├── Business_value_marketing ├── .ipynb_checkpoints │ └── marketing_data-checkpoint.ipynb ├── camera_1.csv ├── marketing_data.ipynb ├── time_average_age_correlation.png ├── time_count_correlation.png └── time_count_gender_correlation.png ├── JunctionAPI ├── TinyFaces │ ├── README.md │ ├── __init__.py │ ├── tiny_face_det.py │ ├── tiny_face_eval.py │ ├── tiny_face_model.py │ ├── tinyface_tf │ │ ├── selfie.jpg │ │ └── selfie_detection.jpg │ ├── uploaded_imgs │ │ ├── img1.jpg │ │ └── img1_detection.jpg │ └── util.py ├── __init__.py ├── api.py ├── app │ ├── __init__.py │ ├── detect │ │ ├── __init__.py │ │ ├── face_detector.py │ │ ├── forms.py │ │ ├── routes.py │ │ ├── routes_opencv.py │ │ └── run_model_server.py │ ├── main │ │ ├── __init__.py │ │ ├── forms.py │ │ └── routes.py │ └── templates │ │ ├── base.html │ │ └── detect │ │ ├── results.html │ │ └── upload.html ├── config.py └── flask_requirements.txt ├── LICENSE ├── README.md ├── api ├── __pycache__ │ ├── api.cpython-35.pyc │ ├── api.cpython-36.pyc │ ├── config.cpython-35.pyc │ └── config.cpython-36.pyc ├── api.py ├── app │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ └── __init__.cpython-36.pyc │ ├── detect │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── camera.cpython-35.pyc │ │ │ ├── customerservices.cpython-35.pyc │ │ │ ├── forms.cpython-35.pyc │ │ │ ├── forms.cpython-36.pyc │ │ │ ├── geo_info_caller.cpython-35.pyc │ │ │ ├── routes.cpython-35.pyc │ │ │ ├── routes.cpython-36.pyc │ │ │ ├── run_model_server.cpython-35.pyc │ │ │ └── run_model_server.cpython-36.pyc │ │ ├── camera.py │ │ ├── camera.pyc │ │ ├── customerservices.py │ │ ├── customerservices.pyc │ │ ├── face_detector.py │ │ ├── facedata.csv │ │ ├── facedata.ods │ │ ├── facedata.xls │ │ ├── forms.py │ │ ├── geo_info_caller.py │ │ ├── morerealdata.csv │ │ ├── outputgenerator.py │ │ ├── routes.py │ │ ├── run_model_server.py │ │ ├── savedheatmap │ │ │ └── heatmap.png │ │ ├── staduim1.png │ │ ├── test_customerService.py │ │ └── tweetGenerator.py │ ├── main │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── routes.cpython-35.pyc │ │ │ └── routes.cpython-36.pyc │ │ ├── forms.py │ │ └── routes.py │ ├── model │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── age_model.cpython-35.pyc │ │ │ ├── age_model.cpython-36.pyc │ │ │ ├── emo_model.cpython-35.pyc │ │ │ ├── emo_model.cpython-36.pyc │ │ │ ├── smallervggnet.cpython-35.pyc │ │ │ └── smallervggnet.cpython-36.pyc │ │ ├── age_model.py │ │ ├── emo_model.py │ │ └── smallervggnet.py │ ├── records │ │ └── camera1.csv │ ├── static │ │ ├── age_status.png │ │ ├── emotion_status.png │ │ ├── gender_status.png │ │ ├── heatmap.png │ │ ├── selfytest_detect.jpeg │ │ ├── test2_detect.jpeg │ │ └── test_detect.jpg │ └── templates │ │ ├── base.html │ │ └── detect │ │ ├── index.html │ │ ├── public.html │ │ ├── results.html │ │ └── upload.html ├── config.py └── requirements.txt ├── face_detection_statistics.png ├── junction.jpg ├── out_putgeneration ├── README.md ├── camera.py ├── camera.pyc ├── customerservices.py ├── customerservices.pyc ├── facedata.csv ├── facedata.ods ├── facedata.xls ├── geo_info_caller.py ├── geo_info_caller.pyc ├── morerealdata.csv ├── outputgenerator.py ├── savedheatmap │ └── heatmap.png ├── staduim1.png ├── test_customerService.py └── tweetGenerator.py └── web_and_mobile.png /Business_value_marketing/.ipynb_checkpoints/marketing_data-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "data = pd.read_csv('camera_1.csv', encoding='utf8')" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 3, 25 | "metadata": {}, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/html": [ 30 | "
\n", 31 | "\n", 44 | "\n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | "
TimeFace_countAgeMaleFemaleEmotion
07:0020258020neutral
17:1525258020happy
27:3030258020neutral
37:4525228020neutral
48:0030288020happy
\n", 104 | "
" 105 | ], 106 | "text/plain": [ 107 | " Time Face_count Age Male Female Emotion\n", 108 | "0 7:00 20 25 80 20 neutral\n", 109 | "1 7:15 25 25 80 20 happy\n", 110 | "2 7:30 30 25 80 20 neutral\n", 111 | "3 7:45 25 22 80 20 neutral\n", 112 | "4 8:00 30 28 80 20 happy" 113 | ] 114 | }, 115 | "execution_count": 3, 116 | "metadata": {}, 117 | "output_type": "execute_result" 118 | } 119 | ], 120 | "source": [ 121 | "data.head()" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 5, 127 | "metadata": {}, 128 | "outputs": [ 129 | { 130 | "name": "stdout", 131 | "output_type": "stream", 132 | "text": [ 133 | "Emotion\n", 134 | "angry 3\n", 135 | "happy 29\n", 136 | "neutral 38\n", 137 | "none 3\n", 138 | "dtype: int64\n" 139 | ] 140 | }, 141 | { 142 | "data": { 143 | "text/plain": [ 144 | "" 145 | ] 146 | }, 147 | "execution_count": 5, 148 | "metadata": {}, 149 | "output_type": "execute_result" 150 | }, 151 | { 152 | "data": { 153 | "image/png": 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154 | "text/plain": [ 155 | "" 156 | ] 157 | }, 158 | "metadata": {}, 159 | "output_type": "display_data" 160 | } 161 | ], 162 | "source": [ 163 | "print(data.groupby('Emotion').size())\n", 164 | "%matplotlib inline\n", 165 | "import matplotlib as plt\n", 166 | "data['Emotion'].value_counts().plot(kind=\"barh\")" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": {}, 173 | "outputs": [], 174 | "source": [] 175 | } 176 | ], 177 | "metadata": { 178 | "kernelspec": { 179 | "display_name": "Python 3", 180 | "language": "python", 181 | "name": "python3" 182 | }, 183 | "language_info": { 184 | "codemirror_mode": { 185 | "name": "ipython", 186 | "version": 3 187 | }, 188 | "file_extension": ".py", 189 | "mimetype": "text/x-python", 190 | "name": "python", 191 | "nbconvert_exporter": "python", 192 | "pygments_lexer": "ipython3", 193 | "version": "3.6.4" 194 | } 195 | }, 196 | "nbformat": 4, 197 | "nbformat_minor": 2 198 | } 199 | -------------------------------------------------------------------------------- /Business_value_marketing/camera_1.csv: -------------------------------------------------------------------------------- 1 | Time,Face_count,Age,Male,Female,Emotion 2 | 7:00,0,0,0,0,none 3 | 7:15,0,0,0,0,none 4 | 7:30,0,0,0,0,none 5 | 7:45,0,0,0,0,none 6 | 8:00,30,20,24,6,happy 7 | 8:15,30,20,24,6,happy 8 | 8:30,30,20,24,6,neutral 9 | 8:45,50,20,40,10,happy 10 | 9:00,60,20,48,12,neutral 11 | 9:15,100,20,75,25,neutral 12 | 9:30,150,20,105,45,neutral 13 | 9:45,250,20,175,75,angry 14 | 10:00,250,20,175,75,neutral 15 | 10:15,170,20,105,65,neutral 16 | 10:30,170,40,105,65,neutral 17 | 10:45,160,40,96,24,happy 18 | 11:00,120,40,96,24,happy 19 | 11:15,120,40,96,24,happy 20 | 11:30,100,40,80,20,neutral 21 | 11:45,100,40,80,20,neutral 22 | 12:00,80,20,40,40,neutral 23 | 12:15,80,20,40,40,neutral 24 | 12:30,80,20,40,40,neutral 25 | 12:45,50,20,35,15,neutral 26 | 13:00,50,20,35,15,neutral 27 | 13:15,50,20,35,15,happy 28 | 13:30,40,20,30,10,happy 29 | 13:45,40,20,30,10,happy 30 | 14:00,50,20,35,15,happy 31 | 14:15,60,20,40,20,happy 32 | 14:30,80,20,60,20,neutral 33 | 14:45,100,20,60,40,neutral 34 | 15:00,120,20,60,60,neutral 35 | 15:15,120,20,60,60,neutral 36 | 15:30,140,20,70,70,neutral 37 | 15:45,150,20,80,70,neutral 38 | 16:00,160,20,130,30,neutral 39 | 16:15,180,20,140,40,neutral 40 | 16:30,200,20,160,40,neutral 41 | 16:45,200,20,160,40,neutral 42 | 17:00,200,20,160,40,neutral 43 | 17:15,300,20,240,60,angry 44 | 17:30,300,30,240,60,angry 45 | 17:45,220,30,180,40,neutral 46 | 18:00,200,40,160,40,neutral 47 | 18:15,200,40,160,40,neutral 48 | 18:30,160,40,120,40,neutral 49 | 18:45,160,40,120,40,neutral 50 | 19:00,100,40,80,20,happy 51 | 19:15,100,40,80,20,happy 52 | 19:30,60,50,40,20,happy 53 | 19:45,50,50,40,10,happy 54 | 20:00,60,50,40,10,happy 55 | 20:15,60,50,40,10,happy 56 | 20:30,60,50,40,10,happy 57 | 20:45,50,50,40,10,happy 58 | 21:00,40,50,36,4,happy 59 | 21:15,30,50,27,3,neutral 60 | 21:30,20,50,18,2,neutral 61 | 21:45,10,50,9,1,neutral 62 | 22:00,10,50,9,1,neutral 63 | 22:15,10,50,9,1,neutral 64 | 22:30,10,60,9,1,happy 65 | 22:45,10,60,9,1,happy 66 | 23:00,10,60,9,1,happy 67 | 23:15,10,60,9,1,happy 68 | 23:30,10,60,9,1,happy 69 | 23:45,10,60,9,1,happy 70 | 0:00,10,60,9,1,happy 71 | 0:15,10,60,9,1,happy 72 | 0:30,0,0,0,0,none 73 | 0:45,0,0,0,0,none 74 | 1:00,0,0,0,0,none -------------------------------------------------------------------------------- /Business_value_marketing/time_average_age_correlation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/Business_value_marketing/time_average_age_correlation.png -------------------------------------------------------------------------------- /Business_value_marketing/time_count_correlation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/Business_value_marketing/time_count_correlation.png -------------------------------------------------------------------------------- /Business_value_marketing/time_count_gender_correlation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/Business_value_marketing/time_count_gender_correlation.png -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/README.md: -------------------------------------------------------------------------------- 1 | # Tiny Face Detector in TensorFlow 2 | 3 | A TensorFlow port(inference only) of Tiny Face Detector from [authors' MatConvNet codes](https://github.com/peiyunh/tiny)[1]. 4 | 5 | # Usage 6 | ## Converting a pretrained model 7 | 8 | `matconvnet_hr101_to_pickle` reads weights of the MatConvNet pretrained model and 9 | write back to a pickle file which is used in a TensorFlow model as initial weights. 10 | 11 | 1. Download a [ResNet101-based pretrained model(hr_res101.mat)](https://www.cs.cmu.edu/%7Epeiyunh/tiny/hr_res101.mat) 12 | from the authors' repo. 13 | 14 | 2. Convert the model to a pickle file by: 15 | ``` 16 | python matconvnet_hr101_to_pickle.py 17 | --matlab_model_path /path/to/pretrained_model 18 | --weight_file_path ./tinyface_tf/tf.pkl 19 | ``` 20 | 3. Don't forget to change the model path in "tiny_face_det.py" file (L:34) 21 | 22 | # Examples 23 | ### selfie with many people 24 | This is the same image as one in [the authors' repo](https://github.com/peiyunh/tiny)[1]. 25 | 26 | ![selfie](https://github.com/cydonia999/Tiny_Faces_in_Tensorflow/blob/master/images/selfie.jpg?raw=true) 27 | 28 | [Original image](https://github.com/peiyunh/tiny/blob/master/data/demo/selfie.jpg) 29 | -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/JunctionAPI/TinyFaces/__init__.py -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/tiny_face_det.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | 6 | import tensorflow as tf 7 | import tiny_face_model 8 | import JunctionAPI.TinyFaces.util 9 | import numpy as np 10 | import cv2 11 | 12 | import time 13 | from scipy.special import expit 14 | 15 | 16 | def overlay_bounding_boxes(raw_img, refined_bboxes, lw): 17 | # Overlay bounding boxes on an image with the color based on the confidence. 18 | for r in refined_bboxes: 19 | _score = expit(r[4]) 20 | cm_idx = int(np.ceil(_score * 255)) 21 | rect_color = [int(np.ceil(x * 255)) for x in JunctionAPI.TinyFaces.util.cm_data[cm_idx]] # parula 22 | _lw = lw 23 | if lw == 0: # line width of each bounding box is adaptively determined. 24 | bw, bh = r[2] - r[0] + 1, r[3] - r[0] + 1 25 | _lw = 1 if min(bw, bh) <= 20 else max(2, min(3, min(bh / 20, bw / 20))) 26 | _lw = int(np.ceil(_lw * _score)) 27 | 28 | _r = [int(x) for x in r[:4]] 29 | cv2.rectangle(raw_img, (_r[0], _r[1]), (_r[2], _r[3]), rect_color, _lw) 30 | 31 | 32 | def evaluate(img_path, prob_thresh=0.5, nms_thresh=0.1, lw=3, display=False): 33 | x = tf.placeholder(tf.float32, [1, None, None, 3]) # n, h, w, c 34 | model = tiny_face_model.Model('/path/to/pkl/file/') 35 | score_final = model.tiny_face(x) 36 | 37 | 38 | average_image = model.get_data_by_key("average_image") 39 | clusters = model.get_data_by_key("clusters") 40 | 41 | # main 42 | with tf.Session() as sess: 43 | sess.run(tf.global_variables_initializer()) 44 | fname = img_path 45 | raw_img = cv2.imread(img_path) 46 | raw_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) 47 | raw_img_f = raw_img.astype(np.float32) 48 | 49 | scales = [0.5, 1, 1.5, 2.0] 50 | start = time.time() 51 | 52 | # initialize output 53 | bboxes = np.empty(shape=(0, 5)) 54 | 55 | # process input at different scales 56 | for s in scales: 57 | print("Processing {} at scale {:.4f}".format(fname, s)) 58 | img = cv2.resize(raw_img_f, (0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR) 59 | img = img - average_image 60 | img = img[np.newaxis, :] 61 | 62 | # we don't run every template on every scale ids of templates to ignore 63 | tids = list(range(4, 12)) + ([] if s <= 1.0 else list(range(18, 25))) 64 | ignoredTids = list(set(range(0, clusters.shape[0])) - set(tids)) 65 | 66 | # run through the net 67 | score_final_tf = sess.run(score_final, feed_dict={x: img}) 68 | 69 | # collect scores 70 | score_cls_tf, score_reg_tf = score_final_tf[:, :, :, :25], score_final_tf[:, :, :, 25:125] 71 | prob_cls_tf = expit(score_cls_tf) 72 | prob_cls_tf[0, :, :, ignoredTids] = 0.0 73 | 74 | def _calc_bounding_boxes(): 75 | # threshold for detection 76 | _, fy, fx, fc = np.where(prob_cls_tf > prob_thresh) 77 | 78 | # interpret heatmap into bounding boxes 79 | cy = fy * 8 - 1 80 | cx = fx * 8 - 1 81 | ch = clusters[fc, 3] - clusters[fc, 1] + 1 82 | cw = clusters[fc, 2] - clusters[fc, 0] + 1 83 | 84 | # extract bounding box refinement 85 | Nt = clusters.shape[0] 86 | tx = score_reg_tf[0, :, :, 0:Nt] 87 | ty = score_reg_tf[0, :, :, Nt:2 * Nt] 88 | tw = score_reg_tf[0, :, :, 2 * Nt:3 * Nt] 89 | th = score_reg_tf[0, :, :, 3 * Nt:4 * Nt] 90 | 91 | # refine bounding boxes 92 | dcx = cw * tx[fy, fx, fc] 93 | dcy = ch * ty[fy, fx, fc] 94 | rcx = cx + dcx 95 | rcy = cy + dcy 96 | rcw = cw * np.exp(tw[fy, fx, fc]) 97 | rch = ch * np.exp(th[fy, fx, fc]) 98 | 99 | scores = score_cls_tf[0, fy, fx, fc] 100 | tmp_bboxes = np.vstack((rcx - rcw / 2, rcy - rch / 2, rcx + rcw / 2, rcy + rch / 2)) 101 | tmp_bboxes = np.vstack((tmp_bboxes / s, scores)) 102 | tmp_bboxes = tmp_bboxes.transpose() 103 | return tmp_bboxes 104 | 105 | tmp_bboxes = _calc_bounding_boxes() 106 | bboxes = np.vstack((bboxes, tmp_bboxes)) # : (5265, 5) 107 | 108 | print("time {:.2f} secs for {}".format(time.time() - start, fname)) 109 | 110 | # non maximum suppression 111 | # refind_idx = util.nms(bboxes, nms_thresh) 112 | refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32), 113 | tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32), 114 | max_output_size=bboxes.shape[0], iou_threshold=nms_thresh) 115 | refind_idx = sess.run(refind_idx) 116 | refined_bboxes = bboxes[refind_idx] 117 | return refined_bboxes 118 | 119 | 120 | def detect_from_img_path(img_path): 121 | with tf.Graph().as_default(): 122 | detections = evaluate(img_path, prob_thresh=0.5, nms_thresh=0.1,lw=3) 123 | return detections 124 | -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/tiny_face_eval.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | import os 6 | os.environ["CUDA_VISIBLE_DEVICES"]="0" 7 | 8 | 9 | import tensorflow as tf 10 | import tiny_face_model 11 | import JunctionAPI.TinyFaces.util 12 | from argparse import ArgumentParser 13 | import numpy as np 14 | import matplotlib.pyplot as plt 15 | import cv2 16 | import pickle 17 | 18 | import pylab as pl 19 | import time 20 | import os 21 | from scipy.special import expit 22 | import glob 23 | 24 | MAX_INPUT_DIM = 5000.0 25 | 26 | def overlay_bounding_boxes(raw_img, refined_bboxes, lw): 27 | """Overlay bounding boxes of face on images. 28 | Args: 29 | raw_img: 30 | A target image. 31 | refined_bboxes: 32 | Bounding boxes of detected faces. 33 | lw: 34 | Line width of bounding boxes. If zero specified, 35 | this is determined based on confidence of each detection. 36 | Returns: 37 | None. 38 | """ 39 | 40 | # Overlay bounding boxes on an image with the color based on the confidence. 41 | for r in refined_bboxes: 42 | _score = expit(r[4]) 43 | cm_idx = int(np.ceil(_score * 255)) 44 | rect_color = [int(np.ceil(x * 255)) for x in JunctionAPI.TinyFaces.util.cm_data[cm_idx]] # parula 45 | _lw = lw 46 | if lw == 0: # line width of each bounding box is adaptively determined. 