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import sys
from facewarp.gen_puppet_utils import *
''' ================================================
FOA face landmark detection
================================================ '''
data_dir = out_dir = 'examples_cartoon'
test_data = sys.argv[1] # for example 'roy_example.png'
CH = test_data[:-4]
use_gt_bb = False
if(not os.path.exists(os.path.join(data_dir, CH + '.pts'))):
from thirdparty.face_of_art.menpo_functions import *
from thirdparty.face_of_art.deep_heatmaps_model_fusion_net import DeepHeatmapsModel
model_path = 'MakeItTalk/examples/ckpt/deep_heatmaps-60000' # model for estimation stage
pdm_path = 'thirdparty/face_of_art/pdm_clm_models/pdm_models/' # models for correction stage
clm_path = 'thirdparty/face_of_art/pdm_clm_models/clm_models/g_t_all' # model for tuning stage
outline_tune = True # if true use tuning stage on eyebrows+jaw, else use tuning stage on jaw only
map_landmarks_to_original_image = True # if True, landmark predictions will be mapped to match original
# input image size. otherwise the predicted landmarks will match the cropped version (256x256) of the images
# load images
bb_dir = os.path.join(data_dir, 'Bounding_Boxes')
bb_dictionary = load_bb_dictionary(bb_dir, mode='TEST', test_data=test_data)
bb_type = 'init'
img_list = load_menpo_image_list(
img_dir=data_dir, test_data=test_data, train_crop_dir=data_dir, img_dir_ns=data_dir, bb_type=bb_type,
bb_dictionary=bb_dictionary, mode='TEST', return_transform=map_landmarks_to_original_image)
# load model
heatmap_model = DeepHeatmapsModel(
mode='TEST', img_path=data_dir, test_model_path=model_path, test_data=test_data, menpo_verbose=False)
print ("\npredicting landmarks for: "+os.path.join(data_dir, test_data))
print ("\nsaving landmarks to: "+out_dir)
for i, img in enumerate(img_list):
if i == 0:
reuse = None
else:
reuse = True
preds = heatmap_model.get_landmark_predictions(img_list=[img], pdm_models_dir=pdm_path, clm_model_path=clm_path,
reuse=reuse, map_to_input_size=map_landmarks_to_original_image)
if map_landmarks_to_original_image:
img = img[0]
if outline_tune:
pred_lms = preds['ECpTp_out']
else:
pred_lms = preds['ECpTp_jaw']
mio.export_landmark_file(PointCloud(pred_lms[0]), os.path.join(out_dir, img.path.stem + '.pts'),
overwrite=True)
print ("\nFOA landmark detection DONE!")
''' ====================================================================
opencv vis and refine landmark
1. visualize the automatic detection result from FOA approach
2. click on landmarks and move them if they are not correct
Press Q to save landmarks and continue.
==================================================================== '''
import cv2
import numpy as np
import os
if(os.path.exists(os.path.join(data_dir, CH + '_face_open_mouth.txt'))):
pts0 = np.loadtxt(os.path.join(data_dir, CH + '_face_open_mouth.txt'))
pts0 = pts0[:, 0:2]
else:
f = open(os.path.join(data_dir, test_data[:-4] + '.pts'), 'r')
lines = f.readlines()
pts = []
for i in range(3, 3+68):
line = lines[i]
line = line[:-1].split(' ')
pts += [float(item) for item in line]
pts0 = np.array(pts).reshape((68, 2))
pts = np.copy(pts0)
img0 = cv2.imread(os.path.join(data_dir, test_data))
img = np.copy(img0)
node = -1
def click_adjust_wireframe(event, x, y, flags, param):
global img, pts, node
def update_img(node, button_up=False):
global img, pts
# update carton points object and get fresh pts list
pts[node, 0], pts[node, 1] = x, y
img = np.copy(img0)
draw_landmarks(img, pts)
# zoom-in feature
if (not button_up):
zoom_in_scale = 2
zoom_in_box_size = int(150 / zoom_in_scale)
zoom_in_range = int(np.min([zoom_in_box_size, x, y,
(img.shape[0] - y) / 2 / zoom_in_scale,
(img.shape[1] - x) / 2 / zoom_in_scale]))
img_zoom_in = img[y - zoom_in_range:y + zoom_in_range,
x - zoom_in_range:x + zoom_in_range].copy()
img_zoom_in = cv2.resize(img_zoom_in, (0, 0), fx=zoom_in_scale,
fy=zoom_in_scale)
cv2.drawMarker(img_zoom_in, (zoom_in_range * zoom_in_scale,
zoom_in_range * zoom_in_scale),
(0, 0, 255),
markerType=cv2.MARKER_CROSS, markerSize=30,
thickness=2, line_type=cv2.LINE_AA)
height, width, depth = np.shape(img_zoom_in)
img[y:y + height, x:x + width] = img_zoom_in
cv2.rectangle(img, (x, y), (x + height, y + width),
(0, 0, 255), thickness=2)
if event == cv2.EVENT_LBUTTONDOWN:
# search for nearest point
node = closest_node((x, y), pts)
if(node >=0):
update_img(node)
if event == cv2.EVENT_LBUTTONUP:
node = closest_node((x, y), pts)
if (node >= 0):
update_img(node, button_up=True)
node = -1
if event == cv2.EVENT_MOUSEMOVE:
# redraw figure
if (node != -1):
update_img(node)
draw_landmarks(img, pts)
cv2.namedWindow("img", cv2.WINDOW_NORMAL)
cv2.setMouseCallback("img", click_adjust_wireframe)
while(True):
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key == ord("q"):
break
cv2.destroyAllWindows()
print('vis and refine landmark Done!')
pts = np.concatenate([pts, np.ones((68, 1))], axis=1)
np.savetxt(os.path.join(data_dir, '{}_face_open_mouth.txt'.format(CH)), pts, fmt='%.4f')
''' =================================================================
find closed mouth landmark and normalize
Input: param are used to change closed mouth strength
param[0]: larger -> outer-upper lip higher
param[1]: larger -> outer-lower lip higher
param[2]: larger -> inner-upper lip higher
param[3]: larger -> inner-lower lip higher
Output: saved as CH_face_open_mouth_norm.txt
CH_scale_shift.txt
CH_face_close_mouth.txt
Press Q or close the image window to continue.
================================================================= '''
norm_anno(data_dir, CH, param=[0.7, 0.4, 0.5, 0.5], show=True)
''' =================================================================
delauney tri
Input: INNER_ONLY indicates whether use the inner lip landmarks only
Output: saved as CH_delauney_tri.txt
Press any key to continue.
================================================================= '''
delauney_tri(data_dir, test_data, INNER_ONLY=False)
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