First commit

README.md

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 # LineCloser
 Unofficial Keras implementation of Joint Gap Detection and Inpainting of Line Drawings.
+
+## Overview
+Joint gap for line-drawings. Model1 uses network from the paper. For stable training, BN was added for all Conv2D. Model2 uses common network for inpaint. Training code will be uploaded soon.
+
+## Dependencies
+* Keras2 (Tensorflow backend)
+* OpenCV3
+
+## Usage
+1. Set up directories.
+
+2. Download the model from release and put it in the same folder with code.
+
+3. Run `predict.py` for prediction.
+
+## Models
+Models are licensed under a CC-BY-NC-SA 4.0 international license.
+
+From **Project HAT** by Hepesu With :heart:

predict.py

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+import os
+
+# Try running on CPU
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+import numpy as np
+import cv2
+from keras.models import load_model
+
+R = 2 ** 4
+MODEL_NAME = 'model1.h5'
+
+model = load_model(MODEL_NAME)
+model.summary()
+
+for root, dirs, files in os.walk('input', topdown=False):
+    for name in files:
+        print(os.path.join(root, name))
+
+        im = cv2.imread(os.path.join(root, name), cv2.IMREAD_GRAYSCALE)
+
+        im_predict = cv2.resize(im, (im.shape[1] // R * R, im.shape[0] // R * R))
+        im_predict = np.reshape(im_predict, (1, im_predict.shape[0], im_predict.shape[1], 1))
+        im_predict = im_predict.astype(np.float32) / 255.
+
+        result = model.predict(im_predict)
+
+        result = np.squeeze(result) * 255.
+        im_res = cv2.resize(result, (im.shape[1], im.shape[0]))
+
+        cv2.imwrite(os.path.join('output', name), im_res)