File size: 11,011 Bytes
7352753
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
import numpy as np
import gradio as gr
import cv2
from copy import deepcopy
import torch
from torchvision import transforms
from PIL import Image, ImageDraw, ImageFont

from sam.efficient_sam.build_efficient_sam import build_efficient_sam_vits
from src.utils.utils import resize_numpy_image

sam = build_efficient_sam_vits()

def show_point_or_box(image, global_points):
    # for point
    if len(global_points) == 1:
        image = cv2.circle(image, global_points[0], 10, (0, 0, 255), -1)
    # for box
    if len(global_points) == 2:
        p1 = global_points[0]
        p2 = global_points[1]
        image = cv2.rectangle(image,(int(p1[0]),int(p1[1])),(int(p2[0]),int(p2[1])),(0,0,255),2)

    return image
    
def segment_with_points(
    image,
    original_image,
    global_points,
    global_point_label,
    evt: gr.SelectData,
    img_direction,
    save_dir = "./tmp"
):
    if original_image is None:
        original_image = image
    else:
        image = original_image
    if img_direction is None:
        img_direction = original_image
    x, y = evt.index[0], evt.index[1]
    image_path = None
    mask_path = None
    if len(global_points) == 0:
        global_points.append([x, y])
        global_point_label.append(2)
        image_with_point= show_point_or_box(image.copy(), global_points)
        return image_with_point, original_image, None, global_points, global_point_label
    elif len(global_points) == 1:
        global_points.append([x, y])
        global_point_label.append(3)
        x1, y1 = global_points[0]
        x2, y2 = global_points[1]
        if x1 < x2 and y1 >= y2:
            global_points[0][0] = x1
            global_points[0][1] = y2
            global_points[1][0] = x2
            global_points[1][1] = y1
        elif x1 >= x2 and y1 < y2:
            global_points[0][0] = x2
            global_points[0][1] = y1
            global_points[1][0] = x1
            global_points[1][1] = y2
        elif x1 >= x2 and y1 >= y2:
            global_points[0][0] = x2
            global_points[0][1] = y2
            global_points[1][0] = x1
            global_points[1][1] = y1
        image_with_point = show_point_or_box(image.copy(), global_points)
        # data process
        input_point = np.array(global_points)
        input_label = np.array(global_point_label)
        pts_sampled = torch.reshape(torch.tensor(input_point), [1, 1, -1, 2])
        pts_labels = torch.reshape(torch.tensor(input_label), [1, 1, -1])
        img_tensor = transforms.ToTensor()(image)
        # sam
        predicted_logits, predicted_iou = sam(
            img_tensor[None, ...],
            pts_sampled,
            pts_labels,
        )
        mask = torch.ge(predicted_logits[0, 0, 0, :, :], 0).float().cpu().detach().numpy()
        mask_image = (mask*255.).astype(np.uint8)
        return image_with_point, original_image, mask_image, global_points, global_point_label
    else:
        global_points=[[x, y]]
        global_point_label=[2]
        image_with_point= show_point_or_box(image.copy(), global_points)
        return image_with_point, original_image, None, global_points, global_point_label


def segment_with_points_paste(
    image,
    original_image,
    global_points,
    global_point_label,
    image_b,
    evt: gr.SelectData,
    dx, 
    dy, 
    resize_scale

):
    if original_image is None:
        original_image = image
    else:
        image = original_image
    x, y = evt.index[0], evt.index[1]
    if len(global_points) == 0:
        global_points.append([x, y])
        global_point_label.append(2)
        image_with_point= show_point_or_box(image.copy(), global_points)
        return image_with_point, original_image, None, global_points, global_point_label, None
    elif len(global_points) == 1:
        global_points.append([x, y])
        global_point_label.append(3)
        x1, y1 = global_points[0]
        x2, y2 = global_points[1]
        if x1 < x2 and y1 >= y2:
            global_points[0][0] = x1
            global_points[0][1] = y2
            global_points[1][0] = x2
            global_points[1][1] = y1
        elif x1 >= x2 and y1 < y2:
            global_points[0][0] = x2
            global_points[0][1] = y1
            global_points[1][0] = x1
            global_points[1][1] = y2
        elif x1 >= x2 and y1 >= y2:
            global_points[0][0] = x2
            global_points[0][1] = y2
            global_points[1][0] = x1
            global_points[1][1] = y1
        image_with_point = show_point_or_box(image.copy(), global_points)
        # data process
        input_point = np.array(global_points)
        input_label = np.array(global_point_label)
        pts_sampled = torch.reshape(torch.tensor(input_point), [1, 1, -1, 2])
        pts_labels = torch.reshape(torch.tensor(input_label), [1, 1, -1])
        img_tensor = transforms.ToTensor()(image)
        # sam
        predicted_logits, predicted_iou = sam(
            img_tensor[None, ...],
            pts_sampled,
            pts_labels,
        )
        mask = torch.ge(predicted_logits[0, 0, 0, :, :], 0).float().cpu().detach().numpy()
        mask_uint8 = (mask*255.).astype(np.uint8)

        return image_with_point, original_image, paste_with_mask_and_offset(image, image_b, mask_uint8, dx, dy, resize_scale), global_points, global_point_label, mask_uint8
    else:
        global_points=[[x, y]]
        global_point_label=[2]
        image_with_point= show_point_or_box(image.copy(), global_points)
        return image_with_point, original_image, None, global_points, global_point_label, None

