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424 строки
11 KiB
Plaintext
Generated
424 строки
11 KiB
Plaintext
Generated
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dcaa3da1",
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"metadata": {},
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"source": [
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"# Notebook for text extraction on image\n",
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"Inga Ulusoy, SSC, July 2022"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cf362e60",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from IPython.display import Image, display\n",
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"import misinformation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6da3a7aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"images = misinformation.find_files(limit=1000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf811ce0",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in images:\n",
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" display(Image(filename=i))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "366e2060",
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"metadata": {},
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"outputs": [],
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"source": [
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"# start with only English\n",
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"mysubfiles = [i for i in images if \"eng\" in i]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b330b267",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in mysubfiles:\n",
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" display(Image(filename=i))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b32409f",
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"metadata": {},
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"outputs": [],
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"source": [
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"mydict = misinformation.utils.initialize_dict(mysubfiles)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "07b7a7a3",
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"metadata": {},
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"source": [
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"# Pre-process the images: Convert to greyscale and increase contrast"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4cacfb0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"from matplotlib import pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c94b02cb-9e96-4812-8448-8bc731bfd8aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install matplotlib\n",
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"! pip install numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8a2d3057",
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"metadata": {},
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"outputs": [],
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"source": [
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"def preprocess(filename):\n",
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" \"\"\"Preprocess the image to enhance features for extraction.\"\"\"\n",
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" image = cv2.imread(filename)\n",
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" # preserve the original image\n",
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" # original = image.copy()\n",
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" # Grayscale, Gaussian blur, Otsu's threshold\n",
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" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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" # sharpen contrast by first smoothing and then substracting the smoothed and thresholded version\n",
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" sharpened = unsharp_mask(gray, amount=1.1, threshold=0.1)\n",
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" inverted = invert_image(sharpened)\n",
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" return gray, sharpened, inverted\n",
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"\n",
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"\n",
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"# use unsharp mask algorithm from opencv\n",
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"# https://docs.opencv.org/4.x/d1/d10/classcv_1_1MatExpr.html#details\n",
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"def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):\n",
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" \"\"\"Return a sharpened version of the image, using an unsharp mask.\n",
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" Amount: 1 is neutral, higher values result in shaprer images. threshold is the value below which the difference between blurred and original image gets discarded.\"\"\"\n",
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" blurred = cv2.GaussianBlur(image, kernel_size, sigma)\n",
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" sharpened = float(amount + 1) * image - float(amount) * blurred\n",
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" sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))\n",
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" sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))\n",
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" sharpened = sharpened.round().astype(np.uint8)\n",
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" if threshold > 0:\n",
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" low_contrast_mask = np.absolute(image - blurred) < threshold\n",
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" np.copyto(sharpened, image, where=low_contrast_mask)\n",
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" return sharpened\n",
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"\n",
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"\n",
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"def invert_image(image):\n",
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" return cv2.bitwise_not(image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a483868b",
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"metadata": {},
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"outputs": [],
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"source": [
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"grey_image = []\n",
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"for i in mysubfiles:\n",
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" grey_image.append(preprocess(i))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "08ed750d",
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"metadata": {},
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"outputs": [],
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"source": [
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"for image in grey_image:\n",
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" # disable default colormap in imshow\n",
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" plt.imshow(image[0], cmap=\"gray\", vmin=0, vmax=255)\n",
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" plt.imshow(image[1], cmap=\"gray\", vmin=0, vmax=255)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "71ac2229",
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"metadata": {},
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"source": [
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"mabe further preprocess in cropping out text regions..?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7786d09c",
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"metadata": {},
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"source": [
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"# Try out different libraries\n",
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"## The standard go-to tool that is slightly complicated: pytesseract\n",
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"Install tesseract and the language libraries:\n",
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"```\n",
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"sudo apt install tesseract-ocr \n",
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"sudo apt install tesseract-ocr-all \n",
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"sudo apt install imagemagick \n",
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"``` "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0d69504c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pytesseract import pytesseract\n",
