test mmdet pipeline

app.py

@@ -14,7 +14,7 @@ except:
     os.system('pip install /home/user/app/main/transformer_utils')
 
 os.system('cp -rf /home/user/app/assets/conversions.py /home/user/.pyenv/versions/3.9.18/lib/python3.9/site-packages/torchgeometry/core/conversions.py')
-DEFAULT_MODEL='smpler_x_h32' # for config
+DEFAULT_MODEL='posotmetro' # for config
 OUT_FOLDER = '/home/user/app/demo_out'
 os.makedirs(OUT_FOLDER, exist_ok=True)
 

common/utils/preprocessing.py

@@ -58,7 +58,7 @@ def sanitize_bbox(bbox, img_width, img_height):
     return bbox
 
 
-def process_bbox(bbox, img_width, img_height, ratio=1.25):
+def process_bbox(bbox, img_width, img_height, ratio=1.2):
     bbox = sanitize_bbox(bbox, img_width, img_height)
     if bbox is None:
         return bbox

main/config/config_postometro.py

@@ -0,0 +1,111 @@
+import os
+import os.path as osp
+
+# will be update in exp
+num_gpus = -1
+exp_name = 'output/exp1/pre_analysis'
+
+# quick access
+save_epoch = 1
+lr = 1e-5
+end_epoch = 10
+train_batch_size = 16
+
+syncbn = True
+bbox_ratio = 1.2
+
+# continue
+continue_train = False
+start_over = True
+
+# dataset setting
+agora_fix_betas = True
+agora_fix_global_orient_transl = True
+agora_valid_root_pose = True
+
+# all
+dataset_list = ['Human36M', 'MSCOCO', 'MPII', 'AGORA', 'EHF', 'SynBody', 'GTA_Human2', \
+    'EgoBody_Egocentric', 'EgoBody_Kinect', 'UBody', 'PW3D', 'MuCo', 'PROX']
+trainset_3d = ['MSCOCO','AGORA', 'UBody']
+trainset_2d = ['PW3D', 'MPII', 'Human36M']
+trainset_humandata = ['BEDLAM', 'SPEC', 'GTA_Human2','SynBody', 'PoseTrack',
+                    'EgoBody_Egocentric', 'PROX', 'CrowdPose',
+                    'EgoBody_Kinect', 'MPI_INF_3DHP', 'RICH', 'MuCo', 'InstaVariety',
+                    'Behave', 'UP3D', 'ARCTIC',
+                    'OCHuman', 'CHI3D', 'RenBody_HiRes', 'MTP', 'HumanSC3D', 'RenBody',
+                    'FIT3D', 'Talkshow' , 'SSP3D', 'LSPET']
+testset = 'EHF'
+
+use_cache = True
+# downsample
+BEDLAM_train_sample_interval = 5
+EgoBody_Kinect_train_sample_interval = 10
+train_sample_interval = 10 # UBody
+MPI_INF_3DHP_train_sample_interval = 5
+InstaVariety_train_sample_interval = 10
+RenBody_HiRes_train_sample_interval = 5
+ARCTIC_train_sample_interval = 10
+# RenBody_train_sample_interval = 10
+FIT3D_train_sample_interval = 10
+Talkshow_train_sample_interval = 10
+
+# strategy 
+data_strategy = 'balance' # 'balance' need to define total_data_len
+total_data_len = 4500000
+
+# model
+smplx_loss_weight = 1.0 #2 for agora_model for smplx shape
+smplx_pose_weight = 10.0
+
+smplx_kps_3d_weight = 100.0
+smplx_kps_2d_weight = 1.0
+net_kps_2d_weight = 1.0
+
+agora_benchmark = 'agora_model' # 'agora_model', 'test_only'
+
+model_type = 'smpler_x_h'
+encoder_config_file = 'main/transformer_utils/configs/smpler_x/encoder/body_encoder_huge.py'
+encoder_pretrained_model_path = 'pretrained_models/vitpose_huge.pth'
+feat_dim = 1280
+
+## =====FIXED ARGS============================================================
+## model setting
+upscale = 4
+hand_pos_joint_num = 20
+face_pos_joint_num = 72
+num_task_token = 24
+num_noise_sample = 0
+
+## UBody setting
+train_sample_interval = 10
+test_sample_interval = 100
+make_same_len = False
+
+## input, output size
+input_img_shape = (256, 256)
+input_body_shape = (256, 256)
+output_hm_shape = (16, 16, 12)
+input_hand_shape = (256, 256)
+output_hand_hm_shape = (16, 16, 16)
+output_face_hm_shape = (8, 8, 8)
+input_face_shape = (192, 192)
+focal = (5000, 5000)  # virtual focal lengths
+princpt = (input_body_shape[1] / 2, input_body_shape[0] / 2)  # virtual principal point position
+body_3d_size = 2
+hand_3d_size = 0.3
+face_3d_size = 0.3
+camera_3d_size = 2.5
+
+## training config
+print_iters = 100
+lr_mult = 1
+
+## testing config
+test_batch_size = 32
+
+## others
+num_thread = 2
+vis = False
+
+## directory
+output_dir, model_dir, vis_dir, log_dir, result_dir, code_dir = None, None, None, None, None, None

main/inference.py

@@ -76,6 +76,7 @@ class Inferer:
             num_bbox = len(mmdet_box)
         
         ## loop all detected bboxes
+        ok_bboxes = []
         for bbox_id in range(num_bbox):
             mmdet_box_xywh = np.zeros((4))
             # xyxy -> xywh
@@ -91,6 +92,8 @@ class Inferer:
             # align these pre-processing steps
             bbox = process_bbox(mmdet_box_xywh, original_img_width, original_img_height)
 
+            ok_bboxes.append(bbox)
+
             # test mmdet pipeline
             if bbox is not None:
                 top_left = (int(bbox[0]), int(bbox[1]))
@@ -142,5 +145,5 @@ class Inferer:
             # vis_img = render_mesh(vis_img, mesh, smpl_x.face, {'focal': focal, 'princpt': princpt}, 
             #                       mesh_as_vertices=mesh_as_vertices)
             # vis_img = vis_img.astype('uint8') 
-        return vis_img, num_bbox, mmdet_box
+        return vis_img, num_bbox, ok_bboxes