(1, -1) # 1*72 + shape = torch.FloatTensor(shape).view(1, -1) # 1*10 + global_t = torch.FloatTensor(global_t).view(1, -1) # 1*3 + smpl_output = smpl_model(betas=shape, pose2rot=True, + body_pose=pose[:, 3:], + global_orient=pose[:, :3], + transl=global_t) + smpl_vertices = smpl_output.vertices[0].numpy() + smpl_vertices_list.append(smpl_vertices) + + # ------------ draw the 2D keypoint skeleton ------------ + skeleton2d_img = draw_skeleton(skeleton2d_img, part) + + # setup the render + renderer = Renderer(focal_length=target_focal_l, img_w=img_w, img_h=img_h, faces=smpl_model.faces) + + smpl_vertices_arr = np.array(smpl_vertices_list) + # ------------ render the front view ------------ + front_view_img = renderer.render_front_view(smpl_vertices_arr, bg_img_rgb=img[:, :, ::-1].copy()) + + # ------------ render the side view ------------ + side_view_img = renderer.render_side_view(smpl_vertices_arr) + + # delete the render + renderer.delete() + + # ------------ show the images ------------ + def on_key_press(event): + global target_index + if event.key == "right": + target_index += 1 + elif event.key == "left": + target_index -= 1 + elif event.key == "escape": + exit() + plt.close() + + images = [img[:, :, ::-1], skeleton2d_img[:, :, ::-1], + front_view_img, side_view_img] + titles = ["image", "2D skeleton", "front view", "side view"] + fig = plt.figure() + for idx, (image, title) in enumerate(zip(images, titles)): + fig.add_subplot(2, 2, idx + 1) + plt.imshow(image) + plt.title(title) + plt.axis("off") + plt.suptitle(target_imgname) + plt.tight_layout() + fig.canvas.mpl_connect("key_press_event", on_key_press) + plt.show() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_dir', help='the image directory') + parser.add_argument('--cliffGT_path', help='path to the pseudo-GT file') + args = parser.parse_args() + + main(args.img_dir, args.cliffGT_path, SMPL_MODEL_DIR) diff --git a/CLIFF/viz_cliffGT_perSubject.py b/CLIFF/viz_cliffGT_perSubject.py new file mode 100644 index 0000000..5d0022a --- /dev/null +++ b/CLIFF/viz_cliffGT_perSubject.py @@ -0,0 +1,101 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import cv2 +import smplx +import torch +import argparse +import numpy as np +from os.path import join as osp +import matplotlib.pyplot as plt + +from common.renderer_pyrd import Renderer +from common.skeleton_drawer import draw_skeleton +from common.constants import SMPL_MODEL_DIR + + +def main(img_dir, cliffGT_path, smpl_path): + # setup the SMPL model + smpl_model = smplx.create(smpl_path, "smpl") + + cliff_gt = np.load(cliffGT_path) + print("cliff_gt keys:", list(cliff_gt.keys())) + + global target_index + target_index = 0 + while True: + # iterate each subject + imgname = cliff_gt["imgname"][target_index] + pose = cliff_gt["pose"][target_index] + shape = cliff_gt["shape"][target_index] + global_t = cliff_gt["global_t"][target_index] + focal_l = cliff_gt["focal_l"][target_index] + part = cliff_gt["part"][target_index] + print("image name:", imgname) + + # get mesh vertices using the SMPL model + pose = torch.FloatTensor(pose).view(1, -1) # 1*72 + shape = torch.FloatTensor(shape).view(1, -1) # 1*10 + global_t = torch.FloatTensor(global_t).view(1, -1) # 1*3 + smpl_output = smpl_model(betas=shape, pose2rot=True, + body_pose=pose[:, 3:], + global_orient=pose[:, :3], + transl=global_t) + smpl_vertices = smpl_output.vertices.numpy() + + # load the image + img_path = osp(img_dir, imgname) + img = cv2.imread(img_path) + img_h, img_w, _ = np.shape(img) + + # setup the render + renderer = Renderer(focal_length=focal_l, img_w=img_w, img_h=img_h, faces=smpl_model.faces) + # ------------ render the front view ------------ + front_view_img = renderer.render_front_view(smpl_vertices, bg_img_rgb=img[:, :, ::-1].copy()) + # ------------ render the side view ------------ + side_view_img = renderer.render_side_view(smpl_vertices) + # delete the render + renderer.delete() + + # ------------ draw the 2D keypoint skeleton ------------ + skeleton2d_img = draw_skeleton(img.copy(), part) + + # ------------ show the images ------------ + def on_key_press(event): + global target_index + if event.key == "right": + target_index += 1 + elif event.key == "left": + target_index -= 1 + elif event.key == "escape": + exit() + plt.close() + + images = [img[:, :, ::-1], skeleton2d_img[:, :, ::-1], + front_view_img, side_view_img] + titles = ["image", "2D skeleton", "front view", "side view"] + fig = plt.figure() + for idx, (image, title) in enumerate(zip(images, titles)): + fig.add_subplot(2, 2, idx + 1) + plt.imshow(image) + plt.title(title) + plt.axis("off") + plt.suptitle(imgname) + plt.tight_layout() + fig.canvas.mpl_connect("key_press_event", on_key_press) + plt.show() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_dir', help='the image directory') + parser.add_argument('--cliffGT_path', help='path to the pseudo-GT file') + args = parser.parse_args() + + main(args.img_dir, args.cliffGT_path, SMPL_MODEL_DIR)

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