### List Directory Contents

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Lists the contents of the current directory after the setup steps. This is useful for verifying that files have been downloaded and built correctly.

```python
% ls
```

--------------------------------

### Basic CMake Project Setup

Source: https://github.com/choyingw/synergynet/blob/main/Sim3DR/tests/CMakeLists.txt

Sets the minimum CMake version, defines the target and project name, and configures C++ compiler flags for building the project.

```cmake
cmake_minimum_required(VERSION 2.8)

set(TARGET test)
project(${TARGET})
```

--------------------------------

### Install SynergyNet Locally

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Install SynergyNet in editable mode using pip. This allows importing the SynergyNet module from other directories.

```bash
pip install -e .
```

--------------------------------

### Run Benchmark Evaluation

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Execute the benchmark script with a specified pretrained model. Results and visualizations for the first 50 examples are saved in the 'results/' directory.

```python
benchmark.py -w pretrained/best.pth.tar
```

--------------------------------

### Simplified API for SynergyNet

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Demonstrates how to use the SynergyNet API to get 3D landmarks, mesh, and face pose from an image. Ensure you have the necessary data and pretrained weights.

```python
import cv2
from synergy3DMM import SynergyNet
model = SynergyNet()
I = cv2.imread(<your image path>)
# get landmark [[y, x, z], 68 (points)], mesh [[y, x, z], 53215 (points)], and face pose (Euler angles [yaw, pitch, roll] and translation [y, x, z])
lmks3d, mesh, pose = model.get_all_outputs(I)
```

--------------------------------

### Activate Conda Environment

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Activate the SynergyNet Conda environment before installing packages.

```conda
conda activate SynergyNet
```

--------------------------------

### Clone SynergyNet Repository

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Clones the SynergyNet GitHub repository. This is the initial step to get the project files.

```python
print('\n> Cloning the repo')
!git clone https://github.com/choyingw/SynergyNet

% cd SynergyNet
```

--------------------------------

### Build Sim3DR Extensions

Source: https://github.com/choyingw/synergynet/blob/main/Sim3DR/readme.md

Builds the Cython extensions for Sim3DR in-place. Ensure you have the necessary build tools installed.

```shell
python3 setup.py build_ext --inplace
```

--------------------------------

### Import Core Libraries for SynergyNet

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Imports essential libraries for SynergyNet, including PyTorch, torchvision, NumPy, OpenCV, and custom utilities for inference and model building. Ensure these libraries are installed in your environment.

```python
import torch
import torchvision.transforms as transforms
import numpy as np
import cv2
from utils.ddfa import ToTensor, Normalize
from model_building import SynergyNet
from utils.inference import crop_img, predict_sparseVert, draw_landmarks, predict_denseVert, predict_pose, draw_axis
import argparse
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
import os
import os.path as osp
import glob
from FaceBoxes import FaceBoxes
from utils.render import render
import scipy.io as sio
```

--------------------------------

### Run Face Detection Demo

Source: https://github.com/choyingw/synergynet/blob/main/FaceBoxes/readme.md

Execute the FaceBoxes.py script using Python 3 to run the face detection demonstration.

```shell script
python3 FaceBoxes.py
```

--------------------------------

### Load and Display Image Outputs (Sample 1)

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Loads the original image and its processed outputs (overlay, landmark, pose) for sample 1 using OpenCV and displays them. Requires `cv2_imshow` from `google.colab.patches`.

```python
from google.colab.patches import cv2_imshow
I_img = cv2.imread('img/sample_1.jpg', -1)
I_overlay = cv2.imread('inference_output/rendering_overlay/sample_1.jpg', -1)
I_landmark = cv2.imread('inference_output/landmarks/sample_1.jpg', -1)
I_pose = cv2.imread('inference_output/poses/sample_1.jpg', -1)
cv2_imshow(I_img)
cv2_imshow(I_overlay)
cv2_imshow(I_landmark)
cv2_imshow(I_pose)
```

--------------------------------

### Load and Display Image Outputs (Sample 2)

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Loads the original image and its processed outputs (overlay, landmark, pose) for sample 2 using OpenCV and displays them. Requires `cv2_imshow` from `google.colab.patches`.

```python
I_img = cv2.imread('img/sample_2.jpg', -1)
I_overlay = cv2.imread('inference_output/rendering_overlay/sample_2.jpg', -1)
I_landmark = cv2.imread('inference_output/landmarks/sample_2.jpg', -1)
I_pose = cv2.imread('inference_output/poses/sample_2.jpg', -1)
cv2_imshow(I_img)
cv2_imshow(I_overlay)
cv2_imshow(I_landmark)
cv2_imshow(I_pose)
```

