├── LICENSE ├── README.md ├── examples ├── cpunk.gif └── geralt.gif ├── infer_vid.py └── vis.py /LICENSE: -------------------------------------------------------------------------------- 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. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. 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 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Densepose on video 2 | 3 | Put the .py files with your detectron installation. 4 | 5 | ##### Use command 6 | ``` 7 | python2 tools/infer_vid.py 8 | --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml 9 | --output-dir DensePoseData/infer_out/ 10 | --wts https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl 11 | --input-file filename 12 | ``` 13 | Note: Requires about 4GB VRAM. 14 | 15 | If you use ResNet101_FPN_s1x model, you need more than 4GB of memory. 16 | If you have only 4GB use ResNet50_FPN_s1x. 17 | 18 | You can get configs and models from here. 19 | * https://github.com/facebookresearch/DensePose/tree/master/configs 20 | * https://github.com/facebookresearch/DensePose/blob/master/MODEL_ZOO.md 21 | 22 | Additional requirement - ffmpeg 23 | 24 | ##### Tested with 25 | * Ubuntu 16.04 26 | * Cuda 9.0 27 | * CUDNN 6 28 | * opencv 3.4 29 | * GTX 1050 Ti 30 | * GTX 1080 Ti 31 | 32 | ##### Example, ran on Geralt doing some witcherin' 33 | ![examples/geralt.gif](examples/geralt.gif) 34 | 35 | 36 | ##### And a bit of Night city. 37 | ![examples/cpunk.gif](examples/cpunk.gif) 38 | -------------------------------------------------------------------------------- /examples/cpunk.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trrahul/densepose-video/2157512c7604da1f00ef9a2065d4c81deff382df/examples/cpunk.gif -------------------------------------------------------------------------------- /examples/geralt.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trrahul/densepose-video/2157512c7604da1f00ef9a2065d4c81deff382df/examples/geralt.gif -------------------------------------------------------------------------------- /infer_vid.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | ############################################################################## 7 | 8 | """Perform inference on a single image or all images with a certain extension 9 | (e.g., .jpg) in a folder. 10 | """ 11 | 12 | from __future__ import absolute_import 13 | from __future__ import division 14 | from __future__ import print_function 15 | from __future__ import unicode_literals 16 | 17 | from collections import defaultdict 18 | import argparse 19 | import cv2 # NOQA (Must import before importing caffe2 due to bug in cv2) 20 | import glob 21 | import logging 22 | import os 23 | import sys 24 | import time 25 | import pdb 26 | import subprocess 27 | from caffe2.python import workspace 28 | 29 | from detectron.core.config import assert_and_infer_cfg 30 | from detectron.core.config import cfg 31 | from detectron.core.config import merge_cfg_from_file 32 | from detectron.utils.io import cache_url 33 | from detectron.utils.logging import setup_logging 34 | from detectron.utils.timer import Timer 35 | import detectron.core.test_engine as infer_engine 36 | import detectron.datasets.dummy_datasets as dummy_datasets 37 | import detectron.utils.c2 as c2_utils 38 | import detectron.utils.vis as vis_utils 39 | 40 | c2_utils.import_detectron_ops() 41 | 42 | # OpenCL may be enabled by default in OpenCV3; disable it because it's not 43 | # thread safe and causes unwanted GPU memory allocations. 