├── .idea ├── crowd-analytics.iml ├── misc.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── Issues.txt ├── LICENSE ├── README.md ├── algo ├── Xval.txt ├── api.py ├── bored.txt ├── camera.py ├── graph.py ├── graphx.txt ├── graphy.txt ├── int_vision_training ├── main.py ├── ml.py ├── ml_mobile.py ├── play.py ├── pool.py ├── smile.txt ├── test.txt └── yval.txt ├── graph.png ├── newTrain ├── Xval.txt ├── analysis.txt ├── api.py ├── happy.txt └── yval.txt ├── range ├── Issues.txt ├── analysed.txt ├── analyze.py ├── meta-chart.jpeg └── test.py └── testImages ├── attentive.jpeg ├── bored.jpeg └── serious.jpg /.idea/crowd-analytics.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 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| 924 | 925 | 926 | 927 | 928 | -------------------------------------------------------------------------------- /Issues.txt: -------------------------------------------------------------------------------- 1 | Check for yawn (Can be done with AWS or custom training for surprise) 2 | 3 | Training images stats: -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Abhijeet Singh 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 | 23 | The Japanese Female Facial Expression (JAFFE) Database 24 | Michael J. Lyons, Shigeru Akemastu, Miyuki Kamachi, Jiro Gyoba. 25 | Coding Facial Expressions with Gabor Wavelets, 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200-205 (1998). 26 | 27 | The University of Milano Bicocca 3D Face Database 28 | A. Colombo, C. Cusano, and R. Schettini, “UMB-DB: A Database of Partially Occluded 3D 29 | Faces,” in Proc. ICCV 2011 Workshops, pp. 2113-2119, 2011. 30 | 31 | The Karolinska Directed Emotional Faces (KDEF) Stimulus Set 32 | E.Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska Directed Emotional Faces - KDEF, CD ROM from Department of Clinical Neuroscience, Psychology section, Karolinska Institutet, ISBN 91-630-7164-9. 33 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # INT-Vision 2 | Crowd analysis tool 3 | 4 | INT-Vision (Interest Vision) is a desktop software to analyse productivity of multiple human subjects simultaneously based on emotions and gestures. It can be easily deployed on any device with a digital camera and both real-time and graph analysis results can be seen on the desktop with the click of a button. 5 | 6 | Possible applications: 7 | - In schools/coachings to analyse interest of students in a particular lecture at various points of time. 8 | - In offices to analyse productivity of employees. 9 | - In workshops/seminars to analyse the interest of audience in the presented content. 10 | 11 | This serves as an AI enhanced surveillance system and eliminates the need for feedback or human monitoring. 12 | 13 | How to use: 14 | 1. Navigate to the algo folder. 15 | ```shell 16 | cd algo 17 | ``` 18 | 2. Run main file using Python. 19 | ```shell 20 | python3 main.py 21 | ``` 22 | 3. Press "Start Webcam" to start analysis using default laptop/PC camera. Press Esc key to stop recording. Wait for processing to finish. 23 | 4. Press "Start Video" to see analysed video with blue boxes indicating interested subjected and red boxes indicating disengaged, sleepy or bored subjects. 24 | 5. Press "Show Graph" to see a graph of interest level of people versus time. 25 | -------------------------------------------------------------------------------- /algo/api.py: -------------------------------------------------------------------------------- 1 | import os 2 | import requests 3 | import numpy as np 4 | 5 | # free, 20 calls/min, limit 30K calls/month 6 | # subscription_key = "bf5951c5f4934e2e90bc11c48ffb57fa" 7 | # premium, 10 calls/sec, no limit, 66 INR / 1000 calls 8 | subscription_key = "02726400482345229652709041c698ba" 9 | assert subscription_key 10 | 11 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 12 | 13 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 14 | params = { 15 | 'returnFaceId': 'false', 16 | 'returnFaceLandmarks': 'false', 17 | 'returnFaceAttributes': 'emotion,smile,headPose' 18 | } 19 | 20 | X = np.empty((0, 8), float) 21 | y = np.empty((0, 1), float) 22 | 23 | 24 | test_folder_name = "bored" 25 | test_file = "bored.txt" 26 | 27 | # remove previous test file 28 | open(test_file,'w') 29 | # writes azure analysed values of files in txt file 30 | def analyse(fname,ara): 31 | 32 | with open(test_file,'a') as myfile: 33 | #myfile.write("anger\tcontempt\tdisgust\tfear\thappiness\tneutral\tsadness\tsurprise\troll\n") 34 | myfile.write("anger=" + str(ara[0]) + " ") 35 | myfile.write("contempt=" + str(ara[1]) + " ") 36 | myfile.write("disgust=" + str(ara[2]) + " ") 37 | myfile.write("fear=" + str(ara[3]) + " ") 38 | myfile.write("happiness=" + str(ara[4])+ " ") 39 | myfile.write("neutral=" + str(ara[5]) + " ") 40 | myfile.write("sadness=" + str(ara[6]) + " ") 41 | myfile.write("surprise=" + str(ara[7]) + " ") 42 | #myfile.write("roll=" + str(ara[8])) 43 | myfile.write("\n") 44 | myfile.write(str(fname)+"\n\n") 45 | 46 | # this is criteria for error 47 | if(False): 48 | shutil.move("/home/abhijeet/Documents/github/crowd-analytics/newTrain/" + test_folder_name + "/" + str(fname), 49 | "/home/abhijeet/Documents/github/crowd-analytics/newTrain/errors/" + str(fname)) 50 | 51 | 52 | # dataset folder 53 | indir = '/home/abhijeet/Documents/github/crowd-analytics/algo/' 54 | 55 | progress = 0 56 | for dirs,dirlist,filenames in os.walk("."): 57 | print(dirs) 58 | 59 | for filename in filenames: 60 | print(filename) 61 | if filename.endswith(".jpeg") or filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".JPG") or filename.endswith(".PNG") or filename.endswith(".JPEG"): 62 | print("taken " + filename) 63 | # print(os.path.join(directory, filename)) 64 | # image_data = open(indir + '/' + filename, "rb").read() 65 | image_data = open(indir + '/' + dirs.split('/')[1]+'/'+filename, "rb").read() 66 | 67 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 68 | response.raise_for_status() 69 | analysis = response.json() 70 | 71 | if analysis: 72 | print("Face detected and done ", progress, "%, analysing", dirs, "currently") 73 | 74 | for i in analysis: 75 | 76 | #print(i) 77 | dic = [] 78 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["anger"]) 79 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["contempt"]) 80 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["disgust"]) 81 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["fear"]) 82 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["happiness"]) 83 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["neutral"]) 84 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["sadness"]) 85 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["surprise"]) 86 | #dic.insert(len(dic), i["faceAttributes"]["smile"]) 87 | #dic.insert(len(dic), abs(i["faceAttributes"]["headPose"]["roll"])) 88 | 89 | # convert list to numpy array 90 | arr = np.array(dic) 91 | 92 | # insert next row to main numpy array 93 | X = np.vstack([X, arr]) 94 | 95 | 96 | # analyse particular folder 97 | if str(dirs[2:]) == test_folder_name: 98 | analyse(filename, arr) 99 | # print(dirs) 100 | # print("---",dirs[2:],"----") 101 | # print(type(dirs)) 102 | 103 | if str(dirs[2:]) == "bored" or str(dirs[2:]) == "openmouth" or str(dirs[2:]) == "sad": 104 | y = np.insert(y, len(y), 1) 105 | else: 106 | y = np.insert(y, len(y), -1) 107 | 108 | # increment value = 100 / no. of directories 109 | progress += 6.66 110 | 111 | #print(X) 112 | #print(y) 113 | 114 | np.savetxt('Xval.txt', X, fmt='%f') 115 | np.savetxt('yval.txt', y, fmt='%d') -------------------------------------------------------------------------------- /algo/bored.txt: -------------------------------------------------------------------------------- 1 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.516 sadness=0.484 surprise=0.0 2 | 1.jpg 3 | 4 | anger=0.0 contempt=0.279 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.715 sadness=0.005 surprise=0.0 5 | 16.jpeg 6 | 7 | anger=0.0 contempt=0.015 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.314 sadness=0.671 surprise=0.0 8 | nch.jpg 9 | 10 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.002 sadness=0.0 surprise=0.998 11 | BM21SUS.JPG 12 | 13 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.002 neutral=0.718 sadness=0.28 surprise=0.0 14 | Young-man-cant-wake-up.jpg 15 | 16 | anger=0.0 contempt=0.003 disgust=0.0 fear=0.001 happiness=0.001 neutral=0.938 sadness=0.055 surprise=0.001 17 | download (4).jpg 18 | 19 | anger=0.0 contempt=0.089 disgust=0.023 fear=0.0 happiness=0.0 neutral=0.084 sadness=0.804 surprise=0.0 20 | bored-kid.jpg 21 | 22 | anger=0.0 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.821 sadness=0.175 surprise=0.0 23 | BM33NES.JPG 24 | 25 | anger=0.0 contempt=0.016 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.968 sadness=0.015 surprise=0.0 26 | 13.jpeg 27 | 28 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.997 sadness=0.001 surprise=0.0 29 | 76762917.jpg 30 | 31 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.981 sadness=0.019 surprise=0.0 32 | 76762917.jpg 33 | 34 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.925 sadness=0.075 surprise=0.0 35 | 76762917.jpg 36 | 37 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.487 sadness=0.512 surprise=0.0 38 | sad-bored-young-couple-with-flour-on-their-faces-kneading-dough-on-G1MB6C.jpg 39 | 40 | anger=0.0 contempt=0.001 disgust=0.001 fear=0.0 happiness=0.003 neutral=0.869 sadness=0.126 surprise=0.0 41 | sad-bored-young-couple-with-flour-on-their-faces-kneading-dough-on-G1MB6C.jpg 42 | 43 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.992 sadness=0.008 surprise=0.0 44 | qut8.png 45 | 46 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.592 sadness=0.408 surprise=0.0 47 | outt11.png 48 | 49 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.061 neutral=0.932 sadness=0.005 surprise=0.0 50 | wut7.png 51 | 52 | anger=0.0 contempt=0.201 disgust=0.001 fear=0.0 happiness=0.012 neutral=0.364 sadness=0.421 surprise=0.0 53 | out23.png 54 | 55 | anger=0.021 contempt=0.001 disgust=0.001 fear=0.0 happiness=0.0 neutral=0.973 sadness=0.004 surprise=0.0 56 | ouut4.png 57 | 58 | anger=0.0 contempt=0.005 disgust=0.0 fear=0.001 happiness=0.0 neutral=0.899 sadness=0.003 surprise=0.092 59 | BF12AFS.JPG 60 | 61 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.971 sadness=0.028 surprise=0.0 62 | o-CELEBRITY-BITCH-FACE-facebook.jpg 63 | 64 | anger=0.0 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.955 sadness=0.041 surprise=0.001 65 | eut8.png 66 | 67 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.87 sadness=0.129 surprise=0.0 68 | AM30NES.JPG 69 | 70 | anger=0.0 contempt=0.005 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.981 sadness=0.013 surprise=0.0 71 | 17.jpeg 72 | 73 | anger=0.001 contempt=0.0 disgust=0.001 fear=0.0 happiness=0.359 neutral=0.034 sadness=0.001 surprise=0.605 74 | download (1).jpg 75 | 76 | anger=0.0 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.365 sadness=0.632 surprise=0.0 77 | images (4).jpg 78 | 79 | anger=0.0 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.985 sadness=0.013 surprise=0.0 80 | 11.jpg 81 | 82 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.878 sadness=0.121 surprise=0.0 83 | images.jpeg 84 | 85 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.06 sadness=0.939 surprise=0.0 86 | bored-asian-woman-student-overworked-computer-young-pretty-chinese-tired-over-worked-her-laptop-wearing-black-shirt-white-39103934.jpg 87 | 88 | anger=0.055 contempt=0.002 disgust=0.007 fear=0.0 happiness=0.0 neutral=0.114 sadness=0.821 surprise=0.0 89 | 26.jpg 90 | 91 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.307 sadness=0.693 surprise=0.0 92 | out19.png 93 | 94 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.007 neutral=0.991 sadness=0.001 surprise=0.0 95 | ouyyt9.png 96 | 97 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.415 sadness=0.585 surprise=0.0 98 | outt8.png 99 | 100 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.922 sadness=0.078 surprise=0.0 101 | AF15NES.JPG 102 | 103 | anger=0.206 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.786 sadness=0.002 surprise=0.002 104 | ouyuyyt13.png 105 | 106 | anger=0.0 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.816 sadness=0.181 surprise=0.0 107 | images (6).jpg 108 | 109 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.998 sadness=0.002 surprise=0.0 110 | 6x9HgMV9.jpg 111 | 112 | anger=0.002 contempt=0.002 disgust=0.003 fear=0.002 happiness=0.001 neutral=0.945 sadness=0.012 surprise=0.033 113 | cce148b0ba8904b977cce3d13e0624df--sherlock-moriarty-sherlock-benedict.jpg 114 | 115 | anger=0.051 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.947 sadness=0.001 surprise=0.0 116 | ouyuyyt5.png 117 | 118 | anger=0.002 contempt=0.002 disgust=0.002 fear=0.001 happiness=0.0 neutral=0.97 sadness=0.021 surprise=0.003 119 | black-male-headshot-gunter-nezhoda.jpg 120 | 121 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.001 happiness=0.0 neutral=0.066 sadness=0.929 surprise=0.003 122 | bored-teen-girl-sitting-making-faces-k-uhd-native-video-70198817.jpg 123 | 124 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.961 sadness=0.039 surprise=0.0 125 | UY.FE2.153.tiff.png 126 | 127 | anger=0.32 contempt=0.018 disgust=0.001 fear=0.0 happiness=0.0 neutral=0.658 sadness=0.003 surprise=0.001 128 | ouyuyyt12.png 129 | 130 | anger=0.0 contempt=0.004 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.891 sadness=0.105 surprise=0.0 131 | AM05NES.JPG 132 | 133 | anger=0.431 contempt=0.01 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.555 sadness=0.002 surprise=0.002 134 | ouyuyyt11.png 135 | 136 | anger=0.001 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neutral=0.566 sadness=0.434 surprise=0.0 650 | outt5.png 651 | 652 | anger=0.001 contempt=0.03 disgust=0.002 fear=0.0 happiness=0.001 neutral=0.384 sadness=0.582 surprise=0.0 653 | KL.AN2.168.tiff.png 654 | 655 | anger=0.0 contempt=0.004 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.806 