File size: 7,673 Bytes
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
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)

    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