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1129 строки
30 KiB
Plaintext
1129 строки
30 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Objects recognition"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebooks shows how to detect objects quickly using [cvlib](https://github.com/arunponnusamy/cvlib) and the [YOLOv4](https://github.com/AlexeyAB/darknet) model. This library detects faces, people, and several inanimate objects; we currently have restricted the output to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.\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": "2023-06-28T06:55:52.977606Z",
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"iopub.status.busy": "2023-06-28T06:55:52.977235Z",
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"iopub.status.idle": "2023-06-28T06:55:52.985673Z",
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"shell.execute_reply": "2023-06-28T06:55:52.984772Z"
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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": "2023-06-28T06:55:52.988359Z",
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"iopub.status.busy": "2023-06-28T06:55:52.988145Z",
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"iopub.status.idle": "2023-06-28T06:56:05.900716Z",
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"shell.execute_reply": "2023-06-28T06:56:05.900069Z"
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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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"import ammico.objects as ob"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Set an image path as input file 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": 3,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-28T06:56:05.905170Z",
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"iopub.status.busy": "2023-06-28T06:56:05.903583Z",
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"iopub.status.idle": "2023-06-28T06:56:05.909863Z",
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"shell.execute_reply": "2023-06-28T06:56:05.909290Z"
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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=\"data/\",\n",
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" limit=10,\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": 4,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-28T06:56:05.912879Z",
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"iopub.status.busy": "2023-06-28T06:56:05.912550Z",
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"iopub.status.idle": "2023-06-28T06:56:05.916852Z",
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"shell.execute_reply": "2023-06-28T06:56:05.916259Z"
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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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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Detect objects and directly write to csv\n",
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"You can 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": "2023-06-28T06:56:05.919860Z",
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"iopub.status.busy": "2023-06-28T06:56:05.919411Z",
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"iopub.status.idle": "2023-06-28T06:56:13.454060Z",
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"shell.execute_reply": "2023-06-28T06:56:13.453412Z"
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}
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},
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"outputs": [
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"text": [
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"Downloading yolov4.cfg from https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg\n",
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"Downloading yolov4.weights from https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights\n"
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},
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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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"Downloading yolov3_classes.txt from https://github.com/arunponnusamy/object-detection-opencv/raw/master/yolov3.txt\n"
|
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]
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},
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}
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],
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"source": [
|
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"for key in mydict:\n",
|
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" mydict[key] = ob.ObjectDetector(mydict[key]).analyse_image()"
|
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]
|
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},
|
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{
|
|
"attachments": {},
|
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"cell_type": "markdown",
|
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"metadata": {},
|
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"source": [
|
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"Convert the dictionary of dictionarys into a dictionary with lists:"
|
|
]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
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"metadata": {
|
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"execution": {
|
|
"iopub.execute_input": "2023-06-28T06:56:13.457836Z",
|
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"iopub.status.busy": "2023-06-28T06:56:13.457210Z",
|
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"iopub.status.idle": "2023-06-28T06:56:13.461136Z",
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"shell.execute_reply": "2023-06-28T06:56:13.460631Z"
|
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}
|
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},
|
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"outputs": [],
|
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"source": [
|
|
"outdict = mutils.append_data_to_dict(mydict)\n",
|
|
"df = mutils.dump_df(outdict)"
|
|
]
|
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},
|
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{
|
|
"attachments": {},
|
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"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
|
"Check the dataframe:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-06-28T06:56:13.463949Z",
|
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"iopub.status.busy": "2023-06-28T06:56:13.463377Z",
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"iopub.status.idle": "2023-06-28T06:56:13.476816Z",
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"shell.execute_reply": "2023-06-28T06:56:13.476320Z"
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}
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},
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"outputs": [
|
|
{
|
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
|
"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
|
" <th>filename</th>\n",
|
|
" <th>person</th>\n",
|
|
" <th>bicycle</th>\n",
|
|
" <th>car</th>\n",
|
|
" <th>motorcycle</th>\n",
|
|
" <th>airplane</th>\n",
|
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" <th>bus</th>\n",
|
|
" <th>train</th>\n",
|
|
" <th>truck</th>\n",
|
|
" <th>boat</th>\n",
|
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" <th>traffic light</th>\n",
|
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" <th>cell phone</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>data/106349S_por.png</td>\n",
|
|
" <td>yes</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>yes</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>data/102141_2_eng.png</td>\n",
|
|
" <td>yes</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>data/102730_eng.png</td>\n",
|
|
" <td>yes</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>yes</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" filename person bicycle car motorcycle airplane bus train \\\n",
|
|
"0 data/106349S_por.png yes no no no no no no \n",
|
|
"1 data/102141_2_eng.png yes no no no no no no \n",
|
|
"2 data/102730_eng.png yes no no no no no no \n",
|
|
"\n",
|
|
" truck boat traffic light cell phone \n",
|
|
"0 no no no yes \n",
|
|
"1 no no no no \n",
|
|
"2 yes no no no "
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.head(10)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Write the csv file:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-06-28T06:56:13.479457Z",
|
|
"iopub.status.busy": "2023-06-28T06:56:13.478925Z",
|
|
"iopub.status.idle": "2023-06-28T06:56:13.483801Z",
|
|
"shell.execute_reply": "2023-06-28T06:56:13.482947Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.to_csv(\"data_out.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Manually inspect what was detected\n",
|
|
"\n",
|
|
"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 directly export a csv file in the step above.\n",
|
|
"Here, we display the object detection results provided by the above library. 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."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-06-28T06:56:13.486281Z",
|
|
"iopub.status.busy": "2023-06-28T06:56:13.486076Z",
|
|
"iopub.status.idle": "2023-06-28T06:56:14.242789Z",
|
|
"shell.execute_reply": "2023-06-28T06:56:14.242087Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "TypeError",
|
|
"evalue": "__init__() got an unexpected keyword argument 'identify'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m analysis_explorer \u001b[38;5;241m=\u001b[39m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAnalysisExplorer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midentify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mobjects\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m analysis_explorer\u001b[38;5;241m.\u001b[39mrun_server(port\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8056\u001b[39m)\n",
|
|
"\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'identify'"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"objects\")\n",
|
|
"analysis_explorer.run_server(port=8056)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.17"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "f1142466f556ab37fe2d38e2897a16796906208adb09fea90ba58bdf8a56f0ba"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|