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spaces demo
Browse files- LICENSE +201 -0
- app.py +48 -0
- demo_MLSD.py +275 -0
- requirements.txt +6 -0
- static/css/app.css +11 -0
- static/favicon.ico +0 -0
- templates/index_scan.html +128 -0
- tflite_models/M-LSD_320_large_fp16.tflite +0 -0
- tflite_models/M-LSD_320_large_fp32.tflite +0 -0
- tflite_models/M-LSD_320_tiny_fp16.tflite +0 -0
- tflite_models/M-LSD_320_tiny_fp32.tflite +0 -0
- tflite_models/M-LSD_512_large_fp16.tflite +0 -0
- tflite_models/M-LSD_512_large_fp32.tflite +0 -0
- tflite_models/M-LSD_512_tiny_fp16.tflite +0 -0
- tflite_models/M-LSD_512_tiny_fp32.tflite +0 -0
- utils.py +511 -0
LICENSE
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app.py
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from PIL import Image
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import cv2
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import numpy as np
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import tensorflow as tf
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from utils import pred_lines, pred_squares
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import gradio as gr
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from urllib.request import urlretrieve
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# Load MLSD 512 Large FP32 tflite
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model_name = 'tflite_models/M-LSD_512_large_fp32.tflite'
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interpreter = tf.lite.Interpreter(model_path=model_name)
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interpreter.allocate_tensors()
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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def gradio_wrapper_for_LSD(img_input, score_thr, dist_thr):
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lines = pred_lines(img_input, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=score_thr, dist_thr=dist_thr)
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img_output = img_input.copy()
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# draw lines
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for line in lines:
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x_start, y_start, x_end, y_end = [int(val) for val in line]
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cv2.line(img_output, (x_start, y_start), (x_end, y_end), [0,255,255], 2)
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return img_output
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urlretrieve("https://www.digsdigs.com/photos/2015/05/a-bold-minimalist-living-room-with-dark-stained-wood-geometric-touches-a-sectional-sofa-and-built-in-lights-for-a-futuristic-feel.jpg","example1.jpg")
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urlretrieve("https://specials-images.forbesimg.com/imageserve/5dfe2e6925ab5d0007cefda5/960x0.jpg","example2.jpg")
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urlretrieve("https://images.livspace-cdn.com/w:768/h:651/plain/https://jumanji.livspace-cdn.com/magazine/wp-content/uploads/2015/11/27170345/atr-1-a-e1577187047515.jpeg","example3.jpg")
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sample_images = [["example1.jpg", 0.2, 10.0], ["example2.jpg", 0.2, 10.0], ["example3.jpg", 0.2, 10.0]]
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iface = gr.Interface(gradio_wrapper_for_LSD,
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["image",
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gr.inputs.Number(default=0.2, label='score_thr (0.0 ~ 1.0)'),
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gr.inputs.Number(default=10.0, label='dist_thr (0.0 ~ 20.0)')
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],
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"image",
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title="Line segment detection with Mobile LSD (M-LSD)",
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description="M-LSD is a light-weight and real-time deep line segment detector, which can run on GPU, CPU, and even on Mobile devices. Try it by uploading an image or clicking on an example. Read more at the links below",
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article="<p style='text-align: center'><a href='https://arxiv.org/abs/2106.00186'>Towards Real-time and Light-weight Line Segment Detection</a> | <a href='https://github.com/navervision/mlsd'>Github Repo</a></p>",
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examples=sample_images,
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allow_screenshot=True)
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iface.launch()
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demo_MLSD.py
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|
|
|
1 |
+
'''
|
2 |
+
M-LSD
|
3 |
+
Copyright 2021-present NAVER Corp.
|
4 |
+
Apache License v2.0
|
5 |
+
'''
|
6 |
+
# for demo
|
7 |
+
import os
|
8 |
+
from flask import Flask, request, session, json, Response, render_template, abort, send_from_directory
|
9 |
+
import requests
|
10 |
+
from urllib.request import urlopen
|
11 |
+
from io import BytesIO
|
12 |
+
import uuid
|
13 |
+
import cv2
|
14 |
+
import time
|
15 |
+
import argparse
|
16 |
+
|
17 |
+
# for tflite
|
18 |
+
import numpy as np
|
19 |
+
from PIL import Image
|
20 |
+
import tensorflow as tf
|
21 |
+
|
22 |
+
# for square detector
|
23 |
+
from utils import pred_squares
|
24 |
+
|
25 |
+
os.environ['CUDA_VISIBLE_DEVICES'] = '' # CPU mode
|
26 |
+
|
27 |
+
# flask
|
28 |
+
app = Flask(__name__)
|
29 |
+
logger = app.logger
|
30 |
+
logger.info('init demo app')
|
31 |
+
|
32 |
+
# config
|
33 |
+
parser = argparse.ArgumentParser()
|
34 |
+
|
35 |
+
## model parameters
|
36 |
+
parser.add_argument('--tflite_path', default='./tflite_models/M-LSD_512_large_fp16.tflite', type=str)
|
37 |
+
parser.add_argument('--input_size', default=512, type=int,
|
38 |
+
help='The size of input images.')
|
39 |
+
|
40 |
+
## LSD parameter
|
41 |
+
parser.add_argument('--score_thr', default=0.10, type=float,
|
42 |
+
help='Discard center points when the score < score_thr.')
|
43 |
+
|
44 |
+
## intersection point parameters
|
45 |
+
parser.add_argument('--outside_ratio', default=0.10, type=float,
|
46 |
+
help='''Discard an intersection point
|
47 |
+
when it is located outside a line segment farther than line_length * outside_ratio.''')
|
48 |
+
parser.add_argument('--inside_ratio', default=0.50, type=float,
|
49 |
+
help='''Discard an intersection point
|
50 |
+
when it is located inside a line segment farther than line_length * inside_ratio.''')
|
51 |
+
|
52 |
+
## ranking boxes parameters
|
53 |
+
parser.add_argument('--w_overlap', default=0.0, type=float,
|
54 |
+
help='''When increasing w_overlap, the final box tends to overlap with
|
55 |
+
the detected line segments as much as possible.''')
