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* colors expression by KMean algorithm * object detection by imageai * object detection by cvlib * add encapsulation of object detection * remove encapsulation of objdetect v0 * objects expression to dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added imageai to requirements * add objects to dictionary * update for AnalysisMethod baseline * add objects dection support explore_analysis display * extend python version of misinf to allow imageai * account for older python * use global functionality for dict to csv convert * update for docker build * docker will build now but ipywidgets still not working * test code * include test data folder in repo * add some sample images * load cvs labels to dict * add test data * retrigger checks * add map to human coding * get orders from dict, missing dep * add module to test accuracy * retrigger checks * retrigger checks * now removing imageai * removed imageai * move labelmanager to analyse * multiple faces in mydict * fix pre-commit issues * map mydict * hide imageai * objects default using cvlib, isolate and disable imageai * correct python version * refactor faces tests * refactor objects tests * sonarcloud issues * refactor utils tests * address code smells * update readme * update notebook without imageai Co-authored-by: Ma Xianghe <825074348@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: iulusoy <inga.ulusoy@uni-heidelberg.de>
104 строки
2.3 KiB
Plaintext
104 строки
2.3 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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"<span style =\" color : green ;font - weight : bold \">ImageAI for Object Detection</span>\n",
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"http://imageai.org/#features"
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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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"A simple, high level, easy-to-use open source Computer Vision library for Python.\n",
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"\n",
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"It was developed with a focus on enabling easy and fast experimentation. Being able to go from an idea to prototype with least amount of delay is key to doing good research.\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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"<p>cvlib detect_common_objects pretrained on coco dataset.</p>\n",
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"Underneath it uses YOLOv3 model trained on COCO dataset capable of detecting 80 common objects in context."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import matplotlib.pyplot as plt\n",
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"import cvlib as cv\n",
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"from cvlib.object_detection import draw_bbox"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"im = cv2.imread(\"image.jpg\")\n",
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"\n",
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"bbox, label, conf = cv.detect_common_objects(im)\n",
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"\n",
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"output_image = draw_bbox(im, bbox, label, conf)\n",
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"\n",
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"plt.imshow(output_image)\n",
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"\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": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"im = cv2.imread(\"image02.jpg\")\n",
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"\n",
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"bbox, label, conf = cv.detect_common_objects(im)\n",
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"\n",
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"output_image = draw_bbox(im, bbox, label, conf)\n",
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"\n",
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"plt.imshow(output_image)\n",
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"\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": "code",
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"execution_count": null,
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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",
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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": "3.7.3"
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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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