spaces demo

LICENSE

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app.py

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+from PIL import Image
+import cv2
+import numpy as np
+import tensorflow as tf
+from utils import pred_lines, pred_squares
+import gradio as gr
+from urllib.request import urlretrieve
+
+
+# Load MLSD 512 Large FP32 tflite
+model_name = 'tflite_models/M-LSD_512_large_fp32.tflite'
+interpreter = tf.lite.Interpreter(model_path=model_name)
+
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+def gradio_wrapper_for_LSD(img_input, score_thr, dist_thr):
+  lines = pred_lines(img_input, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=score_thr, dist_thr=dist_thr)
+  img_output = img_input.copy()
+
+  # draw lines
+  for line in lines:
+    x_start, y_start, x_end, y_end = [int(val) for val in line]
+    cv2.line(img_output, (x_start, y_start), (x_end, y_end), [0,255,255], 2)
+  
+  return img_output
+
+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")
+urlretrieve("https://specials-images.forbesimg.com/imageserve/5dfe2e6925ab5d0007cefda5/960x0.jpg","example2.jpg")
+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")
+sample_images = [["example1.jpg", 0.2, 10.0], ["example2.jpg", 0.2, 10.0], ["example3.jpg", 0.2, 10.0]]
+
+
+
+iface = gr.Interface(gradio_wrapper_for_LSD,
+                     ["image",
+                      gr.inputs.Number(default=0.2, label='score_thr (0.0 ~ 1.0)'),
+                      gr.inputs.Number(default=10.0, label='dist_thr (0.0 ~ 20.0)')
+                     ],
+                     "image",
+                     title="Line segment detection with Mobile LSD (M-LSD)",
+                     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",
+                     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>",
+                     examples=sample_images,
+                     allow_screenshot=True)
+
+iface.launch()

demo_MLSD.py

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+'''
+M-LSD
+Copyright 2021-present NAVER Corp.
+Apache License v2.0
+'''
+# for demo
+import os
+from flask import Flask, request, session, json, Response, render_template, abort, send_from_directory
+import requests
+from urllib.request import urlopen
+from io import BytesIO
+import uuid
+import cv2
+import time
+import argparse
+
+# for tflite
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# for square detector
+from utils import pred_squares
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '' # CPU mode
+
+# flask
+app = Flask(__name__)
+logger = app.logger
+logger.info('init demo app')
+
+# config
+parser = argparse.ArgumentParser()
+
+## model parameters
+parser.add_argument('--tflite_path', default='./tflite_models/M-LSD_512_large_fp16.tflite', type=str)
+parser.add_argument('--input_size', default=512, type=int,
+                    help='The size of input images.')
+
+## LSD parameter
+parser.add_argument('--score_thr', default=0.10, type=float,
+                    help='Discard center points when the score < score_thr.')
+
+## intersection point parameters
+parser.add_argument('--outside_ratio', default=0.10, type=float,
+                    help='''Discard an intersection point 
+                    when it is located outside a line segment farther than line_length * outside_ratio.''')
+parser.add_argument('--inside_ratio', default=0.50, type=float,
+                    help='''Discard an intersection point
+                    when it is located inside a line segment farther than line_length * inside_ratio.''')
+
+## ranking boxes parameters
+parser.add_argument('--w_overlap', default=0.0, type=float,
+                    help='''When increasing w_overlap, the final box tends to overlap with
+                    the detected line segments as much as possible.''')
+parser.add_argument('--w_degree', default=1.14, type=float,
+                    help='''When increasing w_degree, the final box tends to be
+                    a parallel quadrilateral with reference to the angle of the box.''')
+parser.add_argument('--w_length', default=0.03, type=float,
+                    help='''When increasing w_length, the final box tends to be
+                    a parallel quadrilateral with reference to the length of the box.''')
+parser.add_argument('--w_area', default=1.84, type=float,
+                    help='When increasing w_area, the final box tends to be the largest one out of candidates.')
+parser.add_argument('--w_center', default=1.46, type=float,
+                    help='When increasing w_center, the final box tends to be located in the center of input image.')
