From ca133566515c6e06a8fc3e7de05609a23983ae20 Mon Sep 17 00:00:00 2001
From: Harald Scheidl <harald@newpc.com>
Date: Mon, 1 Feb 2021 17:56:15 +0100
Subject: [PATCH] geometric data augmentation
---
src/SamplePreprocessor.py | 76 ++++++++++++++++++++++-----------------
1 file changed, 43 insertions(+), 33 deletions(-)
diff --git a/src/SamplePreprocessor.py b/src/SamplePreprocessor.py
index 5547eec..1d5ffd9 100644
--- a/src/SamplePreprocessor.py
+++ b/src/SamplePreprocessor.py
@@ -9,53 +9,62 @@ def preprocess(img, imgSize, dataAugmentation=False):
# there are damaged files in IAM dataset - just use black image instead
if img is None:
- img = np.zeros([imgSize[1], imgSize[0]])
+ img = np.zeros(imgSize[::-1])
+ # data augmentation
img = img.astype(np.float)
-
- # increase dataset size by applying random stretches to the images
if dataAugmentation:
+ # photometric data augmentation
if random.random() < 0.25:
rand_odd = lambda: random.randint(1, 3) * 2 + 1
img = cv2.GaussianBlur(img, (rand_odd(), rand_odd()), 0)
if random.random() < 0.25:
- img = cv2.dilate(img,np.ones((3,3)))
+ img = cv2.dilate(img, np.ones((3, 3)))
if random.random() < 0.25:
- img = cv2.erode(img,np.ones((3,3)))
+ img = cv2.erode(img, np.ones((3, 3)))
if random.random() < 0.5:
- img = img * (0.25 + random.random() * 0.75)
+ img = img * (0.5 + random.random() * 0.5)
if random.random() < 0.25:
- img = np.clip(img + (np.random.random(img.shape)-0.5) * random.randint(1, 50), 0, 255)
+ img = np.clip(img + (np.random.random(img.shape) - 0.5) * random.randint(1, 50), 0, 255)
if random.random() < 0.1:
img = 255 - img
- stretch = random.random() - 0.5 # -0.5 .. +0.5
- wStretched = max(int(img.shape[1] * (1 + stretch)), 1) # random width, but at least 1
- img = cv2.resize(img, (wStretched, img.shape[0])) # stretch horizontally by factor 0.5 .. 1.5
+ # geometric data augmentation
+ wt, ht = imgSize
+ h, w = img.shape
+ f = min(wt / w, ht / h)
+ fx = f * np.random.uniform(0.75, 1.25)
+ fy = f * np.random.uniform(0.75, 1.25)
- # create target image and copy sample image into it
- (wt, ht) = imgSize
- (h, w) = img.shape
- fx = w / wt
- fy = h / ht
- f = max(fx, fy)
- newSize = (max(min(wt, int(w / f)), 1),
- max(min(ht, int(h / f)), 1)) # scale according to f (result at least 1 and at most wt or ht)
- img = cv2.resize(img, newSize)
- target = np.ones([ht, wt]) * 127.5
-
- r_freedom = target.shape[0] - img.shape[0]
- c_freedom = target.shape[1] - img.shape[1]
-
- if dataAugmentation:
- r_off, c_off = random.randint(0, r_freedom), random.randint(0, c_freedom)
+ # random position around center
+ txc = (wt - w * fx) / 2
+ tyc = (ht - h * fy) / 2
+ freedom_x = wt // 10
+ freedom_y = ht // 10
+ tx = txc + np.random.randint(-freedom_x, freedom_x)
+ ty = tyc + np.random.randint(-freedom_y, freedom_y)
+
+ # map image into target image
+ M = np.float32([[fx, 0, tx], [0, fy, ty]])
+ target = np.ones(imgSize[::-1]) * 255 / 2
+ img = cv2.warpAffine(img, M, dsize=imgSize, dst=target, borderMode=cv2.BORDER_TRANSPARENT)
+
+ # no data augmentation
else:
- r_off, c_off = r_freedom // 2, c_freedom // 2
+ # center image
+ wt, ht = imgSize
+ h, w = img.shape
+ f = min(wt / w, ht / h)
+ tx = (wt - w * f) / 2
+ ty = (ht - h * f) / 2
- target[r_off:img.shape[0]+r_off, c_off:img.shape[1]+c_off] = img
+ # map image into target image
+ M = np.float32([[f, 0, tx], [0, f, ty]])
+ target = np.ones(imgSize[::-1]) * 255 / 2
+ img = cv2.warpAffine(img, M, dsize=imgSize, dst=target, borderMode=cv2.BORDER_TRANSPARENT)
# transpose for TF
- img = cv2.transpose(target)
+ img = cv2.transpose(img)
# convert to range [-1, 1]
img = img / 255 - 0.5
@@ -64,10 +73,11 @@ def preprocess(img, imgSize, dataAugmentation=False):
if __name__ == '__main__':
import matplotlib.pyplot as plt
+
img = cv2.imread('../data/test.png', cv2.IMREAD_GRAYSCALE)
- img_aug = preprocess(img, (128, 32), True)
+ img_aug = preprocess(img, (128, 32), False)
plt.subplot(121)
- plt.imshow(img)
+ plt.imshow(img, cmap='gray')
plt.subplot(122)
- plt.imshow(cv2.transpose(img_aug))
- plt.show()
\ No newline at end of file
+ plt.imshow(cv2.transpose(img_aug), cmap='gray')
+ plt.show()
--
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