|
56 |
+
parser.add_argument('--w_degree', default=1.14, type=float,
|
57 |
+
help='''When increasing w_degree, the final box tends to be
|
58 |
+
a parallel quadrilateral with reference to the angle of the box.''')
|
59 |
+
parser.add_argument('--w_length', default=0.03, type=float,
|
60 |
+
help='''When increasing w_length, the final box tends to be
|
61 |
+
a parallel quadrilateral with reference to the length of the box.''')
|
62 |
+
parser.add_argument('--w_area', default=1.84, type=float,
|
63 |
+
help='When increasing w_area, the final box tends to be the largest one out of candidates.')
|
64 |
+
parser.add_argument('--w_center', default=1.46, type=float,
|
65 |
+
help='When increasing w_center, the final box tends to be located in the center of input image.')
|
66 |
+
|
67 |
+
## flask demo parameter
|
68 |
+
parser.add_argument('--port', default=5000, type=int,
|
69 |
+
help='flask demo will be running on http://0.0.0.0:port/')
|
70 |
+
|
71 |
+
|
72 |
+
class model_graph:
|
73 |
+
def __init__(self, args):
|
74 |
+
self.interpreter, self.input_details, self.output_details = self.load_tflite(args.tflite_path)
|
75 |
+
self.params = {'score': args.score_thr,'outside_ratio': args.outside_ratio,'inside_ratio': args.inside_ratio,
|
76 |
+
'w_overlap': args.w_overlap,'w_degree': args.w_degree,'w_length': args.w_length,
|
77 |
+
'w_area': args.w_area,'w_center': args.w_center}
|
78 |
+
self.args = args
|
79 |
+
|
80 |
+
|
81 |
+
def load_tflite(self, tflite_path):
|
82 |
+
interpreter = tf.lite.Interpreter(model_path=tflite_path)
|
83 |
+
interpreter.allocate_tensors()
|
84 |
+
input_details = interpreter.get_input_details()
|
85 |
+
output_details = interpreter.get_output_details()
|
86 |
+
|
87 |
+
return interpreter, input_details, output_details
|
88 |
+
|
89 |
+
|
90 |
+
def pred_tflite(self, image):
|
91 |
+
segments, squares, score_array, inter_points = pred_squares(image, self.interpreter, self.input_details, self.output_details, [self.args.input_size, self.args.input_size], params=self.params)
|
92 |
+
|
93 |
+
output = {}
|
94 |
+
output['segments'] = segments
|
95 |
+
output['squares'] = squares
|
96 |
+
output['scores'] = score_array
|
97 |
+
output['inter_points'] = inter_points
|
98 |
+
|
99 |
+
return output
|
100 |
+
|
101 |
+
|
102 |
+
def read_image(self, image_url):
|
103 |
+
response = requests.get(image_url, stream=True)
|
104 |
+
image = np.asarray(Image.open(BytesIO(response.content)).convert('RGB'))
|
105 |
+
|
106 |
+
max_len = 1024
|
107 |
+
h, w, _ = image.shape
|
108 |
+
org_shape = [h, w]
|
109 |
+
max_idx = np.argmax(org_shape)
|
110 |
+
|
111 |
+
max_val = org_shape[max_idx]
|
112 |
+
if max_val > max_len:
|
113 |
+
min_idx = (max_idx + 1) % 2
|
114 |
+
ratio = max_len / max_val
|
115 |
+
new_min = org_shape[min_idx] * ratio
|
116 |
+
new_shape = [0, 0]
|
117 |
+
new_shape[max_idx] = 1024
|
118 |
+
new_shape[min_idx] = new_min
|
119 |
+
|
120 |
+
image = cv2.resize(image, (int(new_shape[1]), int(new_shape[0])), interpolation=cv2.INTER_AREA)
|
121 |
+
|
122 |
+
return image
|
123 |
+
|
124 |
+
|
125 |
+
def init_resize_image(self, im, maximum_size=1024):
|
126 |
+
h, w, _ = im.shape
|
127 |
+
size = [h, w]
|
128 |
+
max_arg = np.argmax(size)
|
129 |
+
max_len = size[max_arg]
|
130 |
+
min_arg = max_arg - 1
|
131 |
+
min_len = size[min_arg]
|
132 |
+
if max_len < maximum_size:
|
133 |
+
return im
|
134 |
+
else:
|
135 |
+
ratio = maximum_size / max_len
|
136 |
+
max_len = max_len * ratio
|
137 |
+
min_len = min_len * ratio
|
138 |
+
size[max_arg] = int(max_len)
|
139 |
+
size[min_arg] = int(min_len)
|
140 |
+
|
141 |
+
im = cv2.resize(im, (size[1], size[0]), interpolation = cv2.INTER_AREA)
|
142 |
+
|
143 |
+
return im
|
144 |
+
|
145 |
+
|
146 |
+
def decode_image(self, session_id, rawimg):
|
147 |
+
dirpath = os.path.join('static/results', session_id)
|
148 |
+
|
149 |
+
if not os.path.exists(dirpath):
|
150 |
+
os.makedirs(dirpath)
|
151 |
+
save_path = os.path.join(dirpath, 'input.png')
|
152 |
+
input_image_url = os.path.join(dirpath, 'input.png')
|
153 |
+
|
154 |
+
img = cv2.imdecode(np.frombuffer(rawimg, dtype='uint8'), 1)[:,:,::-1]
|
155 |
+
img = self.init_resize_image(img)
|
156 |
+
cv2.imwrite(save_path, img[:,:,::-1])
|
157 |
+
|
158 |
+
return img, input_image_url
|
159 |
+
|
160 |
+
|
161 |
+
def draw_output(self, image, output, save_path='test.png'):
|
162 |
+
color_dict = {'red': [255, 0, 0],
|
163 |
+
'green': [0, 255, 0],
|
164 |
+
'blue': [0, 0, 255],
|
165 |
+
'cyan': [0, 255, 255],
|
166 |
+
'black': [0, 0, 0],
|
167 |
+
'yellow': [255, 255, 0],
|
168 |
+
'dark_yellow': [200, 200, 0]}
|
169 |
+
|
170 |
+
line_image = image.copy()
|
171 |
+