+
+## flask demo parameter
+parser.add_argument('--port', default=5000, type=int,
+                    help='flask demo will be running on http://0.0.0.0:port/')
+
+
+class model_graph:
+    def __init__(self, args):
+        self.interpreter, self.input_details, self.output_details = self.load_tflite(args.tflite_path)
+        self.params = {'score': args.score_thr,'outside_ratio': args.outside_ratio,'inside_ratio': args.inside_ratio, 
+                       'w_overlap': args.w_overlap,'w_degree': args.w_degree,'w_length': args.w_length,
+                       'w_area': args.w_area,'w_center': args.w_center}
+        self.args = args
+
+
+    def load_tflite(self, tflite_path):
+        interpreter = tf.lite.Interpreter(model_path=tflite_path)
+        interpreter.allocate_tensors()
+        input_details = interpreter.get_input_details()
+        output_details = interpreter.get_output_details()
+
+        return interpreter, input_details, output_details
+
+
+    def pred_tflite(self, image):
+        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)
+
+        output = {}
+        output['segments'] = segments
+        output['squares'] = squares
+        output['scores'] = score_array
+        output['inter_points'] = inter_points
+
+        return output
+
+
+    def read_image(self, image_url):
+        response = requests.get(image_url, stream=True)
+        image = np.asarray(Image.open(BytesIO(response.content)).convert('RGB'))
+        
+        max_len = 1024
+        h, w, _ = image.shape
+        org_shape = [h, w]
+        max_idx = np.argmax(org_shape)
+
+        max_val = org_shape[max_idx]
+        if max_val  > max_len:
+            min_idx = (max_idx + 1) % 2
+            ratio = max_len / max_val
+            new_min = org_shape[min_idx] * ratio
+            new_shape = [0, 0]
+            new_shape[max_idx] = 1024
+            new_shape[min_idx] = new_min
+
+            image = cv2.resize(image, (int(new_shape[1]), int(new_shape[0])), interpolation=cv2.INTER_AREA)
+
+        return image
+    
+
+    def init_resize_image(self, im, maximum_size=1024):
+        h, w, _ = im.shape
+        size = [h, w]
+        max_arg = np.argmax(size)
+        max_len = size[max_arg]
+        min_arg = max_arg - 1
+        min_len = size[min_arg]
+        if max_len < maximum_size:
+            return im
+        else:
+            ratio = maximum_size / max_len
+            max_len = max_len * ratio
+            min_len = min_len * ratio
+            size[max_arg] = int(max_len)
+            size[min_arg] = int(min_len)
+
+            im = cv2.resize(im, (size[1], size[0]), interpolation = cv2.INTER_AREA)
+
+            return im
+
+
+    def decode_image(self, session_id, rawimg):
+        dirpath = os.path.join('static/results', session_id)
+
+        if not os.path.exists(dirpath):
+            os.makedirs(dirpath)
+        save_path = os.path.join(dirpath, 'input.png')
+        input_image_url = os.path.join(dirpath, 'input.png')
+    
+        img = cv2.imdecode(np.frombuffer(rawimg, dtype='uint8'), 1)[:,:,::-1]
+        img = self.init_resize_image(img)
+        cv2.imwrite(save_path, img[:,:,::-1])
+
+        return img, input_image_url
+
+
+    def draw_output(self, image, output, save_path='test.png'):
+        color_dict = {'red': [255, 0, 0],
+                      'green': [0, 255, 0],
+                      'blue': [0, 0, 255],
+                      'cyan': [0, 255, 255],
+                      'black': [0, 0, 0],
+                      'yellow': [255, 255, 0],
+                      'dark_yellow': [200, 200, 0]}
+        
+        line_image = image.copy()
+        square_image = image.copy()
+        square_candidate_image = image.copy()
+        
+        line_thick = 5
+
+        # output > line array
+        for line in output['segments']:
