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326 строки
11 KiB
Plaintext
326 строки
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Color analysis of pictures\n",
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"\n",
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"\n",
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"\n",
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"This notebook shows primary color analysis of color image using K-Means algorithm.\n",
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"The output are N primary colors and their corresponding percentage.\n",
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"\n",
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"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
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"\n",
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"After that, we can import `ammico` and read in the files given a folder path."
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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": 1,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:06:51.157185Z",
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"iopub.status.busy": "2023-06-22T12:06:51.156533Z",
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"iopub.status.idle": "2023-06-22T12:06:51.165679Z",
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"shell.execute_reply": "2023-06-22T12:06:51.165046Z"
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}
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},
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"outputs": [],
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"source": [
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"# if running on google colab\n",
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"# flake8-noqa-cell\n",
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"import os\n",
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"\n",
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"if \"google.colab\" in str(get_ipython()):\n",
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" # update python version\n",
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" # install setuptools\n",
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" # %pip install setuptools==61 -qqq\n",
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" # install ammico\n",
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" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
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" # mount google drive for data and API key\n",
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" from google.colab import drive\n",
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"\n",
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" drive.mount(\"/content/drive\")"
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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": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:06:51.168990Z",
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"iopub.status.busy": "2023-06-22T12:06:51.168555Z",
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"iopub.status.idle": "2023-06-22T12:07:02.844083Z",
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"shell.execute_reply": "2023-06-22T12:07:02.843367Z"
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}
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},
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"outputs": [],
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"source": [
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"import ammico\n",
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"from ammico import utils as mutils\n",
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"from ammico import display as mdisplay\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We select a subset of image files to try the color analysis on, see the `limit` keyword. The `find_files` function finds image files within a given directory:"
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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": 3,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:02.848332Z",
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"iopub.status.busy": "2023-06-22T12:07:02.847598Z",
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"iopub.status.idle": "2023-06-22T12:07:02.851965Z",
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"shell.execute_reply": "2023-06-22T12:07:02.851262Z"
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}
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},
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"outputs": [],
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"source": [
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"# Here you need to provide the path to your google drive folder\n",
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"# or local folder containing the images\n",
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"images = mutils.find_files(\n",
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" path=\"/content/drive/MyDrive/misinformation-data/\",\n",
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" limit=10,\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We need to initialize the main dictionary that contains all information for the images and is updated through each subsequent analysis:"
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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": 4,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:02.855341Z",
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"iopub.status.busy": "2023-06-22T12:07:02.855106Z",
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"iopub.status.idle": "2023-06-22T12:07:02.858208Z",
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"shell.execute_reply": "2023-06-22T12:07:02.857509Z"
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}
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},
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"outputs": [],
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"source": [
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"mydict = mutils.initialize_dict(images)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
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"Here, we display the color detection results provided by `colorgram` and `colour` libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
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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": 5,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:02.861441Z",
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"iopub.status.busy": "2023-06-22T12:07:02.861207Z",
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"iopub.status.idle": "2023-06-22T12:07:02.894620Z",
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"shell.execute_reply": "2023-06-22T12:07:02.893951Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dash is running on http://127.0.0.1:8057/\n",
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"\n"
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]
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},
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{
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"data": {
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"text/html": [
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"\n",
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" <iframe\n",
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" width=\"100%\"\n",
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" height=\"650\"\n",
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" src=\"http://127.0.0.1:8057/\"\n",
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" frameborder=\"0\"\n",
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" allowfullscreen\n",
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" \n",
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" ></iframe>\n",
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" "
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],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0x7f905c50efd0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"colors\")\n",
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"analysis_explorer.run_server(port = 8057)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
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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": 6,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:03.390903Z",
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"iopub.status.busy": "2023-06-22T12:07:03.390288Z",
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"iopub.status.idle": "2023-06-22T12:07:03.394359Z",
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"shell.execute_reply": "2023-06-22T12:07:03.393589Z"
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}
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},
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"outputs": [],
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"source": [
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"for key in mydict.keys():\n",
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" mydict[key] = ammico.colors.ColorDetector(mydict[key]).analyse_image()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
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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": 7,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:03.398008Z",
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"iopub.status.busy": "2023-06-22T12:07:03.397345Z",
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"iopub.status.idle": "2023-06-22T12:07:04.583139Z",
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"shell.execute_reply": "2023-06-22T12:07:04.582297Z"
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}
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},
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"outputs": [
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{
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"ename": "IndexError",
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"evalue": "list index out of range",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m outdict \u001b[38;5;241m=\u001b[39m \u001b[43mmutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend_data_to_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m df \u001b[38;5;241m=\u001b[39m mutils\u001b[38;5;241m.\u001b[39mdump_df(outdict)\n",
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"File \u001b[0;32m~/work/AMMICO/AMMICO/ammico/utils.py:92\u001b[0m, in \u001b[0;36mappend_data_to_dict\u001b[0;34m(mydict)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Append entries from nested dictionaries to keys in a global dict.\"\"\"\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# first initialize empty list for each key that is present\u001b[39;00m\n\u001b[0;32m---> 92\u001b[0m outdict \u001b[38;5;241m=\u001b[39m {key: [] \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mkeys()}\n\u001b[1;32m 93\u001b[0m \u001b[38;5;66;03m# now append the values to each key in a list\u001b[39;00m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m subdict \u001b[38;5;129;01min\u001b[39;00m mydict\u001b[38;5;241m.\u001b[39mvalues():\n",
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"\u001b[0;31mIndexError\u001b[0m: list index out of range"
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]
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}
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],
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"source": [
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"outdict = mutils.append_data_to_dict(mydict)\n",
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"df = mutils.dump_df(outdict)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Check the dataframe:"
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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": 8,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:04.586954Z",
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"iopub.status.busy": "2023-06-22T12:07:04.586692Z",
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"iopub.status.idle": "2023-06-22T12:07:04.625945Z",
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"shell.execute_reply": "2023-06-22T12:07:04.625122Z"
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}
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},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'df' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
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]
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}
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],
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"source": [
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"df.head(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Write the csv file - here you should provide a file path and file name for the csv file to be written."
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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": 9,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-22T12:07:04.629298Z",
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"iopub.status.busy": "2023-06-22T12:07:04.628858Z",
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"iopub.status.idle": "2023-06-22T12:07:04.668480Z",
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"shell.execute_reply": "2023-06-22T12:07:04.667619Z"
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}
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},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'df' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/content/drive/MyDrive/misinformation-data/data_out.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
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]
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}
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],
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"source": [
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"df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
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]
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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": "3.9.17"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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