sadness=0.189 surprise=0.0 656 | out14.png 657 | 658 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.859 sadness=0.14 surprise=0.0 659 | UY.AN1.146.tiff.png 660 | 661 | anger=0.0 contempt=0.006 disgust=0.0 fear=0.0 happiness=0.002 neutral=0.956 sadness=0.037 surprise=0.0 662 | deb891ee24906866d9b7e9b487052384.jpg 663 | 664 | anger=0.052 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.945 sadness=0.001 surprise=0.001 665 | ouyuyyt7.png 666 | 667 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.961 sadness=0.037 surprise=0.0 668 | tut2.png 669 | 670 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.89 sadness=0.11 surprise=0.0 671 | BM32NES.JPG 672 | 673 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.003 neutral=0.991 sadness=0.003 surprise=0.002 674 | ouyyt2.png 675 | 676 | anger=0.0 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.997 sadness=0.001 surprise=0.0 677 | rut7.png 678 | 679 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=1.0 sadness=0.0 surprise=0.0 680 | NM.AN1.104.tiff.png 681 | 682 | anger=0.324 contempt=0.003 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.671 sadness=0.001 surprise=0.001 683 | ouyuyyt17.png 684 | 685 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.99 sadness=0.001 surprise=0.007 686 | 23.jpeg 687 | 688 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.541 sadness=0.459 surprise=0.0 689 | outt6.png 690 | 691 | anger=0.0 contempt=0.004 disgust=0.0 fear=0.001 happiness=0.0 neutral=0.931 sadness=0.064 surprise=0.001 692 | BM27NES.JPG 693 | 694 | anger=0.016 contempt=0.009 disgust=0.0 fear=0.001 happiness=0.0 neutral=0.951 sadness=0.023 surprise=0.0 695 | man-bored-expression-26178496.jpg 696 | 697 | anger=0.019 contempt=0.002 disgust=0.003 fear=0.004 happiness=0.0 neutral=0.944 sadness=0.009 surprise=0.02 698 | ouyuyyt1.png 699 | 700 | anger=0.006 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.992 sadness=0.002 surprise=0.0 701 | ouyuyyt6.png 702 | 703 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.807 sadness=0.192 surprise=0.0 704 | BM30NES.JPG 705 | 706 | anger=0.291 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.704 sadness=0.002 surprise=0.0 707 | ouyuyyt18.png 708 | 709 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.938 sadness=0.062 surprise=0.0 710 | BF19NES.JPG 711 | 712 | anger=0.001 contempt=0.009 disgust=0.0 fear=0.001 happiness=0.003 neutral=0.965 sadness=0.018 surprise=0.001 713 | out11.png 714 | 715 | anger=0.0 contempt=0.011 disgust=0.0 fear=0.0 happiness=0.014 neutral=0.973 sadness=0.002 surprise=0.0 716 | out2.png 717 | 718 | anger=0.0 contempt=0.014 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.405 sadness=0.58 surprise=0.0 719 | 7.jpg 720 | 721 | -------------------------------------------------------------------------------- /algo/camera.py: -------------------------------------------------------------------------------- 1 | # import cv2 2 | # cam = cv2.VideoCapture(0) 3 | # s, im = cam.read() # captures image 4 | # for i in range(10000000): 5 | # cv2.imshow("Test Picture", im) # displays captured image 6 | # cv2.imwrite("test.bmp",im) # writes image test.bmp to disk 7 | 8 | import cv2 9 | import os 10 | 11 | #capture from camera at location 0 12 | cap = cv2.VideoCapture(0) 13 | #set the width and height, and UNSUCCESSFULLY set the exposure time 14 | cap.set(3,1280) 15 | cap.set(4,1024) 16 | cap.set(15, 0.1) 17 | 18 | while True: 19 | ret, img = cap.read() 20 | img = cv2.flip(img, 1) 21 | cv2.imshow("input", img) 22 | #cv2.imshow("thresholded", imgray*thresh2) 23 | 24 | cv2.imwrite("test.bmp", img) # writes image test.bmp to disk 25 | print("haaye haaye") 26 | os.remove("test.bmp") 27 | key = cv2.waitKey(10) 28 | if key == 27: # Esc key 29 | break 30 | 31 | 32 | cv2.destroyAllWindows() 33 | cv2.VideoCapture(0).release() -------------------------------------------------------------------------------- /algo/graph.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | from scipy.interpolate import spline 4 | 5 | # load y-axis values from graphy.txt 6 | y = np.loadtxt('graphy.txt', dtype=int) 7 | # load x-axis values from graphx.txt 8 | x = np.loadtxt('graphx.txt', dtype=int) 9 | 10 | # convert epoch values to seconds 11 | temp = x[0] 12 | for i in range(len(x)): 13 | # print(i) 14 | x[i] -= temp 15 | 16 | # the number (eg. 300) represents number of points to make between x.min() and x.max() 17 | xnew = np.linspace(x.min(), x.max(), 50) 18 | ynew = spline(x, y, xnew) 19 | 20 | # plot graph 21 | plt.ylabel('Number of interested audience') 22 | plt.xlabel('Time (sec)') 23 | plt.plot(xnew, ynew) 24 | plt.savefig('../graph.png') 25 | plt.show() -------------------------------------------------------------------------------- /algo/graphx.txt: -------------------------------------------------------------------------------- 1 | 1528607667 2 | 1528607668 3 | 1528607669 4 | 1528607671 5 | 1528607672 6 | 1528607673 7 | 1528607675 8 | 1528607676 9 | 1528607677 10 | 1528607679 11 | 1528607680 12 | 1528607682 13 | 1528607683 14 | 1528607685 15 | 1528607686 16 | 1528607687 17 | 1528607689 18 | 1528607690 19 | 1528607691 20 | 1528607693 21 | 1528607694 22 | 1528607695 23 | 1528607697 24 | 1528607698 25 | 1528607699 26 | 1528607701 27 | 1528607702 28 | 1528607703 29 | 1528607705 30 | 1528607706 31 | 1528607708 32 | 1528607709 33 | 1528607711 34 | 1528607712 35 | 1528607714 36 | 1528607715 37 | 1528607717 38 | 1528607719 39 | 1528607720 40 | 1528607721 41 | 1528607723 42 | 1528607724 43 | 1528607726 44 | 1528607727 45 | 1528607728 46 | 1528607730 47 | 1528607731 48 | 1528607732 49 | 1528607734 50 | 1528607735 51 | 1528607736 52 | 1528607738 53 | 1528607739 54 | 1528607741 55 | 1528607742 56 | 1528607743 57 | 1528607745 58 | 1528607746 59 | 1528607748 60 | 1528607749 61 | 1528607750 62 | 1528607752 63 | 1528607753 64 | 1528607754 65 | 1528607756 66 | 1528607757 67 | 1528607759 68 | 1528607760 69 | 1528607761 70 | 1528607763 71 | 1528607764 72 | 1528607765 73 | 1528607767 74 | 1528607768 75 | -------------------------------------------------------------------------------- /algo/graphy.txt: -------------------------------------------------------------------------------- 1 | 1 2 | 1 3 | 1 4 | 1 5 | 2 6 | 1 7 | 1 8 | 2 9 | 2 10 | 2 11 | 1 12 | 2 13 | 2 14 | 2 15 | 1 16 | 1 17 | 1 18 | 1 19 | 3 20 | 3 21 | 2 22 | 2 23 | 2 24 | 2 25 | 3 26 | 2 27 | 3 28 | 2 29 | 2 30 | 1 31 | 1 32 | 2 33 | 1 34 | 0 35 | 1 36 | 2 37 | 0 38 | 0 39 | 0 40 | 0 41 | 0 42 | 1 43 | 1 44 | 1 45 | 0 46 | 0 47 | 0 48 | 0 49 | 0 50 | 0 51 | 0 52 | 0 53 | 0 54 | 1 55 | 0 56 | 0 57 | 1 58 | 1 59 | 2 60 | 1 61 | 1 62 | 0 63 | 0 64 | 0 65 | 2 66 | 0 67 | 1 68 | 2 69 | 1 70 | 1 71 | 2 72 | 1 73 | 1 74 | 1 75 | -------------------------------------------------------------------------------- /algo/int_vision_training: -------------------------------------------------------------------------------- 1 | /home/abhijeet/Documents/Machine Learning/int_vision_training -------------------------------------------------------------------------------- /algo/main.