+            x_start, y_start, x_end, y_end = [int(val) for val in line]
+            cv2.line(line_image, (x_start, y_start), (x_end, y_end), color_dict['red'], line_thick)
+        
+        inter_image = line_image.copy()
+               
+        for pt in output['inter_points']:
+            x, y = [int(val) for val in pt]
+            cv2.circle(inter_image, (x, y), 10, color_dict['blue'], -1)
+
+        for square in output['squares']:
+            cv2.polylines(square_candidate_image, [square.reshape([-1, 1, 2])], True, color_dict['dark_yellow'], line_thick)
+
+        for square in output['squares'][0:1]:
+            cv2.polylines(square_image, [square.reshape([-1, 1, 2])], True, color_dict['yellow'], line_thick)
+            for pt in square:
+                cv2.circle(square_image, (int(pt[0]), int(pt[1])), 10, color_dict['cyan'], -1)
+
+        '''
+        square image | square candidates image
+        inter image  | line image
+        '''
+        output_image = self.init_resize_image(square_image, 512)
+        output_image = np.concatenate([output_image, self.init_resize_image(square_candidate_image, 512)], axis=1)
+        output_image_tmp = np.concatenate([self.init_resize_image(inter_image, 512), self.init_resize_image(line_image, 512)], axis=1)
+        output_image = np.concatenate([output_image, output_image_tmp], axis=0)
+
+        cv2.imwrite(save_path, output_image[:,:,::-1])
+
+        return output_image
+
+
+    def save_output(self, session_id, input_image_url, image, output):
+        dirpath = os.path.join('static/results', session_id)
+
+        if not os.path.exists(dirpath):
+            os.makedirs(dirpath)
+        
+        save_path = os.path.join(dirpath, 'output.png')
+        self.draw_output(image, output, save_path=save_path)
+
+        output_image_url = os.path.join(dirpath, 'output.png')
+
+        rst = {}
+        rst['input_image_url'] = input_image_url
+        rst['session_id'] = session_id
+        rst['output_image_url'] = output_image_url
+
+        with open(os.path.join(dirpath, 'results.json'), 'w') as f:
+            json.dump(rst, f)
+
+
+def init_worker(args):
+    global model
+    
+    model = model_graph(args)
+
+   
+@app.route('/')
+def index():
+    return render_template('index_scan.html', session_id='dummy_session_id')
+
+
+@app.route('/', methods=['POST'])
+def index_post():
+    request_start = time.time()
+    configs = request.form
+            
+    session_id = str(uuid.uuid1())
+
+    image_url = configs['image_url'] # image_url
+    
+    if len(image_url) == 0:
+        bio = BytesIO()
+        request.files['image'].save(bio)
+        rawimg = bio.getvalue()
+        image, image_url = model.decode_image(session_id, rawimg)
+    else:
+        image = model.read_image(image_url)
+    
+    output = model.pred_tflite(image)
+    
+    model.save_output(session_id, image_url, image, output)
+
+    return render_template('index_scan.html', session_id=session_id)
+
+
+@app.route('/favicon.ico')
+def favicon():
+    return send_from_directory(os.path.join(app.root_path, 'static'),
+                               'favicon.ico', mimetype='image/vnd.microsoft.icon')
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+
+    init_worker(args)
+
+    app.run(host='0.0.0.0', port=args.port)

requirements.txt

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+numpy
+opencv-python
+pillow
+tensorflow-gpu
+Flask
+gradio

static/css/app.css

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+#app {
+	padding: 20px;
+}
+
+#result .item {
+	padding-bottom: 20px;
+}
+
+.form-content-container {
+	padding-left: 20px;
+}

templates/index_scan.html

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+<!doctype! html>
+<!--
+M-LSD
+Copyright 2021-present NAVER Corp.