47 | bw, bh = r[2] - r[0] + 1, r[3] - r[0] + 1 48 | _lw = 1 if min(bw, bh) <= 20 else max(2, min(3, min(bh / 20, bw / 20))) 49 | _lw = int(np.ceil(_lw * _score)) 50 | 51 | _r = [int(x) for x in r[:4]] 52 | cv2.rectangle(raw_img, (_r[0], _r[1]), (_r[2], _r[3]), rect_color, _lw) 53 | 54 | 55 | def evaluate(weight_file_path, data_dir, output_dir, prob_thresh=0.5, nms_thresh=0.1, lw=3, display=False): 56 | """Detect faces in images. 57 | Args: 58 | prob_thresh: 59 | The threshold of detection confidence. 60 | nms_thresh: 61 | The overlap threshold of non maximum suppression 62 | weight_file_path: 63 | A pretrained weight file in the pickle format 64 | generated by matconvnet_hr101_to_tf.py. 65 | data_dir: 66 | A directory which contains images. 67 | output_dir: 68 | A directory into which images with detected faces are output. 69 | lw: 70 | Line width of bounding boxes. If zero specified, 71 | this is determined based on confidence of each detection. 72 | display: 73 | Display tiny face images on window. 74 | Returns: 75 | None. 76 | """ 77 | 78 | # placeholder of input images. Currently batch size of one is supported. 79 | x = tf.placeholder(tf.float32, [1, None, None, 3]) # n, h, w, c 80 | 81 | # Create the tiny face model which weights are loaded from a pretrained model. 82 | model = tiny_face_model.Model(weight_file_path) 83 | score_final = model.tiny_face(x) 84 | 85 | # Find image files in data_dir. 86 | filenames = [] 87 | for ext in ('*.png', '*.gif', '*.jpg', '*.jpeg'): 88 | filenames.extend(glob.glob(os.path.join(data_dir, ext))) 89 | 90 | # Load an average image and clusters(reference boxes of templates). 91 | with open(weight_file_path, "rb") as f: 92 | _, mat_params_dict = pickle.load(f) 93 | 94 | average_image = model.get_data_by_key("average_image") 95 | clusters = model.get_data_by_key("clusters") 96 | clusters_h = clusters[:, 3] - clusters[:, 1] + 1 97 | clusters_w = clusters[:, 2] - clusters[:, 0] + 1 98 | normal_idx = np.where(clusters[:, 4] == 1) 99 | 100 | # main 101 | with tf.Session() as sess: 102 | sess.run(tf.global_variables_initializer()) 103 | 104 | for filename in filenames: 105 | fname = filename.split(os.sep)[-1] 106 | raw_img = cv2.imread(filename) 107 | raw_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) 108 | raw_img_f = raw_img.astype(np.float32) 109 | 110 | def _calc_scales(): 111 | raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] 112 | min_scale = min(np.floor(np.log2(np.max(clusters_w[normal_idx] / raw_w))), 113 | np.floor(np.log2(np.max(clusters_h[normal_idx] / raw_h)))) 114 | max_scale = min(1.0, -np.log2(max(raw_h, raw_w) / MAX_INPUT_DIM)) 115 | scales_down = pl.frange(min_scale, 0, 1.) 116 | scales_up = pl.frange(0.5, max_scale, 0.5) 117 | scales_pow = np.hstack((scales_down, scales_up)) 118 | scales = np.power(2.0, scales_pow) 119 | return scales 120 | 121 | scales = [0.5, 1, 1.2] # 0.5000, 1.0000, 1.4142, 2.0000] # _calc_scales() 122 | start = time.time() 123 | 124 | # initialize output 125 | bboxes = np.empty(shape=(0, 5)) 126 | 127 | # process input at different scales 128 | for s in scales: 129 | print("Processing {} at scale {:.4f}".format(fname, s)) 130 | img = cv2.resize(raw_img_f, (0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR) 131 | img = img - average_image 132 | img = img[np.newaxis, :] 133 | 134 | # we don't run every template on every scale ids of templates to ignore 135 | tids = list(range(4, 12)) + ([] if s <= 1.0 else list(range(18, 25))) 136 | ignoredTids = list(set(range(0, clusters.shape[0])) - set(tids)) 137 | 138 | # run through the net 139 | score_final_tf = sess.run(score_final, feed_dict={x: img}) 140 | 141 | # collect scores 142 | score_cls_tf, score_reg_tf = score_final_tf[:, :, :, :25], score_final_tf[:, :, :, 25:125] 143 | prob_cls_tf = expit(score_cls_tf) 144 | prob_cls_tf[0, :, :, ignoredTids] = 0.0 145 | 146 | def _calc_bounding_boxes(): 147 | # threshold for detection 148 | _, fy, fx, fc = np.where(prob_cls_tf > prob_thresh) 149 | 150 | # interpret heatmap into bounding boxes 151 | cy = fy * 8 - 1 152 | cx = fx * 8 - 1 153 | ch = clusters[fc, 3] - clusters[fc, 1] + 1 154 | cw = clusters[fc, 2] - clusters[fc, 0] + 1 155 | 156 | # extract bounding box refinement 157 | Nt = clusters.shape[0] 158 | tx = score_reg_tf[0, :, :, 0:Nt] 159 | ty = score_reg_tf[0, :, :, Nt:2*Nt] 160 | tw = score_reg_tf[0, :, :, 2*Nt:3*Nt] 161 | th = score_reg_tf[0, :, :, 3*Nt:4*Nt] 162 | 163 | # refine bounding boxes 164 | dcx = cw * tx[fy, fx, fc] 165 | dcy = ch * ty[fy, fx, fc] 166 | rcx = cx + dcx 167 | rcy = cy + dcy 168 | rcw = cw * np.exp(tw[fy, fx, fc]) 169 | rch = ch * np.exp(th[fy, fx, fc]) 170 | 171 | scores = score_cls_tf[0, fy, fx, fc] 172 | tmp_bboxes = np.vstack((rcx - rcw / 2, rcy - rch / 2, rcx + rcw / 2, rcy + rch / 2)) 173 | tmp_bboxes = np.vstack((tmp_bboxes / s, scores)) 174 | tmp_bboxes = tmp_bboxes.transpose() 175 | return tmp_bboxes 176 | 177 | tmp_bboxes = _calc_bounding_boxes() 178 | bboxes = np.vstack((bboxes, tmp_bboxes)) # : (5265, 5) 179 | 180 | 181 | print("time {:.2f} secs for {}".format(time.time() - start, fname)) 182 | 183 | # non maximum suppression 184 | # refind_idx = util.nms(bboxes, nms_thresh) 185 | refind_idx = tf.image.non_max_suppression(tf.convert_to_tensor(bboxes[:, :4], dtype=tf.float32), 186 | tf.convert_to_tensor(bboxes[:, 4], dtype=tf.float32), 187 | max_output_size=bboxes.shape[0], iou_threshold=nms_thresh) 188 | refind_idx = sess.run(refind_idx) 189 | refined_bboxes = bboxes[refind_idx] 190 | overlay_bounding_boxes(raw_img, refined_bboxes, lw) 191 | 192 | if display: 193 | # plt.axis('off') 194 | plt.imshow(raw_img) 195 | plt.show() 196 | 197 | # save image with bounding boxes 198 | raw_img = cv2.cvtColor(raw_img, cv2.COLOR_RGB2BGR) 199 | cv2.imwrite(os.path.join(output_dir, fname), raw_img) 200 | 201 | def main(): 202 | 203 | argparse = ArgumentParser() 204 | argparse.add_argument('--weight_file_path', type=str, help='Pretrained weight file.', default="/path/to/mat2tf.pkl") 205 | argparse.add_argument('--data_dir', type=str, help='Image data directory.', default="/path/to/input_image_directory") 206 | argparse.add_argument('--output_dir', type=str, help='Output directory for images with faces detected.', default="/path/to/output_directory") 207 | argparse.add_argument('--prob_thresh', type=float, help='The threshold of detection confidence(default: 0.5).', default=0.5) 208 | argparse.add_argument('--nms_thresh', type=float, help='The overlap threshold of non maximum suppression(default: 0.1).', default=0.1) 209 | argparse.add_argument('--line_width', type=int, help='Line width of bounding boxes(0: auto).', default=3) 210 | argparse.add_argument('--display', type=bool, help='Display each image on window.', default=False) 211 | 212 | args = argparse.parse_args() 213 | 214 | # check arguments 215 | assert os.path.exists(args.weight_file_path), "weight file: " + args.weight_file_path + " not found." 216 | assert os.path.exists(args.data_dir), "data directory: " + args.data_dir + " not found." 217 | assert os.path.exists(args.output_dir), "output directory: " + args.output_dir + " not found." 218 | assert args.line_width >= 0, "line_width should be >= 0." 219 | 220 | with tf.Graph().as_default(): 221 | evaluate( 222 | weight_file_path=args.weight_file_path, data_dir=args.data_dir, output_dir=args.output_dir, 223 | prob_thresh=args.prob_thresh, nms_thresh=args.nms_thresh, 224 | lw=args.line_width, display=args.display) 225 | 226 | if __name__ == '__main__': 227 | main() 228 | -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/tiny_face_model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | 6 | import tensorflow as tf 7 | import numpy as np 8 | import pickle 9 | 10 | class Model(): 11 | def __init__(self, weight_file_path): 12 | """Overlay bounding boxes of face on images. 13 | Args: 14 | weight_file_path: 15 | A pretrained weight file in the pickle format 16 | generated by matconvnet_hr101_to_tf.py. 17 | Returns: 18 | None. 19 | """ 20 | self.dtype = tf.float32 21 | self.weight_file_path = weight_file_path 22 | with open(self.weight_file_path, "rb") as f: 23 | self.mat_blocks_dict, self.mat_params_dict = pickle.load(f) 24 | 25 | def get_data_by_key(self, key): 26 | """Helper to access a pretrained model data through a key.""" 27 | assert key in self.mat_params_dict, "key: " + key + " not found." 28 | return self.mat_params_dict[key] 29 | 30 | def _weight_variable_on_cpu(self, name, shape): 31 | """Helper to create a weight Variable stored on CPU memory. 32 | 33 | Args: 34 | name: name of the variable. 35 | shape: list of ints: (height, width, channel, filter). 36 | 37 | Returns: 38 | initializer for Variable. 39 | """ 40 | assert len(shape) == 4 41 | 42 | weights = self.get_data_by_key(name + "_filter") # (h, w, channel, filter) 43 | assert list(weights.shape) == shape 44 | initializer = tf.constant_initializer(weights, dtype=self.dtype) 45 | 46 | with tf.device('/cpu:0'): 47 | var = tf.get_variable(name + "_w", shape, initializer=initializer, dtype=self.dtype) 48 | return var 49 | 50 | def _bias_variable_on_cpu(self, name, shape): 51 | """Helper to create a bias Variable stored on CPU memory. 52 | 53 | Args: 54 | name: name of the variable. 55 | shape: int, filter size. 56 | 57 | Returns: 58 | initializer for Variable. 59 | """ 60 | assert isinstance(shape, int) 61 | bias = self.get_data_by_key(name + "_bias") 62 | assert len(bias) == shape 63 | initializer = tf.constant_initializer(bias, dtype=self.dtype) 64 | 65 | with tf.device('/cpu:0'): 66 | var = tf.get_variable(name + "_b", shape, initializer=initializer, dtype=self.dtype) 67 | return var 68 | 69 | 70 | def _bn_variable_on_cpu(self, name, shape): 71 | """Helper to create a batch normalization Variable stored on CPU memory. 72 | 73 | Args: 74 | name: name of the variable. 75 | shape: int, filter size. 76 | 77 | Returns: 78 | initializer for Variable. 79 | """ 80 | assert isinstance(shape, int) 81 | 82 | name2 = "bn" + name[3:] 83 | if name.startswith("conv"): 84 | name2 = "bn_" + name 85 | 86 | scale = self.get_data_by_key(name2 + '_scale') 87 | offset = self.get_data_by_key(name2 + '_offset') 88 | mean = self.get_data_by_key(name2 + '_mean') 89 | variance = self.get_data_by_key(name2 + '_variance') 90 | 91 | with tf.device('/cpu:0'): 92 | initializer = tf.constant_initializer(scale, dtype=self.dtype) 93 | scale = tf.get_variable(name2 + "_scale", shape, initializer=initializer, dtype=self.dtype) 94 | initializer = tf.constant_initializer(offset, dtype=self.dtype) 95 | offset = tf.get_variable(name2 + "_offset", shape, initializer=initializer, dtype=self.dtype) 96 | initializer = tf.constant_initializer(mean, dtype=self.dtype) 97 | mean = tf.get_variable(name2 + "_mean", shape, initializer=initializer, dtype=self.dtype) 98 | initializer = tf.constant_initializer(variance, dtype=self.dtype) 99 | variance = tf.get_variable(name2 + "_variance", shape, initializer=initializer, dtype=self.dtype) 100 | 101 | return scale, offset, mean, variance 102 | 103 | 104 | def conv_block(self, bottom, name, shape, strides=[1,1,1,1], padding="SAME", 105 | has_bias=False, add_relu=True, add_bn=True, eps=1.0e-5): 106 | """Create a block composed of multiple layers: 107 | a conv layer 108 | a batch normalization layer 109 | an activation layer 110 | 111 | Args: 112 | bottom: A layer before this block. 113 | name: Name of the block. 114 | shape: List of ints: (height, width, channel, filter). 115 | strides: Strides of conv layer. 116 | padding: Padding of conv layer. 117 | has_bias: Whether a bias term is added. 118 | add_relu: Whether a ReLU layer is added. 119 | add_bn: Whether a batch normalization layer is added. 120 | eps: A small float number to avoid dividing by 0, used in a batch normalization layer. 121 | Returns: 122 | a block of layers 123 | """ 124 | assert len(shape) == 4 125 | 126 | weight = self._weight_variable_on_cpu(name, shape) 127 | conv = tf.nn.conv2d(bottom, weight, strides, padding=padding) 128 | if has_bias: 129 | bias = self._bias_variable_on_cpu(name, shape[3]) 130 | 131 | pre_activation = tf.nn.bias_add(conv, bias) if has_bias else conv 132 | 133 | if add_bn: 134 | # scale, offset, mean, variance = self._bn_variable_on_cpu("bn_" + name, shape[-1]) 135 | scale, offset, mean, variance = self._bn_variable_on_cpu(name, shape[-1]) 136 | pre_activation = tf.nn.batch_normalization(pre_activation, mean, variance, offset, scale, variance_epsilon=eps) 137 | 138 | relu = tf.nn.relu(pre_activation) if add_relu else pre_activation 139 | 140 | return relu 141 | 142 | 143 | def conv_trans_layer(self, bottom, name, shape, strides=[1,1,1,1], padding="SAME", has_bias=False): 144 | """Create a block composed of multiple layers: 145 | a transpose of conv layer 146 | an activation layer 147 | 148 | Args: 149 | bottom: A layer before this block. 150 | name: Name of the block. 151 | shape: List of ints: (height, width, channel, filter). 152 | strides: Strides of conv layer. 153 | padding: Padding of conv layer. 154 | has_bias: Whether a bias term is added. 155 | add_relu: Whether a ReLU layer is added. 156 | Returns: 157 | a block of layers 158 | """ 159 | assert len(shape) == 4 160 | 161 | weight = self._weight_variable_on_cpu(name, shape) 162 | nb, h, w, nc = tf.split(tf.shape(bottom), num_or_size_splits=4) 163 | output_shape = tf.stack([nb, (h - 1) * strides[1] - 3 + shape[0], (w - 1) * strides[2] - 3 + shape[1], nc])[:, 0] 164 | conv = tf.nn.conv2d_transpose(bottom, weight, output_shape, strides, padding=padding) 165 | if has_bias: 166 | bias = self._bias_variable_on_cpu(name, shape[3]) 167 | 168 | conv = tf.nn.bias_add(conv, bias) if has_bias else conv 169 | 170 | return conv 171 | 172 | def residual_block(self, bottom, name, in_channel, neck_channel, out_channel, trunk): 173 | """Create a residual block. 174 | 175 | Args: 176 | bottom: A layer before this block. 177 | name: Name of the block. 178 | in_channel: number of channels in a input tensor. 179 | neck_channel: number of channels in a bottleneck block. 180 | out_channel: number of channels in a output tensor. 181 | trunk: a tensor in a identity path. 182 | Returns: 183 | a block of layers 184 | """ 185 | _strides = [1, 2, 2, 1] if name.startswith("res3a") or name.startswith("res4a") else [1, 1, 1, 1] 186 | res = self.conv_block(bottom, name + '_branch2a', shape=[1, 1, in_channel, neck_channel], 187 | strides=_strides, padding="VALID", add_relu=True) 188 | res = self.conv_block(res, name + '_branch2b', shape=[3, 3, neck_channel, neck_channel], 189 | padding="SAME", add_relu=True) 190 | res = self.conv_block(res, name + '_branch2c', shape=[1, 1, neck_channel, out_channel], 191 | padding="VALID", add_relu=False) 192 | 193 | res = trunk + res 194 | res = tf.nn.relu(res) 195 | 196 | return res 197 | 198 | def tiny_face(self, image): 199 | """Create a tiny face model. 200 | 201 | Args: 202 | image: an input image. 203 | Returns: 204 | a score tensor 205 | """ 206 | img = tf.pad(image, [[0, 0], [3, 3], [3, 3], [0, 0]], "CONSTANT") 207 | conv = self.conv_block(img, 'conv1', shape=[7, 7, 3, 64], strides=[1, 2, 2, 1], padding="VALID", add_relu=True) 208 | pool1 = tf.nn.max_pool(conv, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME') 209 | 210 | res2a_branch1 = self.conv_block(pool1, 'res2a_branch1', shape=[1, 1, 64, 256], padding="VALID", add_relu=False) 211 | res2a = self.residual_block(pool1, 'res2a', 64, 64, 256, res2a_branch1) 212 | res2b = self.residual_block(res2a, 'res2b', 256, 64, 256, res2a) 213 | res2c = self.residual_block(res2b, 'res2c', 256, 64, 256, res2b) 214 | 215 | res3a_branch1 = self.conv_block(res2c, 'res3a_branch1', shape=[1, 1, 256, 512], strides=[1, 2, 2, 1], padding="VALID", add_relu=False) 216 | res3a = self.residual_block(res2c, 'res3a', 256, 128, 512, res3a_branch1) 217 | 218 | res3b1 = self.residual_block(res3a, 'res3b1', 512, 128, 512, res3a) 219 | res3b2 = self.residual_block(res3b1, 'res3b2', 512, 128, 512, res3b1) 220 | res3b3 = self.residual_block(res3b2, 'res3b3', 512, 128, 512, res3b2) 221 | 222 | res4a_branch1 = self.conv_block(res3b3, 'res4a_branch1', shape=[1, 1, 512, 1024], strides=[1, 2, 2, 1], padding="VALID", add_relu=False) 223 | res4a = self.residual_block(res3b3, 'res4a', 512, 256, 1024, res4a_branch1) 224 | 225 | res4b = res4a 226 | for i in range(1, 23): 227 | res4b = self.residual_block(res4b, 'res4b' + str(i), 1024, 256, 1024, res4b) 228 | 229 | score_res4 = self.conv_block(res4b, 'score_res4', shape=[1, 1, 1024, 125], padding="VALID", 230 | has_bias=True, add_relu=False, add_bn=False) 231 | score4 = self.conv_trans_layer(score_res4, 'score4', shape=[4, 4, 125, 125], strides=[1, 2, 2, 1], padding="SAME") 232 | score_res3 = self.conv_block(res3b3, 'score_res3', shape=[1, 1, 512, 125], padding="VALID", 233 | has_bias=True, add_bn=False, add_relu=False) 234 | 235 | bs, height, width = tf.split(tf.shape(score4), num_or_size_splits=4)[0:3] 236 | _size = tf.convert_to_tensor([height[0], width[0]]) 237 | _offsets = tf.zeros([bs[0], 2]) 238 | score_res3c = tf.image.extract_glimpse(score_res3, _size, _offsets, centered=True, normalized=False) 239 | 240 | score_final = score4 + score_res3c 241 | return score_final 242 | -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/tinyface_tf/selfie.