44 | cv2.ocl.setUseOpenCL(False) 45 | 46 | 47 | def parse_args(): 48 | parser = argparse.ArgumentParser(description='End-to-end inference') 49 | parser.add_argument( 50 | '--cfg', 51 | dest='cfg', 52 | help='cfg model file (/path/to/model_config.yaml)', 53 | default=None, 54 | type=str 55 | ) 56 | parser.add_argument( 57 | '--wts', 58 | dest='weights', 59 | help='weights model file (/path/to/model_weights.pkl)', 60 | default=None, 61 | type=str 62 | ) 63 | parser.add_argument( 64 | '--output-dir', 65 | dest='output_dir', 66 | help='Directory to keep the output files', 67 | default='/tmp/infer_vid', 68 | type=str 69 | ) 70 | parser.add_argument( 71 | '--input-file', 72 | dest='input', 73 | help='Input video file', 74 | default=None, 75 | type=str 76 | ) 77 | if len(sys.argv) == 1: 78 | parser.print_help() 79 | sys.exit(1) 80 | return parser.parse_args() 81 | 82 | 83 | 84 | def main(args): 85 | logger = logging.getLogger(__name__) 86 | merge_cfg_from_file(args.cfg) 87 | cfg.NUM_GPUS = 1 88 | args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE) 89 | assert_and_infer_cfg(cache_urls=False) 90 | model = infer_engine.initialize_model_from_cfg(args.weights) 91 | dummy_coco_dataset = dummy_datasets.get_coco_dataset() 92 | frame_no =0 93 | # print( "capturing video") 94 | cap = cv2.VideoCapture(args.input) 95 | total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 96 | # pdb.set_trace() 97 | grab =1; 98 | if (cap.isOpened()== False): 99 | print("Error opening video stream or file") 100 | exit 101 | while (cap.isOpened() and grab <= total_frames): 102 | print( "|Processing Frame {0}/{1} ".format(grab,total_frames)) 103 | grab += 1 104 | captime = time.time() 105 | ret_val, im = cap.read() 106 | print('\t-Frame read in{: .3f}s'.format(time.time() - captime)) 107 | #skips intermediate frames 108 | if grab%2 !=0: 109 | continue 110 | #uncomment to resize image 111 | im = cv2.resize(im, (int(1280/1),int(720/1))) 112 | timers = defaultdict(Timer) 113 | t = time.time() 114 | with c2_utils.NamedCudaScope(0): 115 | cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all( 116 | model, im, None, timers=timers 117 | ) 118 | print('\t | Inference time: {:.3f}s'.format(time.time() - t)) 119 | for k, v in timers.items(): 120 | print('\t | {}: {:.3f}s'.format(k, v.average_time)) 121 | if 0: 122 | logger.info( 123 | ' \ Note: inference on the first image will be slower than the ' 124 | 'rest (caches and auto-tuning need to warm up)' 125 | ) 126 | output_name = 'out.mp4' 127 | ret = vis_utils.vis_one_image( 128 | im[:, :, ::-1], # BGR -> RGB for visualization 129 | output_name, 130 | args.output_dir, 131 | cls_boxes, 132 | cls_segms, 133 | cls_keyps, 134 | cls_bodys, 135 | dataset=dummy_coco_dataset, 136 | box_alpha=0.3, 137 | show_class=False, 138 | thresh=0.7, 139 | kp_thresh=2 140 | ) 141 | if ret == True: 142 | frame_no = frame_no +1 143 | cap.release() 144 | cv2.destroyAllWindows() 145 | subprocess.call('ffmpeg -framerate 20 -i {}/file%02d.png -c:v libx264 -r 30 -pix_fmt yuv420p vid/out.mp4' 146 | .format(os.path.join(args.output_dir, 'vid')), 147 | shell=True) 148 | if __name__ == '__main__': 149 | workspace.GlobalInit(['caffe2', '--caffe2_log_level=0']) 150 | setup_logging(__name__) 151 | args = parse_args() 152 | main(args) 153 | 154 | -------------------------------------------------------------------------------- /vis.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | ############################################################################## 7 | 8 | """Detection output visualization module.""" 