square_image = image.copy()
|
172 |
+
square_candidate_image = image.copy()
|
173 |
+
|
174 |
+
line_thick = 5
|
175 |
+
|
176 |
+
# output > line array
|
177 |
+
for line in output['segments']:
|
178 |
+
x_start, y_start, x_end, y_end = [int(val) for val in line]
|
179 |
+
cv2.line(line_image, (x_start, y_start), (x_end, y_end), color_dict['red'], line_thick)
|
180 |
+
|
181 |
+
inter_image = line_image.copy()
|
182 |
+
|
183 |
+
for pt in output['inter_points']:
|
184 |
+
x, y = [int(val) for val in pt]
|
185 |
+
cv2.circle(inter_image, (x, y), 10, color_dict['blue'], -1)
|
186 |
+
|
187 |
+
for square in output['squares']:
|
188 |
+
cv2.polylines(square_candidate_image, [square.reshape([-1, 1, 2])], True, color_dict['dark_yellow'], line_thick)
|
189 |
+
|
190 |
+
for square in output['squares'][0:1]:
|
191 |
+
cv2.polylines(square_image, [square.reshape([-1, 1, 2])], True, color_dict['yellow'], line_thick)
|
192 |
+
for pt in square:
|
193 |
+
cv2.circle(square_image, (int(pt[0]), int(pt[1])), 10, color_dict['cyan'], -1)
|
194 |
+
|
195 |
+
'''
|
196 |
+
square image | square candidates image
|
197 |
+
inter image | line image
|
198 |
+
'''
|
199 |
+
output_image = self.init_resize_image(square_image, 512)
|
200 |
+
output_image = np.concatenate([output_image, self.init_resize_image(square_candidate_image, 512)], axis=1)
|
201 |
+
output_image_tmp = np.concatenate([self.init_resize_image(inter_image, 512), self.init_resize_image(line_image, 512)], axis=1)
|
202 |
+
output_image = np.concatenate([output_image, output_image_tmp], axis=0)
|
203 |
+
|
204 |
+
cv2.imwrite(save_path, output_image[:,:,::-1])
|
205 |
+
|
206 |
+
return output_image
|
207 |
+
|
208 |
+
|
209 |
+
def save_output(self, session_id, input_image_url, image, output):
|
210 |
+
dirpath = os.path.join('static/results', session_id)
|
211 |
+
|
212 |
+
if not os.path.exists(dirpath):
|
213 |
+
os.makedirs(dirpath)
|
214 |
+
|
215 |
+
save_path = os.path.join(dirpath, 'output.png')
|
216 |
+
self.draw_output(image, output, save_path=save_path)
|
217 |
+
|
218 |
+
output_image_url = os.path.join(dirpath, 'output.png')
|
219 |
+
|
220 |
+
rst = {}
|
221 |
+
rst['input_image_url'] = input_image_url
|
222 |
+
rst['session_id'] = session_id
|
223 |
+
rst['output_image_url'] = output_image_url
|
224 |
+
|
225 |
+
with open(os.path.join(dirpath, 'results.json'), 'w') as f:
|
226 |
+
json.dump(rst, f)
|
227 |
+
|
228 |
+
|
229 |
+
def init_worker(args):
|
230 |
+
global model
|
231 |
+
|
232 |
+
model = model_graph(args)
|
233 |
+
|
234 |
+
|
235 |
+
@app.route('/')
|
236 |
+
def index():
|
237 |
+
return render_template('index_scan.html', session_id='dummy_session_id')
|
238 |
+
|
239 |
+
|
240 |
+
@app.route('/', methods=['POST'])
|
241 |
+
def index_post():
|
242 |
+
request_start = time.time()
|
243 |
+
configs = request.form
|
244 |
+
|
245 |
+
session_id = str(uuid.uuid1())
|
246 |
+
|
247 |
+
image_url = configs['image_url'] # image_url
|
248 |
+
|
249 |
+
if len(image_url) == 0:
|
250 |
+
bio = BytesIO()
|
251 |
+
request.files['image'].save(bio)
|
252 |
+
rawimg = bio.getvalue()
|
253 |
+
image, image_url = model.decode_image(session_id, rawimg)
|
254 |
+
else:
|
255 |
+
image = model.read_image(image_url)
|
256 |
+
|
257 |
+
output = model.pred_tflite(image)
|
258 |
+
|
259 |
+
model.save_output(session_id, image_url, image, output)
|
260 |
+
|
261 |
+
return render_template('index_scan.html', session_id=session_id)
|
262 |
+
|
263 |
+
|
264 |
+
@app.route('/favicon.ico')
|
265 |
+
def favicon():
|
266 |
+
return send_from_directory(os.path.join(app.root_path, 'static'),
|
267 |
+
'favicon.ico', mimetype='image/vnd.microsoft.icon')
|
268 |
+
|
269 |
+
|
270 |
+
if __name__ == '__main__':
|
271 |
+
args = parser.parse_args()
|
272 |
+
|
273 |
+
init_worker(args)
|
274 |
+
|
275 |
+
app.run(host='0.0.0.0', port=args.port)
|
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
numpy
|
2 |
+
opencv-python
|
3 |
+
pillow
|
4 |
+
tensorflow-gpu
|
5 |
+
Flask
|
6 |
+
gradio
|
static/css/app.css
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#app {
|
2 |
+
padding: 20px;
|
3 |
+
}
|
4 |
+
|
5 |
+
#result .item {
|
6 |
+
padding-bottom: 20px;
|
7 |
+
}
|
8 |
+
|
9 |
+
.form-content-container {
|
10 |
+
padding-left: 20px;
|
11 |
+
}
|
static/favicon.ico
ADDED
|
templates/index_scan.html
ADDED
@@ -0,0 +1,128 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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<!doctype! html>
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<!--
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M-LSD
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Copyright 2021-present NAVER Corp.
