image-preprocessing | 易学教程

Programmatically divide scanned images into separate images

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问题 In order to improve OCR quality, I need to preprocess my scanned images. Sometimes I need to OCR the image with few pictures (components on the page and they are at different angles - for example, a few paper documents scanned at one time), for example: Is it possible to automatically programmatically divide such images into separate images that will contain every logical document? For example with a tool like ImageMagick or something else? Is there any solutions/technics exists for such

How to implement ZCA Whitening? Python

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问题 Im trying to implement ZCA whitening and found some articles to do it, but they are a bit confusing.. can someone shine a light for me? Any tip or help is appreciated! Here is the articles i read : http://courses.media.mit.edu/2010fall/mas622j/whiten.pdf http://bbabenko.tumblr.com/post/86756017649/learning-low-level-vision-feautres-in-10-lines-of I tried several things but most of them i didnt understand and i got locked at some step. Right now i have this as base to start again : dtype = np

How to implement ZCA Whitening? Python

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Convert grayscale png to RGB png image

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问题 I have a dataset of medical images in grayscale Png format which must be converted to RGB format. Tried many solutions but in vain. 回答1: If you want to just convert the format, the following method will help you: In python3, using PILLOW and Numpy: From PIL import Image import numpy as np im = Image.open(path/to/image, 'r').convert('L') im = np.stack((im,)*3, axis=-1) im = Image.fromarray(im) im.save(path/to/save) But if you want to colorize the image, know that colorization is an well-known

preprocessing images generated using keras function ImageDataGenerator() to train resnet50 model

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问题 I am trying to train resnet50 model for image classification problem.I have loaded the 'imagenet' pretrained weights before training the model on the image dataset I have. I am using keras function flow_from_directory() to load images from directory. train_datagen = ImageDataGenerator() train_generator = train_datagen.flow_from_directory( './train_qcut_2_classes', batch_size=batch_size, shuffle=True, target_size=input_size[1:], class_mode='categorical') test_datagen = ImageDataGenerator()

Keras VGG16 preprocess_input modes

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问题 I'm using the Keras VGG16 model. I've seen it there is a preprocess_input method to use in conjunction with the VGG16 model. This method appears to call the preprocess_input method in imagenet_utils.py which (depending on the case) calls _preprocess_numpy_input method in imagenet_utils.py. The preprocess_input has a mode argument which expects "caffe", "tf", or "torch". If I'm using the model in Keras with TensorFlow backend, should I absolutely use mode="tf" ? If yes, is this because the

How to determine amount of augmented images in Keras?

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问题 I am working with Keras 2.0.0 and I'd like to train a deep model with a huge amount of parameters on a GPU. As my data are big, I have to use the ImageDataGenerator . To be honest, I want to abuse the ImageDataGenerator in that sense, that I don't want to perform any augmentations. I just want to put my training images into batches (and rescale them), so I can feed them to model.fit_generator . I adapted the code from here and did some small changes according to my data (i.e. changing binary

Numpy:zero mean data and standardization

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问题 I saw in tutorial (there were no further explanation) that we can process data to zero mean with x -= np.mean(x, axis=0) and normalize data with x /= np.std(x, axis=0) . Can anyone elaborate on these two pieces on code, only thing I got from documentations is that np.mean calculates arithmetic mean calculates mean along specific axis and np.std does so for standard deviation. 回答1: This is a so called zscore. SciPy has a utility for it: >>> from scipy import stats >>> stats.zscore([ 0.7972, 0

Image Preprocessing for OCR - Tessaract

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问题 Obviously this image is pretty tough as it is low clarity and is not a real word. However, with this code, I'm detecting nothing close: import pytesseract from PIL import Image, ImageEnhance, ImageFilter image_name = 'NedNoodleArms.jpg' im = Image.open(image_name) im = im.filter(ImageFilter.MedianFilter()) enhancer = ImageEnhance.Contrast(im) im = enhancer.enhance(2) im = im.convert('1') im.save(image_name) text = pytesseract.image_to_string(Image.open(image_name)) print(text) outputs ,

Image Preprocessing for OCR - Tessaract

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Obviously this image is pretty tough as it is low clarity and is not a real word. However, with this code, I'm detecting nothing close: import pytesseract from PIL import Image, ImageEnhance, ImageFilter image_name = 'NedNoodleArms.jpg' im = Image.open(image_name) im = im.filter(ImageFilter.MedianFilter()) enhancer = ImageEnhance.Contrast(im) im = enhancer.enhance(2) im = im.convert('1') im.save(image_name) text = pytesseract.image_to_string(Image.open(image_name)) print(text) outputs , Mdﬁaodﬁamms Any ideas here? The image my contrasting function produces is: Which looks decent? I don't have