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363 строки
10 KiB
Plaintext
363 строки
10 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 Detector\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": "2023-11-08T13:57:08.580446Z",
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"iopub.status.busy": "2023-11-08T13:57:08.580013Z",
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"iopub.status.idle": "2023-11-08T13:57:08.588398Z",
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"shell.execute_reply": "2023-11-08T13:57:08.587819Z"
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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": "2023-11-08T13:57:08.591760Z",
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"iopub.status.busy": "2023-11-08T13:57:08.591090Z",
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"iopub.status.idle": "2023-11-08T13:57:23.381785Z",
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"shell.execute_reply": "2023-11-08T13:57:23.381109Z"
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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": "2023-11-08T13:57:23.386221Z",
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"iopub.status.busy": "2023-11-08T13:57:23.384585Z",
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"iopub.status.idle": "2023-11-08T13:57:23.390401Z",
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"shell.execute_reply": "2023-11-08T13:57:23.389828Z"
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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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"image_dict = mutils.find_files(\n",
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" path=\"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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"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": 4,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-11-08T13:57:23.393980Z",
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"iopub.status.busy": "2023-11-08T13:57:23.393760Z",
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"iopub.status.idle": "2023-11-08T13:57:23.433263Z",
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"shell.execute_reply": "2023-11-08T13:57:23.432554Z"
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}
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},
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"outputs": [
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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 0x7f7653f8ee20>"
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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(image_dict)\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": 5,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-11-08T13:57:23.436771Z",
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"iopub.status.busy": "2023-11-08T13:57:23.436514Z",
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"iopub.status.idle": "2023-11-08T13:57:30.074608Z",
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"shell.execute_reply": "2023-11-08T13:57:30.073892Z"
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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 image_dict.keys():\n",
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" image_dict[key] = ammico.colors.ColorDetector(image_dict[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": 6,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-11-08T13:57:30.078052Z",
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"iopub.status.busy": "2023-11-08T13:57:30.077824Z",
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"iopub.status.idle": "2023-11-08T13:57:30.083115Z",
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"shell.execute_reply": "2023-11-08T13:57:30.082497Z"
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}
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},
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"outputs": [],
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"source": [
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"image_df = ammico.get_dataframe(image_dict)"
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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": 7,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-11-08T13:57:30.086436Z",
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"iopub.status.busy": "2023-11-08T13:57:30.085935Z",
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"iopub.status.idle": "2023-11-08T13:57:30.100156Z",
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"shell.execute_reply": "2023-11-08T13:57:30.099490Z"
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}
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},
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"outputs": [
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{
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"data": {
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" <thead>\n",
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" <th>0</th>\n",
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" <td>data/106349S_por.png</td>\n",
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" <td>0.01</td>\n",
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" <td>0.01</td>\n",
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" <th>1</th>\n",
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" <td>data/102141_2_eng.png</td>\n",
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" <td>0.04</td>\n",
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"metadata": {},
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"source": [
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"image_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": 8,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-11-08T13:57:30.104152Z",
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"iopub.status.busy": "2023-11-08T13:57:30.103577Z",
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"iopub.status.idle": "2023-11-08T13:57:30.109533Z",
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"shell.execute_reply": "2023-11-08T13:57:30.108756Z"
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}
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},
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"outputs": [],
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"source": [
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"image_df.to_csv(\"data_out.csv\")"
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]
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}
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],
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