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test_image.py
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test_image.py
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from __future__ import division
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import sys
from shutil import copyfile
# tf
import numpy as np
import tensorflow as tf
import torch
# save result
import face_alignment
import cv2
import PIL.Image as pil
import matplotlib.pyplot as plt
import trimesh
# path
_curr_path = os.path.abspath(__file__) # /home/..../face
_cur_dir = os.path.dirname(_curr_path) # ./
# save result
from src_common.common.face_io import write_self_camera, write_self_lm
from tools.preprocess.detect_landmark import LM_detector_howfar
from tools.preprocess.crop_image_affine import *
# graph
from src_tfGraph.build_graph import MGC_TRAIN
flags = tf.app.flags
#
flags.DEFINE_string("dic_image", "data/test/", "Dataset directory")
flags.DEFINE_string("output_dir", "data/output_test_one", "Output directory")
flags.DEFINE_string("ckpt_file", "model/model-400000", "checkpoint file")
#flags.DEFINE_string("ckpt_file", "/home/jiaxiangshang/Downloads/202008/70_31_warpdepthepi_reg/model-400000", "checkpoint file")
#
flags.DEFINE_integer("batch_size", 1, "The size of of a sample batch")
flags.DEFINE_integer("img_width", 224, "Image(square) size")
flags.DEFINE_integer("img_height", 224, "Image(square) size")
# gpmm
flags.DEFINE_string("path_gpmm", "model/bfm09_trim_exp_uv_presplit.h5", "Dataset directory")
flags.DEFINE_integer("light_rank", 27, "3DMM coeffient rank")
flags.DEFINE_integer("gpmm_rank", 80, "3DMM coeffient rank")
flags.DEFINE_integer("gpmm_exp_rank", 64, "3DMM coeffient rank")
#
flags.DEFINE_boolean("flag_eval", True, "3DMM coeffient rank")
flags.DEFINE_boolean("flag_visual", True, "")
flags.DEFINE_boolean("flag_fore", False, "")
# visual
flags.DEFINE_boolean("flag_overlay_save", True, "")
flags.DEFINE_boolean("flag_overlayOrigin_save", True, "")
flags.DEFINE_boolean("flag_main_save", True, "")
FLAGS = flags.FLAGS
if __name__ == '__main__':
FLAGS.dic_image = os.path.join(_cur_dir, FLAGS.dic_image)
FLAGS.output_dir = os.path.join(_cur_dir, FLAGS.output_dir)
FLAGS.ckpt_file = os.path.join(_cur_dir, FLAGS.ckpt_file)
FLAGS.path_gpmm = os.path.join(_cur_dir, FLAGS.path_gpmm)
if not os.path.exists(FLAGS.dic_image):
print("Error: no dataset_dir found")
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
print("Finish copy")
"""
preprocess
"""
lm_d_hf = LM_detector_howfar(lm_type=int(3), device='cpu', face_detector='sfd')
"""
build graph
"""
system = MGC_TRAIN(FLAGS)
system.build_test_graph(
FLAGS, img_height=FLAGS.img_height, img_width=FLAGS.img_width, batch_size=FLAGS.batch_size
)
"""
load model
"""
test_var = tf.global_variables()#tf.model_variables()
# this because we need using the
test_var = [tv for tv in test_var if tv.op.name.find('VertexNormalsPreSplit') == -1]
saver = tf.train.Saver([var for var in test_var])
#config = tf.ConfigProto()
config=tf.ConfigProto(device_count={'cpu':0})
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.graph.finalize()
saver.restore(sess, FLAGS.ckpt_file)
#
import time
# preprocess
path_image = os.path.join(FLAGS.dic_image, 'image04275.jpg')
image_bgr = cv2.imread(path_image)
image_rgb = image_bgr[..., ::-1]
if image_bgr is None:
print("Error: can not find ", path_image)
with torch.no_grad():
lm_howfar = lm_d_hf.lm_detection_howfar(image_bgr)
lm_howfar = lm_howfar[:, :2]
# face image align by landmark
# we also provide a tools to generate 'std_224_bfm09'
lm_trans, img_warped, tform = crop_align_affine_transform(lm_howfar, image_rgb, FLAGS.img_height, std_224_bfm09)
image_rgb_b = img_warped[None, ...]