--------------------------------

### Load and Display Image Outputs (Sample 3)

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Loads the original image and its processed outputs (overlay, landmark, pose) for sample 3 using OpenCV and displays them. Requires `cv2_imshow` from `google.colab.patches`.

```python
I_img = cv2.imread('img/sample_3.jpg', -1)
I_overlay = cv2.imread('inference_output/rendering_overlay/sample_3.jpg', -1)
I_landmark = cv2.imread('inference_output/landmarks/sample_3.jpg', -1)
I_pose = cv2.imread('inference_output/poses/sample_3.jpg', -1)
cv2_imshow(I_img)
cv2_imshow(I_overlay)
cv2_imshow(I_landmark)
cv2_imshow(I_pose)
```

--------------------------------

### Navigate to SynergyNet Directory

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Change the current directory to the cloned SynergyNet repository.

```bash
cd SynergyNet
```

--------------------------------

### Execute Training Script

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Run the training script. Hyperparameters like learning rate, epochs, and GPU device can be configured within the script. Adjust batch size and learning rate proportionally if GPU memory is limited.

```bash
train_script.sh
```

--------------------------------

### Load and Display Image Outputs (Sample 4)

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Loads the original image and its processed outputs (overlay, landmark, pose) for sample 4 using OpenCV and displays them. Requires `cv2_imshow` from `google.colab.patches`.

```python
I_img = cv2.imread('img/sample_4.jpg', -1)
I_overlay = cv2.imread('inference_output/rendering_overlay/sample_4.jpg', -1)
I_landmark = cv2.imread('inference_output/landmarks/sample_4.jpg', -1)
I_pose = cv2.imread('inference_output/poses/sample_4.jpg', -1)
cv2_imshow(I_img)
cv2_imshow(I_overlay)
cv2_imshow(I_landmark)
cv2_imshow(I_pose)
```

--------------------------------

### Build CPU Version of NMS

Source: https://github.com/choyingw/synergynet/blob/main/FaceBoxes/readme.md

Navigate to the utils directory and build the NMS extension in-place using Python 3.

```shell script
cd utils
python3 build.py build_ext --inplace
```

--------------------------------

### Parse Command-Line Arguments for SynergyNet

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Sets up argument parsing for the SynergyNet demo script. It defines options for input files, image format, image size, and batch size.

```python
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--files', default='./img/', help='path to a single image or path to a folder containing multiple images')
parser.add_argument("--png", action="store_true", help="if images are with .png extension")
parser.add_argument('--img_size', default=120, type=int)
parser.add_argument('-b', '--batch-size', default=1, type=int)

args = parser.parse_args()
args.files = 'img'


main(args)
```

--------------------------------

### Build CPU Version of NMS (Alternative)

Source: https://github.com/choyingw/synergynet/blob/main/FaceBoxes/readme.md

Alternatively, execute the build_cpu_nms.sh script to build the CPU version of NMS.

```shell script
sh ./build_cpu_nms.sh
```

--------------------------------

### Build Sim3DR and FaceBoxes Dependencies

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Navigates into Sim3DR and FaceBoxes directories to build their respective dependencies using shell scripts. This step is crucial for the functionality of these components.

```python
% cd Sim3DR
!sh ./build_sim3dr.sh

% cd ../FaceBoxes
!sh ./build_cpu_nms.sh
% cd ..
```

--------------------------------

### Create Conda Environment

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Create a new Conda environment named SynergyNet for project dependencies.

```conda
conda create --name SynergyNet
```

--------------------------------

### Process and Save Image Outputs

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

This section outlines the processing steps for each image, indicating the saving locations for mesh, landmark, and pose results. It is repeated for each sample image.

```text
Process the image:  img/sample_1.jpg
Save mesh result to inference_output/rendering_overlay/sample_1.jpg
Save landmark result to inference_output/landmarks/sample_1.jpg
Save pose result to inference_output/poses/sample_1.jpg
```

```text
Process the image:  img/sample_2.jpg
Save mesh result to inference_output/rendering_overlay/sample_2.jpg
Save landmark result to inference_output/landmarks/sample_2.jpg
Save pose result to inference_output/poses/sample_2.jpg
```

```text
Process the image:  img/sample_3.jpg
Save mesh result to inference_output/rendering_overlay/sample_3.jpg
Save landmark result to inference_output/landmarks/sample_3.jpg
Save pose result to inference_output/poses/sample_3.jpg
```

```text
Process the image:  img/sample_4.jpg
Save mesh result to inference_output/rendering_overlay/sample_4.jpg
Save landmark result to inference_output/landmarks/sample_4.jpg
Save pose result to inference_output/poses/sample_4.jpg
```