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import os 3 | import tkinter as tk 4 | from tkinter import * 5 | 6 | def call_ml(): 7 | os.system('python3 ml.py') 8 | def call_mobile_ml(): 9 | os.system('python3 ml_mobile.py') 10 | def play_video(): 11 | os.system('python3 play.py') 12 | def show_graph(): 13 | os.system('python3 graph.py') 14 | 15 | if __name__ == "__main__": 16 | # -------configure window 17 | root = tk.Tk() 18 | var1 = StringVar() 19 | 20 | label1 = Message( root, textvariable=var1, relief=RAISED,aspect=150,width=2000,pady=50,padx=800,bg='light sea green',font=("Helvetica", 30, "bold") ) 21 | 22 | var1.set("INT-Vision") 23 | label1.pack() 24 | root.geometry("%dx%d+%d+0" % (1000, 1000,500)) 25 | 26 | startbutton=tk.Button(root,width=15,height=2,text='WEBCAM START',command=call_ml) 27 | startbutton.place(x=100,y=100) 28 | #stopbutton=tk.Button(root,width=10,height=1,text='STOP', command=stoprecording) 29 | startbutton.pack() 30 | #stopbutton.pack() 31 | bu=tk.Button(root,width=15,height=2,text='MOBILE CAM START',command=call_mobile_ml) 32 | bu.place(x=100,y=100) 33 | #stopbutton=tk.Button(root,width=10,height=1,text='STOP', command=stoprecording) 34 | bu.pack() 35 | bu1=tk.Button(root,width=15,height=2,text='PLAY VIDEO',command=play_video) 36 | bu1.place(x=100,y=100) 37 | #stopbutton=tk.Button(root,width=10,height=1,text='STOP', command=stoprecording) 38 | bu1.pack() 39 | bu2=tk.Button(root,width=15,height=2,text='SHOW GRAPH',command=show_graph) 40 | bu2.place(x=100,y=100) 41 | #stopbutton=tk.Button(root,width=10,height=1,text='STOP', command=stoprecording) 42 | bu2.pack() 43 | 44 | 45 | var2=StringVar() 46 | label2 = Message( root, textvariable=var2, relief=RAISED,aspect=150,width=2000,pady=50,padx=800,bg='light sea green',font=("Helvetica", 30, "bold"), ) 47 | var2.set("Red shows Disengaged and Blue shows Engaged") 48 | label2.pack(side=BOTTOM) 49 | 50 | 51 | # -------begin 52 | root.mainloop() 53 | -------------------------------------------------------------------------------- /algo/ml.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import requests 4 | from sklearn import svm 5 | import cv2 6 | import time 7 | import threading 8 | 9 | # check for Python version to decide which queue module to import 10 | import sys 11 | is_py2 = sys.version[0] == '2' 12 | if is_py2: 13 | from Queue import Queue 14 | else: 15 | from queue import Queue 16 | 17 | # creating arrays to save in graphx and graphy text files 18 | graphXarr = np.empty((0, 1), int) 19 | graphYarr = np.empty((0, 1), int) 20 | 21 | open('test.txt','w') 22 | with open('test.txt','a') as myfile: 23 | myfile.write("anger contempt disgust fear happiness neutral sadness surprise roll\n") 24 | 25 | # create queue to store video frames 26 | que = Queue() 27 | array_rec = [] 28 | yhatf=2 29 | w1=-1 30 | t1=-1 31 | l1=-1 32 | h1=-1 33 | 34 | cap = cv2.VideoCapture(0) 35 | exit = 0 36 | 37 | def waste_facerec(img , array_rec): 38 | for rec in array_rec: 39 | w1 = rec["w1"] 40 | t1 = rec["t1"] 41 | h1 = rec["h1"] 42 | l1 = rec["l1"] 43 | yhatf = rec["yhat"] 44 | 45 | if yhatf > 0: 46 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (0, 0, 255), 2) 47 | else: 48 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (255, 0, 0), 2) 49 | return img 50 | 51 | def facerec(img): 52 | print("w1=", w1, "t1=", t1, "l1=", l1, "h1=", h1) 53 | if yhatf > 0: 54 | # if bored, then red rectangle 55 | with open('test.txt','a') as myfile: 56 | myfile.write("bored\n") 57 | 58 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (0, 0, 255), 2) 59 | else: 60 | # blue rectangle 61 | with open('test.txt','a') as myfile: 62 | myfile.write("not bored\n") 63 | 64 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (255, 0, 0), 2) 65 | return img 66 | 67 | def writeframe(img): 68 | for i in range(6): 69 | out.write(img) 70 | 71 | # function to increase opencv frame brightness 72 | def adjust_gamma(image, gamma=1.0): 73 | 74 | invGamma = 1.0 / gamma 75 | table = np.array([((i / 255.0) ** invGamma) * 255 76 | for i in np.arange(0, 256)]).astype("uint8") 77 | 78 | return cv2.LUT(image, table) 79 | 80 | def showframe(): 81 | global cap 82 | 83 | # set the width and height, and UNSUCCESSFULLY set the exposure time 84 | cap.set(3, 1280) 85 | cap.set(4, 1024) 86 | cap.set(15, 0.1) 87 | 88 | while True: 89 | ret, img = cap.read() 90 | 91 | img = cv2.flip(img, 1) 92 | 93 | #increase brightness 94 | gamma = 1.8 95 | img = adjust_gamma(img, gamma=gamma) 96 | 97 | 98 | # frame show function 99 | # cv2.imshow("thresholded", imgray*thresh2) 100 | winname = "Input" 101 | cv2.namedWindow(winname) # Create a named window 102 | cv2.moveWindow(winname, 700,300) # Move it to (40,30) 103 | cv2.imshow(winname, img) 104 | 105 | global que 106 | # print("size=", que.qsize()) 107 | # writes image test.bmp to disk 108 | que.put(img) 109 | 110 | key = cv2.waitKey(10) 111 | if key == 27: # Esc key 112 | break 113 | cv2.destroyAllWindows() 114 | cv2.VideoCapture(0).release() 115 | print("finished camera") 116 | global exit 117 | exit = 1 118 | 119 | def func(image_data): 120 | print("in func function") 121 | 122 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 123 | #print response 124 | response.raise_for_status() 125 | analysis = response.json() 126 | 127 | print("func") 128 | 129 | diic = [] 130 | video = [] 131 | for i in analysis: 132 | with open('test.txt','a') as myfile: 133 | 134 | print(i) 135 | video.append({"faceRectangle":i["faceRectangle"]}) 136 | dic = [] 137 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["anger"]) 138 | myfile.write("anger="+str(i["faceAttributes"]["emotion"]["anger"])+"\t") 139 | 140 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["contempt"]) 141 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["disgust"]) 142 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["fear"]) 143 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["happiness"]) 144 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["neutral"]) 145 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["sadness"]) 146 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["surprise"]) 147 | #dic.insert(len(dic), i["faceAttributes"]["smile"]) 148 | #dic.insert(len(dic), abs(i["faceAttributes"]["headPose"]["roll"])) 149 | 150 | myfile.write("contempt="+str(i["faceAttributes"]["emotion"]["contempt"])+"\t") 151 | myfile.write("disgust="+str(i["faceAttributes"]["emotion"]["disgust"])+"\t") 152 | myfile.write("fear="+str(i["faceAttributes"]["emotion"]["fear"])+"\t") 153 | myfile.write("happiness="+str(i["faceAttributes"]["emotion"]["happiness"])+"\t") 154 | myfile.write("neutral="+str(i["faceAttributes"]["emotion"]["neutral"])+"\t") 155 | myfile.write("sadness="+str(i["faceAttributes"]["emotion"]["sadness"])+"\t") 156 | myfile.write("surprise="+str(i["faceAttributes"]["emotion"]["surprise"])+"\n") 157 | #myfile.write("smile="+str(i["faceAttributes"]["smile"])+"\n") 158 | #myfile.write("roll="+str(abs(i["faceAttributes"]["headPose"]["roll"]))+"\n") 159 | 160 | diic.insert(len(diic), dic) 