update Readme

Readme.md

@@ -2,7 +2,7 @@
  * @Date: 2021-01-13 20:32:12
  * @Author: Qing Shuai
  * @LastEditors: Qing Shuai
- * @LastEditTime: 2021-01-25 19:35:14
+ * @LastEditTime: 2021-01-29 19:50:10
  * @FilePath: /EasyMocapRelease/Readme.md
 -->
 # EasyMocap
@@ -16,8 +16,9 @@ In this project, we provide the basic code for fitting SMPL[1]/SMPL+H[2]/SMPLX[3
 
 > We plan to intergrate more interesting algorithms, please stay tuned!
 
-1. [Multi-Person from Multiple Views](https://github.com/zju3dv/mvpose)
-2. [Mocap from Multiple **Uncalibrated** and **Unsynchronized** Videos](https://arxiv.org/pdf/2008.07931.pdf)
+1. [[CVPR19] Multi-Person from Multiple Views](https://github.com/zju3dv/mvpose)
+2. [[ECCV20] Mocap from Multiple **Uncalibrated** and **Unsynchronized** Videos](https://arxiv.org/pdf/2008.07931.pdf)
+![](doc/imocap/frame_00036_036.jpg)
 3. [Dense Reconstruction and View Synthesis from **Sparse Views**](https://zju3dv.github.io/neuralbody/)
 
 ## Installation

code/mytools/utils.py

@@ -2,7 +2,7 @@
   @ Date: 2021-01-15 11:12:00
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-01-15 11:19:55
+  @ LastEditTime: 2021-01-25 21:36:42
   @ FilePath: /EasyMocap/code/mytools/utils.py
 '''
 import time
@@ -18,4 +18,4 @@ class Timer:
     def __exit__(self, exc_type, exc_value, exc_tb):
         end = time.time()
         if not self.silent:
-            print('-> [{}]: {:.2f}s'.format(self.name, end-self.start))
+            print('-> [{:20s}]: {:5.1f}ms'.format(self.name, (end-self.start)*1000))

scripts/preprocess/extract_video.py

@@ -2,7 +2,7 @@
   @ Date: 2021-01-13 20:38:33
   @ Author: Qing Shuai
   @ LastEditors: Qing Shuai
-  @ LastEditTime: 2021-01-25 14:41:56
+  @ LastEditTime: 2021-01-27 10:41:48
   @ FilePath: /EasyMocap/scripts/preprocess/extract_video.py
 '''
 import os, sys
@@ -145,8 +145,8 @@ def detect_frame(detector, img, pid=0):
         annots.append(annot)
     return annots
     
-def extract_yolo_hrnet(image_root, annot_root):
-    imgnames = sorted(glob(join(image_root, '*.jpg')))
+def extract_yolo_hrnet(image_root, annot_root, ext='jpg'):
+    imgnames = sorted(glob(join(image_root, '*.{}'.format(ext))))
     import torch
     device = torch.device('cuda')
     from estimator.detector import Detector
@@ -170,7 +170,7 @@ def extract_yolo_hrnet(image_root, annot_root):
     }
     detector = Detector('yolo', 'hrnet', device, config)
     for nf, imgname in enumerate(tqdm(imgnames)):
-        annotname = join(annot_root, os.path.basename(imgname).replace('.jpg', '.json'))
+        annotname = join(annot_root, os.path.basename(imgname).replace('.{}'.format(ext), '.json'))
         annot = create_annot_file(annotname, imgname)
         img0 = cv2.imread(imgname)
         annot['annots'] = detect_frame(detector, img0, 0)
@@ -188,6 +188,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('path', type=str, default=None)
     parser.add_argument('--mode', type=str, default='openpose', choices=['openpose', 'yolo-hrnet'])
+    parser.add_argument('--ext', type=str, default='jpg', choices=['jpg', 'png'])
     parser.add_argument('--handface', action='store_true')
     parser.add_argument('--openpose', type=str, 
         default='/media/qing/Project/openpose')
@@ -226,6 +227,6 @@ if __name__ == "__main__":
                         dst=annot_root
                     )
                 elif mode == 'yolo-hrnet':
-                    extract_yolo_hrnet(image_root, annot_root)
+                    extract_yolo_hrnet(image_root, annot_root, args.ext)
     else:
         print(args.path, ' not exists')