-
Notifications
You must be signed in to change notification settings - Fork 13
/
utils_ootd.py
235 lines (206 loc) · 11.6 KB
/
utils_ootd.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
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
import pdb
import numpy as np
import cv2
from PIL import Image, ImageDraw
label_map = {
"background": 0,
"hat": 1,
"hair": 2,
"sunglasses": 3,
"upper_clothes": 4,
"skirt": 5,
"pants": 6,
"dress": 7,
"belt": 8,
"left_shoe": 9,
"right_shoe": 10,
"head": 11,
"left_leg": 12,
"right_leg": 13,
"left_arm": 14,
"right_arm": 15,
"bag": 16,
"scarf": 17,
}
def extend_arm_mask(wrist, elbow, scale):
wrist = elbow + scale * (wrist - elbow)
return wrist
def hole_fill(img):
img = np.pad(img[1:-1, 1:-1], pad_width = 1, mode = 'constant', constant_values=0)
img_copy = img.copy()
mask = np.zeros((img.shape[0] + 2, img.shape[1] + 2), dtype=np.uint8)
cv2.floodFill(img, mask, (0, 0), 255)
img_inverse = cv2.bitwise_not(img)
dst = cv2.bitwise_or(img_copy, img_inverse)
return dst
def refine_mask(mask):
contours, hierarchy = cv2.findContours(mask.astype(np.uint8),
cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)
area = []
for j in range(len(contours)):
a_d = cv2.contourArea(contours[j], True)
area.append(abs(a_d))
refine_mask = np.zeros_like(mask).astype(np.uint8)
if len(area) != 0:
i = area.index(max(area))
cv2.drawContours(refine_mask, contours, i, color=255, thickness=-1)
return refine_mask
def get_mask_location(model_type, category, model_parse: Image.Image, keypoint: dict, width=384,height=512):
im_parse = model_parse.resize((width, height), Image.NEAREST)
parse_array = np.array(im_parse)
if model_type == 'hd':
arm_width = 60
elif model_type == 'dc':
arm_width = 45
else:
raise ValueError("model_type must be \'hd\' or \'dc\'!")
parse_head = (parse_array == 1).astype(np.float32) + \
(parse_array == 3).astype(np.float32) + \
(parse_array == 11).astype(np.float32)
parser_mask_fixed = (parse_array == label_map["left_shoe"]).astype(np.float32) + \
(parse_array == label_map["right_shoe"]).astype(np.float32) + \
(parse_array == label_map["hat"]).astype(np.float32) + \
(parse_array == label_map["sunglasses"]).astype(np.float32) + \
(parse_array == label_map["bag"]).astype(np.float32)
# (parse_array == label_map["scarf"]).astype(np.float32) + \
parser_mask_changeable = (parse_array == label_map["background"]).astype(np.float32)
arms_left = (parse_array == 14).astype(np.float32)
arms_right = (parse_array == 15).astype(np.float32)
arms = arms_left + arms_right
if category == 'dresses':
# 裙子加腿 mask
parse_mask = (parse_array == 7).astype(np.float32) + \
(parse_array == 4).astype(np.float32) + \
(parse_array == 5).astype(np.float32) + \
(parse_array == 6).astype(np.float32)
# (parse_array == 12).astype(np.float32) + \
# (parse_array == 13).astype(np.float32) + \
# 除fixed的区域，都是parser_mask_changeable
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'upper_body':
# 上衣mask
parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
# 下半身衣物fixed
parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
(parse_array == label_map["pants"]).astype(np.float32)
# parser_mask_fixed_lower_cloth = cv2.erode(parser_mask_fixed_lower_cloth, np.ones((5, 5), np.uint16))
parser_mask_fixed += parser_mask_fixed_lower_cloth
# 除fixed的区域，都是parser_mask_changeable
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'lower_body':
# 裤子加腿 mask
parse_mask = (parse_array == 6).astype(np.float32) + \
(parse_array == 12).astype(np.float32) + \
(parse_array == 13).astype(np.float32) + \
(parse_array == 5).astype(np.float32)
# 手臂fixed
parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
(parse_array == 14).astype(np.float32) + \
(parse_array == 15).astype(np.float32)
# 除fixed的区域，都是parser_mask_changeable
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
else:
raise NotImplementedError
# Load pose points
pose_data = keypoint["pose_keypoints_2d"]
pose_data = np.array(pose_data)
pose_data = pose_data.reshape((-1, 2))
im_arms_left = Image.new('L', (width, height))
im_arms_right = Image.new('L', (width, height))
arms_draw_left = ImageDraw.Draw(im_arms_left)
arms_draw_right = ImageDraw.Draw(im_arms_right)
if category == 'dresses' or category == 'upper_body':
shoulder_right = np.multiply(tuple(pose_data[2][:2]), height / 512.0)
shoulder_left = np.multiply(tuple(pose_data[5][:2]), height / 512.0)
elbow_right = np.multiply(tuple(pose_data[3][:2]), height / 512.0)
elbow_left = np.multiply(tuple(pose_data[6][:2]), height / 512.0)
wrist_right = np.multiply(tuple(pose_data[4][:2]), height / 512.0)
wrist_left = np.multiply(tuple(pose_data[7][:2]), height / 512.0)
ARM_LINE_WIDTH = int(arm_width / 512 * height)