161 | print(diic) 162 | 163 | 164 | return diic, video 165 | 166 | # api code 167 | subscription_key = "02726400482345229652709041c698ba" 168 | assert subscription_key 169 | 170 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 171 | 172 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 173 | params = { 174 | 'returnFaceLandmarks': 'false', 175 | 'returnFaceAttributes': 'emotion,smile,headPose' 176 | } 177 | 178 | # scikit code 179 | X = np.loadtxt('Xval.txt', dtype=float) 180 | y = np.loadtxt('yval.txt', dtype=int) 181 | 182 | clf = svm.SVC() 183 | clf.fit(X, y) 184 | thread1 = threading.Thread(target=showframe, args=()) 185 | thread1.start() 186 | 187 | frame_width = int(cap.get(3)) 188 | frame_height = int(cap.get(4)) 189 | 190 | # Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file. 191 | out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height)) 192 | framecount=0 193 | 194 | i=50 195 | while True: 196 | if que.qsize() >= 16: 197 | img = que.get() 198 | 199 | if not que.empty(): 200 | 201 | print("----------------------------------------") 202 | print(que.qsize()) 203 | 204 | print("----------------------------------------") 205 | 206 | cv2.imwrite("test.bmp", img) 207 | 208 | image_data = open("test.bmp", "rb").read() 209 | 210 | diic, video = func(image_data) 211 | 212 | print("got diic") 213 | 214 | if (diic != []): 215 | Xhat = np.array(diic) 216 | Yhat = clf.predict(Xhat) 217 | 218 | # store time of capturing frame in graphx.txt 219 | graphXarr = np.insert(graphXarr, len(graphXarr), time.time()) 220 | 221 | count = 0 222 | array_rec = [] 223 | for one in video: 224 | rec = one["faceRectangle"] 225 | 226 | if( (Xhat[count][5] > 0.85) and (Xhat[count][6] > 0.002) ): 227 | Yhat[count] = 1 228 | 229 | # positive value of yhatf means that person can be categorised as bored 230 | # correct errors here using Xhat 231 | yhatf = Yhat[count] 232 | 233 | w1 = rec["width"] 234 | t1 = rec["top"] 235 | h1 = rec["height"] 236 | l1 = rec["left"] 237 | array_rec.append({"w1":w1 ,"t1":t1 ,"h1":h1 ,"l1":l1, "yhat":yhatf }) 238 | img = facerec(img) 239 | count = count + 1 240 | 241 | 242 | # store number of people bored in graphyval temporarily and insert it to numpy array for every frame captured 243 | graphyval = 0 244 | for i in Yhat: 245 | if i < 0: 246 | graphyval += 1 247 | # insert into numpy array 248 | graphYarr = np.insert(graphYarr, len(graphYarr), graphyval) 249 | 250 | writeframe(img) 251 | 252 | print("Prediction Array", Yhat) 253 | mScore = clf.score(X, y) 254 | print("Model Score", mScore) 255 | 256 | #cv2.rectangle(img, (50, 50), (50 + 50, 50 + 50), (255, 0, 0), 2) 257 | 258 | if w1 != -1: 259 | for cc in range(15): 260 | img = que.get() 261 | waste_facerec(img,array_rec) 262 | #writeframe(img) 263 | 264 | os.remove("test.bmp") 265 | 266 | else: 267 | #print("queue is empty") 268 | if exit == 1: 269 | print("process finished") 270 | break 271 | 272 | thread1.join() 273 | out.release() 274 | 275 | # save created numpy arrays in respective text files to create graph 276 | np.savetxt('graphx.txt', graphXarr, fmt='%d') 277 | np.savetxt('graphy.txt', graphYarr, fmt='%d') 278 | -------------------------------------------------------------------------------- /algo/ml_mobile.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import requests 4 | from sklearn import svm 5 | import cv2 6 | import time 7 | import threading 8 | import sys 9 | import urllib 10 | 11 | 12 | # check for Python version to decide which queue module to import 13 | is_py2 = sys.version[0] == '2' 14 | if is_py2: 15 | from Queue import Queue 16 | else: 17 | from queue import Queue 18 | 19 | 20 | img11=urllib.request.urlopen('http://192.168.14.187:8080/shot.jpg') 21 | imgnp=np.array(bytearray(img11.read()),dtype=np.uint8) 22 | imgcv=cv2.imdecode(imgnp,-1) 23 | image_height, image_width, channels = imgcv.shape 24 | def getcvimg(): 25 | img11=urllib.request.urlopen('http://192.168.14.187:8080/shot.jpg') 26 | imgnp=np.array(bytearray(img11.read()),dtype=np.uint8) 27 | imgcv=cv2.imdecode(imgnp,-1) 28 | #imgcv1 = cv2.resize(imgcv, (80, 24)) 29 | 30 | return imgcv 31 | 32 | # creating arrays to save in graphx and graphy text files 33 | graphXarr = np.empty((0, 1), int) 34 | graphYarr = np.empty((0, 1), int) 35 | 36 | open('test.txt','w') 37 | with open('test.txt','a') as myfile: 38 | myfile.write("anger contempt disgust fear happiness neutral sadness surprise roll\n") 39 | 40 | # create queue to store video frames 41 | que = Queue() 42 | array_rec = [] 43 | yhatf=2 44 | w1=-1 45 | t1=-1 46 | l1=-1 47 | h1=-1 48 | 49 | cap = cv2.VideoCapture() 50 | #cap = cv2.VideoCapture(0) # for webcam 51 | exit = 0 52 | 53 | def waste_facerec(img , array_rec): 54 | for rec in array_rec: 55 | w1 = rec["w1"] 56 | t1 = rec["t1"] 57 | h1 = rec["h1"] 58 | l1 = rec["l1"] 59 | yhatf = rec["yhat"] 60 | 61 | if yhatf > 0: 62 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (0, 0, 255), 2) 63 | else: 64 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (255, 0, 0), 2) 65 | return img 66 | 67 | def facerec(img): 68 | print("w1=", w1, "t1=", t1, "l1=", l1, "h1=", h1) 69 | if yhatf > 0: 70 | # if bored, then red rectangle 71 | with open('test.txt','a') as myfile: 72 | myfile.write("bored\n") 73 | 74 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (0, 0, 255), 2) 75 | else: 76 | # blue rectangle 77 | with open('test.txt','a') as myfile: 78 | myfile.write("not bored\n") 79 | 80 | cv2.rectangle(img, (l1, t1), (l1 + w1, t1 + h1), (255, 0, 0), 2) 81 | return img 82 | 83 | def writeframe(img): 84 | for i in range(6): 85 | out.write(img) 86 | 87 | # function to increase opencv frame brightness 88 | def adjust_gamma(image, gamma=2.0): 89 | 90 | invGamma = 1.0 / gamma 91 | table = np.array([((i / 255.0) ** invGamma) * 255 92 | for i in np.arange(0, 256)]).astype("uint8") 93 | 94 | return cv2.LUT(image, table) 95 | 96 | def showframe(): 97 | global cap 98 | 99 | # set the width and height, and UNSUCCESSFULLY set the exposure time 100 | cap.set(3, image_width) 101 | cap.set(4, image_height) 102 | cap.set(15, 0.1) 103 | 104 | while True: 105 | img = getcvimg(); 106 | #print(img) 107 | #img = cv2.flip(img, 1) 108 | 109 | #increase brightness 110 | gamma = 2.7 111 | img = adjust_gamma(img, gamma=gamma) 112 | 113 | 114 | # frame show function 115 | # cv2.imshow("thresholded", imgray*thresh2) 116 | cv2.imshow("input", img) 117 | global que 118 | # print("size=", que.qsize()) 119 | # writes image test.bmp to disk 120 | que.put(img) 121 | 122 | key = cv2.waitKey(10) 123 | if key == 27: # Esc key 124 | print("Escape key pressed") 125 | break 126 | cv2.destroyAllWindows() 127 | cap.release() 128 | #cv2.VideoCapture().release() 129 | print("finished camera") 130 | global exit 131 | exit = 1 132 | 133 | def func(image_data): 134 | print("in func function") 135 | 136 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 137 | #print response 138 | response.raise_for_status() 139 | analysis = response.json() 140 | 141 | print("func") 142 | 143 | diic = [] 144 | video = [] 145 | for i in