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Apache License v2.0
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-->
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<html>
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<head>
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<title>MLSD demo</title>
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<link rel="stylesheet" href="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" type="text/css">
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<link rel="stylesheet" href="/static/css/app.css" type="text/css">
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<script src="https://cdn.staticfile.org/jquery/3.2.1/jquery.min.js"></script>
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<script src="https://cdn.staticfile.org/tether/1.4.0/js/tether.min.js"></script>
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<script src="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/vue@2.x/dist/vue.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/vuetify@2.x/dist/vuetify.js"></script>
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</head>
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<style>
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.container {
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width: 1000em;
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overflow-x: auto;
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white-space: nowrap;
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}
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.image {
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position: relative;
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}
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h2 {
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position: absolute;
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top: 200px;
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left: 10px;
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width: 100px;
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color: white;
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background: rgb(0, 0, 0);
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background: rgba(0, 0, 0, 0.7);
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}
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</style>
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<body>
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<div id="app">
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<div>
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<form id="upload-form" method="post" enctype="multipart/form-data">
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<h5>MLSD demo</h5>
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<div class="form-content-container">
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image_url: <input id="upload_url" type="text" name="image_url" /><br>
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image_data: <input id="upload_image" type="file" name="image" /><br>
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<input id="upload_button" type="submit" value="Submit" />
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</div>
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</form>
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</div>
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<hr>
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<div id="result" v-if="show">
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<div class="item">
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<div><h5>Output_image</h5>
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<ul>
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<img id="output_image" :src="output_image_url" style="float:left;margin:10px;">
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</ul>
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<br style="clear:both">
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<div><h5>Input_image</h5></div>
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<ul>
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<img id="input_image" :src="input_image_url" height="224" style="float:left;margin:10px;">
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</ul>
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<br style="clear:both" />
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</div>
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</div>
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<hr>
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<footer>
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Github url: <a href="https://github.com/navervision/mlsd">https://github.com/navervision/mlsd</a>
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</footer>
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</div>
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<script>
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$(function() {
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function getQueryStrings() {
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var vars = [], hash, hashes;
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if (window.location.href.indexOf('#') === -1) {
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hashes = window.location.href.slice(window.location.href.indexOf('?') + 1).split('&');
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} else {
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hashes = window.location.href.slice(window.location.href.indexOf('?') + 1, window.location.href.indexOf('#')).split('&');
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}
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for(var i = 0; i < hashes.length; i++) {
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hash = hashes[i].split('=');
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vars.push(hash[0]);
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vars[hash[0]] = hash[1];
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}
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return vars;
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}
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var session_id = '{{session_id}}';
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var app = new Vue({
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el: '#app',
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data: {
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session_id: session_id,
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show: false,
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},
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});
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var render = function(session_id) {
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app.session_id = session_id;
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app.server_info = ['loading'];
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$.get('/static/results/' + session_id + '/results.json', function(data) {
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if (typeof data == 'string') {
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data = JSON.parse(data);
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}
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app.input_image_url = data.input_image_url;
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app.session_id = data.session_id;
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app.output_image_url = data.output_image_url;
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app.show = true
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});
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}
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if (session_id != 'dummy_session_id') {
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window.history.pushState({},"", '/?r=' + session_id);
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render(session_id);
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} else {
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var queryStrings = getQueryStrings();
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var rid = queryStrings['r'];
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if (rid) {
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render(rid);
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}
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}
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})
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</script>
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</body>
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</html>
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tflite_models/M-LSD_320_large_fp16.tflite
ADDED
Binary file (3.12 MB). View file
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tflite_models/M-LSD_320_large_fp32.tflite
ADDED
Binary file (6.14 MB). View file
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tflite_models/M-LSD_320_tiny_fp16.tflite
ADDED
Binary file (1.28 MB). View file
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tflite_models/M-LSD_320_tiny_fp32.tflite
ADDED
Binary file (2.49 MB). View file
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tflite_models/M-LSD_512_large_fp16.tflite
ADDED
Binary file (3.12 MB). View file
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tflite_models/M-LSD_512_large_fp32.tflite
ADDED
Binary file (6.14 MB). View file
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tflite_models/M-LSD_512_tiny_fp16.tflite
ADDED
Binary file (1.28 MB). View file
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tflite_models/M-LSD_512_tiny_fp32.tflite
ADDED
Binary file (2.49 MB). View file
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utils.py
ADDED
@@ -0,0 +1,511 @@
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|
1 |
+
'''
|
2 |
+
M-LSD
|
3 |
+
Copyright 2021-present NAVER Corp.
