For some reason, using the numpy array has a different result than mine.
Numpy array:
[image: hi.png]
Loop through pixels:
[image: hi.png]
The second was is more accurate but way slower.
On Thursday, December 30, 2021 at 11:43:01 AM UTC-8 zdenop wrote:
> try this:
>
> import numpy as np
> from PIL import Image
>
> filter_colors = [(51, 51, 51), (69, 69, 65), (65, 64, 60), (59, 58, 56),
> (67, 66, 62),
>
> (67, 67, 63), (67, 67, 62), (53, 53, 53), (54, 54, 53), (61, 61,
> 58),
> (62, 62, 60), (55, 55, 54), (59, 59, 57), (56, 56, 55)]
> image = np.array(Image.open('mai.png').convert("RGB"))
> mask = np.isin(image, filter_colors, invert=True)
> img = Image.fromarray(mask.any(axis=2))
>
>
> Zdenko
>
>
> št 30. 12. 2021 o 18:14 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>
>> I also tried many things like cropping, colour changing, colour
>> replacing, and mixing them together.
>>
>> I landed on checking if a pixel is not one of these:
>>
>> [(51, 51, 51), (69, 69, 65), (65, 64, 60), (59, 58, 56), (67, 66, 62),
>> (67, 67, 63), (67, 67, 62), (53, 53, 53), (54, 54, 53), (61, 61, 58), (62,
>> 62, 60), (55, 55, 54), (59, 59, 57), (56, 56, 55)]
>>
>> colours, replace it with white. It is pretty accurate but is there a way
>> to do this with numpy arrays?
>>
>> (code)
>> for x in range(im.width):
>> if pixels[x, y] not in [(51, 51, 51), (69, 69, 65), (65, 64, 60),
>> (59, 58, 56), (67, 66, 62), (67, 67, 63), (67, 67, 62), (53, 53, 53), (54,
>> 54, 53), (61, 61, 58), (62, 62, 60), (55, 55, 54), (59, 59, 57), (56, 56,
>> 55)]:
>> pixels[x, y] = (255, 255, 255)
>> On Thursday, December 30, 2021 at 8:46:51 AM UTC-8 zdenop wrote:
>>
>>> OK. I played a little bit ;-):
>>>
>>> I tested the speed of your code with your image:
>>>
>>> import timeit
>>>
>>> pil_color_replace = """
>>> from PIL import Image
>>>
>>> im = Image.open('mai.png').convert("RGB")
>>>
>>> pixdata = im.load()
>>> for y in range(im.height):
>>> for x in range(im.width):
>>> if pixdata[x, y] != (51, 51, 51):
>>> pixdata[x, y] = (255, 255, 255)
>>> """
>>>
>>> elapsed_time = timeit.timeit(pil_color_replace, number=100)/100
>>> print(f"duration: {elapsed_time:.4} seconds")
>>>
>>> I got an average speed 0.08547 seconds on my computer.
>>> On internet I found the suggestion to use numpy for this and I finished
>>> with the following code:
>>>
>>> np_color_replace_rgb = """
>>> import numpy as np
>>> from PIL import Image
>>>
>>> data = np.array(Image.open('mai.png').convert("RGB"))
>>> mask = (data == [51, 51, 51]).all(-1)
>>> img = Image.fromarray(np.invert(mask))
>>> """
>>>
>>> elapsed_time = timeit.timeit(np_color_replace_rgb, number=100)/100
>>> print(f"duration: {elapsed_time:.4} seconds")
>>>
>>> I got an average speed 0.01774 seconds e.g. 4.8 faster than the PIL code.
>>> It is a little bit cheating as it does not replace colors - just take a
>>> mask of target color and return it as a binarized image, what is exactly
>>> what you need for OCR ;-)
>>>
>>> Also, I would like to point out that the result OCR output is not so
>>> perfect (compared to OCR of unmodified text areas), as this kind of
>>> binarization is very simple.
