-
Notifications
You must be signed in to change notification settings - Fork 60
/
ootd_utils.py
168 lines (143 loc) · 7.48 KB
/
ootd_utils.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
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)
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':
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)
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'upper_body':
parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
(parse_array == label_map["pants"]).astype(np.float32)
parser_mask_fixed += parser_mask_fixed_lower_cloth
parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
elif category == 'lower_body':
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)
parser_mask_fixed += (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
(parse_array == 14).astype(np.float32) + \
(parse_array == 15).astype(np.float32)
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.:
im_arms_right = arms_right
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.:
im_arms_left = arms_left
else:
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)
hands_left = np.logical_and(np.logical_not(im_arms_left), arms_left)
hands_right = np.logical_and(np.logical_not(im_arms_right), arms_right)
parser_mask_fixed += hands_left + hands_right
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)
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)
arm_mask = cv2.dilate(np.logical_or(im_arms_left, im_arms_right).astype('float32'), np.ones((5, 5), np.uint16), iterations=4)
parse_mask += np.logical_or(parse_mask, arm_mask)
parse_mask = np.logical_and(parser_mask_changeable, np.logical_not(parse_mask))
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)
return mask, mask_gray