Gradiodemo.py extension: selectable outputs

Added radioboxes to implement the support for the other output formats available in the demo.py Results are stored in ./examples/results folder with 5 digit randomized serial tags. Chances are low but files may get overwritten so don't pile up on the outputs. A little like a note to my self> Implementation to windows os is possible if bit tricky with all the lost dependencies and stuff. A little more tweak on the code is necessary beyond what is aforementioned at the readme.md - MSVC alone wont be enough for the command line build. check for cl.exe and if it's included in the path env. variables - long is deprecated in the recent numpy releases. np.long should be converted to np.longlong in the ./bfm/bfm.py -due to the library collision, additional line was necessary at the header for the gradiodemo.py to work ``` os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' ``` -there is no 'input'/'output' attribute in gradio gradiodemo.py ``` # before import, make sure FaceBoxes and Sim3DR are built successfully, e.g., import sys from subprocess import call import os import torch os.environ['KMP_DUPLICATE_LIB_OK']='True' import random import string torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Solvay_conference_1927.jpg/1400px-Solvay_conference_1927.jpg', 'solvay.jpg') def run_cmd(command): try: print(command) call(command, shell=True) except Exception as e: print(f"Errorrrrr: {e}!") print(os.getcwd()) #os.chdir("/FaceBoxes/utils") print(os.getcwd()) #run_cmd("python build.py build_ext --inplace") #os.chdir("/Sim3DR") print(os.getcwd()) #run_cmd("python setup.py build_ext --inplace") print(os.getcwd()) #os.chdir("/utils/asset") print(os.getcwd()) #run_cmd("gcc -shared -Wall -O3 render.c -o render.so -fPIC") #os.chdir("/app") print(os.getcwd()) import cv2 import yaml from FaceBoxes import FaceBoxes from TDDFA import TDDFA from utils.render import render from utils.depth import depth from utils.pncc import pncc from utils.uv import uv_tex from utils.pose import viz_pose from utils.serialization import ser_to_ply, ser_to_obj from utils.functions import draw_landmarks, get_suffix from utils.tddfa_util import str2bool import matplotlib.pyplot as plt from skimage import io import gradio as gr # load config cfg = yaml.load(open('configs/mb1_120x120.yml'), Loader=yaml.SafeLoader) # Init FaceBoxes and TDDFA, recommend using onnx flag onnx_flag = True # or True to use ONNX to speed up if onnx_flag: import os os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' os.environ['OMP_NUM_THREADS'] = '4' from FaceBoxes.FaceBoxes_ONNX import FaceBoxes_ONNX from TDDFA_ONNX import TDDFA_ONNX face_boxes = FaceBoxes_ONNX() tddfa = TDDFA_ONNX(**cfg) else: face_boxes = FaceBoxes() tddfa = TDDFA(gpu_mode=False, **cfg) def inference (img, radio_option): #set dense_flag from the radiobox option dense_flag = radio_option in ('2d_dense', '3d', 'depth', 'pncc', 'uv_tex', 'ply', 'obj') new_suffix = f'.{radio_option}' if radio_option in ('ply', 'obj') else '.jpg' wfp = f'examples/results/output_{radio_option}_'+''.join(random.choices(string.ascii_uppercase + string.digits, k=5)) + new_suffix # face detection boxes = face_boxes(img) # regress 3DMM params param_lst, roi_box_lst = tddfa(img, boxes) # reconstruct vertices ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag) #decide output according to radiobox arguments if radio_option == '2d_sparse': draw_landmarks(img, ver_lst, show_flag=False, dense_flag=dense_flag, wfp=wfp) elif radio_option == '2d_dense': draw_landmarks(img, ver_lst, show_flag=False, dense_flag=dense_flag, wfp=wfp) elif radio_option == '3d': render(img, ver_lst, tddfa.tri, alpha=0.6, show_flag=False, wfp=wfp) elif radio_option == 'depth': # if `with_bf_flag` is False, the background is black depth(img, ver_lst, tddfa.tri, show_flag=False, wfp=wfp, with_bg_flag=True) elif radio_option == 'pncc': pncc(img, ver_lst, tddfa.tri, show_flag=False, wfp=wfp, with_bg_flag=True) elif radio_option == 'uv_tex': uv_tex(img, ver_lst, tddfa.tri, show_flag=False, wfp=wfp) elif radio_option == 'pose': viz_pose(img, param_lst, ver_lst, show_flag=False,wfp=wfp) elif radio_option == 'ply': ser_to_ply(ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp) elif radio_option == 'obj': ser_to_obj(img, ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp) else: raise ValueError(f'Unknown opt {radio_option}') #fetch processed image if radio_option in ('2d_sparse', '2d_dense', '3d', 'depth', 'pncc', 'uv_tex'): outimg = cv2.imread(wfp) resized_img = cv2.resize(outimg, (img.shape[1],img.shape[0])) else: #no output image(.obj or .ply) resized_img = img #if dense_flag is false recalc ver_list with dense flag up if dense_flag == False: ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=True) return render(resized_img, ver_lst, tddfa.tri, alpha=0.6, show_flag=False); title = "3DDFA V2" description = "demo for 3DDFA V2. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2009.09960'>Towards Fast, Accurate and Stable 3D Dense Face Alignment</a> | <a href='https://github.com/cleardusk/3DDFA_V2'>Github Repo</a></p>" examples = [ ['solvay.jpg'], ['examples/inputs/emma.jpg'], ['examples/inputs/JianzhuGuo.jpg'], ['examples/inputs/trump_hillary.jpg'] ] gr.Interface( inference, [ gr.Image(type="numpy", label="Input"), gr.Radio(['2d_sparse', '2d_dense', '3d', 'depth', 'pncc', 'uv_tex', 'ply', 'obj'], value='2d_sparse') ], gr.Image(type="numpy", label="Output"), title=title, description=description, article=article, examples=examples ).launch() ``` I'm by no means a python expert just managed to work it out from the official docs and APIs. Any optimization/modification would be welcomed.