+Apache License v2.0
+-->
+<html>
+	<head>
+		<title>MLSD demo</title>
+		<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+		<link rel="stylesheet" href="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" type="text/css">
+		<link rel="stylesheet" href="/static/css/app.css" type="text/css">
+
+		<script src="https://cdn.staticfile.org/jquery/3.2.1/jquery.min.js"></script>
+		<script src="https://cdn.staticfile.org/tether/1.4.0/js/tether.min.js"></script>
+		<script src="https://cdn.staticfile.org/twitter-bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"></script>
+        <script src="https://cdn.jsdelivr.net/npm/vue@2.x/dist/vue.js"></script>
+        <script src="https://cdn.jsdelivr.net/npm/vuetify@2.x/dist/vuetify.js"></script>
+	</head>
+    <style>
+        .container {
+            width: 1000em;
+            overflow-x: auto;
+            white-space: nowrap;
+        }
+        .image {
+           position: relative;
+        }
+
+        h2 {
+           position: absolute;
+           top: 200px;
+           left: 10px;
+           width: 100px;
+           color: white;
+           background: rgb(0, 0, 0);
+           background: rgba(0, 0, 0, 0.7);
+        }
+    </style>
+	<body>
+		<div id="app">
+			<div>
+				<form id="upload-form" method="post" enctype="multipart/form-data">
+					<h5>MLSD demo</h5>
+					<div class="form-content-container">
+						image_url: <input id="upload_url" type="text" name="image_url" /><br>
+                        image_data: <input id="upload_image" type="file" name="image" /><br>
+						<input id="upload_button" type="submit" value="Submit" />
+                    </div>
+				</form>
+			</div>
+			<hr>
+			<div id="result" v-if="show">
+                <div class="item">
+					<div><h5>Output_image</h5>
+                    <ul>
+                        <img id="output_image" :src="output_image_url" style="float:left;margin:10px;">
+                    </ul>
+                    <br style="clear:both">
+					
+                    <div><h5>Input_image</h5></div>
+                    <ul>
+                        <img id="input_image" :src="input_image_url" height="224" style="float:left;margin:10px;">
+                    </ul>
+                    <br style="clear:both" />
+				</div>
+			</div>
+			<hr>
+			<footer>
+            Github url: <a href="https://github.com/navervision/mlsd">https://github.com/navervision/mlsd</a>
+			</footer>
+		</div>
+
+		<script>
+			$(function() {
+				function getQueryStrings() {
+					var vars = [], hash, hashes;
+					if (window.location.href.indexOf('#') === -1) {
+						hashes = window.location.href.slice(window.location.href.indexOf('?') + 1).split('&');
+					} else {
+						hashes = window.location.href.slice(window.location.href.indexOf('?') + 1, window.location.href.indexOf('#')).split('&');
+					}
+					for(var i = 0; i < hashes.length; i++) {
+						hash = hashes[i].split('=');
+						vars.push(hash[0]);
+						vars[hash[0]] = hash[1];
+					}
+					return vars;
+				}
+
+				var session_id = '{{session_id}}';
+
+				var app = new Vue({
+					el: '#app',
+					data: {
+						session_id: session_id,
+                        show: false,
+					},
+				});
+
+				var render = function(session_id) {
+					app.session_id = session_id;
+                    app.server_info = ['loading'];
+					$.get('/static/results/' + session_id + '/results.json', function(data) {
+						if (typeof data == 'string') {
+							data = JSON.parse(data);
+						}
+                        app.input_image_url = data.input_image_url;
+                        app.session_id = data.session_id;
+                        app.output_image_url = data.output_image_url;
+                        app.show = true
+					});
+				}
+
+				if (session_id != 'dummy_session_id') {
+					window.history.pushState({},"", '/?r=' + session_id);
+					render(session_id);
+				} else {
+					var queryStrings = getQueryStrings();
+					var rid = queryStrings['r'];
+					if (rid) {
+						render(rid);
+					}
+				}
+			})
+		</script>
+	</body>
+</html>

utils.py

@@ -0,0 +1,511 @@
+'''
+M-LSD
+Copyright 2021-present NAVER Corp.