analysis: 146 | with open('test.txt','a') as myfile: 147 | 148 | print(i) 149 | video.append({"faceRectangle":i["faceRectangle"]}) 150 | dic = [] 151 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["anger"]) 152 | myfile.write("anger="+str(i["faceAttributes"]["emotion"]["anger"])+"\t") 153 | 154 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["contempt"]) 155 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["disgust"]) 156 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["fear"]) 157 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["happiness"]) 158 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["neutral"]) 159 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["sadness"]) 160 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["surprise"]) 161 | #dic.insert(len(dic), i["faceAttributes"]["smile"]) 162 | #dic.insert(len(dic), abs(i["faceAttributes"]["headPose"]["roll"])) 163 | 164 | myfile.write("contempt="+str(i["faceAttributes"]["emotion"]["contempt"])+"\t") 165 | myfile.write("disgust="+str(i["faceAttributes"]["emotion"]["disgust"])+"\t") 166 | myfile.write("fear="+str(i["faceAttributes"]["emotion"]["fear"])+"\t") 167 | myfile.write("happiness="+str(i["faceAttributes"]["emotion"]["happiness"])+"\t") 168 | myfile.write("neutral="+str(i["faceAttributes"]["emotion"]["neutral"])+"\t") 169 | myfile.write("sadness="+str(i["faceAttributes"]["emotion"]["sadness"])+"\t") 170 | myfile.write("surprise="+str(i["faceAttributes"]["emotion"]["surprise"])+"\n") 171 | #myfile.write("smile="+str(i["faceAttributes"]["smile"])+"\n") 172 | #myfile.write("roll="+str(abs(i["faceAttributes"]["headPose"]["roll"]))+"\n") 173 | 174 | diic.insert(len(diic), dic) 175 | print(diic) 176 | 177 | 178 | return diic, video 179 | 180 | # api code 181 | subscription_key = "02726400482345229652709041c698ba" 182 | assert subscription_key 183 | 184 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 185 | 186 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 187 | params = { 188 | 'returnFaceLandmarks': 'false', 189 | 'returnFaceAttributes': 'emotion,smile,headPose' 190 | } 191 | 192 | # scikit code 193 | X = np.loadtxt('Xval.txt', dtype=float) 194 | y = np.loadtxt('yval.txt', dtype=int) 195 | 196 | clf = svm.SVC() 197 | clf.fit(X, y) 198 | thread1 = threading.Thread(target=showframe, args=()) 199 | thread1.start() 200 | 201 | frame_width = image_width 202 | frame_height = image_height 203 | print("frame_width=",frame_width," ","frame_height=",frame_height) 204 | 205 | # Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file. 206 | out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height)) 207 | framecount=0 208 | let_enter = 0 209 | i=50 210 | while True: 211 | if que.qsize() >= 20 or let_enter == 1: 212 | if que.qsize() <=1: 213 | print("breaking") 214 | break 215 | img = que.get() 216 | 217 | if not que.empty(): 218 | 219 | print("----------------------------------------") 220 | print(que.qsize()) 221 | 222 | print("----------------------------------------") 223 | 224 | cv2.imwrite("test.bmp", img) 225 | 226 | 227 | image_data = open("test.bmp", "rb").read() 228 | 229 | diic, video = func(image_data) 230 | 231 | print("dictionary acquired") 232 | 233 | if (diic != []): 234 | Xhat = np.array(diic) 235 | Yhat = clf.predict(Xhat) 236 | 237 | # store time of capturing frame in graphx.txt 238 | graphXarr = np.insert(graphXarr, len(graphXarr), time.time()) 239 | 240 | count = 0 241 | array_rec = [] 242 | for one in video: 243 | rec = one["faceRectangle"] 244 | 245 | if( (Xhat[count][5] > 0.85) and (Xhat[count][6] > 0.002) ): 246 | Yhat[count] = 1 247 | 248 | # positive value of yhatf means that person can be categorised as bored 249 | # correct errors here using Xhat 250 | yhatf = Yhat[count] 251 | 252 | w1 = rec["width"] 253 | t1 = rec["top"] 254 | h1 = rec["height"] 255 | l1 = rec["left"] 256 | array_rec.append({"w1":w1 ,"t1":t1 ,"h1":h1 ,"l1":l1, "yhat":yhatf }) 257 | img = facerec(img) 258 | count = count + 1 259 | 260 | # store number of people bored in graphyval temporarily and insert it to numpy array for every frame captured 261 | graphyval = 0 262 | for i in Yhat: 263 | if i > 0: 264 | graphyval += 1 265 | # insert into numpy array 266 | graphYarr = np.insert(graphYarr, len(graphYarr), graphyval) 267 | 268 | writeframe(img) 269 | 270 | print("Prediction Array", Yhat) 271 | mScore = clf.score(X, y) 272 | print("Model Score", mScore) 273 | 274 | #cv2.rectangle(img, (50, 50), (50 + 50, 50 + 50), (255, 0, 0), 2) 275 | 276 | if w1 != -1 and que.qsize() > 5: 277 | for cc in range(1): 278 | img = que.get() 279 | waste_facerec(img,array_rec) 280 | #writeframe(img) 281 | 282 | os.remove("test.bmp") 283 | 284 | else: 285 | #print("queue is empty") 286 | 287 | if exit == 1: 288 | print("exit=1") 289 | let_enter = 1 290 | if que.qsize() < 5: 291 | print("process finished") 292 | break 293 | 294 | thread1.join() 295 | out.release() 296 | 297 | # save created numpy arrays in respective text files to create graph 298 | np.savetxt('graphx.txt', graphXarr, fmt='%d') 299 | np.savetxt('graphy.txt', graphYarr, fmt='%d') 300 | -------------------------------------------------------------------------------- /algo/play.py: -------------------------------------------------------------------------------- 1 | # import numpy as np 2 | # import cv2 3 | 4 | # cap = cv2.VideoCapture('outpy.avi') 5 | # length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 6 | # for i in range(length): 7 | # ret, frame = cap.read() 8 | # winname = "Output" 9 | # cv2.namedWindow(winname) # Create a named window 10 | # cv2.moveWindow(winname, 700,300) # Move it to (40,30) 11 | # cv2.imshow(winname, frame) 12 | 13 | 14 | 15 | # if cv2.waitKey(80) & 0xFF == ord('q'): 16 | # break 17 | 18 | # cap.release() 19 | # cv2.destroyAllWindows() 20 | import os 21 | os.system('totem outpy.avi') -------------------------------------------------------------------------------- /algo/pool.py: -------------------------------------------------------------------------------- 1 | import multiprocessing.pool as mpool 2 | 3 | def worker(task): 4 | # work on task 5 | print(task) # substitute your migration code here. 6 | 7 | # create a pool of 10 threads 8 | pool = mpool.ThreadPool(10) 9 | N = 100 10 | 11 | for task in range(N): 12 | pool.apply_async(worker, args = (task, )) 13 | 14 | pool.close() 15 | pool.join() -------------------------------------------------------------------------------- /algo/smile.