9 | 10 | from __future__ import absolute_import 11 | from __future__ import division 12 | from __future__ import print_function 13 | from __future__ import unicode_literals 14 | 15 | import cv2 16 | import numpy as np 17 | import os 18 | import pdb 19 | import pycocotools.mask as mask_util 20 | import time 21 | 22 | from detectron.utils.colormap import colormap 23 | import detectron.utils.env as envu 24 | import detectron.utils.keypoints as keypoint_utils 25 | 26 | # Matplotlib requires certain adjustments in some environments 27 | # Must happen before importing matplotlib 28 | envu.set_up_matplotlib() 29 | import matplotlib.pyplot as plt 30 | from matplotlib.patches import Polygon 31 | from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas 32 | from matplotlib.figure import Figure 33 | 34 | plt.rcParams['pdf.fonttype'] = 42 # For editing in Adobe Illustrator 35 | 36 | 37 | _GRAY = (218, 227, 218) 38 | _GREEN = (18, 127, 15) 39 | _WHITE = (255, 255, 255) 40 | 41 | 42 | def kp_connections(keypoints): 43 | kp_lines = [ 44 | [keypoints.index('left_eye'), keypoints.index('right_eye')], 45 | [keypoints.index('left_eye'), keypoints.index('nose')], 46 | [keypoints.index('right_eye'), keypoints.index('nose')], 47 | [keypoints.index('right_eye'), keypoints.index('right_ear')], 48 | [keypoints.index('left_eye'), keypoints.index('left_ear')], 49 | [keypoints.index('right_shoulder'), keypoints.index('right_elbow')], 50 | [keypoints.index('right_elbow'), keypoints.index('right_wrist')], 51 | [keypoints.index('left_shoulder'), keypoints.index('left_elbow')], 52 | [keypoints.index('left_elbow'), keypoints.index('left_wrist')], 53 | [keypoints.index('right_hip'), keypoints.index('right_knee')], 54 | [keypoints.index('right_knee'), keypoints.index('right_ankle')], 55 | [keypoints.index('left_hip'), keypoints.index('left_knee')], 56 | [keypoints.index('left_knee'), keypoints.index('left_ankle')], 57 | [keypoints.index('right_shoulder'), keypoints.index('left_shoulder')], 58 | [keypoints.index('right_hip'), keypoints.index('left_hip')], 59 | ] 60 | return kp_lines 61 | 62 | 63 | def convert_from_cls_format(cls_boxes, cls_segms, cls_keyps): 64 | """Convert from the class boxes/segms/keyps format generated by the testing 65 | code. 66 | """ 67 | box_list = [b for b in cls_boxes if len(b) > 0] 68 | if len(box_list) > 0: 69 | boxes = np.concatenate(box_list) 70 | else: 71 | boxes = None 72 | if cls_segms is not None: 73 | segms = [s for slist in cls_segms for s in slist] 74 | else: 75 | segms = None 76 | if cls_keyps is not None: 77 | keyps = [k for klist in cls_keyps for k in klist] 78 | else: 79 | keyps = None 80 | classes = [] 81 | for j in range(len(cls_boxes)): 82 | classes += [j] * len(cls_boxes[j]) 83 | return boxes, segms, keyps, classes 84 | 85 | 86 | def get_class_string(class_index, score, dataset): 87 | class_text = dataset.classes[class_index] if dataset is not None else \ 88 | 'id{:d}'.format(class_index) 89 | return class_text + ' {:0.2f}'.format(score).lstrip('0') 90 | 91 | 92 | def vis_mask(img, mask, col, alpha=0.4, show_border=True, border_thick=1): 93 | """Visualizes a single binary mask.""" 94 | 95 | img = img.astype(np.float32) 96 | idx = np.nonzero(mask) 97 | 98 | img[idx[0], idx[1], :] *= 1.0 - alpha 99 | img[idx[0], idx[1], :] += alpha * col 100 | 101 | if show_border: 102 | _, contours, _ = cv2.findContours( 103 | mask.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) 104 | cv2.drawContours(img, contours, -1, _WHITE, border_thick, cv2.LINE_AA) 105 | 106 | return img.astype(np.uint8) 107 | 108 | 109 | def vis_class(img, pos, class_str, font_scale=0.35): 110 | """Visualizes the class.""" 111 | x0, y0 = int(pos[0]), int(pos[1]) 112 | # Compute text size. 113 | txt = class_str 114 | font = cv2.FONT_HERSHEY_SIMPLEX 115 | ((txt_w, txt_h), _) = cv2.getTextSize(txt, font, font_scale, 1) 116 | # Place text background. 117 | back_tl = x0, y0 - int(1.3 * txt_h) 118 | back_br = x0 + txt_w, y0 119 | cv2.rectangle(img, back_tl, back_br, _GREEN, -1) 120 | # Show text. 121 | txt_tl = x0, y0 - int(0.3 * txt_h) 122 | cv2.putText(img, txt, txt_tl, font, font_scale, _GRAY, lineType=cv2.LINE_AA) 123 | return img 124 | 125 | 126 | def vis_bbox(img, bbox, thick=1): 127 | """Visualizes a bounding box.""" 128 | (x0, y0, w, h) = bbox 129 | x1, y1 = int(x0 + w), int(y0 + h) 130 | x0, y0 = int(x0), int(y0) 131 | cv2.rectangle(img, (x0, y0), (x1, y1), _GREEN, thickness=thick) 132 | return img 133 | 134 | 135 | def vis_keypoints(img, kps, kp_thresh=2, alpha=0.7): 136 | """Visualizes keypoints (adapted from vis_one_image). 