+Apache License v2.0
+'''
+import os
+import numpy as np
+import cv2
+import tensorflow as tf
+
+
+def pred_lines(image, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=0.10, dist_thr=20.0):
+    h, w, _ = image.shape
+    h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
+
+    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)
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    interpreter.set_tensor(input_details[0]['index'], batch_image)
+    interpreter.invoke()
+
+    pts = interpreter.get_tensor(output_details[0]['index'])[0]
+    pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
+    vmap = interpreter.get_tensor(output_details[2]['index'])[0]
+
+    start = vmap[:,:,:2]
+    end = vmap[:,:,2:]
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    segments_list = []
+    for center, score in zip(pts, pts_score):
+        y, x = center
+        distance = dist_map[y, x]
+        if score > score_thr and distance > dist_thr:
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            x_start = x + disp_x_start
+            y_start = y + disp_y_start
+            x_end = x + disp_x_end
+            y_end = y + disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+    
+    lines = 2 * np.array(segments_list) # 256 > 512
+    lines[:,0] = lines[:,0] * w_ratio
+    lines[:,1] = lines[:,1] * h_ratio
+    lines[:,2] = lines[:,2] * w_ratio
+    lines[:,3] = lines[:,3] * h_ratio
+
+    return lines
+
+
+def pred_squares(image,
+                 interpreter,
+                 input_details,
+                 output_details,
+                 input_shape=[512, 512],
+                 params={'score': 0.06,
+                         'outside_ratio': 0.28,
+                         'inside_ratio': 0.45,
+                         'w_overlap': 0.0,
+                         'w_degree': 1.95,
+                         'w_length': 0.0,
+                         'w_area': 1.86,
+                         'w_center': 0.14}):
+    h, w, _ = image.shape
+    original_shape = [h, w]
+
+    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)
+    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
+    interpreter.set_tensor(input_details[0]['index'], batch_image)
+    interpreter.invoke()
+
+    pts = interpreter.get_tensor(output_details[0]['index'])[0]
+    pts_score = interpreter.get_tensor(output_details[1]['index'])[0]
+    vmap = interpreter.get_tensor(output_details[2]['index'])[0]
+    
+    start = vmap[:,:,:2] # (x, y)
+    end = vmap[:,:,2:] # (x, y)
+    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
+
+    junc_list = []
+    segments_list = []
+    for junc, score in zip(pts, pts_score):
+        y, x = junc
+        distance = dist_map[y, x]
+        if score > params['score'] and distance > 20.0:
+            junc_list.append([x, y])
+            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
+            d_arrow = 1.0
+            x_start = x + d_arrow * disp_x_start
+            y_start = y + d_arrow * disp_y_start
+            x_end = x + d_arrow * disp_x_end
+            y_end = y + d_arrow * disp_y_end
+            segments_list.append([x_start, y_start, x_end, y_end])
+            
+    segments = np.array(segments_list)
+    
+    ####### post processing for squares
+    # 1. get unique lines
+    point = np.array([[0, 0]])
+    point = point[0]
+    start = segments[:,:2]
+    end = segments[:,2:]
+    diff = start - end
+    a = diff[:, 1]
+    b = -diff[:, 0]
+    c = a * start[:,0] + b * start[:,1]
+
+    d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
+    theta = np.arctan2(diff[:,0], diff[:,1]) * 180 / np.pi
+    theta[theta < 0.0] += 180
+    hough = np.concatenate([d[:,None], theta[:,None]], axis=-1)
+
+    d_quant = 1
+    theta_quant = 2
+    hough[:,0] //= d_quant
+    hough[:,1] //= theta_quant
+    _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
+    
+    acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
+    idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
+    yx_indices = hough[indices,:].astype('int32')
+    acc_map[yx_indices[:,0], yx_indices[:,1]] = counts
+    idx_map[yx_indices[:,0], yx_indices[:,1]] = indices
+    
+    acc_map_np = acc_map
+    acc_map = acc_map[None,:,:,None]
+    
+    ### fast suppression using tensorflow op
+    acc_map = tf.constant(acc_map, dtype=tf.float32)
+    max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5,5), strides=1, padding='same')(acc_map)
+    acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
+    flatten_acc_map = tf.reshape(acc_map, [1, -1])
+    topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
+    _, h, w, _ = acc_map.shape
+    y = tf.expand_dims(topk_indices // w, axis=-1)
+    x = tf.expand_dims(topk_indices % w, axis=-1)
+    yx = tf.concat([y, x], axis=-1)
+    ###
+
+    yx = yx[0].numpy()