def paste_with_mask_and_offset(image_a, image_b, mask, x_offset=0, y_offset=0, delta=1):
    try:
        numpy_mask = np.array(mask)
        y_coords, x_coords = np.nonzero(numpy_mask)  
        x_min = x_coords.min()  
        x_max = x_coords.max()  
        y_min = y_coords.min()  
        y_max = y_coords.max()
        target_center_x = int((x_min + x_max) / 2)
        target_center_y = int((y_min + y_max) / 2)

        image_a = Image.fromarray(image_a)
        image_b = Image.fromarray(image_b)
        mask = Image.fromarray(mask)

        if image_a.size != mask.size:
            mask = mask.resize(image_a.size)

        cropped_image = Image.composite(image_a, Image.new('RGBA', image_a.size, (0, 0, 0, 0)), mask)
        x_b = int(target_center_x * (image_b.width / cropped_image.width))
        y_b = int(target_center_y * (image_b.height / cropped_image.height))
        x_offset = x_offset - int((delta - 1) * x_b)
        y_offset = y_offset - int((delta - 1) * y_b)
        cropped_image = cropped_image.resize(image_b.size)
        new_size = (int(cropped_image.width * delta), int(cropped_image.height * delta))
        cropped_image = cropped_image.resize(new_size)
        image_b.putalpha(128) 
        result_image = Image.new('RGBA', image_b.size, (0, 0, 0, 0))
        result_image.paste(image_b, (0, 0))
        result_image.paste(cropped_image, (x_offset, y_offset), mask=cropped_image)

        return result_image
    except:
        return None

def upload_image_move(img, original_image):
    if original_image is not None:
        return original_image
    else:
        return img

def fun_clear(*args):
    result = []
    for arg in args:
        if isinstance(arg, list):
            result.append([])
        else:
            result.append(None)
    return tuple(result)

def clear_points(img):
    image, mask = img["image"], np.float32(img["mask"][:, :, 0]) / 255.
    if mask.sum() > 0:
        mask = np.uint8(mask > 0)
        masked_img = mask_image(image, 1 - mask, color=[0, 0, 0], alpha=0.3)
    else:
        masked_img = image.copy()

    return [], masked_img

def get_point(img, sel_pix, evt: gr.SelectData):
    sel_pix.append(evt.index)
    points = []
    for idx, point in enumerate(sel_pix):
        if idx % 2 == 0:
            cv2.circle(img, tuple(point), 10, (0, 0, 255), -1)
        else:
            cv2.circle(img, tuple(point), 10, (255, 0, 0), -1)
        points.append(tuple(point))
        if len(points) == 2:
            cv2.arrowedLine(img, points[0], points[1], (255, 255, 255), 4, tipLength=0.5)
            points = []
    return img if isinstance(img, np.ndarray) else np.array(img)

def calculate_translation_percentage(ori_shape, selected_points):
    dx = selected_points[1][0] - selected_points[0][0]
    dy = selected_points[1][1] - selected_points[0][1]
    dx_percentage = dx / ori_shape[1]
    dy_percentage = dy / ori_shape[0]
    
    return dx_percentage, dy_percentage

def get_point_move(original_image, img, sel_pix, evt: gr.SelectData):
    if original_image is not None:
        img = original_image.copy()
    else:
        original_image = img.copy()
    if len(sel_pix)<2:
        sel_pix.append(evt.index)
    else:
        sel_pix = [evt.index]
    points = []
    dx, dy = 0, 0
    for idx, point in enumerate(sel_pix):
        if idx % 2 == 0:
            cv2.circle(img, tuple(point), 10, (0, 0, 255), -1)
        else:
            cv2.circle(img, tuple(point), 10, (255, 0, 0), -1)
        points.append(tuple(point))
        if len(points) == 2:
            cv2.arrowedLine(img, points[0], points[1], (255, 255, 255), 4, tipLength=0.5)
            ori_shape = original_image.shape
            dx, dy = calculate_translation_percentage(original_image.shape, sel_pix)
            points = []
    img = np.array(img)

    return img, original_image, sel_pix, dx, dy

def store_img(img):
    image, mask = img["image"], np.float32(img["mask"][:, :, 0]) / 255.
    if mask.sum() > 0:
        mask = np.uint8(mask > 0)
        masked_img = mask_image(image, 1 - mask, color=[0, 0, 0], alpha=0.3)
    else:
        masked_img = image.copy()

    return image, masked_img, mask

def store_img_move(img, mask=None):
    if mask is not None:
        image = img["image"]
        return image, None, mask
    image, mask = img["image"], np.float32(img["mask"][:, :, 0]) / 255.
    if mask.sum() > 0:
        mask = np.uint8(mask > 0)
        masked_img = mask_image(image, 1 - mask, color=[0, 0, 0], alpha=0.3)
    else:
        masked_img = image.copy()

    return image, masked_img, (mask*255.).astype(np.uint8)

def mask_image(image, mask, color=[255,0,0], alpha=0.5, max_resolution=None):
    """ Overlay mask on image for visualization purpose. 
    Args:
        image (H, W, 3) or (H, W): input image
        mask (H, W): mask to be overlaid
        color: the color of overlaid mask
        alpha: the transparency of the mask
    """
    if max_resolution is not None:
        image, _ = resize_numpy_image(image, max_resolution*max_resolution)
        mask = cv2.resize(mask, (image.shape[1], image.shape[0]),interpolation=cv2.INTER_NEAREST)

    out = deepcopy(image)
    img = deepcopy(image)
    img[mask == 1] = color
    out = cv2.addWeighted(img, alpha, out, 1-alpha, 0, out)
    contours = cv2.findContours(np.uint8(deepcopy(mask)), cv2.RETR_TREE, 
                        cv2.CHAIN_APPROX_SIMPLE)[-2:]
    return out