increase parameter in getStructuringElement from 4 to 5 when creating mask:
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 5))
Zdenko
po 3. 1. 2022 o 0:08 Cyrus Yip <cyruscmy...@gmail.com> napísal(a):
> 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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>>>>>>>>>>>>>>>>>>> .
>>>>>>>>>>>>>>>>>>>
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