|
4 |
+
Apache License v2.0
|
5 |
+
'''
|
6 |
+
import os
|
7 |
+
import numpy as np
|
8 |
+
import cv2
|
9 |
+
import tensorflow as tf
|
10 |
+
|
11 |
+
|
12 |
+
def pred_lines(image, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=0.10, dist_thr=20.0):
|
13 |
+
h, w, _ = image.shape
|
14 |
+
h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
|
15 |
+
|
16 |
+
resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
|
17 |
+
batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
|
18 |
+
interpreter.set_tensor(input_details[0]['index'], batch_image)
|
19 |
+
interpreter.invoke()
|
20 |
+
|
21 |
+
pts = interpreter.get_tensor(output_details[0]['index'])[0]
|
22 |
+
pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
|
23 |
+
vmap = interpreter.get_tensor(output_details[2]['index'])[0]
|
24 |
+
|
25 |
+
start = vmap[:,:,:2]
|
26 |
+
end = vmap[:,:,2:]
|
27 |
+
dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
|
28 |
+
|
29 |
+
segments_list = []
|
30 |
+
for center, score in zip(pts, pts_score):
|
31 |
+
y, x = center
|
32 |
+
distance = dist_map[y, x]
|
33 |
+
if score > score_thr and distance > dist_thr:
|
34 |
+
disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
|
35 |
+
x_start = x + disp_x_start
|
36 |
+
y_start = y + disp_y_start
|
37 |
+
x_end = x + disp_x_end
|
38 |
+
y_end = y + disp_y_end
|
39 |
+
segments_list.append([x_start, y_start, x_end, y_end])
|
40 |
+
|
41 |
+
lines = 2 * np.array(segments_list) # 256 > 512
|
42 |
+
lines[:,0] = lines[:,0] * w_ratio
|
43 |
+
lines[:,1] = lines[:,1] * h_ratio
|
44 |
+
lines[:,2] = lines[:,2] * w_ratio
|
45 |
+
lines[:,3] = lines[:,3] * h_ratio
|
46 |
+
|
47 |
+
return lines
|
48 |
+
|
49 |
+
|
50 |
+
def pred_squares(image,
|
51 |
+
interpreter,
|
52 |
+
input_details,
|
53 |
+
output_details,
|
54 |
+
input_shape=[512, 512],
|
55 |
+
params={'score': 0.06,
|
56 |
+
'outside_ratio': 0.28,
|
57 |
+
'inside_ratio': 0.45,
|
58 |
+
'w_overlap': 0.0,
|
59 |
+
'w_degree': 1.95,
|
60 |
+
'w_length': 0.0,
|
61 |
+
'w_area': 1.86,
|
62 |
+
'w_center': 0.14}):
|
63 |
+
h, w, _ = image.shape
|
64 |
+
original_shape = [h, w]
|
65 |
+
|
66 |
+
resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
|
67 |
+
batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
|
68 |
+
interpreter.set_tensor(input_details[0]['index'], batch_image)
|
69 |
+
interpreter.invoke()
|
70 |
+
|
71 |
+
pts = interpreter.get_tensor(output_details[0]['index'])[0]
|
72 |
+
pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
|
73 |
+
vmap = interpreter.get_tensor(output_details[2]['index'])[0]
|
74 |
+
|
75 |
+
start = vmap[:,:,:2] # (x, y)
|
76 |
+
end = vmap[:,:,2:] # (x, y)
|
77 |
+
dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
|
78 |
+
|
79 |
+
junc_list = []
|
80 |
+
segments_list = []
|
81 |
+
for junc, score in zip(pts, pts_score):
|
82 |
+
y, x = junc
|
83 |
+
distance = dist_map[y, x]
|
84 |
+
if score > params['score'] and distance > 20.0:
|
85 |
+
junc_list.append([x, y])
|
86 |
+
disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
|
87 |
+
d_arrow = 1.0
|
88 |
+
x_start = x + d_arrow * disp_x_start
|
89 |
+
y_start = y + d_arrow * disp_y_start
|
90 |
+
x_end = x + d_arrow * disp_x_end
|
91 |
+
y_end = y + d_arrow * disp_y_end
|
92 |
+
segments_list.append([x_start, y_start, x_end, y_end])
|
93 |
+
|
94 |
+
segments = np.array(segments_list)
|
95 |
+
|
96 |
+
####### post processing for squares
|
97 |
+
# 1. get unique lines
|
98 |
+
point = np.array([[0, 0]])
|
99 |
+
point = point[0]
|
100 |
+
start = segments[:,:2]
|
101 |
+
end = segments[:,2:]
|
102 |
+
diff = start - end
|
103 |
+
a = diff[:, 1]
|
104 |
+
b = -diff[:, 0]
|
105 |
+
c = a * start[:,0] + b * start[:,1]
|
106 |
+
|
107 |
+
d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
|
108 |
+
theta = np.arctan2(diff[:,0], diff[:,1]) * 180 / np.pi
|
109 |
+
theta[theta < 0.0] += 180
|
110 |
+
hough = np.concatenate([d[:,None], theta[:,None]], axis=-1)
|
111 |
+
|
112 |
+
d_quant = 1
|
113 |
+
theta_quant = 2
|
114 |
+
hough[:,0] //= d_quant
|
115 |
+
hough[:,1] //= theta_quant
|
116 |
+
_, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
|
117 |
+
|
118 |
+
acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
|
119 |
+
idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
|
120 |
+
yx_indices = hough[indices,:].astype('int32')
|
121 |
+
acc_map[yx_indices[:,0], yx_indices[:,1]] = counts
|
122 |
+
idx_map[yx_indices[:,0], yx_indices[:,1]] = indices
|
123 |
+
|
124 |
+
acc_map_np = acc_map
|
125 |
+
acc_map = acc_map[None,:,:,None]
|
126 |
+
|
127 |
+
### fast suppression using tensorflow op
|
128 |
+
acc_map = tf.constant(acc_map, dtype=tf.float32)
|
129 |
+
max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5,5), strides=1, padding='same')(acc_map)
|
130 |
+
acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
|
131 |
+
flatten_acc_map = tf.reshape(acc_map, [1, -1])
|
132 |
+
topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
|
133 |
+
_, h, w, _ = acc_map.shape
|
134 |
+
y = tf.expand_dims(topk_indices // w, axis=-1)
|
135 |
+
x = tf.expand_dims(topk_indices % w, axis=-1)
|
136 |
+
yx = tf.concat([y, x], axis=-1)
|
137 |
+
###
|
138 |
+
|
139 |
+
yx = yx[0].numpy()
|
140 |
+
indices = idx_map[yx[:,0], yx[:,1]]
|
141 |
+