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/JunctionAPI/TinyFaces/tinyface_tf/selfie.jpg -------------------------------------------------------------------------------- /JunctionAPI/TinyFaces/tinyface_tf/selfie_detection.jpg: -------------------------------------------------------------------------------- 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/JunctionAPI/TinyFaces/util.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import absolute_import 3 | from __future__ import division 4 | from __future__ import print_function 5 | 6 | import numpy as np 7 | 8 | def nms(dets, prob_thresh): 9 | x1 = dets[:, 0] 10 | y1 = dets[:, 1] 11 | x2 = dets[:, 2] 12 | y2 = dets[:, 3] 13 | scores = dets[:, 4] 14 | 15 | areas = (x2 - x1 + 1) * (y2 - y1 + 1) 16 | 17 | order = scores.argsort()[::-1] 18 | 19 | keep = [] 20 | while order.size > 0: 21 | i = order[0] 22 | keep.append(i) 23 | xx1 = np.maximum(x1[i], x1[order[1:]]) 24 | yy1 = np.maximum(y1[i], y1[order[1:]]) 25 | xx2 = np.minimum(x2[i], x2[order[1:]]) 26 | yy2 = np.minimum(y2[i], y2[order[1:]]) 27 | w = np.maximum(0.0, xx2 - xx1 + 1) 28 | h = np.maximum(0.0, yy2 - yy1 + 1) 29 | inter = w * h 30 | 31 | ovr = inter / (areas[i] + areas[order[1:]] - inter) 32 | inds = np.where(ovr <= prob_thresh)[0] 33 | 34 | order = order[inds + 1] 35 | return keep 36 | 37 | # colormap parula borrowed from 38 | # https://github.com/BIDS/colormap/blob/master/fake_parula.py 39 | cm_data = [[ 0.26710521, 0.03311059, 0.6188155 ], 40 | [ 0.26493929, 0.04780926, 0.62261795], 41 | [ 0.26260545, 0.06084214, 0.62619176], 42 | [ 0.26009691, 0.07264411, 0.62951561], 43 | [ 0.25740785, 0.08360391, 0.63256745], 44 | [ 0.25453369, 0.09395358, 0.63532497], 45 | [ 0.25147146, 0.10384228, 0.6377661 ], 46 | [ 0.24822014, 0.11337029, 0.6398697 ], 47 | [ 0.24478105, 0.12260661, 0.64161629], 48 | [ 0.24115816, 0.131599 , 0.6429888 ], 49 | [ 0.23735836, 0.14038009, 0.64397346], 50 | [ 0.23339166, 0.14897137, 0.64456048], 51 | [ 0.22927127, 0.15738602, 0.64474476], 52 | [ 0.22501278, 0.16563165, 0.64452595], 53 | [ 0.22063349, 0.17371215, 0.64390834], 54 | [ 0.21616055, 0.18162302, 0.64290515], 55 | [ 0.21161851, 0.18936156, 0.64153295], 56 | [ 0.20703353, 0.19692415, 0.63981287], 57 | [ 0.20243273, 0.20430706, 0.63776986], 58 | [ 0.19784363, 0.211507 , 0.63543183], 59 | [ 0.19329361, 0.21852157, 0.63282872], 60 | [ 0.18880937, 0.2253495 , 0.62999156], 61 | [ 0.18442119, 0.23198815, 0.62695569], 62 | [ 0.18014936, 0.23844124, 0.62374886], 63 | [ 0.17601569, 0.24471172, 0.62040016], 64 | [ 0.17204028, 0.25080356, 0.61693715], 65 | [ 0.16824123, 0.25672163, 0.6133854 ], 66 | [ 0.16463462, 0.26247158, 0.60976836], 67 | [ 0.16123449, 0.26805963, 0.60610723], 68 | [ 0.15805279, 0.27349243, 0.60242099], 69 | [ 0.15509948, 0.27877688, 0.59872645], 70 | [ 0.15238249, 0.28392004, 0.59503836], 71 | [ 0.14990781, 0.28892902, 0.59136956], 72 | [ 0.14767951, 0.29381086, 0.58773113], 73 | [ 0.14569979, 0.29857245, 0.58413255], 74 | [ 0.1439691 , 0.30322055, 0.58058191], 75 | [ 0.14248613, 0.30776167, 0.57708599], 76 | [ 0.14124797, 0.31220208, 0.57365049], 77 | [ 0.14025018, 0.31654779, 0.57028011], 78 | [ 0.13948691, 0.32080454, 0.5669787 ], 79 | [ 0.13895174, 0.32497744, 0.56375063], 80 | [ 0.13863958, 0.32907012, 0.56060453], 81 | [ 0.138537 , 0.3330895 , 0.55753513], 82 | [ 0.13863384, 0.33704026, 0.55454374], 83 | [ 0.13891931, 0.34092684, 0.55163126], 84 | [ 0.13938212, 0.34475344, 0.54879827], 85 | [ 0.14001061, 0.34852402, 0.54604503], 86 | [ 0.14079292, 0.35224233, 0.54337156], 87 | [ 0.14172091, 0.35590982, 0.54078769], 88 | [ 0.14277848, 0.35953205, 0.53828312], 89 | [ 0.14395358, 0.36311234, 0.53585661], 90 | [ 0.1452346 , 0.36665374, 0.5335074 ], 91 | [ 0.14661019, 0.3701591 , 0.5312346 ], 92 | [ 0.14807104, 0.37363011, 0.52904278], 93 | [ 0.1496059 , 0.3770697 , 0.52692951], 94 | [ 0.15120289, 0.3804813 , 0.52488853], 95 | [ 0.15285214, 0.38386729, 0.52291854], 96 | [ 0.15454421, 0.38722991, 0.52101815], 97 | [ 0.15627225, 0.39056998, 0.5191937 ], 98 | [ 0.15802555, 0.39389087, 0.5174364 ], 99 | [ 0.15979549, 0.39719482, 0.51574311], 100 | [ 0.16157425, 0.40048375, 0.51411214], 101 | [ 0.16335571, 0.40375871, 0.51254622], 102 | [ 0.16513234, 0.40702178, 0.51104174], 103 | [ 0.1668964 , 0.41027528, 0.50959299], 104 | [ 0.16864151, 0.41352084, 0.50819797], 105 | [ 0.17036277, 0.41675941, 0.50685814], 106 | [ 0.1720542 , 0.41999269, 0.50557008], 107 | [ 0.17370932, 0.42322271, 0.50432818], 108 | [ 0.17532301, 0.42645082, 0.50313007], 109 | [ 0.17689176, 0.42967776, 0.50197686], 110 | [ 0.17841013, 0.43290523, 0.5008633 ], 111 | [ 0.17987314, 0.43613477, 0.49978492], 112 | [ 0.18127676, 0.43936752, 0.49873901], 113 | [ 0.18261885, 0.44260392, 0.49772638], 114 | [ 0.18389409, 0.44584578, 0.49673978], 115 | [ 0.18509911, 0.44909409, 0.49577605], 116 | [ 0.18623135, 0.4523496 , 0.494833 ], 117 | [ 0.18728844, 0.45561305, 0.49390803], 118 | [ 0.18826671, 0.45888565, 0.49299567], 119 | [ 0.18916393, 0.46216809, 0.49209268], 120 | [ 0.18997879, 0.46546084, 0.49119678], 121 | [ 0.19070881, 0.46876472, 0.49030328], 122 | [ 0.19135221, 0.47208035, 0.48940827], 123 | [ 0.19190791, 0.47540815, 0.48850845], 124 | [ 0.19237491, 0.47874852, 0.4876002 ], 125 | [ 0.19275204, 0.48210192, 0.48667935], 126 | [ 0.19303899, 0.48546858, 0.48574251], 127 | [ 0.19323526, 0.48884877, 0.48478573], 128 | [ 0.19334062, 0.49224271, 0.48380506], 129 | [ 0.19335574, 0.49565037, 0.4827974 ], 130 | [ 0.19328143, 0.49907173, 0.48175948], 131 | [ 0.19311664, 0.50250719, 0.48068559], 132 | [ 0.192864 , 0.50595628, 0.47957408], 133 | [ 0.19252521, 0.50941877, 0.47842186], 134 | [ 0.19210087, 0.51289469, 0.47722441], 135 | [ 0.19159194, 0.516384 , 0.47597744], 136 | [ 0.19100267, 0.51988593, 0.47467988], 137 | [ 0.19033595, 0.52340005, 0.47332894], 138 | [ 0.18959113, 0.5269267 , 0.47191795], 139 | [ 0.18877336, 0.530465 , 0.47044603], 140 | [ 0.18788765, 0.53401416, 0.46891178], 141 | [ 0.18693822, 0.53757359, 0.46731272], 142 | [ 0.18592276, 0.54114404, 0.46563962], 143 | [ 0.18485204, 0.54472367, 0.46389595], 144 | [ 0.18373148, 0.5483118 , 0.46207951], 145 | [ 0.18256585, 0.55190791, 0.4601871 ], 146 | [ 0.18135481, 0.55551253, 0.45821002], 147 | [ 0.18011172, 0.55912361, 0.45615277], 148 | [ 0.17884392, 0.56274038, 0.45401341], 149 | [ 0.17755858, 0.56636217, 0.45178933], 150 | [ 0.17625543, 0.56998972, 0.44946971], 151 | [ 0.174952 , 0.57362064, 0.44706119], 152 | [ 0.17365805, 0.57725408, 0.44456198], 153 | [ 0.17238403, 0.58088916, 0.4419703 ], 154 | [ 0.17113321, 0.58452637, 0.43927576], 155 | [ 0.1699221 , 0.58816399, 0.43648119], 156 | [ 0.1687662 , 0.5918006 , 0.43358772], 157 | [ 0.16767908, 0.59543526, 0.43059358], 158 | [ 0.16667511, 0.59906699, 0.42749697], 159 | [ 0.16575939, 0.60269653, 0.42428344], 160 | [ 0.16495764, 0.6063212 , 0.42096245], 161 | [ 0.16428695, 0.60993988, 0.41753246], 162 | [ 0.16376481, 0.61355147, 0.41399151], 163 | [ 0.16340924, 0.61715487, 0.41033757], 164 | [ 0.16323549, 0.62074951, 0.40656329], 165 | [ 0.16326148, 0.62433443, 0.40266378], 166 | [ 0.16351136, 0.62790748, 0.39864431], 167 | [ 0.16400433, 0.63146734, 0.39450263], 168 | [ 0.16475937, 0.63501264, 0.39023638], 169 | [ 0.16579502, 0.63854196, 0.38584309], 170 | [ 0.16712921, 0.64205381, 0.38132023], 171 | [ 0.168779 , 0.64554661, 0.37666513], 172 | [ 0.17075915, 0.64901912, 0.37186962], 173 | [ 0.17308572, 0.65246934, 0.36693299], 174 | [ 0.1757732 , 0.65589512, 0.36185643], 175 | [ 0.17883344, 0.65929449, 0.3566372 ], 176 | [ 0.18227669, 0.66266536, 0.35127251], 177 | [ 0.18611159, 0.66600553, 0.34575959], 178 | [ 0.19034516, 0.66931265, 0.34009571], 179 | [ 0.19498285, 0.67258423, 0.3342782 ], 180 | [ 0.20002863, 0.67581761, 0.32830456], 181 | [ 0.20548509, 0.67900997, 0.3221725 ], 182 | [ 0.21135348, 0.68215834, 0.31587999], 183 | [ 0.2176339 , 0.68525954, 0.30942543], 184 | [ 0.22432532, 0.68831023, 0.30280771], 185 | [ 0.23142568, 0.69130688, 0.29602636], 186 | [ 0.23893914, 0.69424565, 0.28906643], 187 | [ 0.2468574 , 0.69712255, 0.28194103], 188 | [ 0.25517514, 0.69993351, 0.27465372], 189 | [ 0.26388625, 0.70267437, 0.26720869], 190 | [ 0.27298333, 0.70534087, 0.25961196], 191 | [ 0.28246016, 0.70792854, 0.25186761], 192 | [ 0.29232159, 0.71043184, 0.2439642 ], 193 | [ 0.30253943, 0.71284765, 0.23594089], 194 | [ 0.31309875, 0.71517209, 0.22781515], 195 | [ 0.32399522, 0.71740028, 0.21959115], 196 | [ 0.33520729, 0.71952906, 0.21129816], 197 | [ 0.3467003 , 0.72155723, 0.20298257], 198 | [ 0.35846225, 0.72348143, 0.19466318], 199 | [ 0.3704552 , 0.72530195, 0.18639333], 200 | [ 0.38264126, 0.72702007, 0.17822762], 201 | [ 0.39499483, 0.72863609, 0.17020921], 202 | [ 0.40746591, 0.73015499, 0.1624122 ], 203 | [ 0.42001969, 0.73158058, 0.15489659], 204 | [ 0.43261504, 0.73291878, 0.14773267], 205 | [ 0.44521378, 0.73417623, 0.14099043], 206 | [ 0.45777768, 0.73536072, 0.13474173], 207 | [ 0.47028295, 0.73647823, 0.1290455 ], 208 | [ 0.48268544, 0.73753985, 0.12397794], 209 | [ 0.49497773, 0.73854983, 0.11957878], 210 | [ 0.5071369 , 0.73951621, 0.11589589], 211 | [ 0.51913764, 0.74044827, 0.11296861], 212 | [ 0.53098624, 0.74134823, 0.11080237], 213 | [ 0.5426701 , 0.74222288, 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0.19091446], 236 | [ 0.77183123, 0.76120466, 0.19641095], 237 | [ 0.78060815, 0.76205561, 0.20191973], 238 | [ 0.78932717, 0.76290815, 0.20743538], 239 | [ 0.79799213, 0.76376186, 0.21295324], 240 | [ 0.8066067 , 0.76461631, 0.21846931], 241 | [ 0.81517444, 0.76547101, 0.22398014], 242 | [ 0.82369877, 0.76632547, 0.2294827 ], 243 | [ 0.832183 , 0.7671792 , 0.2349743 ], 244 | [ 0.8406303 , 0.76803167, 0.24045248], 245 | [ 0.84904371, 0.76888236, 0.24591492], 246 | [ 0.85742615, 0.76973076, 0.25135935], 247 | [ 0.86578037, 0.77057636, 0.25678342], 248 | [ 0.87410891, 0.77141875, 0.2621846 ], 249 | [ 0.88241406, 0.77225757, 0.26755999], 250 | [ 0.89070781, 0.77308772, 0.27291122], 251 | [ 0.89898836, 0.77391069, 0.27823228], 252 | [ 0.90725475, 0.77472764, 0.28351668], 253 | [ 0.91550775, 0.77553893, 0.28875751], 254 | [ 0.92375722, 0.7763404 , 0.29395046], 255 | [ 0.9320227 , 0.77712286, 0.29909267], 256 | [ 0.94027715, 0.7779011 , 0.30415428], 257 | [ 0.94856742, 0.77865213, 0.3091325 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0.99590494, 0.87727931, 0.24750009], 281 | [ 0.99555225, 0.88285068, 0.2418514 ], 282 | [ 0.99513763, 0.8884501 , 0.23588062], 283 | [ 0.99471252, 0.89405076, 0.2296837 ], 284 | [ 0.99421873, 0.89968246, 0.2230963 ], 285 | [ 0.99370185, 0.90532165, 0.21619768], 286 | [ 0.99313786, 0.91098038, 0.2088926 ], 287 | [ 0.99250707, 0.91666811, 0.20108214], 288 | [ 0.99187888, 0.92235023, 0.19290417], 289 | [ 0.99110991, 0.92809686, 0.18387963], 290 | [ 0.99042108, 0.93379995, 0.17458127], 291 | [ 0.98958484, 0.93956962, 0.16420166], 292 | [ 0.98873988, 0.94533859, 0.15303117], 293 | [ 0.98784836, 0.95112482, 0.14074826], 294 | [ 0.98680727, 0.95697596, 0.12661626]] 295 | -------------------------------------------------------------------------------- /JunctionAPI/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/JunctionAPI/__init__.py -------------------------------------------------------------------------------- /JunctionAPI/api.py: -------------------------------------------------------------------------------- 1 | from app import create_app 2 | 3 | app = create_app() 4 | -------------------------------------------------------------------------------- /JunctionAPI/app/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/JunctionAPI/app/__init__.py -------------------------------------------------------------------------------- /JunctionAPI/app/detect/__init__.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint 2 | 3 | bp = Blueprint('detect', __name__) 4 | 5 | from app.detect import routes 6 | from app.detect import run_model_server 7 | run_model_server = run_model_server.pre_load_model() 8 | -------------------------------------------------------------------------------- /JunctionAPI/app/detect/face_detector.py: -------------------------------------------------------------------------------- 1 | import time 2 | import cv2 3 | 4 | 5 | class FaceDetector(): 6 | def __init__(self): 7 | from ..hr101_mxnet.tiny_fd import TinyFacesDetector 8 | # from tiny_fd import TinyFacesDetector 9 | self.tiny_faces = TinyFacesDetector(model_root='/home/paperspace/Desktop/Junction_ver2/api/app/weights', prob_thresh=0.5, gpu_idx=0) 10 | 11 | def detect(self, fname, txtfname): 12 | self.img = cv2.imread(fname) 13 | tic = time.time() 14 | boxes = self.detector.detect(self.img) 15 | toc = time.time() 16 | elapsed_time = toc - tic 17 | 18 | self.write_to_txt_file(fname, boxes, txtfname) 19 | 20 | return boxes.shape[0], boxes, elapsed_time 21 | 22 | def get_face_bboxes(self, img): 23 | boxes = self.tiny_faces.detect(self.img) 24 | # return boxes.shape[0], boxes 25 | return 127, [1,2,3,4] 26 | def write_to_txt_file(self, fname, boxes, txtfname): 27 | txtfile = open(txtfname, 'w') 28 | txtfile.write(fname + '\n') 29 | txtfile.write(str(boxes.shape[0]) + '\n') 30 | for box in boxes: 31 | line = '' 32 | 33 | x1 = min(max(0, int(box[0])), self.img.shape[0]) 34 | y1 = min(max(0, int(box[1])), self.img.shape[1]) 35 | x2 = min(max(0, int(box[2])), self.img.shape[0]) 36 | y2 = min(max(0, int(box[3])), self.img.shape[1]) 37 | line += str(x1) + ' ' + str(y1) + ' ' + str(x2) + ' ' + str(y2) + '\n' 38 | 39 | txtfile.write(line) 40 | -------------------------------------------------------------------------------- /JunctionAPI/app/detect/forms.py: -------------------------------------------------------------------------------- 1 | from flask_wtf import FlaskForm 2 | 3 | class DetectForm(FlaskForm): 4 | pass 5 | -------------------------------------------------------------------------------- /JunctionAPI/app/detect/routes.py: -------------------------------------------------------------------------------- 1 | from flask import redirect, url_for, request, render_template, flash, session 2 | from flask import current_app as current_app 3 | from PIL import Image 4 | import cv2 5 | from app.detect import bp 6 | from app.detect import run_model_server 7 | import numpy as np 8 | from app.detect.forms import DetectForm 9 | from keras.preprocessing.image import img_to_array 10 | import matplotlib.pyplot as plt 11 | import os 12 | import io 13 | import imutils 14 | import copy 15 | import csv 16 | import shutil 17 | import sys 18 | sys.path.append('../../TinyFaces') 19 | from tiny_face_det import detect_from_img_path 20 | #import tiny_face_eval 21 | 22 | @bp.route('/detect', methods=['GET', 'POST']) 23 | def detect(): 24 | if "processed_data" not in session: 25 | session["processed_data"] = {} 26 | bucket = session["processed_data"] 27 | emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"} 28 | gender_dict = {0: 'man', 1:'woman'} 29 | 30 | # For data analysis 31 | emo_count = {"Angry":0, "Disgusted":0, "Fearful":0, "Happy":0, "Neutral":0, "Sad":0, "Surprised":0} 32 | gender_count = {"man":0, "woman":0} 33 | age_class = {} 34 | 35 | if "processed_data" not in session: 36 | # To store the processed_data 37 | session["processed_data"] = {} 38 | form = DetectForm() 39 | current_app.config.update(DROPZONE_UPLOAD_ON_CLICK=True) 40 | 41 | if request.method == "POST": 42 | for (key, f) in (request.files.items()): 43 | image = f.read() 44 | image = Image.open(io.BytesIO(image)).convert('RGB') 45 | opencv_img = np.array(image) 46 | opencv_img = opencv_img[:, :, ::-1].copy() 47 | age_img_size = run_model_server.age_mdl.input.shape.as_list()[1] 48 | frame = imutils.resize(opencv_img, width=400) 49 | (h, w) = frame.shape[:2] 50 | img_path = '../../TinyFaces/uploaded_imgs/' 51 | cv2.imwrite(img_path, frame) 52 | detections = detect_from_img_path(img_path) 53 | print(detections) 54 | count = 0 55 | 56 | history_data = [] 57 | # loop over the detections 58 | for det in detections: 59 | count += 1 60 | box = det[:-1] 61 | startX = int(box[0]) 62 | startY = int(box[1]) 63 | endX = int(box[2]) 64 | endY = int(box[3]) 65 | 66 | # Extract the patches 67 | img_patch = frame[startY:startY + (endY-startY), startX:startX + (endX - startX)] 68 | 69 | # Patch for Emo detect 70 | emo_patch = cv2.cvtColor(img_patch, cv2.COLOR_RGB2GRAY) 71 | emo_patch = np.expand_dims(np.expand_dims(cv2.resize( 72 | emo_patch, (48, 48)), -1), 0) 73 | 74 | # Patch for gender detect 75 | gender_patch = cv2.resize(img_patch, (96, 96)) 76 | gender_patch = gender_patch.astype("float") / 255.0 77 | gender_patch = img_to_array(gender_patch) 78 | gender_patch = np.expand_dims(gender_patch, axis=0) 79 | 80 | # Path for age detect 81 | age_patch = cv2.cvtColor(img_patch, cv2.COLOR_BGR2RGB) 82 | age_patch = cv2.resize(age_patch, (age_img_size, age_img_size)) 83 | age_patch = np.expand_dims(age_patch, axis=0) 84 | 85 | graph = run_model_server.graph 86 | with graph.as_default(): 87 | predicted_age = run_model_server.age_mdl.predict(age_patch) 88 | ages = np.arange(0, 101).reshape(101, 1) 89 | predicted_age = int(predicted_age.dot(ages).flatten()) 90 | 91 | detected_gender = run_model_server.gender_mdl.predict(gender_patch)[0] 92 | gender_index = int(np.argmax(detected_gender)) 93 | 94 | predicted_emo = run_model_server.emo_mdl.predict(emo_patch) 95 | 96 | emo_index = int(np.argmax(predicted_emo)) 97 | emo_count[emotion_dict[emo_index]] += 1 98 | gender_count[gender_dict[gender_index]] += 1 99 | if str(predicted_age) in age_class: 100 | age_class[str(predicted_age)] +=1 101 | else: 102 | age_class[str(predicted_age)] = 1 103 | history_data.append([emotion_dict[emo_index],gender_dict[gender_index],predicted_age,(startX,startY, endX, endY)]) 104 | 105 | # draw the bounding box of the face along with the associated 106 | # probability 107 | y = startY - 10 if startY - 10 > 10 else startY + 10 108 | cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 255, 0), 2) 109 | 110 | static_file_path = os.path.join(os.getcwd(), "app", "static") 111 | # Generate Emotion Status 112 | xs = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Neutral', 'Sad', 'Surprised'] 113 | ys = [] 114 | data = copy.deepcopy(emo_count) 115 | print(data) 116 | ys = [data['Angry'], data['Disgusted'], data['Fearful'], data['Happy'], data['Neutral'], data['Sad'], data['Surprised']] 117 | plt.bar(list(xs), ys, align='center', alpha=0.5) 118 | 119 | plt.ylabel('Number of people') 120 | plt.title('Emotion Classification') 121 | plt.savefig(os.path.join(static_file_path,'emotion_status.png')) 122 | plt.cla() 123 | plt.clf() 124 | # Generate Gender Count 125 | xs = ['MAN', 'WOMAN'] 126 | ys = [] 127 | data = copy.deepcopy(gender_count) 128 | ys = [data['man'], data['woman']] 129 | plt.bar(list(xs), ys, align='center', alpha=0.5) 130 | 131 | plt.ylabel('Number of people') 132 | plt.title('Gender Classification') 133 | plt.savefig(os.path.join(static_file_path,'gender_status.png')) 134 | plt.cla() 135 | plt.clf() 136 | # Age Classification 137 | data = copy.deepcopy(age_class) 138 | xs = [] 139 | ys = [] 140 | for item, lst in data.items(): 141 | xs.append(item) 142 | ys.append(data[item]) 143 | #plt.plot(list(xs), ys, label='Crowd Count', color='blue') 144 | plt.plot(list(xs), ys, 'bo', label='Crowd Count') 145 | plt.title("Age Classification Status") 146 | plt.xlabel("Age") 147 | plt.ylabel("Number of people") 148 | plt.legend(loc='upper right') 149 | plt.savefig(os.path.join(static_file_path,'age_status.png')) 150 | plt.cla() 151 | plt.clf() 152 | file_name = "{}_detect.{}".format(f.filename.split('.')[0],f.filename.split('.')[1]) 153 | cv2.imwrite(os.path.join(static_file_path, file_name), frame) 154 | # Note we must return 200 HTTP OK response. 155 | # Saving csv for historical data analysis. 156 | # TODO: Change to database. 157 | with open(os.path.join(os.getcwd(),'app', 'records', 'camera1.csv'), mode='a') as data_file: 158 | data_file= csv.writer(data_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) 159 | for i in range(len(history_data)): 160 | data_file.writerow(list(history_data[i])) 161 | 162 | bucket['file_name'] = file_name 163 | bucket['detected_person'] = count 164 | session['processed_data'] = bucket 165 | return "Successfully processed.", 200 166 | else: 167 | return render_template('detect/../templates/detect/upload.html', title="Tokyo Junction", 168 | form=form) 169 | 170 | @bp.route('/result') 171 | def result(): 172 | if "processed_data" not in session: 173 | return redirect(url_for('detect.detect')) 174 | processed_data = session["processed_data"] 175 | file_name = processed_data['file_name'] 176 | person_count = processed_data['detected_person'] 177 | session.pop('processed_data', None) 178 | # TO DO 179 | # Display the processed data on the page 180 | print("Here result") 181 | return render_template('detect/../templates/detect/results.html', file_name=file_name, people_count=person_count) 182 | 183 | @bp.route('/public') 184 | def public(): 185 | print("Public...") 186 | return render_template('detect/../templates/detect/public.html') 187 | -------------------------------------------------------------------------------- /JunctionAPI/app/detect/routes_opencv.py: -------------------------------------------------------------------------------- 1 | from flask import redirect, url_for, request, render_template, flash, session 2 | from flask import current_app as current_app 3 | from PIL import Image 4 | import cv2 5 | from app.detect import bp 6 | from app.detect import run_model_server 7 | import numpy as np 8 | from app.detect.forms import DetectForm 9 | from keras.preprocessing.image import img_to_array 10 | import matplotlib.pyplot as plt 11 | import os 12 | import io 13 | import imutils 14 | import csv 15 | import sys 16 | sys.path.append('/home/paperspace/Desktop/MLT/Tiny_Faces_in_Tensorflow') 17 | from tiny_face_det import get_img_path 18 | #import tiny_face_eval 19 | 20 | @bp.route('/detect', methods=['GET', 'POST']) 21 | def detect(): 22 | if "processed_data" not in session: 23 | session["processed_data"] = {} 24 | bucket = session["processed_data"] 25 | emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"} 26 | gender_dict = {0: 'man', 1:'woman'} 27 | 28 | # For data analysis 29 | emo_count = {"Angry":0, "Disgusted":0, "Fearful":0, "Happy":0, "Neutral":0, 30 | "Sad":0, "Surprised":0} 31 | gender_count = {"man":0, "woman":0} 32 | age_class = {} 33 | 34 | if "processed_data" not in session: 35 | # To store the processed_data 36 | session["processed_data"] = {} 37 | form = DetectForm() 38 | current_app.config.update(DROPZONE_UPLOAD_ON_CLICK=True) 39 | # if request.method == 'GET': 40 | # print("Here visited") 41 | # return render_template('detect/upload.html', title="Upload Files") 42 | # else: 43 | # print("Posted Done") 44 | # return "Successfully processed.", 200 45 | if request.method == "POST": 46 | for (key, f) in (request.files.items()): 47 | # f.save(os.path.join(os.getcwd(),"app","uploads", f.filename)) 48 | image = f.read() 49 | image = Image.open(io.BytesIO(image)).convert('RGB') 50 | opencv_img = np.array(image) 51 | opencv_img = opencv_img[:, :, ::-1].copy() 52 | age_img_size = run_model_server.age_mdl.input.shape.as_list()[1] 53 | frame = imutils.resize(opencv_img, width=400) 54 | (h, w) = frame.shape[:2] 55 | blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,(300, 300), (104.0, 177.0, 123.0)) 56 | # pass the blob through the network and obtain the detections and 57 | # predictions 58 | run_model_server.net.setInput(blob) 59 | detections = run_model_server.net.forward() 60 | print(detections) 61 | #img_path = '/home/paperspace/img.jpg' 62 | #cv2.imwrite(img_path, frame) 63 | #detections = get_img_path(img_path) 64 | count = 0 65 | 66 | history_data = [] 67 | # loop over the detections 68 | for i in range(0, detections.shape[2]): 69 | # extract the confidence (i.e., probability) associated with the 70 | # prediction 71 | confidence = detections[0, 0, i, 2] 72 | 73 | # filter out weak detections by ensuring the `confidence` is 74 | # greater than the minimum confidence 75 | if confidence < 0.5: 76 | continue 77 | count += 1 78 | # compute the (x, y)-coordinates of the bounding box for the 79 | # object 80 | box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) 81 | (startX, startY, endX, endY) = box.astype("int") 82 | 83 | # Extract the patches 84 | img_patch = frame[startY:startY + (endY-startY), startX:startX + (endX - startX)] 85 | 86 | # Patch for Emo detect 87 | emo_patch = cv2.cvtColor(img_patch, cv2.COLOR_RGB2GRAY) 88 | emo_patch = np.expand_dims(np.expand_dims(cv2.resize( 89 | emo_patch, (48, 48)), -1), 0) 90 | 91 | # Patch for gender detect 92 | gender_patch = cv2.resize(img_patch, (96, 96)) 93 | gender_patch = gender_patch.astype("float") / 255.0 94 | gender_patch = img_to_array(gender_patch) 95 | gender_patch = np.expand_dims(gender_patch, axis=0) 96 | 97 | # Path for age detect 98 | age_patch = cv2.cvtColor(img_patch, cv2.COLOR_BGR2RGB) 99 | age_patch = cv2.resize(age_patch, (age_img_size, age_img_size)) 100 | age_patch = np.expand_dims(age_patch, axis=0) 101 | 102 | graph = run_model_server.graph 103 | with graph.as_default(): 104 | predicted_age = run_model_server.age_mdl.predict(age_patch) 105 | ages = np.arange(0, 101).reshape(101, 1) 106 | predicted_age = int(predicted_age.dot(ages).flatten()) 107 | 108 | detected_gender = run_model_server.gender_mdl.predict(gender_patch)[0] 109 | gender_index = int(np.argmax(detected_gender)) 110 | 111 | predicted_emo = run_model_server.emo_mdl.predict(emo_patch) 112 | 113 | emo_index = int(np.argmax(predicted_emo)) 114 | emo_count[emotion_dict[emo_index]] += 1 115 | gender_count[gender_dict[gender_index]] += 1 116 | if str(predicted_age) in age_class: 117 | age_class[str(predicted_age)] +=1 118 | else: 119 | age_class[str(predicted_age)] = 1 120 | history_data.append([emotion_dict[emo_index],gender_dict[gender_index],predicted_age,(startX,startY, endX, endY)]) 121 | 122 | # draw the bounding box of the face along with the associated 123 | # probability 124 | y = startY - 10 if startY - 10 > 10 else startY + 10 125 | cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 255, 0), 2) 126 | 127 | static_file_path = os.path.join(os.getcwd(), "app", "static") 128 | # Generate Emotion Status 129 | xs = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Neutral', 'Sad', 'Surprised'] 130 | ys = [] 131 | data = emo_count 132 | ys = [data['Angry'], data['Disgusted'], data['Fearful'], data['Happy'], data['Neutral'], data['Sad'], data['Surprised'] 133 | plt.bar(list(xs), ys, align='center', alpha=0.5) 134 | 135 | plt.ylabel('Number of people') 136 | plt.title('Emotion Classification') 137 | plt.savefig(os.path.join(static_file_path,'emotion_status.png')) 138 | plt.cla() 139 | 140 | # Generate Gender Count 141 | xs = ['MAN', 'WOMAN'] 142 | ys = [] 143 | data = gender_count 144 | ys = [data['man'], data['woman']] 145 | plt.bar(list(xs), ys, align='center', alpha=0.5) 146 | 147 | plt.ylabel('Number of people') 148 | plt.title('Gender Classification') 149 | plt.savefig(os.path.join(static_file_path,'gender_status.png')) 150 | plt.cla() 151 | 152 | # Age Classification 153 | data = age_class 154 | xs = [] 155 | ys = [] 156 | for item, lst in data.items(): 157 | xs.append(item) 158 | ys.append(data[item]) 159 | plt.plot(list(xs), ys, label='Crowd Count', color='blue') 160 | plt.title("Age Classification Status") 161 | plt.xlabel("Age") 162 | plt.ylabel("Number of people") 163 | plt.legend(loc='upper right') 164 | plt.savefig(os.path.join(static_file_path,'age_status.png')) 165 | 166 | file_name = "{}_detect.{}".format(f.filename.split('.')[0],f.filename.split('.')[1]) 167 | cv2.imwrite(os.path.join(static_file_path, file_name), frame) 168 | # Note we must return 200 HTTP OK response. 169 | # Saving csv for historical data analysis. 170 | # TODO: Change to database. 171 | with open(os.path.join(os.getcwd(),'app', 'records', 'camera1.csv'), mode='a') as data_file: 172 | data_file= csv.writer(data_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) 173 | for i in range(len(history_data)): 174 | data_file.writerow(list(history_data[i])) 175 | 176 | bucket['file_name'] = file_name 177 | bucket['detected_person'] = count 178 | session['processed_data'] = bucket 179 | return "Successfully processed.", 200 180 | else: 181 | return render_template('detect/../templates/detect/upload.html', title="Tokyo Junction", 182 | form=form) 183 | 184 | @bp.route('/result') 185 | def result(): 186 | if "processed_data" not in session: 187 | return redirect(url_for('detect.detect')) 188 | processed_data = session["processed_data"] 189 | file_name = processed_data['file_name'] 190 | person_count = processed_data['detected_person'] 191 | session.pop('processed_data', None) 192 | # TO DO 193 | # Display the processed data on the page 194 | print("Here result") 195 | return render_template('detect/../templates/detect/results.html', file_name=file_name, people_count=person_count) 196 | 197 | @bp.route('/public') 198 | def public(): 199 | print("Public...") 200 | return render_template('detect/../templates/detect/public.html') 201 | -------------------------------------------------------------------------------- /JunctionAPI/app/detect/run_model_server.py: -------------------------------------------------------------------------------- 1 | # import the necessary packages 2 | # from keras.applications import ResNet50 3 | from keras.preprocessing.image import img_to_array 4 | from keras.preprocessing.image import array_to_img 5 | from keras.models import load_model 6 | from app.model import emo_model, age_model 7 | import numpy as np 8 | import time 9 | import cv2 10 | from PIL import Image 11 | import os 12 | import cv2 13 | import tensorflow as tf 14 | 15 | # Initialize Global Variable 16 | emo_mdl = None 17 | gender_mdl = None 18 | age_mdl = None 19 | graph = None 20 | 21 | 22 | args = {'prototxt': "deploy.prototxt.txt", 23 | 'face_model': "res10_300x300_ssd_iter_140000.caffemodel", 24 | 'emo_model': "emo_model.h5", 25 | 'gender_model': "gender_detection.model", 26 | 'age_model': "age_only_resnet50_weights.061-3.300-4.410.hdf5", 27 | 'confidence': 0.5} 28 | 29 | def pre_load_model(): 30 | global emo_mdl 31 | global gender_mdl 32 | global age_mdl 33 | global net 34 | global graph 35 | 36 | # load the Keras Model 37 | emo_mdl = emo_model.build() 38 | emo_mdl.load_weights(os.path.join(os.getcwd(),'app', 'weights', args["emo_model"])) 39 | gender_mdl = load_model(os.path.join(os.getcwd(),'app', 'weights', args["gender_model"])) 40 | age_mdl = age_model.get_model(model_name="ResNet50") 41 | age_mdl.load_weights(os.path.join(os.getcwd(), 'app', 'weights', args["age_model"])) 42 | net = cv2.dnn.readNetFromCaffe( 43 | os.path.join(os.path.join(os.getcwd(), 'app', 'weights', args['prototxt'])), 44 | os.path.join(os.path.join(os.getcwd(),'app','weights', args['face_model'])) 45 | ) 46 | graph = tf.get_default_graph() 47 | -------------------------------------------------------------------------------- /JunctionAPI/app/main/__init__.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint 2 | 3 | bp = Blueprint('main', __name__) 4 | 5 | from app.main import routes 6 | -------------------------------------------------------------------------------- /JunctionAPI/app/main/forms.py: -------------------------------------------------------------------------------- 1 | from flask import request 2 | -------------------------------------------------------------------------------- /JunctionAPI/app/main/routes.py: -------------------------------------------------------------------------------- 1 | from flask import redirect, url_for, request, render_template, flash 2 | from app.main import bp 3 | from flask import current_app as app 4 | 5 | @bp.route('/', methods=['GET']) 6 | def index(): 7 | return "Hello" 8 | 9 | 10 | 11 | -------------------------------------------------------------------------------- /JunctionAPI/app/templates/base.html: -------------------------------------------------------------------------------- 1 | {% extends 'bootstrap/base.html' %} 2 | 3 | {% block title %} 4 | {% if title %}{{ title }}{% else %}{{ ('Junction Tokyo') }}{% endif %} 5 | {% endblock %} 6 | 7 | {% block content %} 8 |