# M_inv is used to back project the face reconstruction result to origin image
M_inv = np.linalg.inv(tform.params)
M = tform.params
#print(np.matmul(M_inv, M))
"""
Start
"""
time_st = time.time()
pred = system.inference(sess, image_rgb_b)
time_end = time.time()
print("Time each batch: ", time_end - time_st)
# name
dic_image, name_image = os.path.split(path_image)
name_image_pure, _ = os.path.splitext(name_image)
"""
Render
"""
image_input = image_rgb_b
"""
NP
"""
b = 0
vertex_shape = pred['vertex_shape'][0][b, :, :]
vertex_color = pred['vertex_color'][0][b, :, :]
vertex_color = np.clip(vertex_color, 0, 1)
#vertex_color_rgba = np.concatenate([vertex_color, np.ones([vertex_color.shape[0], 1])], axis=1)
vertex_color_ori = pred['vertex_color_ori'][0][b, :, :]
vertex_color_ori = np.clip(vertex_color_ori, 0, 1)
if FLAGS.flag_eval:
mesh_tri = trimesh.Trimesh(
vertex_shape.reshape(-1, 3),
system.h_lrgp.h_curr.mesh_tri_np.reshape(-1, 3),
vertex_colors=vertex_color.reshape(-1, 3),
process=False
)
mesh_tri.visual.kind == 'vertex'
path_mesh_save = os.path.join(FLAGS.output_dir, name_image_pure + ".ply")
mesh_tri.export(path_mesh_save)
"""
Landmark 3D
"""
path_lm3d_save = os.path.join(FLAGS.output_dir, name_image_pure + "_lm3d.txt")
lm_68 = vertex_shape[system.h_lrgp.h_curr.idx_lm68_np]
write_self_lm(path_lm3d_save, lm_68)
"""
Landmark 2D
"""
lm2d = pred['lm2d'][0][b, :, :]
path_lm2d_save = os.path.join(FLAGS.output_dir, name_image_pure + "_lm2d.txt")
write_self_lm(path_lm2d_save, lm2d)
"""
Pose
"""
path_cam_save = os.path.join(FLAGS.output_dir, name_image_pure + "_cam.txt")
pose = pred['gpmm_pose'][0][b, :]
intrinsic = pred['gpmm_intrinsic'][b, :, :]
write_self_camera(path_cam_save, FLAGS.img_width, FLAGS.img_height, intrinsic, pose)
"""
Common visual
"""
if FLAGS.flag_visual:
# visual
result_overlayMain_255 = pred['overlayMain_255'][0][b, :, :]
result_overlayTexMain_255 = pred['overlayTexMain_255'][0][b, :, :]
result_overlayGeoMain_255 = pred['overlayGeoMain_255'][0][b, :, :]
result_overlayLightMain_255 = pred['overlayLightMain_255'][0][b, :, :]
result_apper_mulPose_255 = pred['apper_mulPose_255'][0][b, :, :]
result_overlay_255 = pred['overlay_255'][0][b, :, :]
result_overlayTex_255 = pred['overlayTex_255'][0][b, :, :]
result_overlayGeo_255 = pred['overlayGeo_255'][0][b, :, :]
result_overlayLight_255 = pred['overlayLight_255'][0][b, :, :]
# common
visual_concat = np.concatenate([image_input[0], result_overlay_255, result_overlayGeo_255, result_apper_mulPose_255], axis=1)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_mulPoses.jpg")
plt.imsave(path_image_save, visual_concat)
if FLAGS.flag_overlayOrigin_save:
gpmm_render_mask = pred['gpmm_render_mask'][0][b, :, :]
gpmm_render_mask = np.tile(gpmm_render_mask, reps=(1, 1, 3))
path_image_origin = os.path.join(dic_image, name_image_pure + ".jpg")
image_origin = cv2.imread(path_image_origin)
gpmm_render_overlay_wo = inverse_affine_warp_overlay(
M_inv, image_origin, result_overlay_255, gpmm_render_mask)
gpmm_render_overlay_texture_wo = inverse_affine_warp_overlay(
M_inv, image_origin, result_overlayTex_255, gpmm_render_mask)
gpmm_render_overlay_gary_wo = inverse_affine_warp_overlay(
M_inv, image_origin, result_overlayGeo_255, gpmm_render_mask)
gpmm_render_overlay_illu_wo = inverse_affine_warp_overlay(
M_inv, image_origin, result_overlayLight_255, gpmm_render_mask)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayOrigin.jpg")
cv2.imwrite(path_image_save, gpmm_render_overlay_wo)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayTexOrigin.jpg")
# cv2.imwrite(path_image_save, gpmm_render_overlay_texture_wo)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayGeoOrigin.jpg")
cv2.imwrite(path_image_save, gpmm_render_overlay_gary_wo)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayLightOrigin.jpg")
# cv2.imwrite(path_image_save, gpmm_render_overlay_illu_wo)
if FLAGS.flag_main_save:
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayMain.jpg")
plt.imsave(path_image_save, result_overlayMain_255)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayTexMain.jpg")
#plt.imsave(path_image_gray_main_overlay, gpmm_render_overlay)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayGeoMain.jpg")
plt.imsave(path_image_save, result_overlayGeoMain_255)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayLightMain.jpg")
#cv2.imwrite(path_image_save, result_overlayLightMain_255)
if FLAGS.flag_overlay_save:
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlay.jpg")
plt.imsave(path_image_save, result_overlay_255)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayTex.jpg")
plt.imsave(path_image_save, result_overlayTex_255)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayGeo.jpg")
plt.imsave(path_image_save, result_overlayGeo_255)
path_image_save = os.path.join(FLAGS.output_dir, name_image_pure + "_overlayLight.jpg")
plt.imsave(path_image_save, result_overlayLight_255)