+    indices = idx_map[yx[:,0], yx[:,1]]
+    topk_values = topk_values.numpy()[0]
+    basis = 5 // 2
+
+    merged_segments = []
+    for yx_pt, max_indice, value in zip(yx, indices, topk_values):
+        y, x = yx_pt
+        if max_indice == -1 or value == 0:
+            continue
+        segment_list = []
+        for y_offset in range(-basis, basis+1):
+            for x_offset in range(-basis, basis+1):
+                indice = idx_map[y+y_offset,x+x_offset]
+                cnt = int(acc_map_np[y+y_offset,x+x_offset])
+                if indice != -1:
+                    segment_list.append(segments[indice])
+                if cnt > 1:
+                    check_cnt = 1
+                    current_hough = hough[indice]
+                    for new_indice, new_hough in enumerate(hough):
+                        if (current_hough == new_hough).all() and indice != new_indice:
+                            segment_list.append(segments[new_indice])
+                            check_cnt += 1
+                        if check_cnt == cnt:
+                            break
+        group_segments = np.array(segment_list).reshape([-1, 2])
+        sorted_group_segments = np.sort(group_segments, axis=0)
+        x_min, y_min = sorted_group_segments[0,:]
+        x_max, y_max = sorted_group_segments[-1,:]
+
+        deg = theta[max_indice]
+        if deg >= 90:
+            merged_segments.append([x_min, y_max, x_max, y_min])
+        else:
+            merged_segments.append([x_min, y_min, x_max, y_max])
+
+    # 2. get intersections
+    new_segments = np.array(merged_segments) # (x1, y1, x2, y2)
+    start = new_segments[:,:2] # (x1, y1)
+    end = new_segments[:,2:] # (x2, y2)
+    new_centers = (start + end) / 2.0
+    diff = start - end
+    dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
+
+    # ax + by = c
+    a = diff[:,1]
+    b = -diff[:,0]
+    c = a * start[:,0] + b * start[:,1]
+    pre_det = a[:,None] * b[None,:]
+    det = pre_det - np.transpose(pre_det)
+
+    pre_inter_y = a[:,None] * c[None,:]
+    inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
+    pre_inter_x = c[:,None] * b[None,:]
+    inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
+    inter_pts = np.concatenate([inter_x[:,:,None], inter_y[:,:,None]], axis=-1).astype('int32')
+    
+    # 3. get corner information
+    # 3.1 get distance
+    '''
+    dist_segments:
+        | dist(0), dist(1), dist(2), ...|
+    dist_inter_to_segment1:
+        | dist(inter,0), dist(inter,0), dist(inter,0), ... |
+        | dist(inter,1), dist(inter,1), dist(inter,1), ... |
+        ...
+    dist_inter_to_semgnet2:
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
+        ...
+    '''
+
+    dist_inter_to_segment1_start = np.sqrt(np.sum(((inter_pts - start[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
+    dist_inter_to_segment1_end = np.sqrt(np.sum(((inter_pts - end[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_start = np.sqrt(np.sum(((inter_pts - start[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
+    dist_inter_to_segment2_end = np.sqrt(np.sum(((inter_pts - end[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1]
+    
+    # sort ascending
+    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]
+    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]
+
+    # 3.2 get degree
+    inter_to_start = new_centers[:,None,:] - inter_pts
+    deg_inter_to_start = np.arctan2(inter_to_start[:,:,1], inter_to_start[:,:,0]) * 180 / np.pi
+    deg_inter_to_start[deg_inter_to_start < 0.0] += 360
+    inter_to_end = new_centers[None,:,:] - inter_pts
+    deg_inter_to_end = np.arctan2(inter_to_end[:,:,1], inter_to_end[:,:,0]) * 180 / np.pi
+    deg_inter_to_end[deg_inter_to_end < 0.0] += 360
+    
+    '''
+    0 -- 1
+    |    |
+    3 -- 2
+    '''
+    # rename variables
+    deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
+    # sort deg ascending
+    deg_sort = np.sort(np.concatenate([deg1_map[:,:,None], deg2_map[:,:,None]], axis=-1), axis=-1)
+    
+    deg_diff_map = np.abs(deg1_map - deg2_map)
+    # we only consider the smallest degree of intersect
+    deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
+    
+    # define available degree range
+    deg_range = [60, 120]
+    
+    corner_dict = {corner_info: [] for corner_info in range(4)}
+    inter_points = []
+    for i in range(inter_pts.shape[0]):
+        for j in range(i + 1, inter_pts.shape[1]):
+            # i, j > line index, always i < j
+            x, y = inter_pts[i, j, :]
+            deg1, deg2 = deg_sort[i, j, :]
+            deg_diff = deg_diff_map[i, j]
+            
+            check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
+
+            outside_ratio = params['outside_ratio'] # over ratio >>> drop it!
+            inside_ratio = params['inside_ratio'] # over ratio >>> drop it!