topk_values = topk_values.numpy()[0]
|
142 |
+
basis = 5 // 2
|
143 |
+
|
144 |
+
merged_segments = []
|
145 |
+
for yx_pt, max_indice, value in zip(yx, indices, topk_values):
|
146 |
+
y, x = yx_pt
|
147 |
+
if max_indice == -1 or value == 0:
|
148 |
+
continue
|
149 |
+
segment_list = []
|
150 |
+
for y_offset in range(-basis, basis+1):
|
151 |
+
for x_offset in range(-basis, basis+1):
|
152 |
+
indice = idx_map[y+y_offset,x+x_offset]
|
153 |
+
cnt = int(acc_map_np[y+y_offset,x+x_offset])
|
154 |
+
if indice != -1:
|
155 |
+
segment_list.append(segments[indice])
|
156 |
+
if cnt > 1:
|
157 |
+
check_cnt = 1
|
158 |
+
current_hough = hough[indice]
|
159 |
+
for new_indice, new_hough in enumerate(hough):
|
160 |
+
if (current_hough == new_hough).all() and indice != new_indice:
|
161 |
+
segment_list.append(segments[new_indice])
|
162 |
+
check_cnt += 1
|
163 |
+
if check_cnt == cnt:
|
164 |
+
break
|
165 |
+
group_segments = np.array(segment_list).reshape([-1, 2])
|
166 |
+
sorted_group_segments = np.sort(group_segments, axis=0)
|
167 |
+
x_min, y_min = sorted_group_segments[0,:]
|
168 |
+
x_max, y_max = sorted_group_segments[-1,:]
|
169 |
+
|
170 |
+
deg = theta[max_indice]
|
171 |
+
if deg >= 90:
|
172 |
+
merged_segments.append([x_min, y_max, x_max, y_min])
|
173 |
+
else:
|
174 |
+
merged_segments.append([x_min, y_min, x_max, y_max])
|
175 |
+
|
176 |
+
# 2. get intersections
|
177 |
+
new_segments = np.array(merged_segments) # (x1, y1, x2, y2)
|
178 |
+
start = new_segments[:,:2] # (x1, y1)
|
179 |
+
end = new_segments[:,2:] # (x2, y2)
|
180 |
+
new_centers = (start + end) / 2.0
|
181 |
+
diff = start - end
|
182 |
+
dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
|
183 |
+
|
184 |
+
# ax + by = c
|
185 |
+
a = diff[:,1]
|
186 |
+
b = -diff[:,0]
|
187 |
+
c = a * start[:,0] + b * start[:,1]
|
188 |
+
pre_det = a[:,None] * b[None,:]
|
189 |
+
det = pre_det - np.transpose(pre_det)
|
190 |
+
|
191 |
+
pre_inter_y = a[:,None] * c[None,:]
|
192 |
+
inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
|
193 |
+
pre_inter_x = c[:,None] * b[None,:]
|
194 |
+
inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
|
195 |
+
inter_pts = np.concatenate([inter_x[:,:,None], inter_y[:,:,None]], axis=-1).astype('int32')
|
196 |
+
|
197 |
+
# 3. get corner information
|
198 |
+
# 3.1 get distance
|
199 |
+
'''
|
200 |
+
dist_segments:
|
201 |
+
| dist(0), dist(1), dist(2), ...|
|
202 |
+
dist_inter_to_segment1:
|
203 |
+
| dist(inter,0), dist(inter,0), dist(inter,0), ... |
|
204 |
+
| dist(inter,1), dist(inter,1), dist(inter,1), ... |
|
205 |
+
...
|
206 |
+
dist_inter_to_semgnet2:
|
207 |
+
| dist(inter,0), dist(inter,1), dist(inter,2), ... |
|
208 |
+
| dist(inter,0), dist(inter,1), dist(inter,2), ... |
|
209 |
+
...
|
210 |
+
'''
|
211 |
+
|
212 |
+
dist_inter_to_segment1_start = np.sqrt(np.sum(((inter_pts - start[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
|
213 |
+
dist_inter_to_segment1_end = np.sqrt(np.sum(((inter_pts - end[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
|
214 |
+
dist_inter_to_segment2_start = np.sqrt(np.sum(((inter_pts - start[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
|
215 |
+
dist_inter_to_segment2_end = np.sqrt(np.sum(((inter_pts - end[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
|
216 |
+
|
217 |
+
# sort ascending
|
218 |
+
dist_inter_to_segment1 = np.sort(np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1), axis=-1) # [n_batch, n_batch, 2]
|
219 |
+
dist_inter_to_segment2 = np.sort(np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1), axis=-1) # [n_batch, n_batch, 2]
|
220 |
+
|
221 |
+
# 3.2 get degree
|
222 |
+
inter_to_start = new_centers[:,None,:] - inter_pts
|
223 |
+
deg_inter_to_start = np.arctan2(inter_to_start[:,:,1], inter_to_start[:,:,0]) * 180 / np.pi
|
224 |
+
deg_inter_to_start[deg_inter_to_start < 0.0] += 360
|
225 |
+
inter_to_end = new_centers[None,:,:] - inter_pts
|
226 |
+
deg_inter_to_end = np.arctan2(inter_to_end[:,:,1], inter_to_end[:,:,0]) * 180 / np.pi
|
227 |
+
deg_inter_to_end[deg_inter_to_end < 0.0] += 360
|
228 |
+
|
229 |
+
'''
|
230 |
+
0 -- 1
|
231 |
+
| |
|
232 |
+
3 -- 2
|
233 |
+
'''
|
234 |
+
# rename variables
|
235 |
+
deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
|
236 |
+
# sort deg ascending
|
237 |
+
deg_sort = np.sort(np.concatenate([deg1_map[:,:,None], deg2_map[:,:,None]], axis=-1), axis=-1)
|
238 |
+
|
239 |
+
deg_diff_map = np.abs(deg1_map - deg2_map)
|
240 |
+
# we only consider the smallest degree of intersect
|
241 |
+
deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
|
242 |
+
|
243 |
+
# define available degree range
|
244 |
+
deg_range = [60, 120]
|
245 |
+
|
246 |
+
corner_dict = {corner_info: [] for corner_info in range(4)}
|
247 |
+
inter_points = []
|
248 |
+
for i in range(inter_pts.shape[0]):
|
249 |
+
for j in range(i + 1, inter_pts.shape[1]):
|
250 |
+
# i, j > line index, always i < j
|
251 |
+
x, y = inter_pts[i, j, :]
|
252 |
+
deg1, deg2 = deg_sort[i, j, :]
|
253 |
+
deg_diff = deg_diff_map[i, j]
|
254 |
+
|
255 |
+
check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
|
256 |
+
|
257 |
+
outside_ratio = params['outside_ratio'] # over ratio >>> drop it!
|
258 |
+
inside_ratio = params['inside_ratio'] # over ratio >>> drop it!
