forked from adobe-research/MakeItTalk
-
Notifications
You must be signed in to change notification settings - Fork 219
/
main_gen_new_puppet.py
198 lines (145 loc) · 6.9 KB
/
main_gen_new_puppet.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
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 = '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)