All changes required to run in 2025

.gitignore

@@ -15,6 +15,8 @@
 *.rar
 *.tar
 *.zip
+.idea
+data
 
 # OS generated files #
 ######################

README.md

@@ -1,3 +1,38 @@
+# Update to run in 2025
+
+OS: Ubuntu 20.04<br/>
+GPU: L4<br/>
+CUDA: 12.4v<br/>
+
+Step 1: Setup Env
+>add-apt-repository ppa:deadsnakes/ppa -y<br/>
+add-apt-repository ppa:ubuntu-toolchain-r/test -y<br/>
+apt update<br/>
+apt-get install python3.10 python3.10-venv python3.10-distutils libgl1 python3.10-dev gcc-11 g++-11<br/>
+
+Step 2: Clone Repo & Setup DECA
+>cd ~/DECA<br/>
+python3.10 -m venv --without-pip deca<br/>
+source deca/bin/activate<br/>
+python --version<br/>
+curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py <br/>
+python get-pip.py <br/>
+pip install --upgrade pip setuptools wheel build cython<br/>
+pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 <br/>
+pip install -r requirements.txt<br/>
+bash fetch_data.sh<br/>
+
+Step 4: Copy Relevant Data
+1. Download file from "https://drive.usercontent.google.com/download?id=1rp8kdyLPvErw2dTmqtjISRVvQLj6Yzje&export=download&authuser=0"
+2. Move it to data folder.
+3. Follow https://github.com/TimoBolkart/BFM_to_FLAME to get FLAME_albedo_from_BFM.npz and copy to data folder. This is required to run demo_teaser.
+
+Step 5: Run Reconstruct
+>python demos/demo_reconstruct.py -i TestSamples/personal --saveDepth True --saveObj True -s TestSamples/personal/output --saveVis true --saveKpt true <br/>
+
+Use Meshlab to view the output .obj files.<br/>
+Or any other use case you want to. Other usecases, I have not tried, you are welcome to try them and fix them in your own forks.
+
 # DECA: Detailed Expression Capture and Animation (SIGGRAPH2021)
 
 <p align="center"> 

decalib/datasets/datasets.py

@@ -46,7 +46,7 @@ def video2sequence(video_path, sample_step=10):
     return imagepath_list
 
 class TestData(Dataset):
-    def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detector='fan', sample_step=10):
+    def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detector='fan', sample_step=10, device='cuda'):
         '''
             testpath: folder, imagepath_list, image path, video path
         '''
@@ -68,7 +68,7 @@ def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detect
         self.iscrop = iscrop
         self.resolution_inp = crop_size
         if face_detector == 'fan':
-            self.face_detector = detectors.FAN()
+            self.face_detector = detectors.FAN(device)
         # elif face_detector == 'mtcnn':
         #     self.face_detector = detectors.MTCNN()
         else:

decalib/datasets/detectors.py

@@ -17,9 +17,9 @@
 import torch
 
 class FAN(object):
-    def __init__(self):
+    def __init__(self, device='cuda'):
         import face_alignment
-        self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)
+        self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=False, device=device)
 
     def run(self, image):
         '''
@@ -31,30 +31,31 @@ def run(self, image):
             return [0], 'kpt68'
         else:
             kpt = out[0].squeeze()
-            left = np.min(kpt[:,0]); right = np.max(kpt[:,0]); 
-            top = np.min(kpt[:,1]); bottom = np.max(kpt[:,1])
-            bbox = [left,top, right, bottom]
+            left = np.min(kpt[:, 0]);
+            right = np.max(kpt[:, 0]);
+            top = np.min(kpt[:, 1]);
+            bottom = np.max(kpt[:, 1])
+            bbox = [left, top, right, bottom]
             return bbox, 'kpt68'
 
+
 class MTCNN(object):
-    def __init__(self, device = 'cpu'):
+    def __init__(self, device='cpu'):
         '''
         https://github.com/timesler/facenet-pytorch/blob/master/examples/infer.ipynb
         '''
         from facenet_pytorch import MTCNN as mtcnn
         self.device = device
         self.model = mtcnn(keep_all=True)
+
     def run(self, input):
         '''
         image: 0-255, uint8, rgb, [h, w, 3]
         return: detected box
         '''
-        out = self.model.detect(input[None,...])
+        out = self.model.detect(input[None, ...])
         if out[0][0] is None:
             return [0]
         else:
             bbox = out[0][0].squeeze()
             return bbox, 'bbox'
-
-
-

decalib/deca.py

@@ -86,7 +86,10 @@ def _create_model(self, model_cfg):
         model_path = self.cfg.pretrained_modelpath
         if os.path.exists(model_path):
             print(f'trained model found. load {model_path}')
-            checkpoint = torch.load(model_path)
+            if self.device == 'cpu':
+                checkpoint = torch.load(model_path, map_location='cpu')
+            else:
+                checkpoint = torch.load(model_path)
             self.checkpoint = checkpoint
             util.copy_state_dict(self.E_flame.state_dict(), checkpoint['E_flame'])
             util.copy_state_dict(self.E_detail.state_dict(), checkpoint['E_detail'])

decalib/utils/config.py

@@ -1,10 +1,11 @@
 '''
 Default config for DECA
 '''
-from yacs.config import CfgNode as CN
 import argparse
-import yaml
 import os
+import yaml
+
+from yacs.config import CfgNode as CN
 
 cfg = CN()
 
@@ -24,13 +25,13 @@
 # texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip
 cfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')
 cfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')
-cfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') 
-cfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') 
-cfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') 
-cfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') 
-cfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') 
-cfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') 
-cfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM
+cfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl')
+cfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy')
+cfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png')
+cfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png')
+cfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg')
+cfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz')
+cfg.model.tex_type = 'BFM'  # BFM, FLAME, albedoMM
 cfg.model.uv_size = 256
 cfg.model.param_list = ['shape', 'tex', 'exp', 'pose', 'cam', 'light']
 cfg.model.n_shape = 100
@@ -40,7 +41,8 @@
 cfg.model.n_pose = 6
 cfg.model.n_light = 27
 cfg.model.use_tex = True
-cfg.model.jaw_type = 'aa' # default use axis angle, another option: euler. Note that: aa is not stable in the beginning
+cfg.model.extract_tex = True
+cfg.model.jaw_type = 'aa'  # default use axis angle, another option: euler. Note that: aa is not stable in the beginning
 # face recognition model
 cfg.model.fr_model_path = os.path.join(cfg.deca_dir, 'data', 'resnet50_ft_weight.pkl')
 
@@ -100,7 +102,7 @@
 cfg.loss.reg_exp = 1e-04
 cfg.loss.reg_tex = 1e-04
 cfg.loss.reg_light = 1.
-cfg.loss.reg_jaw_pose = 0. #1.
+cfg.loss.reg_jaw_pose = 0.  # 1.
 cfg.loss.use_gender_prior = False
 cfg.loss.shape_consistency = True
 # loss for detail
@@ -119,14 +121,16 @@ def get_cfg_defaults():
     # This is for the "local variable" use pattern
     return cfg.clone()
 
+
 def update_cfg(cfg, cfg_file):
     cfg.merge_from_file(cfg_file)
     return cfg.clone()
 
+
 def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--cfg', type=str, help='cfg file path')
-    parser.add_argument('--mode', type=str, default = 'train', help='deca mode')
+    parser.add_argument('--mode', type=str, default='train', help='deca mode')
 
     args = parser.parse_args()
     print(args, end='\n\n')