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1092 строки
28 KiB
Plaintext
1092 строки
28 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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"# Objects Expression 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 some preliminary work on detecting objects expressions with cvlib. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
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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": "2023-05-16T11:46:43.072679Z",
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"iopub.status.busy": "2023-05-16T11:46:43.072202Z",
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"iopub.status.idle": "2023-05-16T11:46:58.136488Z",
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"shell.execute_reply": "2023-05-16T11:46:58.135748Z"
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},
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"tags": []
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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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"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": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-05-16T11:46:58.140475Z",
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"iopub.status.busy": "2023-05-16T11:46:58.139659Z",
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"iopub.status.idle": "2023-05-16T11:46:58.143901Z",
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"shell.execute_reply": "2023-05-16T11:46:58.143209Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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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": 3,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-05-16T11:46:58.146946Z",
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"iopub.status.busy": "2023-05-16T11:46:58.146507Z",
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"iopub.status.idle": "2023-05-16T11:46:58.149810Z",
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"shell.execute_reply": "2023-05-16T11:46:58.149134Z"
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},
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"tags": []
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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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"## Manually inspect what was detected\n",
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"\n",
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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."
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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": "2023-05-16T11:46:58.153871Z",
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"iopub.status.busy": "2023-05-16T11:46:58.153428Z",
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"iopub.status.idle": "2023-05-16T11:46:59.301016Z",
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"shell.execute_reply": "2023-05-16T11:46:59.300195Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "module 'ammico.display' has no attribute 'explore_analysis'",
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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;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplore_analysis\u001b[49m(mydict, identify\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjects\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"\u001b[0;31mAttributeError\u001b[0m: module 'ammico.display' has no attribute 'explore_analysis'"
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]
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}
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],
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"source": [
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"mdisplay.explore_analysis(mydict, identify=\"objects\")"
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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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"## Detect objects and directly write to csv"
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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": "2023-05-16T11:46:59.305468Z",
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"iopub.status.busy": "2023-05-16T11:46:59.304965Z",
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"iopub.status.idle": "2023-05-16T11:47:07.038626Z",
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"shell.execute_reply": "2023-05-16T11:47:07.037611Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"text": [
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},
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"name": "stdout",
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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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"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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"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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{
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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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{
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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": "2023-05-16T11:47:07.042525Z",
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"iopub.status.busy": "2023-05-16T11:47:07.042007Z",
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"iopub.status.idle": "2023-05-16T11:47:07.046366Z",
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"shell.execute_reply": "2023-05-16T11:47:07.045638Z"
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"tags": []
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},
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"outputs": [],
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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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"cell_type": "code",
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"execution": {
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"iopub.execute_input": "2023-05-16T11:47:07.049479Z",
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>filename</th>\n",
|
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" <th>person</th>\n",
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" <th>bicycle</th>\n",
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" <th>car</th>\n",
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" <th>boat</th>\n",
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" <tbody>\n",
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" <tr>\n",
|
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" <th>0</th>\n",
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" <td>data/102730_eng.png</td>\n",
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" <td>yes</td>\n",
|
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|
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|
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" <td>yes</td>\n",
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" </tr>\n",
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" <tr>\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>yes</td>\n",
|
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" <td>no</td>\n",
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|
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|
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|
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|
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|
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|
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|
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|
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" <td>no</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>2</th>\n",
|
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" <td>data/106349S_por.png</td>\n",
|
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" <td>yes</td>\n",
|
|
" <td>no</td>\n",
|
|
" <td>no</td>\n",
|
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" <td>no</td>\n",
|
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" <td>no</td>\n",
|
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|
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|
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|
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" <td>no</td>\n",
|
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" <td>no</td>\n",
|
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" <td>yes</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" filename person bicycle car motorcycle airplane bus train \n",
|
|
"0 data/102730_eng.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/106349S_por.png yes no no no no no no \n",
|
|
"\n",
|
|
" truck boat traffic light cell phone \n",
|
|
"0 yes no no no \n",
|
|
"1 no no no no \n",
|
|
"2 no no no yes "
|
|
]
|
|
},
|
|
"execution_count": 7,
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|
"metadata": {},
|
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|
|
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|
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],
|
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"source": [
|
|
"df.head(10)"
|
|
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|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Write the csv file:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-05-16T11:47:07.065960Z",
|
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|
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|
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|
|
},
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.to_csv(\"./data_out.csv\")"
|
|
]
|
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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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