--------------------------------

### Generate Textured Artistic Face Meshes (Folder)

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Process all images in a folder to generate textured artistic face meshes. Ensure artistic faces data and predicted UV maps are extracted in the root folder.

```python
artistic.py -f art-all --png
```

--------------------------------

### Compile FaceBoxes NMS

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Compile the CPU NMS module for FaceBoxes. Navigate to the FaceBoxes directory first.

```bash
cd ../FaceBoxes
```

```bash
./build_cpu_nms.sh
```

```bash
cd ..
```

--------------------------------

### SynergyNet Simplified API Usage

Source: https://github.com/choyingw/synergynet/blob/main/README.md

This snippet demonstrates how to import and use the SynergyNet model for single image inference. It shows how to load an image and obtain 3D landmarks, mesh, and face pose.

```APIDOC
## SynergyNet Simplified API

We provide a simple API for convenient usage if you want to plug in this method into your work.

```python
import cv2
from synergy3DMM import SynergyNet
model = SynergyNet()
I = cv2.imread(<your image path>)
# get landmark [[y, x, z], 68 (points)], mesh [[y, x, z], 53215 (points)], and face pose (Euler angles [yaw, pitch, roll] and translation [y, x, z])
lmks3d, mesh, pose = model.get_all_outputs(I)
```

To use this API, ensure you have installed the library using `pip install -e .` and have downloaded the necessary 3DMM data and pretrained weights. You can then import `SynergyNet` from `synergy3DMM` in your Python projects.
```

--------------------------------

### Run Single Image Inference

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Execute the single image inference script with the 'img' directory as input. A compatible GPU is recommended.

```python
python singleImage.py -f img
```

--------------------------------

### Compile Sim3DR

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Compile the Sim3DR module. Ensure you are in the Sim3DR directory.

```bash
cd Sim3DR
```

```bash
./build_sim3dr.sh
```

--------------------------------

### Download Additional Data

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Downloads another required data file using gdown. This is a supplementary data download step.

```python
!gdown --id 1BVHbiLTfX6iTeJcNbh-jgHjWDoemfrzG
```

--------------------------------

### Include Directories

Source: https://github.com/choyingw/synergynet/blob/main/Sim3DR/tests/CMakeLists.txt

Specifies private include directories for the target, adding the project's source directory to the include path.

```cmake
target_include_directories(${TARGET} PRIVATE ${PROJECT_SOURCE_DIR})
```

--------------------------------

### Generate Textured Artistic Face Meshes (Single Image)

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Process a single image to generate a textured artistic face mesh. Ensure artistic faces data and predicted UV maps are extracted in the root folder.

```python
artistic.py -f art-all/122.png
```

--------------------------------

### Run Inference on a Folder of Real Faces

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Execute the inference script on an entire folder containing real face images. The results will be saved in the 'inference_output' directory.

```python
uv_texture_realFaces.py -f texture_data/real --png
```

--------------------------------

### Move Model Weights

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Moves the downloaded 'best.pth.tar' model weights file to the 'pretrained' directory. This is a common step after downloading model checkpoints.

```python
!mv best.pth.tar  ./pretrained
% ls
```

--------------------------------

### Clone SynergyNet Repository

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Clone the SynergyNet repository from GitHub.

```git
git clone https://github.com/choyingw/SynergyNet
```

--------------------------------

### Run Inference on a Single Real Face Image

Source: https://github.com/choyingw/synergynet/blob/main/README.md

Execute the inference script on a single real face image. The results will be saved in the 'inference_output' directory.

```python
uv_texture_realFaces.py -f texture_data/real/image00002_real_A.png
```

--------------------------------

### Compile CPU NMS Extension

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Compiles the CPU Non-Maximum Suppression (NMS) extension using g++. This is often a necessary step for performance-critical components.

```bash
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/nms/cpu_nms.o -o /content/SynergyNet/FaceBoxes/utils/nms/cpu_nms.cpython-37m-x86_64-linux-gnu.so
```

--------------------------------

### Download and Unzip 3DMM Data

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

Downloads the 3DMM data using gdown and then unzips the downloaded file. This data is required for 3D model fitting.

```python
!gdown --id 1SQsMhvAmpD1O8Hm0yEGom0C0rXtA0qs8
!unzip -n 3dmm_data
```

