demo.py

"""
Author: Soubhik Sanyal
Copyright (c) 2019, Soubhik Sanyal
All rights reserved.
based on github.com/akanazawa/hmr
"""
## Demo of RingNet.
## Note that RingNet requires a loose crop of the face in the image.
## Sample usage:
## Run the following command to generate check the RingNet predictions on loosely cropped face images
# python -m demo --img_path *.jpg --out_folder ./RingNet_output
## To output the meshes run the following command
# python -m demo --img_path *.jpg --out_folder ./RingNet_output --save_obj_file=True
## To output both meshes and flame parameters run the following command
# python -m demo --img_path *.jpg --out_folder ./RingNet_output --save_obj_file=True --save_flame_parameters=True
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import os
from absl import flags
import numpy as np
import skimage.io as io
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from psbody.mesh import Mesh
from smpl_webuser.serialization import load_model

from util import renderer as vis_util
from util import image as img_util
from config_test import get_config
from run_RingNet import RingNet_inference

def visualize(img, proc_param, verts, cam, img_name='test_image'):
    """
    Renders the result in original image coordinate frame.
    """
    cam_for_render, vert_shifted = vis_util.get_original(
        proc_param, verts, cam, img_size=img.shape[:2])

    # Render results
    rend_img_overlay = renderer(
        vert_shifted*1.0, cam=cam_for_render, img=img, do_alpha=True)
    rend_img = renderer(
        vert_shifted*1.0, cam=cam_for_render, img_size=img.shape[:2])
    rend_img_vp1 = renderer.rotated(
        vert_shifted, 30, cam=cam_for_render, img_size=img.shape[:2])

    import matplotlib.pyplot as plt
    fig = plt.figure(1)
    plt.clf()
    plt.subplot(221)
    plt.imshow(img)
    plt.title('input')
    plt.axis('off')
    plt.subplot(222)
    plt.imshow(rend_img_overlay)
    plt.title('3D Mesh overlay')
    plt.axis('off')
    plt.subplot(223)
    plt.imshow(rend_img)
    plt.title('3D mesh')
    plt.axis('off')
    plt.subplot(224)
    plt.imshow(rend_img_vp1)
    plt.title('diff vp')
    plt.axis('off')
    plt.draw()
    plt.show(block=False)
    fig.savefig(img_name + '.png')
    # import ipdb
    # ipdb.set_trace()

def preprocess_image(img_path):
    img = io.imread(img_path)
    if np.max(img.shape[:2]) != config.img_size:
        print('Resizing so the max image size is %d..' % config.img_size)
        scale = (float(config.img_size) / np.max(img.shape[:2]))
    else:
        scale = 1.0#scaling_factor
    center = np.round(np.array(img.shape[:2]) / 2).astype(int)
    # image center in (x,y)
    center = center[::-1]
    crop, proc_param = img_util.scale_and_crop(img, scale, center,
                                               config.img_size)
    # import ipdb; ipdb.set_trace()
    # Normalize image to [-1, 1]
    # plt.imshow(crop/255.0)
    # plt.show()
    crop = 2 * ((crop / 255.) - 0.5)

    return crop, proc_param, img


def main(config):
    sess = tf.Session()
    model = RingNet_inference(config, sess=sess)
    input_img, proc_param, img = preprocess_image(config.img_path)
    vertices, flame_parameters = model.predict(np.expand_dims(input_img, axis=0), get_parameters=True)
    cams = flame_parameters[0][:3]
    visualize(img, proc_param, vertices[0], cams, img_name=config.out_folder + '/images/' + config.img_path.split('/')[-1][:-4])

    if config.save_obj_file:
        if not os.path.exists(config.out_folder + '/mesh'):
            os.mkdir(config.out_folder + '/mesh')
        template_mesh = load_model(config.flame_model_path)
        mesh = Mesh(v=vertices[0], f=template_mesh.f)
        mesh.write_obj(config.out_folder + '/mesh/' + config.img_path.split('/')[-1][:-4] + '.obj')

    if config.save_flame_parameters:
        if not os.path.exists(config.out_folder + '/params'):
            os.mkdir(config.out_folder + '/params')
        flame_parameters_ = {'cam':  flame_parameters[0][:3], 'pose': flame_parameters[0][3:3+config.pose_params], 'shape': flame_parameters[0][3+config.pose_params:3+config.pose_params+config.shape_params],
         'expression': flame_parameters[0][3+config.pose_params+config.shape_params:]}
        np.save(config.out_folder + '/params/' + config.img_path.split('/')[-1][:-4] + '.npy', flame_parameters_)


if __name__ == '__main__':
    config = get_config()
    template_mesh = load_model(config.flame_model_path)
    renderer = vis_util.SMPLRenderer(faces=template_mesh.f)

    if not os.path.exists(config.out_folder):
        os.makedirs(config.out_folder)

    if not os.path.exists(config.out_folder + '/images'):
        os.mkdir(config.out_folder + '/images')

    main(config)