+            check_distance = ((dist_inter_to_segment1[i,j,1] >= dist_segments[i] and \
+                                 dist_inter_to_segment1[i,j,0] <= dist_segments[i] * outside_ratio) or \
+                                (dist_inter_to_segment1[i,j,1] <= dist_segments[i] and \
+                                 dist_inter_to_segment1[i,j,0] <= dist_segments[i] * inside_ratio)) and \
+                             ((dist_inter_to_segment2[i,j,1] >= dist_segments[j] and \
+                                 dist_inter_to_segment2[i,j,0] <= dist_segments[j] * outside_ratio) or \
+                                (dist_inter_to_segment2[i,j,1] <= dist_segments[j] and \
+                                 dist_inter_to_segment2[i,j,0] <= dist_segments[j] * inside_ratio))
+
+            if check_degree and check_distance:
+                corner_info = None
+
+                if (deg1 >= 0 and deg1 <= 45 and deg2 >=45 and deg2 <= 120) or \
+                     (deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
+                    corner_info, color_info = 0, 'blue'
+                elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
+                    corner_info, color_info = 1, 'green'
+                elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
+                    corner_info, color_info = 2, 'black'
+                elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
+                     (deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
+                    corner_info, color_info = 3, 'cyan'
+                else:
+                    corner_info, color_info = 4, 'red' # we don't use it
+                    continue
+                
+                corner_dict[corner_info].append([x, y, i, j])
+                inter_points.append([x, y])
+    
+    square_list = []
+    connect_list = []
+    segments_list = []
+    for corner0 in corner_dict[0]:
+        for corner1 in corner_dict[1]:
+            connect01 = False
+            for corner0_line in corner0[2:]:
+                if corner0_line in corner1[2:]:
+                    connect01 = True
+                    break
+            if connect01:
+                for corner2 in corner_dict[2]:
+                    connect12 = False
+                    for corner1_line in corner1[2:]:
+                        if corner1_line in corner2[2:]:
+                            connect12 = True
+                            break
+                    if connect12:
+                        for corner3 in corner_dict[3]:
+                            connect23 = False
+                            for corner2_line in corner2[2:]:
+                                if corner2_line in corner3[2:]:
+                                    connect23 = True
+                                    break
+                            if connect23:
+                                for corner3_line in corner3[2:]:
+                                    if corner3_line in corner0[2:]:
+                                        # SQUARE!!!
+                                        '''
+                                        0 -- 1
+                                        |    |
+                                        3 -- 2
+                                        square_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
+                                            ...
+                                        connect_list:
+                                            order: 01 > 12 > 23 > 30
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
+                                            ...
+                                        segments_list:
+                                            order: 0 > 1 > 2 > 3
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
+                                            ...
+                                        '''
+                                        square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
+                                        connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
+                                        segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
+    
+    def check_outside_inside(segments_info, connect_idx):
+        # return 'outside or inside', min distance, cover_param, peri_param
+        if connect_idx == segments_info[0]:
+            check_dist_mat = dist_inter_to_segment1
+        else:
+            check_dist_mat = dist_inter_to_segment2
+        
+        i, j = segments_info
+        min_dist, max_dist = check_dist_mat[i, j, :]
+        connect_dist = dist_segments[connect_idx]
+        if max_dist > connect_dist:
+            return 'outside', min_dist, 0, 1
+        else:
+            return 'inside', min_dist, -1, -1
+
+
+    top_square = None
+
+    try:
+        map_size = input_shape[0] / 2
+        squares = np.array(square_list).reshape([-1,4,2])
+        score_array = []
+        connect_array = np.array(connect_list)
+        segments_array = np.array(segments_list).reshape([-1,4,2])
+        
+        # get degree of corners:
+        squares_rollup = np.roll(squares, 1, axis=1)
+        squares_rolldown = np.roll(squares, -1, axis=1)
+        vec1 = squares_rollup - squares
+        normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
+        vec2 = squares_rolldown - squares
+        normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
+        inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1) # [n_squares, 4]