size_left = [shoulder_left[0] - ARM_LINE_WIDTH // 2, shoulder_left[1] - ARM_LINE_WIDTH // 2, shoulder_left[0] + ARM_LINE_WIDTH // 2, shoulder_left[1] + ARM_LINE_WIDTH // 2]
size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2,
shoulder_right[1] + ARM_LINE_WIDTH // 2]
if wrist_right[0] <= 1. and wrist_right[1] <= 1.:
if elbow_right[0] <= 1. and elbow_right[1] <= 1.:
# arms_draw_left.line(
# np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white',
# 60, 'curve')
# arms_draw_left.arc(size_left, 0, 360, 'white', 30)
im_arms_right = arms_right
else:
# arms_draw_left.line(
# np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white',
# 60, 'curve')
# arms_draw_left.arc(size_left, 0, 360, 'white', 30)
im_arms_right = arms_right
# arms_draw_right.line(np.concatenate((shoulder_right, elbow_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
# arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH//2)
else:
wrist_right = extend_arm_mask(wrist_right, elbow_right, 1.2)
arms_draw_right.line(np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
arms_draw_right.arc(size_right, 0, 360, 'white', ARM_LINE_WIDTH // 2)
if wrist_left[0] <= 1. and wrist_left[1] <= 1.:
if elbow_left[0] <= 1. and elbow_left[1] <= 1.:
# arms_draw_right.line(
# np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(),
# 'white', 60, 'curve')
# arms_draw_right.arc(size_right, 0, 360, 'white', 30)
im_arms_left = arms_left
else:
# arms_draw_left.line(np.concatenate((elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
im_arms_left = arms_left
# arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH//2)
# arms_draw_right.line(
# np.concatenate((shoulder_right, elbow_right, wrist_right)).astype(np.uint16).tolist(),
# 'white', 60, 'curve')
# arms_draw_right.arc(size_right, 0, 360, 'white', 30)
else:
# extend arm scale 1.2
wrist_left = extend_arm_mask(wrist_left, elbow_left, 1.2)
arms_draw_left.line(np.concatenate((wrist_left, elbow_left, shoulder_left)).astype(np.uint16).tolist(), 'white', ARM_LINE_WIDTH, 'curve')
arms_draw_left.arc(size_left, 0, 360, 'white', ARM_LINE_WIDTH // 2)
# im_arms_left.save("leftarm.jpg")
# im_arms_right.save("rightarm.jpg")
hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left)
# hands_left_refine = refine_mask(hands_left)
# im_arms_left += np.logical_and(np.logical_not(hands_left_refine), hands_left)
# Image.fromarray(((hands_left > 0) * 127.5 + 127.5).astype(np.uint8)).save("hands_left.jpg")
hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right)
# hands_right_refine = refine_mask(hands_right)
# im_arms_right += np.logical_and(np.logical_not(hands_right_refine), hands_right)
# Image.fromarray(((hands_right > 0) * 127.5 + 127.5).astype(np.uint8)).save("hands_right.jpg")
# Image.fromarray(((parser_mask_fixed > 0) * 127.5 + 127.5).astype(np.uint8)).save("fixed_before.jpg")
parser_mask_fixed += hands_left + hands_right
# Image.fromarray(((parser_mask_fixed > 0) * 127.5 + 127.5).astype(np.uint8)).save("fixed_after.jpg")
# parser_mask_fixed 加入脸部和双肩以上脖子
parser_mask_fixed = np.logical_or(parser_mask_fixed, parse_head)
parse_mask = cv2.dilate(parse_mask, np.ones((5, 5), np.uint16), iterations=5)
if category == 'dresses' or category == 'upper_body':
neck_mask = (parse_array == 18).astype(np.float32)
# Image.fromarray(((parse_mask > 0) * 127.5 + 127.5).astype(np.uint8)).save("mask_before.jpg")
neck_mask = cv2.dilate(neck_mask, np.ones((5, 5), np.uint16), iterations=1)
neck_mask = np.logical_and(neck_mask, np.logical_not(parse_head))
parse_mask = np.logical_or(parse_mask, neck_mask)
# Image.fromarray(((parse_mask > 0) * 127.5 + 127.5).astype(np.uint8)).save("mask_after.jpg")
# 单独控制手臂部分大小
arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4)
# Image.fromarray(((arm_mask > 0) * 127.5 + 127.5).astype(np.uint8)).save("arm_mask.jpg")
parse_mask += np.logical_or(parse_mask, arm_mask)
# pdb.set_trace()
# 除了fix（hands排除）就是changeable，not parse_mask 除了要mask的 其余置1（除了fixed区域与mask区域，其余都为1）
# Image.fromarray(((parse_mask > 0) * 127.5 + 127.5).astype(np.uint8)).save("mask_addarm.jpg")
parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask))
# parser_mask_fixed + parse_mask 结合，把fixed加入，只剩要mask区域（若有侵蚀到fixed的操作也会被fixed重新取回）
# Image.fromarray(((parser_mask_fixed > 0).cpu().numpy() * 127.5 + 127.5).astype(np.uint8)).save("mask_fixed.jpg")
parse_mask_total = np.logical_or(parse_mask, parser_mask_fixed)
inpaint_mask = 1 - parse_mask_total
img = np.where(inpaint_mask, 255, 0)
dst = hole_fill(img.astype(np.uint8))
dst = refine_mask(dst)
inpaint_mask = dst / 255 * 1
mask = Image.fromarray(inpaint_mask.astype(np.uint8) * 255)
mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
# print("save")
# mask.save("mask.png")
# mask_gray.save("mask_gray.png")
return mask, mask_gray