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"\n",
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"pytesseract.tesseract_cmd = r\"tesseract\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b27c98c-b437-4c8b-8844-96d8718eea49",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install pytesseract"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "529de8d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"myimage = grey_image[1]\n",
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"plt.imshow(myimage[0], cmap=\"gray\", vmin=0, vmax=255)\n",
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"plt.show()\n",
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"\n",
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"plt.imshow(myimage[1], cmap=\"gray\", vmin=0, vmax=255)\n",
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"plt.show()\n",
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"\n",
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"plt.imshow(myimage[2], cmap=\"gray\", vmin=0, vmax=255)\n",
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"plt.show()\n",
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"\n",
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"text = pytesseract.image_to_string(myimage[0])\n",
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"print(text)\n",
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"text = pytesseract.image_to_string(myimage[1])\n",
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"print(text)\n",
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"text = pytesseract.image_to_string(myimage[2])\n",
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"print(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e25dd39e",
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"metadata": {},
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"source": [
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"Here we probably would need to compare extractopm from different preprocessed images and overlay in a final text."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36495f3f",
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"metadata": {},
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"outputs": [],
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"source": [
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"for image in mysubfiles:\n",
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" # Loading image using OpenCV\n",
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" img = cv2.imread(image)\n",
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"\n",
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" # Preprocessing image\n",
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" # Converting to grayscale\n",
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" gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
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"\n",
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" # creating Binary image by selecting proper threshold\n",
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" binary_image = cv2.threshold(\n",
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" gray_image, 130, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU\n",
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" )[1]\n",
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"\n",
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" # Inverting the image\n",
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" inverted_bin = cv2.bitwise_not(binary_image)\n",
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"\n",
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" # Some noise reduction\n",
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" kernel = np.ones((2, 2), np.uint8)\n",
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" processed_img = cv2.erode(inverted_bin, kernel, iterations=1)\n",
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" processed_img = cv2.dilate(processed_img, kernel, iterations=1)\n",
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"\n",
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" # Applying image_to_string method\n",
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" text = pytesseract.image_to_string(processed_img)\n",
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" plt.imshow(processed_img, cmap=\"gray\", vmin=0, vmax=255)\n",
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" plt.show()\n",
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" print(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d6532019",
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"metadata": {},
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"source": [
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"## keras-ocr\n",
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"Not sure how to create an image object without a url.\n",
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"https://keras-ocr.readthedocs.io/en/latest/examples/using_pretrained_models.html"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "44e38871",
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"metadata": {},
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"outputs": [],
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"source": [
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"import keras_ocr\n",
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"\n",
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"pipeline = keras_ocr.pipeline.Pipeline()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2bb55068-ddd4-4b90-ae94-90181980d3c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install keras-ocr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0002f2c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"images = [\n",
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" keras_ocr.tools.read(url)\n",
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" for url in [\n",
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" \"https://storage.googleapis.com/gcptutorials.com/examples/keras-ocr-img-1.jpg\",\n",
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" \"https://storage.googleapis.com/gcptutorials.com/examples/keras-ocr-img-2.png\",\n",
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" ]\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1567dc85",
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction_groups = pipeline.recognize(images)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb1ca152",
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"metadata": {},
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"outputs": [],
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"source": [
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"predicted_image_1 = prediction_groups[0]\n",
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"for text, box in predicted_image_1:\n",
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" print(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b8b929f",
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"metadata": {},
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"source": [
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"## google cloud vision API\n",
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"First 1000 images per month are free."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c6ecc88",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\n",
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" \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
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"] = \"/home/inga/projects/misinformation-project/misinformation-notes/seismic-bonfire-329406-412821a70264.json\"\n",
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"misinformation.explore_analysis(mydict, identify=\"text-on-image\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d54407ad",
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"metadata": {},
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"source": [
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"## MS Azure\n",
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"https://docs.microsoft.com/en-us/azure/search/cognitive-search-concept-image-scenarios"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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},
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"vscode": {
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"interpreter": {
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"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|