|
259 |
+
check_distance = ((dist_inter_to_segment1[i,j,1] >= dist_segments[i] and \
|
260 |
+
dist_inter_to_segment1[i,j,0] <= dist_segments[i] * outside_ratio) or \
|
261 |
+
(dist_inter_to_segment1[i,j,1] <= dist_segments[i] and \
|
262 |
+
dist_inter_to_segment1[i,j,0] <= dist_segments[i] * inside_ratio)) and \
|
263 |
+
((dist_inter_to_segment2[i,j,1] >= dist_segments[j] and \
|
264 |
+
dist_inter_to_segment2[i,j,0] <= dist_segments[j] * outside_ratio) or \
|
265 |
+
(dist_inter_to_segment2[i,j,1] <= dist_segments[j] and \
|
266 |
+
dist_inter_to_segment2[i,j,0] <= dist_segments[j] * inside_ratio))
|
267 |
+
|
268 |
+
if check_degree and check_distance:
|
269 |
+
corner_info = None
|
270 |
+
|
271 |
+
if (deg1 >= 0 and deg1 <= 45 and deg2 >=45 and deg2 <= 120) or \
|
272 |
+
(deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
|
273 |
+
corner_info, color_info = 0, 'blue'
|
274 |
+
elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
|
275 |
+
corner_info, color_info = 1, 'green'
|
276 |
+
elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
|
277 |
+
corner_info, color_info = 2, 'black'
|
278 |
+
elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
|
279 |
+
(deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
|
280 |
+
corner_info, color_info = 3, 'cyan'
|
281 |
+
else:
|
282 |
+
corner_info, color_info = 4, 'red' # we don't use it
|
283 |
+
continue
|
284 |
+
|
285 |
+
corner_dict[corner_info].append([x, y, i, j])
|
286 |
+
inter_points.append([x, y])
|
287 |
+
|
288 |
+
square_list = []
|
289 |
+
connect_list = []
|
290 |
+
segments_list = []
|
291 |
+
for corner0 in corner_dict[0]:
|
292 |
+
for corner1 in corner_dict[1]:
|
293 |
+
connect01 = False
|
294 |
+
for corner0_line in corner0[2:]:
|
295 |
+
if corner0_line in corner1[2:]:
|
296 |
+
connect01 = True
|
297 |
+
break
|
298 |
+
if connect01:
|
299 |
+
for corner2 in corner_dict[2]:
|
300 |
+
connect12 = False
|
301 |
+
for corner1_line in corner1[2:]:
|
302 |
+
if corner1_line in corner2[2:]:
|
303 |
+
connect12 = True
|
304 |
+
break
|
305 |
+
if connect12:
|
306 |
+
for corner3 in corner_dict[3]:
|
307 |
+
connect23 = False
|
308 |
+
for corner2_line in corner2[2:]:
|
309 |
+
if corner2_line in corner3[2:]:
|
310 |
+
connect23 = True
|
311 |
+
break
|
312 |
+
if connect23:
|
313 |
+
for corner3_line in corner3[2:]:
|
314 |
+
if corner3_line in corner0[2:]:
|
315 |
+
# SQUARE!!!
|
316 |
+
'''
|
317 |
+
0 -- 1
|
318 |
+
| |
|
319 |
+
3 -- 2
|
320 |
+
square_list:
|
321 |
+
order: 0 > 1 > 2 > 3
|
322 |
+
| x0, y0, x1, y1, x2, y2, x3, y3 |
|
323 |
+
| x0, y0, x1, y1, x2, y2, x3, y3 |
|
324 |
+
...
|
325 |
+
connect_list:
|
326 |
+
order: 01 > 12 > 23 > 30
|
327 |
+
| line_idx01, line_idx12, line_idx23, line_idx30 |
|
328 |
+
| line_idx01, line_idx12, line_idx23, line_idx30 |
|
329 |
+
...
|
330 |
+
segments_list:
|
331 |
+
order: 0 > 1 > 2 > 3
|
332 |
+
| line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
|
333 |
+
| line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
|
334 |
+
...
|
335 |
+
'''
|
336 |
+
square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
|
337 |
+
connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
|
338 |
+
segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
|
339 |
+
|
340 |
+
def check_outside_inside(segments_info, connect_idx):
|
341 |
+
# return 'outside or inside', min distance, cover_param, peri_param
|
342 |
+
if connect_idx == segments_info[0]:
|
343 |
+
check_dist_mat = dist_inter_to_segment1
|
344 |
+
else:
|
345 |
+
check_dist_mat = dist_inter_to_segment2
|
346 |
+
|
347 |
+
i, j = segments_info
|
348 |
+
min_dist, max_dist = check_dist_mat[i, j, :]
|
349 |
+
connect_dist = dist_segments[connect_idx]
|
350 |
+
if max_dist > connect_dist:
|
351 |
+
return 'outside', min_dist, 0, 1
|
352 |
+
else:
|
353 |
+
return 'inside', min_dist, -1, -1
|
354 |
+
|
355 |
+
|
356 |
+
top_square = None
|
357 |
+
|
358 |
+
try:
|
359 |
+
map_size = input_shape[0] / 2
|
360 |
+
squares = np.array(square_list).reshape([-1,4,2])
|
361 |
+
score_array = []
|
362 |
+
connect_array = np.array(connect_list)
|
363 |
+
segments_array = np.array(segments_list).reshape([-1,4,2])
|
364 |
+
|
365 |
+
# get degree of corners:
|
366 |
+
squares_rollup = np.roll(squares, 1, axis=1)
|
367 |
+
squares_rolldown = np.roll(squares, -1, axis=1)
|
368 |
+
vec1 = squares_rollup - squares
|
369 |
+
normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
|
370 |
+
vec2 = squares_rolldown - squares
|
371 |
+
normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
|
372 |
+
inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1) # [n_squares, 4]
|
373 |
+
squares_degree = np.arccos(inner_products) * 180 / np.pi # [n_squares, 4]
|
374 |
+
|
375 |
+
# get square score
|
376 |
+
overlap_scores = []
|
377 |
+
degree_scores = []
|
378 |
+
length_scores = []
|
379 |
+
|
380 |
+
for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
|
381 |
+
'''
|
382 |
+
0 -- 1
|
383 |
+
| |
|
384 |
+
3 -- 2
|
385 |
+
|
386 |
+
# segments: [4, 2]
|
387 |
+