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cseas/crowd-analytics/3911d47c9aa02b75823787cebe53026bddf764f8/algo/smile.txt -------------------------------------------------------------------------------- /algo/test.txt: -------------------------------------------------------------------------------- 1 | anger contempt disgust fear happiness neutral sadness surprise roll 2 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.998 sadness=0.0 surprise=0.001 3 | not bored 4 | anger=0.001 contempt=0.023 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.972 sadness=0.0 surprise=0.004 5 | not bored 6 | anger=0.001 contempt=0.014 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.969 sadness=0.001 surprise=0.015 7 | not bored 8 | anger=0.001 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.981 sadness=0.0 surprise=0.016 9 | not bored 10 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.974 sadness=0.0 surprise=0.024 11 | not bored 12 | anger=0.001 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.972 sadness=0.0 surprise=0.025 13 | not bored 14 | anger=0.0 contempt=0.015 disgust=0.0 fear=0.0 happiness=0.002 neutral=0.983 sadness=0.0 surprise=0.0 15 | not bored 16 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.997 sadness=0.0 surprise=0.001 17 | not bored 18 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.999 sadness=0.0 surprise=0.0 19 | not bored 20 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=1.0 sadness=0.0 surprise=0.0 21 | not bored 22 | anger=0.0 contempt=0.004 disgust=0.0 fear=0.0 happiness=0.001 neutral=0.995 sadness=0.0 surprise=0.0 23 | not bored 24 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.97 sadness=0.0 surprise=0.029 25 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.995 sadness=0.0 surprise=0.004 26 | not bored 27 | not bored 28 | anger=0.001 contempt=0.002 disgust=0.0 fear=0.0 happiness=0.0 neutral=0.994 sadness=0.0 surprise=0.003 29 | not bored 30 | -------------------------------------------------------------------------------- /algo/yval.txt: -------------------------------------------------------------------------------- 1 | -1 2 | -1 3 | -1 4 | -1 5 | -1 6 | -1 7 | -1 8 | -1 9 | -1 10 | -1 11 | -1 12 | -1 13 | -1 14 | -1 15 | -1 16 | -1 17 | -1 18 | -1 19 | -1 20 | -1 21 | -1 22 | -1 23 | -1 24 | -1 25 | -1 26 | -1 27 | -1 28 | -1 29 | -1 30 | -1 31 | -1 32 | -1 33 | -1 34 | -1 35 | -1 36 | -1 37 | -1 38 | -1 39 | -1 40 | -1 41 | -1 42 | -1 43 | -1 44 | -1 45 | -1 46 | -1 47 | -1 48 | -1 49 | -1 50 | -1 51 | -1 52 | -1 53 | -1 54 | -1 55 | -1 56 | -1 57 | -1 58 | -1 59 | -1 60 | -1 61 | -1 62 | -1 63 | -1 64 | -1 65 | -1 66 | -1 67 | -1 68 | -1 69 | -1 70 | -1 71 | -1 72 | -1 73 | -1 74 | -1 75 | -1 76 | -1 77 | -1 78 | -1 79 | -1 80 | -1 81 | -1 82 | -1 83 | -1 84 | -1 85 | -1 86 | -1 87 | -1 88 | -1 89 | -1 90 | -1 91 | -1 92 | -1 93 | -1 94 | -1 95 | -1 96 | -1 97 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-------------------------------------------------------------------------------- /newTrain/analysis.txt: -------------------------------------------------------------------------------- 1 | neutral > 0.95 2 | angry > 0.002 (double zero) -------------------------------------------------------------------------------- /newTrain/api.py: -------------------------------------------------------------------------------- 1 | import os, shutil 2 | import requests 3 | import numpy as np 4 | 5 | # free, 20 calls/min, limit 30K calls/month 6 | # subscription_key = "bf5951c5f4934e2e90bc11c48ffb57fa" 7 | # premium, 10 calls/sec, no limit, 66 INR / 1000 calls 8 | subscription_key = "02726400482345229652709041c698ba" 9 | assert subscription_key 10 | 11 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 12 | 13 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 14 | params = { 15 | 'returnFaceId': 'false', 16 | 'returnFaceLandmarks': 'false', 17 | 'returnFaceAttributes': 'emotion,smile,headPose' 18 | } 19 | 20 | X = np.empty((0, 8), float) 21 | y = np.empty((0, 1), float) 22 | 23 | 24 | test_folder_name = "happy" 25 | test_file = "happy.txt" 26 | 27 | # remove previous test file 28 | open(test_file,'w') 29 | # writes azure analysed values of files in txt file 30 | def analyse(fname,ara): 31 | 32 | with open(test_file,'a') as myfile: 33 | #myfile.write("anger\tcontempt\tdisgust\tfear\thappiness\tneutral\tsadness\tsurprise\troll\n") 34 | myfile.write("anger=" + str(ara[0]) + " ") 35 | myfile.write("contempt=" + str(ara[1]) + " ") 36 | myfile.write("disgust=" + str(ara[2]) + " ") 37 | myfile.write("fear=" + str(ara[3]) + " ") 38 | myfile.write("happiness=" + str(ara[4])+ " ") 39 | myfile.write("neutral=" + str(ara[5]) + " ") 40 | myfile.write("sadness=" + str(ara[6]) + " ") 41 | myfile.write("surprise=" + str(ara[7]) + " ") 42 | myfile.write("roll=" + str(ara[8])) 43 | myfile.write("\n") 44 | myfile.write(str(fname)+"\n\n") 45 | 46 | # this is criteria for error 47 | if(ara[4] < 0.4): 48 | shutil.move("/home/abhijeet/Documents/github/crowd-analytics/newTrain/" + test_folder_name + "/" + str(fname), 49 | "/home/abhijeet/Documents/github/crowd-analytics/newTrain/errors/" + str(fname)) 50 | 51 | 52 | # dataset folder 53 | indir = '/home/abhijeet/Documents/github/crowd-analytics/newTrain/' 54 | 55 | for dirs,dirlist,filenames in os.walk("."): 56 | print(dirs) 57 | 58 | for filename in filenames: 59 | print(filename) 60 | if filename.endswith(".jpeg") or filename.endswith(".jpg") or filename.endswith(".png") or filename.endswith(".JPG") or filename.endswith(".PNG") or filename.endswith(".JPEG"): 61 | print("taken " + filename) 62 | # print(os.path.join(directory, filename)) 63 | # image_data = open(indir + '/' + filename, "rb").read() 64 | image_data = open(indir + '/' + dirs.split('/')[1]+'/'+filename, "rb").read() 65 | 66 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 67 | response.raise_for_status() 68 | analysis = response.json() 69 | 70 | if analysis: 71 | print("Face detected\n") 72 | 73 | for i in analysis: 74 | 75 | #print(i) 76 | dic = [] 77 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["anger"]) 78 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["contempt"]) 79 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["disgust"]) 80 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["fear"]) 81 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["happiness"]) 82 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["neutral"]) 83 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["sadness"]) 84 | dic.insert(len(dic), i["faceAttributes"]["emotion"]["surprise"]) 85 | #dic.insert(len(dic), i["faceAttributes"]["smile"]) 86 | #dic.insert(len(dic), abs(i["faceAttributes"]["headPose"]["roll"])) 87 | 88 | # convert list to numpy array 89 | arr = np.array(dic) 90 | 91 | # insert next row to main numpy array 92 | X = np.vstack([X, arr]) 93 | 94 | 95 | # analyse particular folder 96 | if str(dirs[2:]) == test_folder_name: 97 | analyse(filename, arr) 98 | # print(dirs) 99 | # print("---",dirs[2:],"----") 100 | # print(type(dirs)) 101 | 102 | if str(dirs[2:]) == "bored" or str(dirs[2:]) == "openmouth" or str(dirs[2:]) == "sad": 103 | y = np.insert(y, len(y), 1) 104 | else: 105 | y = np.insert(y, len(y), -1) 106 | 107 | #print(X) 108 | #print(y) 109 | 110 | np.savetxt('Xval.txt', X, fmt='%f') 111 | np.savetxt('yval.txt', y, fmt='%d') -------------------------------------------------------------------------------- /newTrain/happy.txt: -------------------------------------------------------------------------------- 1 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.3 2 | BM29HAS.JPG 3 | 4 