support NTU

README.md

@@ -46,6 +46,7 @@ The SMPL human body layer for Pytorch is from the [smplpytorch](https://github.c
     - [CMU Mocap](https://ericguo5513.github.io/action-to-motion/)
     - [UTD-MHAD](https://personal.utdallas.edu/~kehtar/UTD-MHAD.html)
     - [Human3.6M](http://vision.imar.ro/human3.6m/description.php)
+    - [NTU]([ROSE Lab (ntu.edu.sg)](https://rose1.ntu.edu.sg/dataset/actionRecognition/))
     
 - Set the **DATASET.PATH** in the corresponding configuration file to the location of dataset.
 

fit/configs/NTU.json

@@ -0,0 +1,94 @@
+{
+    "MODEL": {
+        "GENDER": "neutral"
+    },
+    "TRAIN": {
+        "LEARNING_RATE": 5e-2,
+        "MAX_EPOCH": 1000,
+        "WRITE": 10,
+        "OPTIMIZE_SCALE":0,
+        "OPTIMIZE_SHAPE":1
+    },
+    "USE_GPU": 1,
+    "DATASET": {
+        "NAME": "NTU",
+        "PATH": "../NTU RGB+D/skeleton_npy",
+        "TARGET_PATH": "",
+        "DATA_MAP": [
+            [
+                0,
+                0
+            ],
+            [
+                1,
+                12
+            ],
+            [
+                2,
+                16
+            ],
+            [
+                4,
+                13
+            ],
+            [
+                5,
+                17
+            ],
+            [
+                6,
+                1
+            ],
+            [
+                7,
+                14
+            ],
+            [
+                8,
+                18
+            ],
+            [
+                9,
+                20
+            ],
+
+            [
+                12,
+                2
+            ],
+            [
+                13,
+                4
+            ],
+            [
+                14,
+                8
+            ],
+            [
+                18,
+                5
+            ],
+            [
+                19,
+                9
+            ],
+            [
+                20,
+                6
+            ],
+            [
+                21,
+                10
+            ],
+            [
+                22,
+                22
+            ],
+            [
+                23,
+                24
+            ]
+        ]
+    },
+    "DEBUG": 0
+}