9 | {% with messages = get_flashed_messages() %} 10 | {% if messages %} 11 | {% for message in messages %} 12 | 13 | {% endfor %} 14 | {% endif %} 15 | {% endwith %} 16 | {# application content needs to be provided in the app_content block #} 17 | {% block app_content %}{% endblock %} 18 |
19 | {% endblock %} 20 | {% block styles %} 21 | {{ super() }} 22 | 25 | {% endblock %} 26 | -------------------------------------------------------------------------------- /JunctionAPI/app/templates/detect/results.html: -------------------------------------------------------------------------------- 1 | {% extends "../base.html" %} 2 | {% import 'bootstrap/wtf.html' as wtf %} 3 | 4 | {% block app_content %} 5 |
6 |
7 |
8 |
9 | {% if people_count %}
{% endif %} 10 |
11 | {% if file_name %} 12 |
13 |
14 |
15 | 16 | 17 |

18 |
19 | 20 |
21 |
22 |
23 | 24 | 25 | 38 | 51 | 64 | 65 | {% endif %} 66 | 67 | {% endblock %} 68 | -------------------------------------------------------------------------------- /JunctionAPI/app/templates/detect/upload.html: -------------------------------------------------------------------------------- 1 | {% extends "../base.html" %} 2 | {% import 'bootstrap/wtf.html' as wtf %} 3 | 4 | {% block head %} 5 | {{ super() }} 6 | {{ dropzone.load_css() }} 7 | {{ dropzone.style('border: 2px dashed #0087F7; margin: 10px 0 10px; min-height: 400px;') }} 8 | {% endblock %} 9 | {% block app_content %} 10 |
11 |
12 |
13 |
14 | {{ form.hidden_tag() }} 15 |
26 | 27 | 28 |
29 | 30 | 31 |

32 |
33 | 34 | 35 | 36 | 37 | 		39 | 40 | 41 |
42 | 43 | 44 |

45 |
46 | 47 | 48 | 49 | 50 | 		52 | 53 | 54 |
55 | 56 | 57 |

58 |
59 | 60 | 61 | 62 | 63 |
16 | 17 | 18 | 19 | 20 | 21 | 22 |
{{ dropzone.create(action="detect.detect") }}
23 | {{ dropzone.load_js() }} 24 | {{ dropzone.config( 25 | custom_init=' 26 | this.on("success", function(file, response){ 27 | window.location="' + url_for("detect.result") +'" 28 | }); 29 | this.on("sending", function(file, xhr, formData) { 30 | csrf_name=$("#csrf_token").attr("name"); 31 | csrf_value=$("#csrf_token").attr("value"); 32 | formData.append(csrf_name, csrf_value); 33 | }); 34 | ', 35 | custom_options="addRemoveLinks: true" 36 | ) }} 37 | {% endblock %} 38 | -------------------------------------------------------------------------------- /JunctionAPI/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | basedir = os.path.abspath(os.path.dirname(__file__)) 4 | 5 | 6 | class Config(object): 7 | SECRET_KEY = os.environ.get('SECRET_KEY') or 'you-will-never-guess' 8 | -------------------------------------------------------------------------------- /JunctionAPI/flask_requirements.txt: -------------------------------------------------------------------------------- 1 | alembic==0.9.6 2 | astroid==2.0.4 3 | Babel==2.5.1 4 | beautifulsoup4==4.6.3 5 | blinker==1.4 6 | certifi==2017.7.27.1 7 | chardet==3.0.4 8 | click==6.7 9 | dominate==2.3.1 10 | elasticsearch==6.1.1 11 | Flask==1.0.2 12 | Flask-Babel==0.11.2 13 | Flask-Blogging==1.0.2 14 | Flask-Bootstrap==3.3.7.1 15 | Flask-Cache==0.13.1 16 | Flask-Dropzone==1.5.3 17 | Flask-FileUpload==0.5.0 18 | Flask-HTTPAuth==3.2.3 19 | Flask-Login==0.4.0 20 | Flask-Mail==0.9.1 21 | Flask-Migrate==2.1.1 22 | Flask-Moment==0.5.2 23 | Flask-Principal==0.4.0 24 | Flask-SQLAlchemy==2.3.2 25 | Flask-Uploads==0.2.1 26 | Flask-WTF==0.14.2 27 | guess-language-spirit==0.5.3 28 | idna==2.6 29 | isort==4.3.4 30 | itsdangerous==0.24 31 | Jinja2==2.10 32 | lazy-object-proxy==1.3.1 33 | Mako==1.0.7 34 | Markdown==2.6.11 35 | MarkupSafe==1.0 36 | mccabe==0.6.1 37 | micawber==0.3.5 38 | peewee==3.7.0 39 | Pygments==2.2.0 40 | PyJWT==1.5.3 41 | pylint==2.1.1 42 | python-dateutil==2.6.1 43 | python-dotenv==0.7.1 44 | python-editor==1.0.3 45 | python-slugify==1.2.6 46 | pytz==2017.2 47 | redis==2.10.6 48 | requests==2.18.4 49 | rq==0.9.2 50 | shortuuid==0.5.0 51 | six==1.11.0 52 | SQLAlchemy==1.1.14 53 | typed-ast==1.1.0 54 | Unidecode==1.0.22 55 | urllib3==1.22 56 | visitor==0.1.3 57 | Werkzeug==0.14.1 58 | wrapt==1.10.11 59 | WTForms==2.1 60 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Machine Learning Tokyo 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MLT x 2020 2 | 3 | More than half a million people are expected to come to Tokyo for the Olympics 2020. We built a highly scalable system for face detection and count, age, gender and emotion prediction for 4 | 5 | * managing crowds, 6 | * making personalized recommendation for users and 7 | * optimizing marketing campaigns and ad placement (e.g. ads on screens based on average age or gender). 8 | 9 | We combined 3 Deep Learning Models and 3 APIs (Twitter, Google Maps, Google Translate). This helps to facilitate Safety, Efficiency and Business Value for the Tokyo Olympics 2020. 10 | 11 | **Presentation slides:** https://goo.gl/hBwi4U 12 | 13 | ![alt text](face_detection_statistics.png) 14 | 15 | 16 | # MLT Teams at Junction Tokyo 17 | 18 |

19 |

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Bootstrap 3 | from flask_dropzone import Dropzone 4 | from flask_uploads import UploadSet, configure_uploads, IMAGES, \ 5 | patch_request_class 6 | from flask_wtf.csrf import CSRFProtect 7 | from config import Config 8 | 9 | bootstrap = Bootstrap() 10 | dropzone = Dropzone() 11 | csrf = CSRFProtect() 12 | 13 | def create_app(config_class=Config): 14 | app = Flask(__name__) 15 | app.config.from_object(config_class) 16 | bootstrap.init_app(app) 17 | dropzone.init_app(app) 18 | csrf.init_app(app) 19 | 20 | from app.main import bp as main_bp 21 | app.register_blueprint(main_bp) 22 | 23 | from app.detect import bp as detect_bp 24 | app.register_blueprint(detect_bp) 25 | 26 | return app 27 | -------------------------------------------------------------------------------- /api/app/__pycache__/__init__.cpython-35.pyc: -------------------------------------------------------------------------------- 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pip install: 13 | * tweepy 14 | * googletrans 15 | * matplotlib 16 | * pandas 17 | 18 | # Error: 19 | * duplicate tweet -> clean the twitter post directly or add the time.min in the tweet 20 | -------------------------------------------------------------------------------- /api/app/detect/__init__.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint 2 | 3 | bp = Blueprint('detect', __name__) 4 | 5 | from app.detect import routes 6 | from app.detect import run_model_server 7 | run_model_server = run_model_server.pre_load_model() 8 | -------------------------------------------------------------------------------- /api/app/detect/__pycache__/__init__.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/__pycache__/__init__.cpython-35.pyc 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-------------------------------------------------------------------------------- /api/app/detect/camera.py: -------------------------------------------------------------------------------- 1 | class camera(): 2 | 3 | def __init__(self, cameraID, X, Y, databaseID, name, Ctype): 4 | self.cameraID = cameraID 5 | self.coorX = X 6 | self.coorY = Y 7 | self.databaseID = databaseID 8 | self.name = name 9 | self.Ctype = Ctype 10 | 11 | 12 | 13 | -------------------------------------------------------------------------------- /api/app/detect/camera.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/camera.pyc -------------------------------------------------------------------------------- /api/app/detect/customerservices.py: -------------------------------------------------------------------------------- 1 | from collections import namedtuple 2 | import pandas as pd 3 | from app.detect.geo_info_caller import get_geo_info 4 | from app.detect import camera 5 | import os 6 | 7 | GeoCamera = namedtuple('GeoCamera', 'camera address distance duration') 8 | 9 | class customerservices: 10 | def __init__(self, dur_thr=200): 11 | self.dur_thr = dur_thr * 60 12 | self.serviceType = "return closest camera based on user input and position" 13 | file_path = os.path.join(os.getcwd(), "app", "detect", "morerealdata.csv") 14 | self.database = pd.read_csv(file_path,delimiter=";") 15 | self.result = pd.DataFrame() 16 | 17 | def getcamIDfromCtype(self, Camlist, type): 18 | goodTypeCam = [] 19 | for cam in Camlist: 20 | if(cam.Ctype == type ): 21 | goodTypeCam.append(cam) 22 | return(goodTypeCam) 23 | 24 | def get_geo_cameras(self, goodTypeCam, geo_info): 25 | geo_cams = [] 26 | for i, v_dur in enumerate(geo_info.duration_values): 27 | print(v_dur) 28 | if v_dur <= self.dur_thr: 29 | geo_cam = GeoCamera(goodTypeCam[i], geo_info.destinations[i], geo_info.distance_texts[i], geo_info.duration_texts[i]) 30 | geo_cams.append(geo_cam) 31 | 32 | return geo_cams 33 | 34 | def findclosestCam(self, goodTypeCam, userPosition): 35 | dest_coords = [tuple([c.coorX, c.coorY]) for c in goodTypeCam] 36 | geo_info = get_geo_info(userPosition, dest_coords) 37 | geo_cams = self.get_geo_cameras(goodTypeCam, geo_info) 38 | return geo_cams 39 | 40 | def returnAllcorrespondingData(self, geo_cameras): 41 | for geo_cam in geo_cameras: 42 | self.result = self.result.append(self.database.loc[self.database['camera'] == geo_cam.camera.databaseID], ignore_index = True) 43 | return self.result, geo_cameras 44 | 45 | def generateRecommandationList(self, results, geocameras): 46 | # Generate the recommandation list: 47 | recommandation = results 48 | recommandation["name"] = "" 49 | recommandation["distance"] = "" 50 | recommandation["duration"] = "" 51 | recommandation["address"] = "" 52 | 53 | #print(recommandation) 54 | 55 | for c in geocameras: 56 | # Get the good index: 57 | index = recommandation.index[recommandation.camera == c.camera.databaseID] 58 | # Set the values: 59 | recommandation.set_value(index,'distance', c.distance) 60 | recommandation.set_value(index,'duration', c.duration) 61 | recommandation.set_value(index,'name', c.camera.name) 62 | recommandation.set_value(index,'address', c.address) 63 | 64 | recommandation.set_index('camera', inplace = True) 65 | recommandation.sort_values(by='waiting', inplace = True) 66 | #print(recommandation) 67 | return(recommandation) 68 | 69 | def solve_request(self, Camlist, Ctype, userPosition): 70 | goodTypeCam = self.getcamIDfromCtype(Camlist, Ctype) 71 | 72 | geo_cameras = self.findclosestCam(goodTypeCam, userPosition) 73 | results, geocameras = self.returnAllcorrespondingData(geo_cameras) 74 | recommandation = self.generateRecommandationList(results, geocameras) 75 | 76 | return(recommandation) 77 | 78 | -------------------------------------------------------------------------------- /api/app/detect/customerservices.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/customerservices.pyc -------------------------------------------------------------------------------- /api/app/detect/face_detector.py: -------------------------------------------------------------------------------- 1 | import time 2 | import cv2 3 | 4 | 5 | class FaceDetector(): 6 | def __init__(self): 7 | from ..hr101_mxnet.tiny_fd import TinyFacesDetector 8 | # from tiny_fd import TinyFacesDetector 9 | self.tiny_faces = TinyFacesDetector(model_root='/home/paperspace/Desktop/Junction/api/app/hr101_mxnet/', prob_thresh=0.5, gpu_idx=0) 10 | 11 | def detect(self, fname, txtfname): 12 | self.img = cv2.imread(fname) 13 | tic = time.time() 14 | boxes = self.detector.detect(self.img) 15 | toc = time.time() 16 | elapsed_time = toc - tic 17 | 18 | self.write_to_txt_file(fname, boxes, txtfname) 19 | 20 | return boxes.shape[0], boxes, elapsed_time 21 | 22 | def get_face_bboxes(self, img): 23 | boxes = self.tiny_faces.detect(self.img) 24 | # return boxes.shape[0], boxes 25 | return 127, [1,2,3,4] 26 | def write_to_txt_file(self, fname, boxes, txtfname): 27 | txtfile = open(txtfname, 'w') 28 | txtfile.write(fname + '\n') 29 | txtfile.write(str(boxes.shape[0]) + '\n') 30 | for box in boxes: 31 | line = '' 32 | 33 | x1 = min(max(0, int(box[0])), self.img.shape[0]) 34 | y1 = min(max(0, int(box[1])), self.img.shape[1]) 35 | x2 = min(max(0, int(box[2])), self.img.shape[0]) 36 | y2 = min(max(0, int(box[3])), self.img.shape[1]) 37 | line += str(x1) + ' ' + str(y1) + ' ' + str(x2) + ' ' + str(y2) + '\n' 38 | 39 | txtfile.write(line) -------------------------------------------------------------------------------- /api/app/detect/facedata.csv: -------------------------------------------------------------------------------- 1 | camera;time;nbpeople;waiting;age;gender;emotion 2 | C1;10:00:00;10;50;25;guy;happy 3 | C1;12:00:00;40;200;35;guy;happy 4 | C1;02:00:00;40;200;30;mix;happy 5 | C1;04:00:00;20;100;22;guy;happy 6 | C1;06:00:00;0;0;0;mix;neutral 7 | C2;10:00:00;3;15;40;girl;happy 8 | C2;12:00:00;6;30;45;girl;angry 9 | C2;02:00:00;3;15;50;girl;angry 10 | C2;04:00:00;6;30;45;guy;angry 11 | C2;06:00:00;8;40;45;guy;happy 12 | C3;10:00:00;12;60;15;girl;happy 13 | C3;12:00:00;34;170;18;girl;angry 14 | C3;02:00:00;34;170;16;girl;happy 15 | C3;04:00:00;12;60;25;girl;happy 16 | C3;06:00:00;5;25;22;guy;angry 17 | C4;10:00:00;24;120;35;mix;angry 18 | C4;12:00:00;56;280;35;mix;angry 19 | C4;02:00:00;80;400;35;mix;angry 20 | C4;04:00:00;43;215;35;guy;angry 21 | C4;06:00:00;12;60;35;mix;angry 22 | -------------------------------------------------------------------------------- /api/app/detect/facedata.ods: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/facedata.ods -------------------------------------------------------------------------------- /api/app/detect/facedata.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/facedata.xls -------------------------------------------------------------------------------- /api/app/detect/forms.py: -------------------------------------------------------------------------------- 1 | from flask_wtf import FlaskForm 2 | 3 | class DetectForm(FlaskForm): 4 | pass 5 | -------------------------------------------------------------------------------- /api/app/detect/geo_info_caller.py: -------------------------------------------------------------------------------- 1 | import googlemaps 2 | from datetime import datetime 3 | from collections import namedtuple 4 | import sys 5 | 6 | GeoObj = namedtuple('geo_object', ['destinations', 'distance_texts', 'distance_values', 7 | 'duration_texts', 'duration_values']) 8 | 9 | def get_geo_info(user_coords, dest_coords, mode='walking'): 10 | user_coords = tuple(user_coords) 11 | dest_coords = list(map(tuple, dest_coords)) 12 | gmaps = googlemaps.Client(key='AIzaSyDjm1LEi97iX2-DBTu0d2xnAQrO9ElYDE8') 13 | distance_result = gmaps.distance_matrix(origins=user_coords, destinations=dest_coords, mode=mode) 14 | 15 | dests = distance_result['destination_addresses'] 16 | dists = [elem['distance']['text'] for elem in distance_result['rows'][0]['elements']] 17 | durs = [elem['duration']['text'] for elem in distance_result['rows'][0]['elements']] 18 | 19 | v_dists = [elem['distance']['value'] for elem in distance_result['rows'][0]['elements']] 20 | v_durs = [elem['duration']['value'] for elem in distance_result['rows'][0]['elements']] 21 | 22 | geo_info = GeoObj(dests, dists, v_dists, durs, v_durs) 23 | return geo_info 24 | 25 | 26 | if __name__ == '__main__': 27 | user_coords = sys.argv[1] 28 | user_coords = tuple(map(float, user_coords.split(','))) 29 | dest_coords = sys.argv[2:] 30 | # dest_coords = dest_coords.split(' ') 31 | dest_coords = [tuple(map(float, dest_coord.split(','))) for dest_coord in dest_coords] 32 | geo_info = get_geo_info(user_coords, dest_coords, mode='walking') 33 | 34 | -------------------------------------------------------------------------------- /api/app/detect/morerealdata.csv: -------------------------------------------------------------------------------- 1 | camera;nbpeople;waiting;age;gender;emotion 2 | C1;12;48;23;girl;happy 3 | C2;20;80;32;mixed;angry 4 | C3;3;12;34;boy;angry 5 | C4;6;24;18;girl;happy 6 | C5;2;8;67;girl;happy 7 | -------------------------------------------------------------------------------- /api/app/detect/outputgenerator.