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| author | gtoderici <gtoderici@users.noreply.github.com> | 2016-10-12 17:26:22 -0700 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2016-10-12 17:26:22 -0700 |
| commit | b98313851963e9b0a92825e37f1722fedf07067b (patch) | |
| tree | 1ccdce8bb5484bb855f6b552d874d8394f40b797 | |
| parent | 4524885253aafb9928e0859fb1505ce9c22ab06c (diff) | |
Change interpolation method from bicubic to area.
Change interpolation method from bicubic to area. This is necessary because when downsizing high resolution images, the bicubic algorithm generates high frequency noise (for example, try resizing an image containing gravel with bicubic vs. area).
I guess ideally there should be a check to determine whether it's upsampling/downsampling and choose Lanczos for upsampling. However, given how slow this is... maybe we only need to worry about downsampling?
| -rw-r--r-- | neural_style.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/neural_style.py b/neural_style.py index b62de21..6dc2cf5 100644 --- a/neural_style.py +++ b/neural_style.py @@ -707,10 +707,10 @@ def get_content_image(content_img): # resize if > max size if h > w and h > mx: w = (float(mx) / float(h)) * w - img = cv2.resize(img, dsize=(int(w), mx), interpolation=cv2.INTER_CUBIC) + img = cv2.resize(img, dsize=(int(w), mx), interpolation=cv2.INTER_AREA) if w > mx: h = (float(mx) / float(w)) * h - img = cv2.resize(img, dsize=(mx, int(h)), interpolation=cv2.INTER_CUBIC) + img = cv2.resize(img, dsize=(mx, int(h)), interpolation=cv2.INTER_AREA) img = preprocess(img, vgg19_mean) return img @@ -721,7 +721,7 @@ def get_style_images(content_img): path = os.path.join(args.style_imgs_dir, style_fn) # bgr image img = cv2.imread(path, cv2.IMREAD_COLOR).astype(np.float32) - img = cv2.resize(img, dsize=(cw, ch)) + img = cv2.resize(img, dsize=(cw, ch), interpolation=cv2.INTER_AREA) img = preprocess(img, vgg19_mean) style_imgs.append(img) return style_imgs @@ -735,7 +735,7 @@ def get_noise_image(noise_ratio, content_img): def get_mask_image(mask_img, width, height): path = os.path.join(args.content_img_dir, mask_img) img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) - img = cv2.resize(img, dsize=(width, height)).astype(np.float32) + img = cv2.resize(img, dsize=(width, height), interpolation=cv2.INTER_AREA).astype(np.float32) mx = np.amax(img) img /= mx return img @@ -836,4 +836,4 @@ def main(): else: render_single_image() if __name__ == '__main__': - main()
\ No newline at end of file + main() |