+        squares_degree = np.arccos(inner_products) * 180 / np.pi # [n_squares, 4]
+
+        # get square score
+        overlap_scores = []
+        degree_scores = []
+        length_scores = []
+        
+        for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
+            '''
+            0 -- 1
+            |    |
+            3 -- 2
+            
+            # segments: [4, 2]
+            # connects: [4]
+            '''
+            
+            ###################################### OVERLAP SCORES
+            cover = 0
+            perimeter = 0
+            # check 0 > 1 > 2 > 3
+            square_length = []
+             
+            for start_idx in range(4):
+                end_idx = (start_idx + 1) % 4
+                
+                connect_idx = connects[start_idx] # segment idx of segment01
+                start_segments = segments[start_idx]
+                end_segments = segments[end_idx]
+                
+                start_point = square[start_idx]
+                end_point = square[end_idx]
+                
+                # check whether outside or inside
+                start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments, connect_idx)
+                end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
+                
+                cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
+                perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
+                
+                square_length.append(dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
+            
+            overlap_scores.append(cover / perimeter)    
+            ######################################
+            ###################################### DEGREE SCORES
+            '''
+            deg0 vs deg2
+            deg1 vs deg3
+            '''
+            deg0, deg1, deg2, deg3 = degree
+            deg_ratio1 = deg0 / deg2
+            if deg_ratio1 > 1.0:
+                deg_ratio1 = 1 / deg_ratio1
+            deg_ratio2 = deg1 / deg3
+            if deg_ratio2 > 1.0:
+                deg_ratio2 = 1 / deg_ratio2
+            degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
+            ######################################
+            ###################################### LENGTH SCORES
+            '''
+            len0 vs len2
+            len1 vs len3
+            '''
+            len0, len1, len2, len3 = square_length
+            len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
+            len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
+            length_scores.append((len_ratio1 + len_ratio2) / 2)
+
+            ######################################
+        
+        overlap_scores = np.array(overlap_scores)
+        overlap_scores /= np.max(overlap_scores)
+            
+        degree_scores = np.array(degree_scores)
+        #degree_scores /= np.max(degree_scores)
+        
+        length_scores = np.array(length_scores)
+
+        ###################################### AREA SCORES
+        area_scores = np.reshape(squares, [-1, 4, 2])
+        area_x = area_scores[:,:,0]
+        area_y = area_scores[:,:,1]
+        correction = area_x[:,-1] * area_y[:,0] - area_y[:,-1] * area_x[:,0]
+        area_scores = np.sum(area_x[:,:-1] * area_y[:,1:], axis=-1) - np.sum(area_y[:,:-1] * area_x[:,1:], axis=-1)
+        area_scores = 0.5 * np.abs(area_scores + correction)
+        area_scores /= (map_size * map_size) #np.max(area_scores)
+        ######################################
+        
+        ###################################### CENTER SCORES
+        centers = np.array([[256 // 2, 256 // 2]], dtype='float32') # [1, 2]
+        # squares: [n, 4, 2]
+        square_centers = np.mean(squares, axis=1) # [n, 2]
+        center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
+        center_scores = center2center / (map_size / np.sqrt(2.0))
+
+
+        '''
+        score_w = [overlap, degree, area, center, length]
+        '''
+        score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
+        score_array = params['w_overlap'] * overlap_scores \
+                        + params['w_degree'] * degree_scores \
+                        + params['w_area'] * area_scores \
+                        - params['w_center'] * center_scores \
+                        + params['w_length'] * length_scores
+
+        best_square = []
+
+        sorted_idx = np.argsort(score_array)[::-1]
+        score_array = score_array[sorted_idx]
+        squares = squares[sorted_idx]
+
+    except Exception as e:
+        pass
+    
+    try:
+        new_segments[:,0] = new_segments[:,0] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:,1] = new_segments[:,1] * 2 / input_shape[0] * original_shape[0]
+        new_segments[:,2] = new_segments[:,2] * 2 / input_shape[1] * original_shape[1]
+        new_segments[:,3] = new_segments[:,3] * 2 / input_shape[0] * original_shape[0]
+    except:
+        new_segments = []
+    
+    try:
+        squares[:,:,0] = squares[:,:,0] * 2 / input_shape[1] * original_shape[1]
+        squares[:,:,1] = squares[:,:,1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        squares = []
+        score_array = []
+
+    try:
+        inter_points = np.array(inter_points)
+        inter_points[:,0] = inter_points[:,0] * 2 / input_shape[1] * original_shape[1]
+        inter_points[:,1] = inter_points[:,1] * 2 / input_shape[0] * original_shape[0]
+    except:
+        inter_points = []
+        
+
+    return new_segments, squares, score_array, inter_points