Gradio Hub Web Demo (#2)

* gradio demo

* fix

* imports

* model path

* article

* update readme

Author: AK391

README.md

@@ -60,6 +60,9 @@ The notebook supports interactive UI with [Gradio](https://gradio.app/) as below
 
 <img src=".github/gradio_example.png" height="350">
 
+### Gradio Hub Web Demo
+[Gradio Web Demo](https://gradio.app/g/AK391/mlsd)
+
 ## Citation
 If you find *M-LSD* useful in your project, please consider to cite the following paper.
 

linedemo.py

@@ -0,0 +1,48 @@
+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()

requirements.txt

@@ -3,3 +3,4 @@ opencv-python
 pillow
 tensorflow-gpu
 Flask
+gradio