137 | kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob). 138 | """ 139 | dataset_keypoints, _ = keypoint_utils.get_keypoints() 140 | kp_lines = kp_connections(dataset_keypoints) 141 | 142 | # Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv. 143 | cmap = plt.get_cmap('rainbow') 144 | colors = [cmap(i) for i in np.linspace(0, 1, len(kp_lines) + 2)] 145 | colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors] 146 | 147 | # Perform the drawing on a copy of the image, to allow for blending. 148 | kp_mask = np.copy(img) 149 | 150 | # Draw mid shoulder / mid hip first for better visualization. 151 | mid_shoulder = ( 152 | kps[:2, dataset_keypoints.index('right_shoulder')] + 153 | kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0 154 | sc_mid_shoulder = np.minimum( 155 | kps[2, dataset_keypoints.index('right_shoulder')], 156 | kps[2, dataset_keypoints.index('left_shoulder')]) 157 | mid_hip = ( 158 | kps[:2, dataset_keypoints.index('right_hip')] + 159 | kps[:2, dataset_keypoints.index('left_hip')]) / 2.0 160 | sc_mid_hip = np.minimum( 161 | kps[2, dataset_keypoints.index('right_hip')], 162 | kps[2, dataset_keypoints.index('left_hip')]) 163 | nose_idx = dataset_keypoints.index('nose') 164 | if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh: 165 | cv2.line( 166 | kp_mask, tuple(mid_shoulder), tuple(kps[:2, nose_idx]), 167 | color=colors[len(kp_lines)], thickness=2, lineType=cv2.LINE_AA) 168 | if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: 169 | cv2.line( 170 | kp_mask, tuple(mid_shoulder), tuple(mid_hip), 171 | color=colors[len(kp_lines) + 1], thickness=2, lineType=cv2.LINE_AA) 172 | 173 | # Draw the keypoints. 174 | for l in range(len(kp_lines)): 175 | i1 = kp_lines[l][0] 176 | i2 = kp_lines[l][1] 177 | p1 = kps[0, i1], kps[1, i1] 178 | p2 = kps[0, i2], kps[1, i2] 179 | if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: 180 | cv2.line( 181 | kp_mask, p1, p2, 182 | color=colors[l], thickness=2, lineType=cv2.LINE_AA) 183 | if kps[2, i1] > kp_thresh: 184 | cv2.circle( 185 | kp_mask, p1, 186 | radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) 187 | if kps[2, i2] > kp_thresh: 188 | cv2.circle( 189 | kp_mask, p2, 190 | radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA) 191 | 192 | # Blend the keypoints. 193 | return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) 194 | 195 | 196 | def vis_one_image_opencv( 197 | im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2, 198 | show_box=False, dataset=None, show_class=False): 199 | """Constructs a numpy array with the detections visualized.""" 200 | 201 | if isinstance(boxes, list): 202 | boxes, segms, keypoints, classes = convert_from_cls_format( 203 | boxes, segms, keypoints) 204 | 205 | if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh: 206 | return im 207 | 208 | if segms is not None and len(segms) > 0: 209 | masks = mask_util.decode(segms) 210 | color_list = colormap() 211 | mask_color_id = 0 212 | 213 | # Display in largest to smallest order to reduce occlusion 214 | areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) 215 | sorted_inds = np.argsort(-areas) 216 | 217 | for i in sorted_inds: 218 | bbox = boxes[i, :4] 219 | score = boxes[i, -1] 220 | if score < thresh: 221 | continue 222 | 223 | # show box (off by default) 224 | if show_box: 225 | im = vis_bbox( 226 | im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1])) 227 | 228 | # show class (off by default) 229 | if show_class: 230 | class_str = get_class_string(classes[i], score, dataset) 231 | im = vis_class(im, (bbox[0], bbox[1] - 