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | 4 | # dedicated object 5 | import tweetGenerator as tw 6 | import camera 7 | 8 | # Create Camera object (obtained from registration) 9 | camera1 = camera.camera(1, 80, 200, "C1", "entrance A", "stadium") 10 | camera2 = camera.camera(2, 500, 200,"C2", "entrance B", "stadium") 11 | 12 | # Load the database (currently dummy data) 13 | dummydatabase = pd.read_csv('facedata.csv',delimiter=";") 14 | print(dummydatabase.head()) 15 | 16 | # Get the number of people and waiting time at each camera (time can be set in the query) 17 | TIME = '10:00:00' 18 | 19 | # Camera1 20 | query = dummydatabase.loc[dummydatabase['camera'] == 'C1'].loc[dummydatabase['time']== TIME] 21 | waitingtimeCamera1 = query.waiting 22 | waitingtimeCamera1 = waitingtimeCamera1.reset_index(drop=True)[0] 23 | nbpeopleC1 = query.nbpeople 24 | nbpeopleC1 = nbpeopleC1.reset_index(drop=True)[0] 25 | print(waitingtimeCamera1) 26 | print(nbpeopleC1) 27 | 28 | # Camera 2 29 | query = dummydatabase.loc[dummydatabase['camera'] == 'C2'].loc[dummydatabase['time']== TIME] 30 | waitingtimeCamera2 = query.waiting 31 | waitingtimeCamera2 = waitingtimeCamera2.reset_index(drop=True)[0] 32 | nbpeopleC2 = query.nbpeople 33 | nbpeopleC2 = nbpeopleC2.reset_index(drop=True)[0] 34 | print(waitingtimeCamera2) 35 | print(nbpeopleC2) 36 | 37 | ### Generate the heatmap of the stadium (all the numerical value are here to make the display looks better) 38 | heatmapC1 = plt.Circle((camera1.coorX, camera1.coorY), nbpeopleC1*8, color='r') 39 | heatmapC2 = plt.Circle((camera2.coorX, camera2.coorY), nbpeopleC2*8, color='g') 40 | 41 | img = plt.imread("staduim1.PNG") 42 | fig, ax = plt.subplots() 43 | ax.add_artist(heatmapC1) 44 | ax.add_artist(heatmapC2) 45 | plt.axis('off') 46 | 47 | plt.text(camera1.coorX - 20, camera1.coorY, str(waitingtimeCamera1) + "min", fontsize=12) 48 | plt.text(camera2.coorX - 20, camera2.coorY, str(waitingtimeCamera2) + "min", fontsize=12) 49 | ax.imshow(img) 50 | 51 | plt.savefig('savedheatmap/heatmap.png') 52 | 53 | ### Tweet generation 54 | tweetAPI = tw.TweetGenerator() 55 | englishText = tweetAPI.ruleBaseGenerator(camera1, nbpeopleC1, camera2, nbpeopleC2) 56 | tweetAPI.multilanguageTweet(englishText) 57 | 58 | ### Fancy Bussniss statictics: for demography / live advertissement tunning based on gender / age 59 | dummydatabase.groupby("camera").plot() 60 | 61 | -------------------------------------------------------------------------------- /api/app/detect/routes.py: -------------------------------------------------------------------------------- 1 | from flask import redirect, url_for, request, render_template, flash, session 2 | from flask import current_app as current_app 3 | from PIL import Image 4 | import cv2 5 | from app.detect import bp 6 | from app.detect import run_model_server 7 | import numpy as np 8 | from app.detect.forms import DetectForm 9 | from keras.preprocessing.image import img_to_array 10 | import matplotlib.pyplot as plt 11 | import os 12 | import io 13 | import imutils 14 | import csv 15 | 16 | from app.detect import camera 17 | from app.detect import customerservices as cs 18 | 19 | from app.detect.geo_info_caller import get_geo_info 20 | 21 | @bp.route('/detect', methods=['GET', 'POST']) 22 | def detect(): 23 | if "processed_data" not in session: 24 | session["processed_data"] = {} 25 | bucket = session["processed_data"] 26 | emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"} 27 | gender_dict = {0: 'man', 1:'woman'} 28 | 29 | # For data analysis 30 | emo_count = {"Angry":0, "Disgusted":0, "Fearful":0, "Happy":0, "Neutral":0, 31 | "Sad":0} 32 | gender_count = {"man":0, "woman":0} 33 | age_class = {} 34 | 35 | if "processed_data" not in session: 36 | # To store the processed_data 37 | session["processed_data"] = {} 38 | form = DetectForm() 39 | current_app.config.update(DROPZONE_UPLOAD_ON_CLICK=True) 40 | # if request.method == 'GET': 41 | # print("Here visited") 42 | # return render_template('detect/upload.html', title="Upload Files") 43 | # else: 44 | # print("Posted Done") 45 | # return "Successfully processed.", 200 46 | if request.method == "POST": 47 | for (key, f) in (request.files.items()): 48 | # f.save(os.path.join(os.getcwd(),"app","uploads", f.filename)) 49 | image = f.read() 50 | image = Image.open(io.BytesIO(image)).convert('RGB') 51 | opencv_img = np.array(image) 52 | opencv_img = opencv_img[:, :, ::-1].copy() 53 | age_img_size = run_model_server.age_mdl.input.shape.as_list()[1] 54 | frame = imutils.resize(opencv_img, width=400) 55 | (h, w) = frame.shape[:2] 56 | blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,(300, 300), (104.0, 177.0, 123.0)) 57 | # pass the blob through the network and obtain the detections and 58 | # predictions 59 | run_model_server.net.setInput(blob) 60 | detections = run_model_server.net.forward() 61 | count = 0 62 | 63 | history_data = [] 64 | # loop over the detections 65 | for i in range(0, detections.shape[2]): 66 | # extract the confidence (i.e., probability) associated with the 67 | # prediction 68 | confidence = detections[0, 0, i, 2] 69 | 70 | # filter out weak detections by ensuring the `confidence` is 71 | # greater than the minimum confidence 72 | if confidence < 0.5: 73 | continue 74 | count += 1 75 | # compute the (x, y)-coordinates of the bounding box for the 76 | # object 77 | box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) 78 | (startX, startY, endX, endY) = box.astype("int") 79 | 80 | # Extract the patches 81 | img_patch = frame[startY:startY + (endY-startY), startX:startX + (endX - startX)] 82 | 83 | # Patch for Emo detect 84 | emo_patch = cv2.cvtColor(img_patch, cv2.COLOR_RGB2GRAY) 85 | emo_patch = np.expand_dims(np.expand_dims(cv2.resize( 86 | emo_patch, (48, 48)), -1), 0) 87 | 88 | # Patch for gender detect 89 | gender_patch = cv2.resize(img_patch, (96, 96)) 90 | gender_patch = gender_patch.astype("float") / 255.0 91 | gender_patch = img_to_array(gender_patch) 92 | gender_patch = np.expand_dims(gender_patch, axis=0) 93 | 94 | # Path for age detect 95 | age_patch = cv2.cvtColor(img_patch, cv2.COLOR_BGR2RGB) 96 | age_patch = cv2.resize(age_patch, (age_img_size, age_img_size)) 97 | age_patch = np.expand_dims(age_patch, axis=0) 98 | 99 | graph = run_model_server.graph 100 | with graph.as_default(): 101 | predicted_age = run_model_server.age_mdl.predict(age_patch) 102 | ages = np.arange(0, 101).reshape(101, 1) 103 | predicted_age = int(predicted_age.dot(ages).flatten()) 104 | 105 | detected_gender = run_model_server.gender_mdl.predict(gender_patch)[0] 106 | gender_index = int(np.argmax(detected_gender)) 107 | 108 | predicted_emo = run_model_server.emo_mdl.predict(emo_patch) 109 | 110 | emo_index = int(np.argmax(predicted_emo)) 111 | emo_count[emotion_dict[emo_index]] += 1 112 | gender_count[gender_dict[gender_index]] += 1 113 | if str(predicted_age) in age_class: 114 | age_class[str(predicted_age)] +=1 115 | else: 116 | age_class[str(predicted_age)] = 1 117 | history_data.append([emotion_dict[emo_index],gender_dict[gender_index],predicted_age,(startX,startY, endX, endY)]) 118 | 119 | # draw the bounding box of the face along with the associated 120 | # probability 121 | y = startY - 10 if startY - 10 > 10 else startY + 10 122 | cv2.rectangle(frame, (startX, startY), (endX, endY),(0, 255, 0), 2) 123 | history_data = [history_data] 124 | static_file_path = os.path.join(os.getcwd(), "app", "static") 125 | # Generate Emotion Status 126 | xs = ['Angry', 'Disgusted', 'Fearful', 'Happy', 'Neutral', 'Sad'] 127 | ys = [] 128 | data = emo_count 129 | ys = [data['Angry'], data['Disgusted'], data['Fearful'], data['Happy'], data['Neutral'], data['Sad']] 130 | plt.bar(list(xs), ys, align='center', alpha=0.5) 131 | 132 | plt.ylabel('Number of people') 133 | plt.title('Gender Classification') 134 | plt.savefig(os.path.join(static_file_path,'emotion_status.png')) 135 | plt.cla() 136 | 137 | # Generate Gender Count 138 | xs = ['MAN', 'WOMAN'] 139 | ys = [] 140 | data = gender_count 141 | ys = [data['man'], data['woman']] 142 | plt.bar(list(xs), ys, align='center', alpha=0.5) 143 | 144 | plt.ylabel('Number of people') 145 | plt.title('Gender Classification') 146 | plt.savefig(os.path.join(static_file_path,'gender_status.png')) 147 | plt.cla() 148 | 149 | # Age Classification 150 | data = age_class 151 | xs = [] 152 | ys = [] 153 | for item, lst in data.items(): 154 | xs.append(item) 155 | ys.append(data[item]) 156 | plt.plot(list(xs), ys, label='Crowd Count', color='blue') 157 | plt.title("Age Classification Status") 158 | plt.xlabel("Age") 159 | plt.ylabel("Number of people") 160 | plt.legend(loc='upper right') 161 | plt.savefig(os.path.join(static_file_path,'age_status.png')) 162 | 163 | file_name = "{}_detect.{}".format(f.filename.split('.')[0],f.filename.split('.')[1]) 164 | cv2.imwrite(os.path.join(static_file_path, file_name), frame) 165 | # Note we must return 200 HTTP OK response. 166 | # Saving csv for historical data analysis. 167 | # TODO: Change to database. 168 | with open(os.path.join(os.getcwd(),'app', 'records', 'camera1.csv'), mode='a') as data_file: 169 | data_file= csv.writer(data_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) 170 | for i in range(len(history_data)): 171 | data_file.writerow(list(history_data[i])) 172 | 173 | bucket['file_name'] = file_name 174 | bucket['detected_person'] = count 175 | session['processed_data'] = bucket 176 | return "Successfully processed.", 200 177 | else: 178 | return render_template('detect/upload.html', title="Tokyo Junction", 179 | form=form) 180 | 181 | @bp.route('/result') 182 | def result(): 183 | if "processed_data" not in session: 184 | return redirect(url_for('detect.detect')) 185 | processed_data = session["processed_data"] 186 | file_name = processed_data['file_name'] 187 | person_count = processed_data['detected_person'] 188 | session.pop('processed_data', None) 189 | # TO DO 190 | # Display the processed data on the page 191 | return render_template('detect/results.html', file_name=file_name, people_count=person_count) 192 | 193 | @bp.route('/heatmap') 194 | def public(): 195 | print("Heatmap...") 196 | return render_template('detect/public.html') 197 | 198 | camera1 = camera.camera(1, 35.679221, 139.776093, "C1", "Restaurant A", "sushi") 199 | camera2 = camera.camera(2, 35.670262, 139.775106,"C2", "Restaurant B", "sushi") 200 | camera3 = camera.camera(3, 35.670259, 139.7751036,"C3", "Restaurant C", "sushi") 201 | camera4 = camera.camera(4, 35.670249, 139.7751056,"C4", "Entrance A", "nightclub") 202 | camlist =[camera1, camera2, camera3, camera4] 203 | place_types = ['caffe', 'restaurant', 'sushi', 'nightclub', 'bar', 'toilet'] 204 | service = cs.customerservices() 205 | 206 | @bp.route("/get_places", methods=["GET"]) 207 | def get_places(): 208 | params = request.json 209 | if (params == None): 210 | params = request.args 211 | if (params is None): 212 | return 'Error: Please insert type and coordinates!' 213 | 214 | place_type = params.get('type') 215 | 216 | if place_type not in place_types: 217 | msg = '%s type not \nsupported...' % place_type 218 | msg = msg + '\nSupported types: %s' % ','.join(place_types) 219 | return msg 220 | 221 | coordinates = params.get("coordinates") 222 | 223 | if coordinates is None: 224 | return 'Please enter your coordinates' 225 | 226 | try: 227 | coordinates = list(map(float, coordinates.split(','))) 228 | except: 229 | return 'Wrong coordinates format' 230 | msg = None 231 | msg = service.solve_request(camlist, place_type, coordinates) 232 | msg = msg.reset_index(drop=True) 233 | n_cols = {'nbpeople': 'Queue size', 'waiting': 'waiting time', 234 | 'age': 'average age', 'gender': 'average gender', 235 | 'emotion': 'average emotion', 'duration': 'eta'} 236 | 237 | msg = msg.rename(index=str, columns=n_cols) 238 | args = dict() 239 | args.clear() 240 | args['place_type'] = place_type 241 | args['msg'] = msg 242 | print(args) 243 | return render_template('detect/index.html', **args) 244 | 245 | 246 | 247 | -------------------------------------------------------------------------------- /api/app/detect/run_model_server.py: -------------------------------------------------------------------------------- 1 | # import the necessary packages 2 | # from keras.applications import ResNet50 3 | from keras.preprocessing.image import img_to_array 4 | from keras.preprocessing.image import array_to_img 5 | from keras.models import load_model 6 | from app.model import emo_model, age_model 7 | import numpy as np 8 | import time 9 | import cv2 10 | from PIL import Image 11 | import os 12 | import cv2 13 | import tensorflow as tf 14 | 15 | # Initialize Global Variable 16 | emo_mdl = None 17 | gender_mdl = None 18 | age_mdl = None 19 | graph = None 20 | 21 | 22 | args = {'prototxt': "deploy.prototxt.txt", 23 | 'face_model': "res10_300x300_ssd_iter_140000.caffemodel", 24 | 'emo_model': "emo_model.h5", 25 | 'gender_model': "gender_detection.model", 26 | 'age_model': "age_only_resnet50_weights.061-3.300-4.410.hdf5", 27 | 'confidence': 0.5} 28 | 29 | def pre_load_model(): 30 | global emo_mdl 31 | global gender_mdl 32 | global age_mdl 33 | global net 34 | global graph 35 | 36 | # load the Keras Model 37 | emo_mdl = emo_model.build() 38 | emo_mdl.load_weights(os.path.join(os.getcwd(),'app', 'weights', args["emo_model"])) 39 | gender_mdl = load_model(os.path.join(os.getcwd(),'app', 'weights', args["gender_model"])) 40 | age_mdl = age_model.get_model(model_name="ResNet50") 41 | age_mdl.load_weights(os.path.join(os.getcwd(), 'app', 'weights', args["age_model"])) 42 | net = cv2.dnn.readNetFromCaffe( 43 | os.path.join(os.path.join(os.getcwd(), 'app', 'weights', args['prototxt'])), 44 | os.path.join(os.path.join(os.getcwd(),'app','weights', args['face_model'])) 45 | ) 46 | graph = tf.get_default_graph() 47 | -------------------------------------------------------------------------------- /api/app/detect/savedheatmap/heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/savedheatmap/heatmap.png -------------------------------------------------------------------------------- /api/app/detect/staduim1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/detect/staduim1.png -------------------------------------------------------------------------------- /api/app/detect/test_customerService.py: -------------------------------------------------------------------------------- 1 | # try the servicecustomer class 2 | import camera 3 | import customerservices as cs 4 | 5 | # Generate a list a Camera 6 | # Create Camera object (obtained from registration) 7 | camera1 = camera.camera(1, 35.679221, 139.776093, "C1", "Restaurant A", "sushi") 8 | camera2 = camera.camera(2, 35.670262, 139.775106,"C2", "Restaurant B", "sushi") 9 | camera3 = camera.camera(3, 35.670259, 139.7751036,"C3", "Restaurant C", "sushi") 10 | camera4 = camera.camera(4, 35.670249, 139.7751056,"C4", "Entrance A", "nightclub") 11 | 12 | camlist =[camera1, camera2, camera3, camera4] 13 | 14 | service = cs.customerservices() 15 | 16 | Ctype = 'sushi' 17 | UserPosition = [35.654697, 139.781929] 18 | result = service.solve_request(camlist, Ctype, UserPosition) 19 | print(result) -------------------------------------------------------------------------------- /api/app/detect/tweetGenerator.py: -------------------------------------------------------------------------------- 1 | # Currently using tweepy but will try to use the tweeter API from Rakuten 2 | # Need to try the translation API 3 | 4 | import tweepy 5 | from googletrans import Translator 6 | import datetime 7 | 8 | #import unirest not supported on python3 9 | 10 | class TweetGenerator: 11 | 12 | def __init__(self): 13 | # Setting the key for identification 14 | self.CONSUMER_KEY ="hEejKZrYXMbN2lsQPmHYnCpvY" 15 | self.CONSUMER_SECRET = "k9a8nFVaDbmUJyDZBAwwdmc1miqh8sDWjJu1AohJw03oiUnPn2" 16 | self.ACCESS_KEY = "1096442165448720385-TYxgNnoYL3z5GmKavNjFgcqvXw4ViA" 17 | self.ACCESS_SECRET = "1jNRID3iJhdDqL0Yq2hAlhJCMlA7AubodvZ0997gY5Wfy" 18 | 19 | # Set the access: 20 | self.auth = tweepy.OAuthHandler(self.CONSUMER_KEY, self.CONSUMER_SECRET) 21 | self.auth.set_access_token(self.ACCESS_KEY, self.ACCESS_SECRET) 22 | 23 | # Set up the api: 24 | self.api = tweepy.API(self.auth) 25 | 26 | # Set up the translator: 27 | self.translator = Translator() 28 | 29 | def send_tweet(self, tweetText): 30 | # Send the status 31 | self.api.update_status(tweetText) 32 | 33 | def trnsl_JP(self, englishText): 34 | # Somehow call working API to translate 35 | jpText = self.translator.translate(englishText, dest='ja').text 36 | jpText = "[JAPANESE] " + jpText 37 | return jpText 38 | 39 | def trnsl_KO(self, englishText): 40 | # Somehow call working API to translate 41 | koText = self.translator.translate(englishText, dest='ko').text 42 | koText = "[KOREAN] " + koText 43 | return koText 44 | 45 | def multilanguageTweet(self, englishText): 46 | # Translate and send the different tweet 47 | if(englishText ==''): 48 | return(0) 49 | 50 | koText = self.trnsl_KO(englishText) 51 | self.send_tweet(koText) 52 | print("Korean tweet send") 53 | 54 | jpText = self.trnsl_JP(englishText) 55 | self.send_tweet(jpText) 56 | print("Japanese tweet send") 57 | 58 | self.send_tweet("[ENGLISH] " + englishText) 59 | print("Englsih tweet send") 60 | 61 | def ruleBaseGenerator(self, camera1, nbpeopleC1, camera2, nbpeopleC2): 62 | # Generate the message to tweet based on the camera 63 | currentDT = datetime.datetime.now() 64 | englishText ="" 65 | if(nbpeopleC1 > nbpeopleC2): 66 | englishText = str(currentDT.day) +'/'+ str(currentDT.hour) +" | Please go to " + camera2.name 67 | 68 | if(nbpeopleC2 > nbpeopleC1): 69 | englishText = str(currentDT.day) +'/'+ str(currentDT.hour) +" | Pleae go to " + camera1.name 70 | 71 | return englishText 72 | 73 | 74 | -------------------------------------------------------------------------------- /api/app/main/__init__.