--------------------------------

### Run SynergyNet Face Analysis Pipeline

Source: https://github.com/choyingw/synergynet/blob/main/synergy_demo.ipynb

This function orchestrates the entire face analysis process, from loading the model to saving inference outputs. It requires a pre-trained model checkpoint and handles image loading, face detection, and result visualization.

```python
# Following 3DDFA-V2, we also use 120x120 resolution
IMG_SIZE = 120

def main(args):
    # load pre-tained model
    checkpoint_fp = 'pretrained/best.pth.tar' 
    args.arch = 'mobilenet_v2'
    args.devices_id = [0]

    checkpoint = torch.load(checkpoint_fp, map_location=lambda storage, loc: storage)['state_dict']
    
    model = SynergyNet(args)
    model_dict = model.state_dict()

    # because the model is trained by multiple gpus, prefix 'module' should be removed
    for k in checkpoint.keys():
        model_dict[k.replace('module.', '')] = checkpoint[k]

    model.load_state_dict(model_dict, strict=False)
    model = model.cuda()
    model.eval()

    # face detector
    face_boxes = FaceBoxes()

    # preparation
    transform = transforms.Compose([ToTensor(), Normalize(mean=127.5, std=128)])
    if osp.isdir(args.files):
        if not args.files[-1] == '/':
            args.files = args.files + '/'
        if not args.png:
            files = sorted(glob.glob(args.files+'*.jpg'))
        else:
            files = sorted(glob.glob(args.files+'*.png'))
    else:
        files = [args.files]

    def _to_ctype(arr):
        if not arr.flags.c_contiguous:
            return arr.copy(order='C')
        return arr
    tri = sio.loadmat('./3dmm_data/tri.mat')['tri'] - 1

    for img_fp in files:
        print("Process the image: ", img_fp)

        img_ori = cv2.imread(img_fp)

        # crop faces
        rects = face_boxes(img_ori)

        # storage
        pts_res = []
        poses = []
        vertices_lst = []
        for idx, rect in enumerate(rects):
            roi_box = rect

            # enlarge the bbox a little and do a square crop
            HCenter = (rect[1] + rect[3])/2
            WCenter = (rect[0] + rect[2])/2
            side_len = roi_box[3]-roi_box[1]
            margin = side_len * 1.2 // 2
            roi_box[0], roi_box[1], roi_box[2], roi_box[3] = WCenter-margin, HCenter-margin, WCenter+margin, HCenter+margin

            img = crop_img(img_ori, roi_box)
            img = cv2.resize(img, dsize=(IMG_SIZE, IMG_SIZE), interpolation=cv2.INTER_LINEAR)
            # cv2.imwrite(f'validate_{idx}.png', img)
            
            input = transform(img).unsqueeze(0)
            with torch.no_grad():
                input = input.cuda()
                param = model.forward_test(input)
                param = param.squeeze().cpu().numpy().flatten().astype(np.float32)

            # inferences
            lmks = predict_sparseVert(param, roi_box, transform=True)
            vertices = predict_denseVert(param, roi_box, transform=True)
            angles, translation = predict_pose(param, roi_box)

            pts_res.append(lmks)
            vertices_lst.append(vertices)
            poses.append([angles, translation, lmks])

        if not osp.exists(f'inference_output/rendering_overlay/'):
            os.makedirs(f'inference_output/rendering_overlay/')
        if not osp.exists(f'inference_output/landmarks/'):
            os.makedirs(f'inference_output/landmarks/')
        if not osp.exists(f'inference_output/poses/'):
            os.makedirs(f'inference_output/poses/')
        
        name = img_fp.rsplit('/',1)[-1][:-4]
        img_ori_copy = img_ori.copy()

        # mesh
        render(img_ori, vertices_lst, alpha=0.6, wfp=f'inference_output/rendering_overlay/{name}.jpg', connectivity=tri)
        
        # landmarks
        draw_landmarks(img_ori_copy, pts_res, wfp=f'inference_output/landmarks/{name}.jpg')
        
        # face orientation
        img_axis_plot = img_ori_copy
        for angles, translation, lmks in poses:
            img_axis_plot = draw_axis(img_axis_plot, angles[0], angles[1],
                angles[2], translation[0], translation[1], size = 50, pts68=lmks)
        wfp = f'inference_output/poses/{name}.jpg'
        cv2.imwrite(wfp, img_axis_plot)
        print(f'Save pose result to {wfp}')
```

--------------------------------

### Compiler Flags and Executable Definition

Source: https://github.com/choyingw/synergynet/blob/main/Sim3DR/tests/CMakeLists.txt

Configures C++ compiler flags to enable Position Independent Code (PIC) and C++11 standard, and adds source files to build the executable target.

```cmake
#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC -O3")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -std=c++11")
add_executable(${TARGET} test.cpp rasterize_kernel.cpp io.cpp)
```

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