fit/tools/label.py

@@ -1156,6 +1156,128 @@ CMU_Mocap = {
     "41_09": "Climb"
 }
 
+NTU={
+ "1":" drink water",
+ "2":" eat meal/snack",
+ "3":" brushing teeth", 
+ "4":" brushing hair", 
+ "5":" drop", 
+ "6":" pickup", 
+ "7":" throw", 
+ "8":" sitting down", 
+ "9":" standing up (from sitting position)", 
+ "10":" clapping", 
+ "11":" reading", 
+ "12":" writing", 
+ "13":" tear up paper", 
+ "14":" wear jacket", 
+ "15":" take off jacket", 
+ "16":" wear a shoe", 
+ "17":" take off a shoe", 
+ "18":" wear on glasses", 
+ "19":" take off glasses", 
+ "20":" put on a hat/cap", 
+ "21":" take off a hat/cap", 
+ "22":" cheer up", 
+ "23":" hand waving", 
+ "24":" kicking something", 
+ "25":" reach into pocket", 
+ "26":" hopping (one foot jumping)", 
+ "27":" jump up", 
+ "28":" make a phone call/answer phone", 
+ "29":" playing with phone/tablet", 
+ "30":" typing on a keyboard", 
+ "31":" pointing to something with finger", 
+ "32":" taking a selfie", 
+ "33":" check time (from watch)", 
+ "34":" rub two hands together", 
+ "35":" nod head/bow", 
+ "36":" shake head", 
+ "37":" wipe face", 
+ "38":" salute", 
+ "39":" put the palms together", 
+ "40":" cross hands in front (say stop)", 
+ "41":" sneeze/cough", 
+ "42":" staggering", 
+ "43":" falling", 
+ "44":" touch head (headache)", 
+ "45":" touch chest (stomachache/heart pain)", 
+ "46":" touch back (backache)", 
+ "47":" touch neck (neckache)", 
+ "48":" nausea or vomiting condition", 
+ "49":" use a fan (with hand or paper)/feeling warm",
+ "50":" punching/slapping other person", 
+ "51":" kicking other person", 
+ "52":" pushing other person", 
+ "53":" pat on back of other person", 
+ "54":" point finger at the other person", 
+ "55":" hugging other person", 
+ "56":" giving something to other person", 
+ "57":" touch other person's pocket", 
+ "58":" handshaking", 
+ "59":" walking towards each other", 
+ "60":" walking apart from each other", 
+ "61":" put on headphone", 
+ "62":" take off headphone", 
+ "63":" shoot at the basket", 
+ "64":" bounce ball", 
+ "65":" tennis bat swing", 
+ "66":" juggling table tennis balls", 
+ "67":" hush (quite)", 
+ "68":" flick hair", 
+ "69":" thumb up", 
+ "70":" thumb down", 
+ "71":" make ok sign", 
+ "72":" make victory sign", 
+ "73":" staple book", 
+ "74":" counting money", 
+ "75":" cutting nails", 
+ "76":" cutting paper (using scissors)", 
+ "77":" snapping fingers", 
+ "78":" open bottle", 
+ "79":" sniff (smell)", 
+ "80":" squat down", 
+ "81":" toss a coin", 
+ "82":" fold paper", 
+ "83":" ball up paper",
+ "84":" play magic cube", 
+ "85":" apply cream on face", 
+ "86":" apply cream on hand back", 
+ "87":" put on bag", 
+ "88":" take off bag", 
+ "89":" put something into a bag", 
+ "90":" take something out of a bag", 
+ "91":" open a box", 
+ "92":" move heavy objects", 
+ "93":" shake fist", 
+ "94":" throw up cap/hat", 
+ "95":" hands up (both hands)", 
+ "96":" cross arms", 
+ "97":" arm circles", 
+ "98":" arm swings", 
+ "99":" running on the spot", 
+ "100":"	butt kicks (kick backward)", 
+ "101":"	cross toe touch", 
+ "102":"	side kick", 
+ "103":"	yawn", 
+ "104":"	stretch oneself", 
+ "105":"	blow nose", 
+ "106":"	hit other person with something", 
+ "107":"	wield knife towards other person", 
+ "108":"	knock over other person (hit with body)", 
+ "109":"	grab other person’s stuff", 
+ "110":"	shoot at other person with a gun", 
+ "111":"	step on foot", 
+ "112":"	high-five", 
+ "113":"	cheers and drink", 
+ "114":"	carry something with other person", 
+ "115":"	take a photo of other person", 
+ "116":"	follow other person", 
+ "117":"	whisper in other person’s ear", 
+ "118":"	exchange things with other person", 
+ "119":"	support somebody with hand", 
+ "120":" finger-guessing game (playing rock-paper-scissors)", 
+}
 
 def get_label(file_name, dataset_name):
     if dataset_name == 'HumanAct12':
@@ -1165,5 +1287,8 @@ def get_label(file_name, dataset_name):
         key = file_name.split('_')[0][1:]
         return UTD_MHAD[key]
     elif dataset_name == 'CMU_Mocap':
-        key = file_name.split('.')[0]
+        key = file_name.split(':')[0]
         return CMU_Mocap[key] if key in CMU_Mocap.keys() else ""
+    elif dataset_name == 'NTU':
+        key = str(int(file_name[-3:]))
+        return NTU[key]

fit/tools/load.py

@@ -18,4 +18,6 @@ def load(name, path):
         return np.load(path, allow_pickle=True)
     elif name == "Human3.6M":
         return np.load(path, allow_pickle=True)[0::5] # down_sample
+    elif name == "NTU":
+        return np.load(path, allow_pickle=True)
         

fit/tools/main.py

@@ -114,7 +114,8 @@ if __name__ == "__main__":
             meters.reset_early_stop()
             logger.info("avg_loss:{:.4f}".format(meters.avg))
 