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint 2 | 3 | bp = Blueprint('main', __name__) 4 | 5 | from app.main import routes 6 | -------------------------------------------------------------------------------- /api/app/main/__pycache__/__init__.cpython-35.pyc: 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| 11 | -------------------------------------------------------------------------------- /api/app/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .emo_model import emo_model 2 | from .smallervggnet import SmallerVGGNet 3 | -------------------------------------------------------------------------------- /api/app/model/__pycache__/__init__.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/model/__pycache__/__init__.cpython-35.pyc -------------------------------------------------------------------------------- /api/app/model/__pycache__/__init__.cpython-36.pyc: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/model/__pycache__/smallervggnet.cpython-35.pyc -------------------------------------------------------------------------------- /api/app/model/__pycache__/smallervggnet.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/model/__pycache__/smallervggnet.cpython-36.pyc -------------------------------------------------------------------------------- /api/app/model/age_model.py: -------------------------------------------------------------------------------- 1 | import better_exceptions 2 | from keras.applications import ResNet50, InceptionResNetV2 3 | from keras.layers import Dense 4 | from keras.models import Model 5 | from keras import backend as K 6 | 7 | 8 | def age_mae(y_true, y_pred): 9 | true_age = K.sum(y_true * K.arange(0, 101, dtype="float32"), axis=-1) 10 | pred_age = K.sum(y_pred * K.arange(0, 101, dtype="float32"), axis=-1) 11 | mae = K.mean(K.abs(true_age - pred_age)) 12 | return mae 13 | 14 | 15 | def get_model(model_name="ResNet50"): 16 | base_model = None 17 | 18 | if model_name == "ResNet50": 19 | base_model = ResNet50(include_top=False, weights='imagenet', input_shape=(224, 224, 3), pooling="avg") 20 | elif model_name == "InceptionResNetV2": 21 | base_model = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=(299, 299, 3), pooling="avg") 22 | 23 | prediction = Dense(units=101, kernel_initializer="he_normal", use_bias=False, activation="softmax", 24 | name="pred_age")(base_model.output) 25 | 26 | model = Model(inputs=base_model.input, outputs=prediction) 27 | 28 | return model 29 | 30 | 31 | def main(): 32 | model = get_model("InceptionResNetV2") 33 | model.summary() 34 | 35 | 36 | if __name__ == '__main__': 37 | main() 38 | -------------------------------------------------------------------------------- /api/app/model/emo_model.py: -------------------------------------------------------------------------------- 1 | from keras.models import Sequential 2 | from keras.layers.core import Dense, Dropout, Flatten 3 | from keras.layers.convolutional import Conv2D 4 | from keras.optimizers import Adam 5 | from keras.layers.pooling import MaxPooling2D 6 | 7 | class emo_model: 8 | @staticmethod 9 | def build(): 10 | model = Sequential() 11 | 12 | model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1))) 13 | model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) 14 | model.add(MaxPooling2D(pool_size=(2, 2))) 15 | model.add(Dropout(0.25)) 16 | 17 | model.add(Conv2D(128, kernel_size=(3, 3), activation='relu')) 18 | model.add(MaxPooling2D(pool_size=(2, 2))) 19 | model.add(Conv2D(128, kernel_size=(3, 3), activation='relu')) 20 | model.add(MaxPooling2D(pool_size=(2, 2))) 21 | model.add(Dropout(0.25)) 22 | 23 | model.add(Flatten()) 24 | model.add(Dense(1024, activation='relu')) 25 | model.add(Dropout(0.5)) 26 | model.add(Dense(7, activation='softmax')) 27 | return model 28 | -------------------------------------------------------------------------------- /api/app/model/smallervggnet.py: -------------------------------------------------------------------------------- 1 | from keras.models import Sequential 2 | from keras.layers.normalization import BatchNormalization 3 | from keras.layers.convolutional import Conv2D 4 | from keras.layers.convolutional import MaxPooling2D 5 | from keras.layers.core import Activation 6 | from keras.layers.core import Flatten 7 | from keras.layers.core import Dropout 8 | from keras.layers.core import Dense 9 | from keras import backend as K 10 | 11 | 12 | class SmallerVGGNet: 13 | @staticmethod 14 | def build(width, height, depth, classes): 15 | model = Sequential() 16 | inputShape = (height, width, depth) 17 | chanDim = -1 18 | 19 | if K.image_data_format() == "channels_first": 20 | inputShape = (depth, height, width) 21 | chanDim = 1 22 | 23 | model.add(Conv2D(32, (3,3), padding="same", input_shape=inputShape)) 24 | model.add(Activation("relu")) 25 | model.add(BatchNormalization(axis=chanDim)) 26 | model.add(MaxPooling2D(pool_size=(3,3))) 27 | model.add(Dropout(0.25)) 28 | 29 | model.add(Conv2D(64, (3,3), padding="same")) 30 | model.add(Activation("relu")) 31 | model.add(BatchNormalization(axis=chanDim)) 32 | model.add(Conv2D(64, (3,3), padding="same")) 33 | model.add(Activation("relu")) 34 | model.add(BatchNormalization(axis=chanDim)) 35 | model.add(MaxPooling2D(pool_size=(2,2))) 36 | model.add(Dropout(0.25)) 37 | 38 | model.add(Conv2D(128, (3,3), padding="same")) 39 | model.add(Activation("relu")) 40 | model.add(BatchNormalization(axis=chanDim)) 41 | model.add(Conv2D(128, (3,3), padding="same")) 42 | model.add(Activation("relu")) 43 | model.add(BatchNormalization(axis=chanDim)) 44 | model.add(MaxPooling2D(pool_size=(2,2))) 45 | model.add(Dropout(0.25)) 46 | 47 | model.add(Flatten()) 48 | model.add(Dense(1024)) 49 | model.add(Activation("relu")) 50 | model.add(BatchNormalization()) 51 | model.add(Dropout(0.5)) 52 | 53 | model.add(Dense(classes)) 54 | model.add(Activation("sigmoid")) 55 | 56 | return model 57 | -------------------------------------------------------------------------------- /api/app/records/camera1.csv: -------------------------------------------------------------------------------- 1 | "['Angry', 'man', 55, (218, 28, 277, 112)]","['Happy', 'man', 25, (15, 31, 76, 107)]","['Angry', 'woman', 30, (324, 25, 375, 90)]","['Fearful', 'man', 5, (275, 196, 328, 278)]","['Angry', 'man', 10, (127, 38, 176, 115)]","['Happy', 'woman', 5, (63, 204, 109, 270)]","['Neutral', 'woman', 8, (166, 223, 214, 288)]" 2 | "['Happy', 'man', 13, (110, 55, 141, 93)]","['Happy', 'man', 33, (213, 43, 238, 83)]","['Happy', 'man', 4, (193, 71, 218, 105)]","['Neutral', 'woman', 10, (69, 76, 95, 112)]" 3 | "['Happy', 'man', 13, (110, 55, 141, 93)]","['Happy', 'man', 33, (213, 43, 238, 83)]","['Happy', 'man', 4, (193, 71, 218, 105)]","['Neutral', 'woman', 10, (69, 76, 95, 112)]" 4 | -------------------------------------------------------------------------------- /api/app/static/age_status.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/age_status.png -------------------------------------------------------------------------------- /api/app/static/emotion_status.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/emotion_status.png -------------------------------------------------------------------------------- /api/app/static/gender_status.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/gender_status.png -------------------------------------------------------------------------------- /api/app/static/heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/heatmap.png -------------------------------------------------------------------------------- /api/app/static/selfytest_detect.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/selfytest_detect.jpeg -------------------------------------------------------------------------------- /api/app/static/test2_detect.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/test2_detect.jpeg -------------------------------------------------------------------------------- /api/app/static/test_detect.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/api/app/static/test_detect.jpg -------------------------------------------------------------------------------- /api/app/templates/base.html: -------------------------------------------------------------------------------- 1 | {% extends 'bootstrap/base.html' %} 2 | 3 | {% block title %} 4 | {% if title %}{{ title }}{% else %}{{ ('Junction Tokyo') }}{% endif %} 5 | {% endblock %} 6 | 7 | {% block content %} 8 |
9 | {% with messages = get_flashed_messages() %} 10 | {% if messages %} 11 | {% for message in messages %} 12 | 13 | {% endfor %} 14 | {% endif %} 15 | {% endwith %} 16 | {# application content needs to be provided in the app_content block #} 17 | {% block app_content %}{% endblock %} 18 |
19 | {% endblock %} 20 | {% block styles %} 21 | {{ super() }} 22 | 25 | {% endblock %} 26 | -------------------------------------------------------------------------------- /api/app/templates/detect/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 |

Recommendations for {{ place_type }}

4 | {% block content %} 5 | {{ msg.to_html() | safe }} 6 | {% endblock content %} 7 | 8 | -------------------------------------------------------------------------------- /api/app/templates/detect/public.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% import 'bootstrap/wtf.html' as wtf %} 3 | 4 | {% block app_content %} 5 |
6 |
7 |
8 |
9 |
10 |
11 | {% endblock %} 12 | -------------------------------------------------------------------------------- /api/app/templates/detect/results.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% import 'bootstrap/wtf.html' as wtf %} 3 | 4 | {% block app_content %} 5 |
6 |
7 |
8 |
9 | {% if people_count %}
{% endif %} 10 |
11 | {% if file_name %} 12 |
13 |
14 |
15 | 16 | 17 |

18 |
19 | 20 |
21 |
22 |
23 | 24 | 25 | 38 | 51 | 64 | 65 | {% endif %} 66 | 67 | {% endblock %} 68 | -------------------------------------------------------------------------------- /api/app/templates/detect/upload.html: -------------------------------------------------------------------------------- 1 | {% extends "base.html" %} 2 | {% import 'bootstrap/wtf.html' as wtf %} 3 | 4 | {% block head %} 5 | {{ super() }} 6 | {{ dropzone.load_css() }} 7 | {{ dropzone.style('border: 2px dashed #0087F7; margin: 10px 0 10px; min-height: 400px;') }} 8 | {% endblock %} 9 | {% block app_content %} 10 |
11 |
12 |
13 |
14 | {{ form.hidden_tag() }} 15 |
26 | 27 | 28 |
29 | 30 | 31 |

32 |
33 | 34 | 35 | 36 | 37 | 		39 | 40 | 41 |
42 | 43 | 44 |

45 |
46 | 47 | 48 | 49 | 50 | 		52 | 53 | 54 |
55 | 56 | 57 |

58 |
59 | 60 | 61 | 62 | 63 |
16 | 17 | 18 | 19 | 20 | 21 | 22 |
{{ dropzone.create(action="detect.detect") }}
23 | {{ dropzone.load_js() }} 24 | {{ dropzone.config( 25 | custom_init=' 26 | this.on("success", function(file, response){ 27 | window.location="' + url_for("detect.result") +'" 28 | }); 29 | this.on("sending", function(file, xhr, formData) { 30 | csrf_name=$("#csrf_token").attr("name"); 31 | csrf_value=$("#csrf_token").attr("value"); 32 | formData.append(csrf_name, csrf_value); 33 | }); 34 | ', 35 | custom_options="addRemoveLinks: true" 36 | ) }} 37 | {% endblock %} 38 | -------------------------------------------------------------------------------- /api/config.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | basedir = os.path.abspath(os.path.dirname(__file__)) 4 | 5 | 6 | class Config(object): 7 | SECRET_KEY = os.environ.get('SECRET_KEY') or 'you-will-never-guess' 8 | -------------------------------------------------------------------------------- /api/requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==0.4.0 2 | alembic==0.9.6 3 | asn1crypto==0.24.0 4 | astor==0.7.1 5 | astroid==2.0.4 6 | atomicwrites==1.3.0 7 | attrs==18.2.0 8 | Babel==2.5.1 9 | backcall==0.1.0 10 | beautifulsoup4==4.6.3 11 | better-exceptions==0.2.2 12 | bleach==1.5.0 13 | blinker==1.4 14 | cairocffi==1.0.2 15 | CairoSVG==2.3.0 16 | certifi==2017.7.27.1 17 | cffi==1.11.5 18 | chardet==3.0.4 19 | click==6.7 20 | cloudpickle==0.5.6 21 | commentjson==0.7.1 22 | cryptography==2.3.1 23 | cssselect2==0.2.1 24 | cycler==0.10.0 25 | dask==0.19.2 26 | decorator==4.3.0 27 | defusedxml==0.5.0 28 | dlib==19.16.0 29 | dominate==2.3.1 30 | elasticsearch==6.1.1 31 | entrypoints==0.2.3 32 | Flask==1.0.2 33 | Flask-Babel==0.11.2 34 | Flask-Blogging==1.0.2 35 | Flask-Bootstrap==3.3.7.1 36 | Flask-Cache==0.13.1 37 | Flask-Dropzone==1.5.3 38 | Flask-FileUpload==0.5.0 39 | Flask-HTTPAuth==3.2.3 40 | Flask-Login==0.4.0 41 | Flask-Mail==0.9.1 42 | Flask-Migrate==2.1.1 43 | Flask-Moment==0.5.2 44 | Flask-Principal==0.4.0 45 | Flask-SQLAlchemy==2.3.2 46 | Flask-Uploads==0.2.1 47 | Flask-WTF==0.14.2 48 | gast==0.2.2 49 | gevent==1.3.5 50 | graphviz==0.10.1 51 | greenlet==0.4.14 52 | grpcio==1.18.0 53 | guess-language-spirit==0.5.3 54 | gunicorn==19.9.0 55 | h5py==2.8.0 56 | html5lib==1.0.1 57 | idna==2.6 58 | imutils==0.5.2 59 | ipykernel==5.1.0 60 | ipython==7.1.1 61 | ipython-genutils==0.2.0 62 | ipywidgets==7.4.2 63 | isort==4.3.4 64 | itsdangerous==0.24 65 | jedi==0.13.1 66 | Jinja2==2.10 67 | jsonschema==2.6.0 68 | jupyter==1.0.0 69 | jupyter-client==5.2.3 70 | jupyter-console==6.0.0 71 | jupyter-core==4.4.0 72 | Keras==2.2.2 73 | Keras-Applications==1.0.4 74 | Keras-Preprocessing==1.0.2 75 | kiwisolver==1.0.1 76 | Lasagne==0.1 77 | lazy-object-proxy==1.3.1 78 | mahotas==1.4.4 79 | Mako==1.0.7 80 | Markdown==2.6.11 81 | MarkupSafe==1.0 82 | matplotlib==3.0.0 83 | mccabe==0.6.1 84 | micawber==0.3.5 85 | mistune==0.8.4 86 | more-itertools==6.0.0 87 | nbconvert==5.4.0 88 | nbformat==4.4.0 89 | networkx==2.2 90 | notebook==5.7.2 91 | numpy==1.14.5 92 | opencv-contrib-python==3.4.4.19 93 | opencv-python==3.4.4.19 94 | pandas==0.24.1 95 | pandocfilters==1.4.2 96 | parso==0.3.1 97 | pdf-reports==0.2.3 98 | peewee==3.7.0 99 | pexpect==4.6.0 100 | pickleshare==0.7.5 101 | Pillow==5.2.0 102 | pluggy==0.8.1 103 | prometheus-client==0.4.2 104 | prompt-toolkit==2.0.7 105 | protobuf==3.6.1 106 | ptyprocess==0.6.0 107 | py==1.7.0 108 | pycparser==2.19 109 | pydot==1.4.1 110 | Pygments==2.2.0 111 | PyJWT==1.5.3 112 | pylint==2.2.2 113 | PyMySQL==0.9.2 114 | pyparsing==2.2.1 115 | Pyphen==0.9.5 116 | pypugjs==5.7.2 117 | pytest==4.2.1 118 | python-dateutil==2.6.1 119 | python-dotenv==0.7.1 120 | python-editor==1.0.3 121 | python-slugify==1.2.6 122 | pytz==2017.2 123 | PyWavelets==1.0.0 124 | PyYAML==3.13 125 | pyzmq==17.1.2 126 | qtconsole==4.4.3 127 | redis==2.10.6 128 | requests==2.18.4 129 | rq==0.9.2 130 | scikit-image==0.14.0 131 | scikit-learn==0.19.2 132 | scikit-neuralnetwork==0.7 133 | scipy==1.1.0 134 | Send2Trash==1.5.0 135 | shortuuid==0.5.0 136 | six==1.11.0 137 | SQLAlchemy==1.1.14 138 | tensorboard==1.12.2 139 | tensorflow==1.5.0 140 | tensorflow-tensorboard==1.5.1 141 | termcolor==1.1.0 142 | terminado==0.8.1 143 | testpath==0.4.2 144 | Theano==1.0.4 145 | tinycss2==0.6.1 146 | toolz==0.9.0 147 | tornado==5.1.1 148 | traitlets==4.3.2 149 | typed-ast==1.1.0 150 | Unidecode==1.0.22 151 | urllib3==1.22 152 | visitor==0.1.3 153 | wcwidth==0.1.7 154 | WeasyPrint==44 155 | webencodings==0.5.1 156 | Werkzeug==0.14.1 157 | widgetsnbextension==3.4.2 158 | wrapt==1.10.11 159 | WTForms==2.1 160 | xlrd==1.2.0 161 | -------------------------------------------------------------------------------- /face_detection_statistics.