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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 1, |
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| 6 | "metadata": { |
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| 7 | "pycharm": { |
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| 8 | "is_executing": false |
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| 9 | } |
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| 10 | }, |
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| 11 | "outputs": [], |
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| 12 | "source": [ |
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| 13 | "from mmaction.apis import init_recognizer, inference_recognizer" |
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| 14 | ] |
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| 15 | }, |
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| 16 | { |
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| 17 | "cell_type": "code", |
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| 18 | "execution_count": 2, |
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| 19 | "metadata": { |
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| 20 | "pycharm": { |
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| 21 | "is_executing": false |
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| 22 | } |
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| 23 | }, |
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| 24 | "outputs": [], |
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| 25 | "source": [ |
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| 26 | "config_file = '../configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py'\n", |
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| 27 | "# download the checkpoint from model zoo and put it in `checkpoints/`\n", |
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| 28 | "checkpoint_file = '../checkpoints/tsn_r50_1x1x3_100e_kinetics400_rgb_20200614-e508be42.pth'" |
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| 29 | ] |
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| 30 | }, |
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| 31 | { |
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| 32 | "cell_type": "code", |
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| 33 | "execution_count": 3, |
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| 34 | "metadata": { |
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| 35 | "pycharm": { |
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| 36 | "is_executing": false |
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| 37 | } |
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| 38 | }, |
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| 39 | "outputs": [], |
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| 40 | "source": [ |
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| 41 | "# build the model from a config file and a checkpoint file\n", |
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| 42 | "model = init_recognizer(config_file, checkpoint_file, device='cpu')" |
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| 43 | ] |
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| 44 | }, |
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| 45 | { |
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| 46 | "cell_type": "code", |
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| 47 | "execution_count": 4, |
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| 48 | "metadata": { |
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| 49 | "pycharm": { |
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| 50 | "is_executing": false |
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| 51 | } |
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| 52 | }, |
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| 53 | "outputs": [], |
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| 54 | "source": [ |
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| 55 | "# test a single video and show the result:\n", |
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| 56 | "video = 'demo.mp4'\n", |
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| 57 | "label = '../tools/data/kinetics/label_map_k400.txt'\n", |
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| 58 | "results = inference_recognizer(model, video)\n", |
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| 59 | "\n", |
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| 60 | "labels = open(label).readlines()\n", |
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| 61 | "labels = [x.strip() for x in labels]\n", |
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| 62 | "results = [(labels[k[0]], k[1]) for k in results]" |
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| 63 | ] |
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| 64 | }, |
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| 65 | { |
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| 66 | "cell_type": "code", |
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| 67 | "execution_count": 5, |
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| 68 | "metadata": { |
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| 69 | "collapsed": false, |
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| 70 | "jupyter": { |
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| 71 | "outputs_hidden": false |
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| 72 | }, |
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| 73 | "pycharm": { |
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| 74 | "is_executing": false, |
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| 75 | "name": "#%%\n" |
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| 76 | } |
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| 77 | }, |
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| 78 | "outputs": [ |
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| 79 | { |
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| 80 | "name": "stdout", |
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| 81 | "output_type": "stream", |
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| 82 | "text": [ |
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| 83 | "arm wrestling: 29.61644\n", |
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| 84 | "rock scissors paper: 10.754839\n", |
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| 85 | "shaking hands: 9.9084\n", |
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| 86 | "clapping: 9.189912\n", |
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| 87 | "massaging feet: 8.305307\n" |
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| 88 | ] |
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| 89 | } |
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| 90 | ], |
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| 91 | "source": [ |
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| 92 | "# show the results\n", |
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| 93 | "for result in results:\n", |
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| 94 | " print(f'{result[0]}: ', result[1])" |
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| 95 | ] |
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| 96 | } |
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| 97 | ], |
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| 98 | "metadata": { |
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| 99 | "kernelspec": { |
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| 100 | "display_name": "Python 3", |
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| 101 | "language": "python", |
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| 102 | "name": "python3" |
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| 103 | }, |
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| 104 | "language_info": { |
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| 105 | "codemirror_mode": { |
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| 106 | "name": "ipython", |
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| 107 | "version": 3 |
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| 108 | }, |
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| 109 | "file_extension": ".py", |
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| 110 | "mimetype": "text/x-python", |
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| 111 | "name": "python", |
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| 112 | "nbconvert_exporter": "python", |
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| 113 | "pygments_lexer": "ipython3", |
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| 114 | "version": "3.7.4" |
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| 115 | }, |
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| 116 | "pycharm": { |
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| 117 | "stem_cell": { |
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| 118 | "cell_type": "raw", |
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| 119 | "metadata": { |
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| 120 | "collapsed": false |
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| 121 | }, |
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| 122 | "source": [] |
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| 123 | } |
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| 124 | } |
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| 125 | }, |
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| 126 | "nbformat": 4, |
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| 127 | "nbformat_minor": 4 |
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| 128 | } |