2), class_str) 232 | 233 | # show mask 234 | if segms is not None and len(segms) > i: 235 | color_mask = color_list[mask_color_id % len(color_list), 0:3] 236 | mask_color_id += 1 237 | im = vis_mask(im, masks[..., i], color_mask) 238 | 239 | # show keypoints 240 | if keypoints is not None and len(keypoints) > i: 241 | im = vis_keypoints(im, keypoints[i], kp_thresh) 242 | 243 | return im 244 | 245 | 246 | def vis_one_image( 247 | im, im_name, output_dir, boxes, segms=None, keypoints=None, body_uv=None, thresh=0.9, 248 | kp_thresh=2, dpi=200, box_alpha=0.0, dataset=None, show_class=False, 249 | ext='jpg', frame_no=None): 250 | """Visual debugging of detections.""" 251 | if not os.path.exists(output_dir): 252 | os.makedirs(output_dir) 253 | 254 | if isinstance(boxes, list): 255 | boxes, segms, keypoints, classes = convert_from_cls_format( 256 | boxes, segms, keypoints) 257 | 258 | if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh: 259 | print("Box:None, Shape:0 or MaxBox below thresh") 260 | return 261 | 262 | dataset_keypoints, _ = keypoint_utils.get_keypoints() 263 | 264 | if segms is not None and len(segms) > 0: 265 | masks = mask_util.decode(segms) 266 | 267 | optime = time.time() 268 | color_list = colormap(rgb=True) / 255 269 | 270 | kp_lines = kp_connections(dataset_keypoints) 271 | cmap = plt.get_cmap('rainbow') 272 | colors = [cmap(i) for i in np.linspace(0, 1, len(kp_lines) + 2)] 273 | 274 | fig = plt.figure(frameon=False) 275 | fig.set_size_inches(im.shape[1] / dpi, im.shape[0] / dpi) 276 | ax = plt.Axes(fig, [0., 0., 1., 1.]) 277 | ax.axis('off') 278 | fig.add_axes(ax) 279 | ax.imshow(im) 280 | 281 | # Display in largest to smallest order to reduce occlusion 282 | areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) 283 | sorted_inds = np.argsort(-areas) 284 | 285 | mask_color_id = 0 286 | for i in sorted_inds: 287 | bbox = boxes[i, :4] 288 | score = boxes[i, -1] 289 | if score < thresh: 290 | continue 291 | 292 | # show box (off by default) 293 | ax.add_patch( 294 | plt.Rectangle((bbox[0], bbox[1]), 295 | bbox[2] - bbox[0], 296 | bbox[3] - bbox[1], 297 | fill=False, edgecolor='g', 298 | linewidth=0.5, alpha=box_alpha)) 299 | 300 | if show_class: 301 | ax.text( 302 | bbox[0], bbox[1] - 2, 303 | get_class_string(classes[i], score, dataset), 304 | fontsize=10, 305 | family='serif', 306 | bbox=dict( 307 | facecolor='g', alpha=0.4, pad=0, edgecolor='none'), 308 | color='white') 309 | 310 | # show mask 311 | if segms is not None and len(segms) > i: 312 | img = np.ones(im.shape) 313 | color_mask = color_list[mask_color_id % len(color_list), 0:3] 314 | mask_color_id += 1 315 | 316 | w_ratio = .4 317 | for c in range(3): 318 | color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio 319 | for c in range(3): 320 | img[:, :, c] = color_mask[c] 321 | e = masks[:, :, i] 322 | 323 | _, contour, hier = cv2.findContours( 324 | e.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) 325 | 326 | for c in contour: 327 | polygon = Polygon( 328 | c.reshape((-1, 2)), 329 | fill=True, facecolor=color_mask, 330 | edgecolor='w', linewidth=1.2, 331 | alpha=0.5) 332 | ax.add_patch(polygon) 333 | # show keypoints 334 | if keypoints is not None and len(keypoints) > i: 335 | kps = keypoints[i] 336 | plt.autoscale(False) 337 | for l in range(len(kp_lines)): 338 | i1 = kp_lines[l][0] 339 | i2 = kp_lines[l][1] 340 | if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh: 341 | x = [kps[0, i1], kps[0, i2]] 342 | y = [kps[1, i1], kps[1, i2]] 343 | line = plt.plot(x, y) 344 | plt.setp(line, color=colors[l], linewidth=1.0, alpha=0.7) 345 | if kps[2, i1] > kp_thresh: 346 | plt.plot( 347 | kps[0, i1], kps[1, i1], '.', color=colors[l], 348 | markersize=3.0, alpha=0.7) 349 | 350 | if kps[2, i2] > kp_thresh: 351 | plt.plot( 352 | kps[0, i2], kps[1, i2], '.', color=colors[l], 353 | markersize=3.0, alpha=0.7) 354 | 355 | # add mid shoulder / mid hip for better visualization 356 | mid_shoulder = ( 357 | kps[:2, dataset_keypoints.index('right_shoulder')] + 358 | kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0 359 | sc_mid_shoulder = np.minimum( 360 | kps[2, dataset_keypoints.index('right_shoulder')], 361 | kps[2, dataset_keypoints.index('left_shoulder')]) 362 | mid_hip = ( 363 | kps[:2, dataset_keypoints.index('right_hip')] + 364 | kps[:2, dataset_keypoints.index('left_hip')]) / 2.0 365 | sc_mid_hip = np.minimum( 366 | kps[2, dataset_keypoints.index('right_hip')], 367 | kps[2, dataset_keypoints.index('left_hip')]) 368 | if (sc_mid_shoulder > kp_thresh and 369 | kps[2, dataset_keypoints.index('nose')] > kp_thresh): 370 | x = [mid_shoulder[0], kps[0, dataset_keypoints.index('nose')]] 371 | y = [mid_shoulder[1], kps[1, dataset_keypoints.index('nose')]] 372 | line = plt.plot(x, y) 373 | plt.setp( 374 | line, color=colors[len(kp_lines)], linewidth=1.0, alpha=0.7) 375 | if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh: 376 | x = [mid_shoulder[0], mid_hip[0]] 377 | y = [mid_shoulder[1], mid_hip[1]] 378 | line = plt.plot(x, y) 379 | plt.setp( 380 | line, color=colors[len(kp_lines) + 1], linewidth=1.0, 381 | alpha=0.7) 382 | # DensePose Visualization Starts!! 383 | ## Get full IUV image out 384 | IUV_fields = body_uv[1] 385 | # 386 | All_Coords = np.zeros(im.shape) 387 | All_inds = np.zeros([im.shape[0],im.shape[1]]) 388 | K = 26 389 | ## 390 | inds = np.argsort(boxes[:,4]) 391 | ## 392 | for i, ind in enumerate(inds): 393 | entry = boxes[ind,:] 394 | if entry[4] > 0.65: 395 | entry=entry[0:4].astype(int) 396 | #### 397 | output = IUV_fields[ind] 398 | #### 399 | All_Coords_Old = All_Coords[ entry[1] : entry[1]+output.shape[1],entry[0]:entry[0]+output.shape[2],:] 400 | All_Coords_Old[All_Coords_Old==0]=output.transpose([1,2,0])[All_Coords_Old==0] 401 | All_Coords[ entry[1] : entry[1]+output.shape[1],entry[0]:entry[0]+output.shape[2],:]= All_Coords_Old 402 | ### 403 | CurrentMask = (output[0,:,:]>0).astype(np.float32) 404 | All_inds_old = All_inds[ entry[1] : entry[1]+output.shape[1],entry[0]:entry[0]+output.shape[2]] 405 | All_inds_old[All_inds_old==0] = CurrentMask[All_inds_old==0]*i 406 | All_inds[ entry[1] : entry[1]+output.shape[1],entry[0]:entry[0]+output.shape[2]] = All_inds_old 407 | # 408 | All_Coords[:,:,1:3] = 255. * All_Coords[:,:,1:3] 409 | All_Coords[All_Coords>255] = 255. 410 | All_Coords = All_Coords.astype(np.uint8) 411 | All_inds = All_inds.astype(np.uint8) 412 | # pdb.set_trace() 413 | #draw frame and contours to canvas 414 | plt.contour( All_Coords[:,:,1]/256.,10, linewidths = 1 ) 415 | plt.contour( All_Coords[:,:,2]/256.,10, linewidths = 1 ) 416 | plt.contour( All_inds, linewidths = 3 ) 417 | plt.axis('off') ; 418 | fig.canvas.draw() 419 | # convert canvas to image 420 | img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') 421 | img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,)) 422 | # img is rgb, convert to opencv's default bgr 423 | img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR) 424 | # display image with opencv 425 | cv2.imshow("plot",img) 426 | cv2.waitKey(1) 427 | print('\t-Visualized in {: .3f}s'.format(time.time() - optime)) 428 | output_name = os.path.basename(im_name) + '.' + ext 429 | filetime = time.time() 430 | out_dir = os.path.join(output_dir, 'vid') 431 | if not os.path.exists(out_dir): 432 | os.mkdir(out_dir) 433 | out_file = 'file%02d.png' % frame_no 434 | fig.savefig(os.path.join(out_dir, out_file), dpi=dpi) 435 | print('\t-Output file wrote in {: .3f}s'.format(time.time() - filetime)) 436 | plt.close('all') 437 | return True 438 | --------------------------------------------------------------------------------