>>>
>>>
>>> Zdenko
>>>
>>>
>>> št 30. 12. 2021 o 11:19 Zdenko Podobny <zde...@gmail.com> napísal(a):
>>>
>>>> Just made your tests ;-)
>>>>
>>>> You can use tesserocr (maybe quite difficult installation if you are on
>>>> windows) instead of pytesseract (e.g. initialize tesseract API once and
>>>> use
>>>> is multiple times). But it does not provide DICT output.
>>>>
>>>>
>>>> Zdenko
>>>>
>>>>
>>>> st 29. 12. 2021 o 21:18 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>>>>
>>>>> but won't multiple ocr's and crops use a lot of time?
>>>>>
>>>>> On Wednesday, December 29, 2021 at 10:15:26 AM UTC-8 zdenop wrote:
>>>>>
>>>>>> IMO if the text is always in the same area, cropping and OCR just
>>>>>> that area will be faster.
>>>>>>
>>>>>> Zdenko
>>>>>>
>>>>>>
>>>>>> st 29. 12. 2021 o 18:58 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>>>>>>
>>>>>>> I played around a bit and replacing all colours except for text
>>>>>>> colour and it works pretty well!
>>>>>>>
>>>>>>> The only thing is replacing colours with:
>>>>>>> im = im.convert("RGB")
>>>>>>> pixdata = im.load()
>>>>>>> for y in range(im.height):
>>>>>>> for x in range(im.width):
>>>>>>> if pixdata[x, y] != (51, 51, 51):
>>>>>>> pixdata[x, y] = (255, 255, 255)
>>>>>>> is a bit slow. Do you know a better way to replace pixels in python?
>>>>>>> I don't know if this is off topic.
>>>>>>> On Wednesday, December 29, 2021 at 9:46:13 AM UTC-8 zdenop wrote:
>>>>>>>
>>>>>>>> If you properly crop text areas you get good output. E.g.
>>>>>>>>
>>>>>>>> [image: r_cropped.png]
>>>>>>>>
>>>>>>>> > tesseract r_cropped.png - --dpi 300
>>>>>>>>
>>>>>>>> Rascal Does Not Dream
>>>>>>>> of Bunny Girl Senpai
>>>>>>>>
>>>>>>>> Zdenko
>>>>>>>>
>>>>>>>>
>>>>>>>> st 29. 12. 2021 o 18:21 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>>>>>>>>
>>>>>>>>> here is an example of an image i would like to use ocr on:
>>>>>>>>> [image: drop8.png]
>>>>>>>>> I would like the results to be like:
>>>>>>>>> ["Naruto Uzumaki Naruto", "Mai Sakurajima Rascal Does Not Dream of
>>>>>>>>> Bunny Girl Senpai", "Keqing Genshin Impact"]
>>>>>>>>>
>>>>>>>>> Right now I'm using
>>>>>>>>> region1 = im.crop((0, 55, im.width, 110))
>>>>>>>>> region2 = im.crop((0, 312, im.width, 360))
>>>>>>>>> image = Image.new("RGB", (im.width, region1.height +
>>>>>>>>> region2.height + 20))
>>>>>>>>> image.paste(region1)
>>>>>>>>> image.paste(region2, (0, region1.height + 20))
>>>>>>>>> results = pytesseract.image_to_data(image,
>>>>>>>>> output_type=pytesseract.Output.DICT)
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> the processed image looks like
>>>>>>>>> [image: hi.png]
>>>>>>>>> but getting results like:
>>>>>>>>> [' ', '»MaiSakurajima¥RascalDoesNotDreamofBunnyGirlSenpai',
>>>>>>>>> 'iGenshinImpact']
>>>>>>>>>
>>>>>>>>> How do I optimize the image/configs so the ocr is more accurate?
>>>>>>>>>
>>>>>>>>> Thank you.
>>>>>>>>>
>>>>>>>>> --
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>>>>>>>>>
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>>>>>>>>> .
>>>>>>>>>
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>>>>>>> .
>>>>>>>
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>>>>>
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>>>>> .
>>>>>
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> To view this discussion on the web visit
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>>
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>> .
>>
>
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