Ok, I will look into how to do that. But do you have an idea why some of
the letters go missing?
On Sunday, January 2, 2022 at 1:10:45 PM UTC-8 zdenop wrote:
> All images you presented have the same size and the text is always in the
> same regions.
> So you can create a mask for these regions and apply it to the
> thresholded input images. This could give you extra speed as you do not
> need to create a mask for each image individually...
>
> Zdenko
>
>
> ne 2. 1. 2022 o 21:01 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>
>> I tried the opencv version, but it fails with images like this:
>> [image: drop12.png][image: hi.png]
>>
>> On Saturday, January 1, 2022 at 12:29:34 PM UTC-8 zdenop wrote:
>>
>>> And here is opencv2 version with IMO better quality:
>>>
>>>
>>> import cv2
>>> data = cv2.imread("mina.png")
>>> mask_text = cv2.inRange(data, (51, 51, 51), (51, 51, 51))
>>>
>>> # Morph open to remove noise
>>> kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
>>> morph = cv2.morphologyEx(mask_text, cv2.MORPH_OPEN, kernel, iterations=1)
>>>
>>> kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 4))
>>> dilate = cv2.dilate(morph, kernel, iterations=4)
>>>
>>> tresh = cv2.threshold(cv2.cvtColor(data, cv2.COLOR_BGR2GRAY),
>>> 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
>>> image_final = cv2.bitwise_and(tresh, tresh, mask=dilate)
>>> # replace background with white
>>> mask1 = np.zeros(( image_final.shape[0] + 2, image_final.shape[1] +
>>> 2), np.uint8)
>>> cv2.floodFill(image_final, mask1, (0, 0), 255)
>>>
>>> display(Image.fromarray(image_final))
>>>
>>>
>>> [image: image.png]
>>>
>>>
>>> Zdenko
>>>
>>>
>>> so 1. 1. 2022 o 20:40 Zdenko Podobny <zde...@gmail.com> napísal(a):
>>>
>>>> What is your code? Does it work on your local computer?
>>>>
>>>> BTW: here is proven numpy code:
>>>>
>>>> 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('mina.png').convert("RGB"))
>>>>
>>>> *A, B = image.shape
>>>> mask = (image.reshape((-1,B)) ==
>>>> np.array(filter_colors)[:,None]).all(-1).any(0).reshape(A)
>>>> img = Image.fromarray(~mask)
>>>>
>>>>
>>>> Zdenko
>>>>
>>>>
>>>> so 1. 1. 2022 o 19:49 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>>>>
>>>>> i managed to install tesseract 5, but the numpy mask doesn't work now.
>>>>> it makes pictures like:
>>>>> [image: image.png]
>>>>> not:
>>>>> [image: image.png]
>>>>>
>>>>>
>>>>> Dockerfile:
>>>>> # syntax=docker/dockerfile:1 ARG TOKEN FROM ubuntu:18.04 RUN apt-get
>>>>> update RUN apt-get install -y software-properties-common RUN apt-get
>>>>> install -y python3.8 RUN apt-get install -y python3-pip RUN apt-get
>>>>> update RUN apt-get install -y build-essential RUN apt-get install -y
>>>>> python3-pil COPY requirements.txt requirements.txt RUN pip3 install
>>>>> -r requirements.txt RUN apt-get update RUN add-apt-repository
>>>>> ppa:alex-p/tesseract-ocr5 RUN apt-get update RUN apt-get install -y
>>>>> tesseract-ocr COPY . . CMD ["python3", "bot.py"]
>>>>>
>>>>> On Friday, December 31, 2021 at 10:29:59 AM UTC-8 Cyrus Yip wrote:
>>>>>
>>>>>> better link? <https://www.toptal.com/developers/hastebin/nonepalihe>
>>>>>>
>>>>>> On Friday, December 31, 2021 at 10:27:41 AM UTC-8 Cyrus Yip wrote:
>>>>>>
>>>>>>> Right now I'm installing tesseract 4 in docker with
>>>>>>> RUN apt-get install -y tesseract-ocr
>>>>>>> That might be a reason why it's way slower than on my computer, how
>>>>>>> can I install tesseract 5?
>>>>>>>
>>>>>>> Dockerfile # syntax=docker/dockerfile:1
>>>>>>>
>>>>>>> ARG TOKEN
>>>>>>>
>>>>>>> FROM python:3.8-slim-buster
>>>>>>>
>>>>>>> RUN apt-get update
>>>>>>> RUN apt-get install -y software-properties-common
>>>>>>> RUN apt-get update
>>>>>>> RUN add-apt-repository ppa:alex-p/tesseract-ocr-devel
>>>>>>>
>>>>>>> RUN apt-get update
>>>>>>> RUN apt-get install -y build-essential
>>>>>>>
>>>>>>> COPY requirements.txt requirements.txt
>>>>>>> RUN pip3 install -r requirements.txt
>>>>>>>
>>>>>>> COPY . .
>>>>>>>
>>>>>>> RUN apt-get install -y tesseract
>>>>>>>
>>>>>>> CMD ["python3", "bot.py"]
>>>>>>>
>>>>>>> Build logs
>>>>>>> <https://appbuild-logs-ams3.ams3.digitaloceanspaces.com/a7609af2-64e1-4ba2-8555-87a4fac8a37f/9420eaef-131e-410f-8add-bbfb870b2693/981a4c35-45d7-41b5-8619-3d9125d60c25/build.log?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=2JPIHVK4OTM6S5VRFBCK%2F20211231%2Fams3%2Fs3%2Faws4_request&X-Amz-Date=20211231T182608Z&X-Amz-Expires=900&X-Amz-SignedHeaders=host&X-Amz-Signature=3ae248ce9fb9e6fef0c71955d9cd9496feb8311162bdda8921750a21544f79a6>
>>>>>>>
>>>>>>>
>>>>>>> On Friday, December 31, 2021 at 3:18:18 AM UTC-8 zdenop wrote:
>>>>>>>
>>>>>>>> You are right - np.isin is working another way than I expected
>>>>>>>> (it does not match tuples, but individual values at tuples) and by
>>>>>>>> coincidence, it produces similar results as your code.
>>>>>>>>
>>>>>>>> Here is updated code that produces the same result as PIL. It is
>>>>>>>> faster but with an increasing number of colors in filter_colors, it
>>>>>>>> will
>>>>>>>> be slower.
>>>>>>>>
>>>>>>>> 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.array([], dtype=bool)
>>>>>>>> for color in filter_colors:
>>>>>>>> if mask.size == 0:
>>>>>>>> mask = (image == color).all(-1)
>>>>>>>> else:
>>>>>>>> mask = mask | (image == color).all(-1)
>>>>>>>> img = Image.fromarray(~mask)
>>>>>>>>
>>>>>>>>
>>>>>>>> Zdenko
>>>>>>>>
>>>>>>>>
>>>>>>>> pi 31. 12. 2021 o 1:45 Cyrus Yip <cyrus...@gmail.com> napísal(a):
>>>>>>>>
>>>>>>>>> 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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