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/face_detection_statistics.png -------------------------------------------------------------------------------- /junction.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/junction.jpg -------------------------------------------------------------------------------- /out_putgeneration/README.md: -------------------------------------------------------------------------------- 1 | # Output generation for the frontend 2 | Currenlty using an dummy dataset 3 | 4 | run as: python outputgenerator.py 5 | 6 | # Output: 7 | * tweet in 3 language based on the stadium video 8 | * heatmap of the stadium 9 | * can be used to generate dataset statistic 10 | 11 | # Dependencies: 12 | pip install: 13 | * tweepy 14 | * googletrans 15 | * matplotlib 16 | * pandas 17 | 18 | # Error: 19 | * duplicate tweet -> clean the twitter post directly or add the time.min in the tweet 20 | -------------------------------------------------------------------------------- /out_putgeneration/camera.py: -------------------------------------------------------------------------------- 1 | # Camera class 2 | 3 | class camera(): 4 | 5 | def __init__(self, cameraID, X, Y, databaseID, name, Ctype): 6 | self.cameraID = cameraID 7 | self.coorX = X 8 | self.coorY = Y 9 | self.databaseID = databaseID 10 | self.name = name 11 | self.Ctype = Ctype 12 | 13 | 14 | -------------------------------------------------------------------------------- /out_putgeneration/camera.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/camera.pyc -------------------------------------------------------------------------------- /out_putgeneration/customerservices.py: -------------------------------------------------------------------------------- 1 | from collections import namedtuple 2 | import pandas as pd 3 | from geo_info_caller import get_geo_info 4 | import camera 5 | 6 | GeoCamera = namedtuple('GeoCamera', 'camera address distance duration') 7 | 8 | class customerservices: 9 | def __init__(self, dur_thr=200): 10 | self.dur_thr = dur_thr * 60 11 | self.serviceType = "return closest camera based on user input and position" 12 | self.database = pd.read_csv('morerealdata.csv',delimiter=";") 13 | self.result = pd.DataFrame() 14 | 15 | def getcamIDfromCtype(self, Camlist, type): 16 | goodTypeCam = [] 17 | for cam in Camlist: 18 | if(cam.Ctype == type ): 19 | goodTypeCam.append(cam) 20 | return(goodTypeCam) 21 | 22 | def get_geo_cameras(self, goodTypeCam, geo_info): 23 | geo_cams = [] 24 | for i, v_dur in enumerate(geo_info.duration_values): 25 | print(v_dur) 26 | if v_dur <= self.dur_thr: 27 | geo_cam = GeoCamera(goodTypeCam[i], geo_info.destinations[i], geo_info.distance_texts[i], geo_info.duration_texts[i]) 28 | geo_cams.append(geo_cam) 29 | 30 | return geo_cams 31 | 32 | def findclosestCam(self, goodTypeCam, userPosition): 33 | dest_coords = [tuple([c.coorX, c.coorY]) for c in goodTypeCam] 34 | geo_info = get_geo_info(userPosition, dest_coords) 35 | geo_cams = self.get_geo_cameras(goodTypeCam, geo_info) 36 | return geo_cams 37 | 38 | def returnAllcorrespondingData(self, geo_cameras): 39 | for geo_cam in geo_cameras: 40 | self.result = self.result.append(self.database.loc[self.database['camera'] == geo_cam.camera.databaseID], ignore_index = True) 41 | return self.result, geo_cameras 42 | 43 | def generateRecommandationList(self, results, geocameras): 44 | # Generate the recommandation list: 45 | recommandation = results 46 | recommandation["name"] = "" 47 | recommandation["distance"] = "" 48 | recommandation["duration"] = "" 49 | recommandation["address"] = "" 50 | 51 | #print(recommandation) 52 | 53 | for c in geocameras: 54 | # Get the good index: 55 | index = recommandation.index[recommandation.camera == c.camera.databaseID] 56 | # Set the values: 57 | recommandation.set_value(index,'distance', c.distance) 58 | recommandation.set_value(index,'duration', c.duration) 59 | recommandation.set_value(index,'name', c.camera.name) 60 | recommandation.set_value(index,'address', c.address) 61 | 62 | recommandation.set_index('camera', inplace = True) 63 | recommandation.sort_values(by='waiting', inplace = True) 64 | #print(recommandation) 65 | return(recommandation) 66 | 67 | def solve_request(self, Camlist, Ctype, userPosition): 68 | goodTypeCam = self.getcamIDfromCtype(Camlist, Ctype) 69 | 70 | geo_cameras = self.findclosestCam(goodTypeCam, userPosition) 71 | results, geocameras = self.returnAllcorrespondingData(geo_cameras) 72 | recommandation = self.generateRecommandationList(results, geocameras) 73 | 74 | return(recommandation) 75 | 76 | -------------------------------------------------------------------------------- /out_putgeneration/customerservices.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/customerservices.pyc -------------------------------------------------------------------------------- /out_putgeneration/facedata.csv: -------------------------------------------------------------------------------- 1 | camera;time;nbpeople;waiting;age;gender;emotion 2 | C1;10:00:00;10;50;25;guy;happy 3 | C1;12:00:00;40;200;35;guy;happy 4 | C1;02:00:00;40;200;30;mix;happy 5 | C1;04:00:00;20;100;22;guy;happy 6 | C1;06:00:00;0;0;0;mix;neutral 7 | C2;10:00:00;3;15;40;girl;happy 8 | C2;12:00:00;6;30;45;girl;angry 9 | C2;02:00:00;3;15;50;girl;angry 10 | C2;04:00:00;6;30;45;guy;angry 11 | C2;06:00:00;8;40;45;guy;happy 12 | C3;10:00:00;12;60;15;girl;happy 13 | C3;12:00:00;34;170;18;girl;angry 14 | C3;02:00:00;34;170;16;girl;happy 15 | C3;04:00:00;12;60;25;girl;happy 16 | C3;06:00:00;5;25;22;guy;angry 17 | C4;10:00:00;24;120;35;mix;angry 18 | C4;12:00:00;56;280;35;mix;angry 19 | C4;02:00:00;80;400;35;mix;angry 20 | C4;04:00:00;43;215;35;guy;angry 21 | C4;06:00:00;12;60;35;mix;angry 22 | -------------------------------------------------------------------------------- /out_putgeneration/facedata.ods: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/facedata.ods -------------------------------------------------------------------------------- /out_putgeneration/facedata.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/facedata.xls -------------------------------------------------------------------------------- /out_putgeneration/geo_info_caller.py: -------------------------------------------------------------------------------- 1 | import googlemaps 2 | from datetime import datetime 3 | from collections import namedtuple 4 | import sys 5 | 6 | GeoObj = namedtuple('geo_object', ['destinations', 'distance_texts', 'distance_values', 7 | 'duration_texts', 'duration_values']) 8 | 9 | def get_geo_info(user_coords, dest_coords, mode='walking'): 10 | user_coords = tuple(user_coords) 11 | dest_coords = list(map(tuple, dest_coords)) 12 | gmaps = googlemaps.Client(key='AIzaSyDjm1LEi97iX2-DBTu0d2xnAQrO9ElYDE8') 13 | distance_result = gmaps.distance_matrix(origins=user_coords, destinations=dest_coords, mode=mode) 14 | 15 | dests = distance_result['destination_addresses'] 16 | dists = [elem['distance']['text'] for elem in distance_result['rows'][0]['elements']] 17 | durs = [elem['duration']['text'] for elem in distance_result['rows'][0]['elements']] 18 | 19 | v_dists = [elem['distance']['value'] for elem in distance_result['rows'][0]['elements']] 20 | v_durs = [elem['duration']['value'] for elem in distance_result['rows'][0]['elements']] 21 | 22 | geo_info = GeoObj(dests, dists, v_dists, durs, v_durs) 23 | return geo_info 24 | 25 | 26 | if __name__ == '__main__': 27 | user_coords = sys.argv[1] 28 | user_coords = tuple(map(float, user_coords.split(','))) 29 | dest_coords = sys.argv[2:] 30 | # dest_coords = dest_coords.split(' ') 31 | dest_coords = [tuple(map(float, dest_coord.split(','))) for dest_coord in dest_coords] 32 | geo_info = get_geo_info(user_coords, dest_coords, mode='walking') 33 | 34 | -------------------------------------------------------------------------------- /out_putgeneration/geo_info_caller.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/geo_info_caller.pyc -------------------------------------------------------------------------------- /out_putgeneration/morerealdata.csv: -------------------------------------------------------------------------------- 1 | camera;nbpeople;waiting;age;gender;emotion 2 | C1;12;48;23;girl;happy 3 | C2;20;80;32;mixed;angry 4 | C3;3;12;34;boy;angry 5 | C4;6;24;18;girl;happy 6 | C5;2;8;67;girl;happy 7 | -------------------------------------------------------------------------------- /out_putgeneration/outputgenerator.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import matplotlib.pyplot as plt 3 | 4 | # dedicated object 5 | import tweetGenerator as tw 6 | import camera 7 | 8 | # Create Camera object (obtained from registration) 9 | camera1 = camera.camera(1, 80, 200, "C1", "entrance A", "stadium") 10 | camera2 = camera.camera(2, 500, 200,"C2", "entrance B", "stadium") 11 | 12 | # Load the database (currently dummy data) 13 | dummydatabase = pd.read_csv('facedata.csv',delimiter=";") 14 | print(dummydatabase.head()) 15 | 16 | # Get the number of people and waiting time at each camera (time can be set in the query) 17 | TIME = '10:00:00' 18 | 19 | # Camera1 20 | query = dummydatabase.loc[dummydatabase['camera'] == 'C1'].loc[dummydatabase['time']== TIME] 21 | waitingtimeCamera1 = query.waiting 22 | waitingtimeCamera1 = waitingtimeCamera1.reset_index(drop=True)[0] 23 | nbpeopleC1 = query.nbpeople 24 | nbpeopleC1 = nbpeopleC1.reset_index(drop=True)[0] 25 | print(waitingtimeCamera1) 26 | print(nbpeopleC1) 27 | 28 | # Camera 2 29 | query = dummydatabase.loc[dummydatabase['camera'] == 'C2'].loc[dummydatabase['time']== TIME] 30 | waitingtimeCamera2 = query.waiting 31 | waitingtimeCamera2 = waitingtimeCamera2.reset_index(drop=True)[0] 32 | nbpeopleC2 = query.nbpeople 33 | nbpeopleC2 = nbpeopleC2.reset_index(drop=True)[0] 34 | print(waitingtimeCamera2) 35 | print(nbpeopleC2) 36 | 37 | ### Generate the heatmap of the stadium (all the numerical value are here to make the display looks better) 38 | heatmapC1 = plt.Circle((camera1.coorX, camera1.coorY), nbpeopleC1*8, color='r') 39 | heatmapC2 = plt.Circle((camera2.coorX, camera2.coorY), nbpeopleC2*8, color='g') 40 | 41 | img = plt.imread("staduim1.PNG") 42 | fig, ax = plt.subplots() 43 | ax.add_artist(heatmapC1) 44 | ax.add_artist(heatmapC2) 45 | plt.axis('off') 46 | 47 | plt.text(camera1.coorX - 20, camera1.coorY, str(waitingtimeCamera1) + "min", fontsize=12) 48 | plt.text(camera2.coorX - 20, camera2.coorY, str(waitingtimeCamera2) + "min", fontsize=12) 49 | ax.imshow(img) 50 | 51 | plt.savefig('savedheatmap/heatmap.png') 52 | 53 | ### Tweet generation 54 | tweetAPI = tw.TweetGenerator() 55 | englishText = tweetAPI.ruleBaseGenerator(camera1, nbpeopleC1, camera2, nbpeopleC2) 56 | tweetAPI.multilanguageTweet(englishText) 57 | 58 | ### Fancy Bussniss statictics: for demography / live advertissement tunning based on gender / age 59 | dummydatabase.groupby("camera").plot() 60 | 61 | -------------------------------------------------------------------------------- /out_putgeneration/savedheatmap/heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/savedheatmap/heatmap.png -------------------------------------------------------------------------------- /out_putgeneration/staduim1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/out_putgeneration/staduim1.png -------------------------------------------------------------------------------- /out_putgeneration/test_customerService.py: -------------------------------------------------------------------------------- 1 | # try the servicecustomer class 2 | import camera 3 | import customerservices as cs 4 | 5 | # Generate a list a Camera 6 | # Create Camera object (obtained from registration) 7 | camera1 = camera.camera(1, 35.679221, 139.776093, "C1", "Restaurant A", "sushi") 8 | camera2 = camera.camera(2, 35.670262, 139.775106,"C2", "Restaurant B", "sushi") 9 | camera3 = camera.camera(3, 35.670259, 139.7751036,"C3", "Restaurant C", "sushi") 10 | camera4 = camera.camera(4, 35.670249, 139.7751056,"C4", "Entrance A", "nightclub") 11 | 12 | camlist =[camera1, camera2, camera3, camera4] 13 | 14 | service = cs.customerservices() 15 | 16 | Ctype = 'sushi' 17 | UserPosition = [35.654697, 139.781929] 18 | result = service.solve_request(camlist, Ctype, UserPosition) 19 | print(result) -------------------------------------------------------------------------------- /out_putgeneration/tweetGenerator.py: -------------------------------------------------------------------------------- 1 | # Currently using tweepy but will try to use the tweeter API from Rakuten 2 | # Need to try the translation API 3 | 4 | import tweepy 5 | from googletrans import Translator 6 | import datetime 7 | 8 | #import unirest not supported on python3 9 | 10 | class TweetGenerator: 11 | 12 | def __init__(self): 13 | # Setting the key for identification 14 | self.CONSUMER_KEY ="hEejKZrYXMbN2lsQPmHYnCpvY" 15 | self.CONSUMER_SECRET = "k9a8nFVaDbmUJyDZBAwwdmc1miqh8sDWjJu1AohJw03oiUnPn2" 16 | self.ACCESS_KEY = "1096442165448720385-TYxgNnoYL3z5GmKavNjFgcqvXw4ViA" 17 | self.ACCESS_SECRET = "1jNRID3iJhdDqL0Yq2hAlhJCMlA7AubodvZ0997gY5Wfy" 18 | 19 | # Set the access: 20 | self.auth = tweepy.OAuthHandler(self.CONSUMER_KEY, self.CONSUMER_SECRET) 21 | self.auth.set_access_token(self.ACCESS_KEY, self.ACCESS_SECRET) 22 | 23 | # Set up the api: 24 | self.api = tweepy.API(self.auth) 25 | 26 | # Set up the translator: 27 | self.translator = Translator() 28 | 29 | def send_tweet(self, tweetText): 30 | # Send the status 31 | self.api.update_status(tweetText) 32 | 33 | def trnsl_JP(self, englishText): 34 | # Somehow call working API to translate 35 | jpText = self.translator.translate(englishText, dest='ja').text 36 | jpText = "[JAPANESE] " + jpText 37 | return jpText 38 | 39 | def trnsl_KO(self, englishText): 40 | # Somehow call working API to translate 41 | koText = self.translator.translate(englishText, dest='ko').text 42 | koText = "[KOREAN] " + koText 43 | return koText 44 | 45 | def multilanguageTweet(self, englishText): 46 | # Translate and send the different tweet 47 | if(englishText ==''): 48 | return(0) 49 | 50 | koText = self.trnsl_KO(englishText) 51 | self.send_tweet(koText) 52 | print("Korean tweet send") 53 | 54 | jpText = self.trnsl_JP(englishText) 55 | self.send_tweet(jpText) 56 | print("Japanese tweet send") 57 | 58 | self.send_tweet("[ENGLISH] " + englishText) 59 | print("Englsih tweet send") 60 | 61 | def ruleBaseGenerator(self, camera1, nbpeopleC1, camera2, nbpeopleC2): 62 | # Generate the message to tweet based on the camera 63 | currentDT = datetime.datetime.now() 64 | englishText ="" 65 | if(nbpeopleC1 > nbpeopleC2): 66 | englishText = str(currentDT.day) +'/'+ str(currentDT.hour) +" | Please go to " + camera2.name 67 | 68 | if(nbpeopleC2 > nbpeopleC1): 69 | englishText = str(currentDT.day) +'/'+ str(currentDT.hour) +" | Pleae go to " + camera1.name 70 | 71 | return englishText 72 | 73 | 74 | -------------------------------------------------------------------------------- /web_and_mobile.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Machine-Learning-Tokyo/MLTx2020/0b9a48ad46ec5310780c410f01cb11beaf8ee36b/web_and_mobile.png --------------------------------------------------------------------------------