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Added new benchmark results and added new noise type in BadPhotoCopy.
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kwcckw committed Aug 5, 2023
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82 changes: 41 additions & 41 deletions README.md
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Expand Up @@ -59,49 +59,49 @@ The benchmark results are computed with Augraphy 8.20 and Tobacco3482 dataset (r

| Augmentation |Images per second|Memory usage (MB)|
|--------------------|----------------:|----------------:|
|BadPhotoCopy | 0.38| 178.20|
|BindingsAndFasteners| 29.34| 21.43|
|BleedThrough | 0.39| 684.69|
|BookBinding | 0.08| 611.74|
|Brightness | 5.93| 147.99|
|BrightnessTexturize | 2.12| 181.74|
|ColorPaper | 5.12| 105.66|
|ColorShift | 0.87| 114.26|
|BadPhotoCopy | 0.37| 178.20|
|BindingsAndFasteners| 31.40| 21.43|
|BleedThrough | 0.43| 684.69|
|BookBinding | 0.11| 611.74|
|Brightness | 5.75| 147.99|
|BrightnessTexturize | 2.16| 181.74|
|ColorPaper | 5.07| 105.66|
|ColorShift | 0.88| 114.26|
|DelaunayTessellation| 0.14| 60.00|
|DirtyDrum | 0.82| 482.63|
|DirtyRollers | 1.58| 249.52|
|Dithering | 3.96| 126.82|
|DotMatrix | 0.22| 80.53|
|Faxify | 2.02| 126.75|
|Folding | 2.85| 63.27|
|Gamma | 34.27| 25.36|
|Geometric | 189.37| 12.68|
|GlitchEffect | 1.48| 126.94|
|DirtyDrum | 0.96| 482.63|
|DirtyRollers | 1.63| 249.52|
|Dithering | 3.82| 126.82|
|DotMatrix | 0.64| 80.77|
|Faxify | 2.12| 126.74|
|Folding | 2.62| 63.28|
|Gamma | 34.39| 25.36|
|Geometric | 183.89| 12.68|
|GlitchEffect | 1.49| 126.94|
|Hollow | 0.21| 343.17|
|InkBleed | 3.93| 177.51|
|InkColorSwap | 5.22| 51.99|
|InkMottling | 7.78| 55.99|
|InkShifter | 0.17| 426.89|
|Jpeg | 6.32| 25.85|
|Letterpress | 0.45| 158.10|
|LightingGradient | 0.44| 638.31|
|LinesDegradation | 1.69| 175.38|
|LowInkPeriodicLines | 6.47| 12.74|
|LowInkRandomLines | 114.02| 12.74|
|LowLightNoise | 0.30| 481.95|
|Markup | 2.97| 154.12|
|NoiseTexturize | 0.73| 249.36|
|NoisyLines | 0.86| 446.76|
|PageBorder | 0.32| 201.22|
|PatternGenerator | 1.68| 51.52|
|ReflectedLight | 0.08| 109.97|
|Scribbles | 1.00| 97.13|
|SectionShift | 129.82| 12.96|
|ShadowCast | 0.95| 50.79|
|Squish | 0.26| 465.31|
|SubtleNoise | 1.68| 202.87|
|VoronoiTessellation | 0.08| 57.48|
|WaterMark | 2.70| 352.34|
|InkBleed | 4.00| 177.51|
|InkColorSwap | 5.10| 51.99|
|InkMottling | 7.60| 55.99|
|InkShifter | 0.20| 426.89|
|Jpeg | 6.38| 25.85|
|Letterpress | 0.46| 158.10|
|LightingGradient | 0.47| 638.31|
|LinesDegradation | 1.51| 175.38|
|LowInkPeriodicLines | 6.19| 12.74|
|LowInkRandomLines | 144.08| 12.74|
|LowLightNoise | 0.32| 481.95|
|Markup | 2.54| 154.12|
|NoiseTexturize | 0.96| 249.36|
|NoisyLines | 1.04| 446.89|
|PageBorder | 0.56| 191.84|
|PatternGenerator | 1.00| 51.53|
|ReflectedLight | 0.08| 109.92|
|Scribbles | 0.93| 99.10|
|SectionShift | 154.45| 12.95|
|ShadowCast | 0.86| 50.80|
|Squish | 0.99| 443.70|
|SubtleNoise | 1.82| 202.87|
|VoronoiTessellation | 0.09| 58.18|
|WaterMark | 2.55| 390.55|

# Alternative Augmentation Libraries
There are plenty of choices when it comes to [augmentation libraries](https://github.com/AgaMiko/data-augmentation-review). However, only Augraphy is designed to address everyday office automation needs associated with paper-oriented process distortions that come from printing, faxing, scanning and copy machines. Most other libraries focus on video and images pertinent to camera-oriented data sources and problem domains. Augraphy is focused on supporting problems related to automation of document images such as OCR, form recognition, form data extraction, document classification, barcode decoding, denoising, document restoration, identity document data extraction, document cropping, etc. Eventually, Augraphy will be able to support photo OCR problems with augmentations designed to emulate camera phone distortions.
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11 changes: 4 additions & 7 deletions augraphy/augmentations/badphotocopy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -214,7 +214,7 @@ def apply_augmentation(self, image):
# get image dimensions
ysize, xsize = image.shape[:2]

if self.noise_side == "random" or self.noise_side not in ["left", "top", "right", "bottom"]:
if self.noise_side == "random":
noise_side = random.choice(["left", "top", "right", "bottom"])
else:
noise_side = self.noise_side
Expand All @@ -238,19 +238,16 @@ def apply_augmentation(self, image):
noise_size=self.noise_size,
noise_sparsity=self.noise_sparsity,
noise_concentration=self.noise_concentration,
xsize=xsize,
ysize=ysize,
)

# new size after rotation
mask_ysize, mask_xsize = mask.shape[:2]

# rescale to 0 -255
mask = ((mask - np.min(mask)) / (np.max(mask) - np.min(mask))) * 255
if self.noise_value[0] > self.noise_value[1]:
self.noise_value[0] = self.noise_value[1]

# creates random small dot of noises
mask += random.randint(self.noise_value[0], self.noise_value[1])
mask[mask > 255] = 255
# resize back to original size
mask = cv2.resize(mask, (xsize, ysize)).astype("uint8")

# apply blur to mask of noise
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