-            # save_pic(res,smpl_layer,file,logger,args.dataset_name,target)
             save_params(res, file, logger, args.dataset_name)
+            save_pic(res,smpl_layer,file,logger,args.dataset_name,target)
+            
             torch.cuda.empty_cache()
     logger.info("Fitting finished! Average loss:     {:.9f}".format(meters.avg))

fit/tools/save.py

@@ -1,3 +1,5 @@
+from display_utils import display_model
+from label import get_label
 import sys
 import os
 import re
@@ -6,8 +8,6 @@ import numpy as np
 import pickle
 
 sys.path.append(os.getcwd())
-from label import get_label
-from display_utils import display_model
 
 
 def create_dir_not_exist(path):
@@ -18,10 +18,8 @@ def create_dir_not_exist(path):
 def save_pic(res, smpl_layer, file, logger, dataset_name, target):
     _, _, verts, Jtr = res
     file_name = re.split('[/.]', file)[-2]
-    fit_path = "fit/output/{}/picture/fit/{}".format(dataset_name, file_name)
+    fit_path = "fit/output/{}/picture/{}".format(dataset_name, file_name)
-    # gt_path = "fit/output/{}/picture/gt/{}".format(dataset_name, file_name)
     create_dir_not_exist(fit_path)
-    # create_dir_not_exist(gt_path)
     logger.info('Saving pictures at {}'.format(fit_path))
     for i in tqdm(range(Jtr.shape[0])):
         display_model(
@@ -32,18 +30,8 @@ def save_pic(res, smpl_layer, file, logger, dataset_name, target):
             kintree_table=smpl_layer.kintree_table,
             savepath=os.path.join(fit_path+"/frame_{}".format(i)),
             batch_idx=i,
-            show=True,
+            show=False,
             only_joint=True)
-        # display_model(
-        #     {'verts': verts.cpu().detach(),
-        #      'joints': target.cpu().detach()},
-        #     model_faces=smpl_layer.th_faces,
-        #     with_joints=True,
-        #     kintree_table=smpl_layer.kintree_table,
-        #     savepath=os.path.join(gt_path+"/frame_{}".format(i)),
-        #     batch_idx=i,
-        #     show=False,
-        #     only_joint=True)
     logger.info('Pictures saved')
 
 
@@ -63,6 +51,7 @@ def save_params(res, file, logger, dataset_name):
     params["pose_params"] = pose_params
     params["shape_params"] = shape_params
     params["Jtr"] = Jtr
+    print("label:{}".format(label))
     with open(os.path.join((fit_path),
                            "{}_params.pkl".format(file_name)), 'wb') as f:
         pickle.dump(params, f)

fit/tools/train.py

@@ -68,8 +68,8 @@ def train(smpl_layer, target,
             break
 
         if epoch % cfg.TRAIN.WRITE == 0:
-            # logger.info("Epoch {}, lossPerBatch={:.6f}, scale={:.4f} EarlyStopSatis: {}".format(
+            # logger.info("Epoch {}, lossPerBatch={:.6f}, scale={:.4f}".format(
-            #         epoch, float(loss),float(scale), early_stop.satis_num))
+            #         epoch, float(loss),float(scale)))
             writer.add_scalar('loss', float(loss), epoch)
             writer.add_scalar('learning_rate', float(
                 optimizer.state_dict()['param_groups'][0]['lr']), epoch)

fit/tools/transform.py

@@ -4,7 +4,8 @@ rotate = {
     'HumanAct12': [1., -1., -1.],
     'CMU_Mocap': [0.05, 0.05, 0.05],
     'UTD_MHAD': [-1., 1., -1.],
-    'Human3.6M': [-0.001, -0.001, 0.001]
+    'Human3.6M': [-0.001, -0.001, 0.001],
+    'NTU': [-1., 1., -1.]
 }