# connects: [4]
|
388 |
+
'''
|
389 |
+
|
390 |
+
###################################### OVERLAP SCORES
|
391 |
+
cover = 0
|
392 |
+
perimeter = 0
|
393 |
+
# check 0 > 1 > 2 > 3
|
394 |
+
square_length = []
|
395 |
+
|
396 |
+
for start_idx in range(4):
|
397 |
+
end_idx = (start_idx + 1) % 4
|
398 |
+
|
399 |
+
connect_idx = connects[start_idx] # segment idx of segment01
|
400 |
+
start_segments = segments[start_idx]
|
401 |
+
end_segments = segments[end_idx]
|
402 |
+
|
403 |
+
start_point = square[start_idx]
|
404 |
+
end_point = square[end_idx]
|
405 |
+
|
406 |
+
# check whether outside or inside
|
407 |
+
start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments, connect_idx)
|
408 |
+
end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
|
409 |
+
|
410 |
+
cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
|
411 |
+
perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
|
412 |
+
|
413 |
+
square_length.append(dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
|
414 |
+
|
415 |
+
overlap_scores.append(cover / perimeter)
|
416 |
+
######################################
|
417 |
+
###################################### DEGREE SCORES
|
418 |
+
'''
|
419 |
+
deg0 vs deg2
|
420 |
+
deg1 vs deg3
|
421 |
+
'''
|
422 |
+
deg0, deg1, deg2, deg3 = degree
|
423 |
+
deg_ratio1 = deg0 / deg2
|
424 |
+
if deg_ratio1 > 1.0:
|
425 |
+
deg_ratio1 = 1 / deg_ratio1
|
426 |
+
deg_ratio2 = deg1 / deg3
|
427 |
+
if deg_ratio2 > 1.0:
|
428 |
+
deg_ratio2 = 1 / deg_ratio2
|
429 |
+
degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
|
430 |
+
######################################
|
431 |
+
###################################### LENGTH SCORES
|
432 |
+
'''
|
433 |
+
len0 vs len2
|
434 |
+
len1 vs len3
|
435 |
+
'''
|
436 |
+
len0, len1, len2, len3 = square_length
|
437 |
+
len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
|
438 |
+
len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
|
439 |
+
length_scores.append((len_ratio1 + len_ratio2) / 2)
|
440 |
+
|
441 |
+
######################################
|
442 |
+
|
443 |
+
overlap_scores = np.array(overlap_scores)
|
444 |
+
overlap_scores /= np.max(overlap_scores)
|
445 |
+
|
446 |
+
degree_scores = np.array(degree_scores)
|
447 |
+
#degree_scores /= np.max(degree_scores)
|
448 |
+
|
449 |
+
length_scores = np.array(length_scores)
|
450 |
+
|
451 |
+
###################################### AREA SCORES
|
452 |
+
area_scores = np.reshape(squares, [-1, 4, 2])
|
453 |
+
area_x = area_scores[:,:,0]
|
454 |
+
area_y = area_scores[:,:,1]
|
455 |
+
correction = area_x[:,-1] * area_y[:,0] - area_y[:,-1] * area_x[:,0]
|
456 |
+
area_scores = np.sum(area_x[:,:-1] * area_y[:,1:], axis=-1) - np.sum(area_y[:,:-1] * area_x[:,1:], axis=-1)
|
457 |
+
area_scores = 0.5 * np.abs(area_scores + correction)
|
458 |
+
area_scores /= (map_size * map_size) #np.max(area_scores)
|
459 |
+
######################################
|
460 |
+
|
461 |
+
###################################### CENTER SCORES
|
462 |
+
centers = np.array([[256 // 2, 256 // 2]], dtype='float32') # [1, 2]
|
463 |
+
# squares: [n, 4, 2]
|
464 |
+
square_centers = np.mean(squares, axis=1) # [n, 2]
|
465 |
+
center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
|
466 |
+
center_scores = center2center / (map_size / np.sqrt(2.0))
|
467 |
+
|
468 |
+
|
469 |
+
'''
|
470 |
+
score_w = [overlap, degree, area, center, length]
|
471 |
+
'''
|
472 |
+
score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
|
473 |
+
score_array = params['w_overlap'] * overlap_scores \
|
474 |
+
+ params['w_degree'] * degree_scores \
|
475 |
+
+ params['w_area'] * area_scores \
|
476 |
+
- params['w_center'] * center_scores \
|
477 |
+
+ params['w_length'] * length_scores
|
478 |
+
|
479 |
+
best_square = []
|
480 |
+
|
481 |
+
sorted_idx = np.argsort(score_array)[::-1]
|
482 |
+
score_array = score_array[sorted_idx]
|
483 |
+
squares = squares[sorted_idx]
|
484 |
+
|
485 |
+
except Exception as e:
|
486 |
+
pass
|
487 |
+
|
488 |
+
try:
|
489 |
+
new_segments[:,0] = new_segments[:,0] * 2 / input_shape[1] * original_shape[1]
|
490 |
+
new_segments[:,1] = new_segments[:,1] * 2 / input_shape[0] * original_shape[0]
|
491 |
+
new_segments[:,2] = new_segments[:,2] * 2 / input_shape[1] * original_shape[1]
|
492 |
+
new_segments[:,3] = new_segments[:,3] * 2 / input_shape[0] * original_shape[0]
|
493 |
+
except:
|
494 |
+
new_segments = []
|
495 |
+
|
496 |
+
try:
|
497 |
+
squares[:,:,0] = squares[:,:,0] * 2 / input_shape[1] * original_shape[1]
|
498 |
+
squares[:,:,1] = squares[:,:,1] * 2 / input_shape[0] * original_shape[0]
|
499 |
+
except:
|
500 |
+
squares = []
|
501 |
+
score_array = []
|
502 |
+
|
503 |
+
try:
|
504 |
+
inter_points = np.array(inter_points)
|
505 |
+
inter_points[:,0] = inter_points[:,0] * 2 / input_shape[1] * original_shape[1]
|
506 |
+
inter_points[:,1] = inter_points[:,1] * 2 / input_shape[0] * original_shape[0]
|
507 |
+
except:
|
508 |
+
inter_points = []
|
509 |
+
|
510 |
+
|
511 |
+
return new_segments, squares, score_array, inter_points
|