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.1 5 | BM08HAS.JPG 6 | 7 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.0 8 | AM15HAS.JPG 9 | 10 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.3 11 | AF22HAS.JPG 12 | 13 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.7 14 | BF12HAS.JPG 15 | 16 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.5 17 | BF24HAS.JPG 18 | 19 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=4.0 20 | AM28HAS.JPG 21 | 22 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.0 23 | BF15HAS.JPG 24 | 25 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.7 26 | AM26HAS.JPG 27 | 28 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.2 29 | BM18HAS.JPG 30 | 31 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=3.2 32 | AF02HAS.JPG 33 | 34 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=3.7 35 | AF26HAS.JPG 36 | 37 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.0 38 | AF30HAS.JPG 39 | 40 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.5 41 | BM12HAS.JPG 42 | 43 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.6 44 | BF07HAS.JPG 45 | 46 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.6 47 | KM.HA3.6.tiff.png 48 | 49 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.0 50 | AF14HAS.JPG 51 | 52 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.999 neutral=0.0 sadness=0.0 surprise=0.0 roll=3.1 53 | KL.HA3.160.tiff.png 54 | 55 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.9 56 | AF32HAS.JPG 57 | 58 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.6 59 | AM01HAS.JPG 60 | 61 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.2 62 | AF06HAS.JPG 63 | 64 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.7 65 | BM22HAS.JPG 66 | 67 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.7 68 | YM.HA3.54.tiff.png 69 | 70 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.1 71 | BM04HAS.JPG 72 | 73 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.4 74 | AF19HAS.JPG 75 | 76 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.998 neutral=0.0 sadness=0.0 surprise=0.002 roll=1.7 77 | AF31HAS.JPG 78 | 79 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.98 neutral=0.019 sadness=0.0 surprise=0.0 roll=4.4 80 | TM.HA3.182.tiff.png 81 | 82 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.5 83 | BM26HAS.JPG 84 | 85 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.4 86 | AM07HAS.JPG 87 | 88 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.0 89 | AF25HAS.JPG 90 | 91 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=0.998 neutral=0.0 sadness=0.0 surprise=0.002 roll=0.5 92 | KA.HA2.30.tiff.png 93 | 94 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.8 95 | BF17HAS.JPG 96 | 97 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.5 98 | AF20HAS.JPG 99 | 100 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.0 101 | AM13HAS.JPG 102 | 103 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.3 104 | BM28HAS.JPG 105 | 106 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.5 107 | AF27HAS.JPG 108 | 109 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.3 110 | AM35HAS.JPG 111 | 112 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.0 113 | BM05HAS.JPG 114 | 115 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=0.2 116 | BM07HAS.JPG 117 | 118 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 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anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=1.3 494 | KM.HA1.4.tiff.png 495 | 496 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.0 497 | AF05HAS.JPG 498 | 499 | anger=0.0 contempt=0.001 disgust=0.0 fear=0.0 happiness=0.98 neutral=0.019 sadness=0.0 surprise=0.0 roll=1.4 500 | BF18HAS.JPG 501 | 502 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=3.8 503 | BF03HAS.JPG 504 | 505 | anger=0.0 contempt=0.0 disgust=0.0 fear=0.0 happiness=1.0 neutral=0.0 sadness=0.0 surprise=0.0 roll=2.0 506 | AM08HAS.JPG 507 | 508 | -------------------------------------------------------------------------------- /newTrain/yval.txt: -------------------------------------------------------------------------------- 1 | -1 2 | -1 3 | -1 4 | -1 5 | -1 6 | -1 7 | -1 8 | -1 9 | -1 10 | -1 11 | -1 12 | -1 13 | -1 14 | -1 15 | -1 16 | -1 17 | -1 18 | -1 19 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| -1 140 | -1 141 | -1 142 | -1 143 | -1 144 | -1 145 | -1 146 | -1 147 | -1 148 | -1 149 | -1 150 | -1 151 | -1 152 | -1 153 | -1 154 | -1 155 | -1 156 | -1 157 | -1 158 | -1 159 | -1 160 | -1 161 | -1 162 | -1 163 | -1 164 | -1 165 | -1 166 | -1 167 | -1 168 | -1 169 | -1 170 | -------------------------------------------------------------------------------- /range/Issues.txt: -------------------------------------------------------------------------------- 1 | 1. Solve issue of code showing attentive faces as bored. 2 | Possible fix: 3 | Use mean values of emotions to measure level of bored. 4 | 5 | Possible problem: 6 | Using more images for analysis should mean more accuracy but since we're using max and min values, 7 | more images only increase the range, thus resulting in worse accuracy. -------------------------------------------------------------------------------- /range/analysed.txt: -------------------------------------------------------------------------------- 1 | {"sadness": {"min": 0.0, "max": 0.718}, "contempt": {"min": 0.0, "max": 0.14}, "anger": {"min": 0.0, "max": 0.044}, "disgust": {"min": 0.0, "max": 0.025}, "surprise": {"min": 0.0, "max": 0.015}, "fear": {"min": 0.0, "max": 0.011}, "happiness": {"min": 0.0, "max": 0.156}, "neutral": {"min": 0.274, "max": 1.0}} -------------------------------------------------------------------------------- /range/analyze.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import requests 4 | 5 | # free, 20 calls/min, limit 30K calls/month 6 | subscription_key = "bf5951c5f4934e2e90bc11c48ffb57fa" 7 | # premium, 10 calls/sec, no limit, 66 INR / 1000 calls 8 | # subscription_key = "02726400482345229652709041c698ba" 9 | assert subscription_key 10 | 11 | 12 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 13 | 14 | # image_url = 'https://how-old.net/Images/faces2/main007.jpg' 15 | # image_data = open(image_path, "rb").read() 16 | 17 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 18 | params = { 19 | 'returnFaceId': 'false', 20 | 'returnFaceLandmarks': 'false', 21 | 'returnFaceAttributes': 'emotion', 22 | } 23 | 24 | dic = { 25 | 'sadness':{'min':2.0,'max':0.0}, 26 | 'contempt':{'min':2.0,'max':0.0}, 27 | 'anger':{'min':2.0,'max':0.0}, 28 | 'disgust':{'min':2.0,'max':0.0}, 29 | 'surprise':{'min':2.0,'max':0.0}, 30 | 'fear':{'min':2.0,'max':0.0}, 31 | 'happiness':{'min':2.0,'max':0.0}, 32 | 'neutral':{'min':2.0,'max':0.0} 33 | } 34 | 35 | # analysing images folder to find ranges of emotions for bored expression 36 | indir = '/home/abhijeet/Documents/python/faceapi/boredImages' 37 | for root, dirs, filenames in os.walk(indir): 38 | for filename in filenames: 39 | if filename.endswith(".jpeg") or filename.endswith(".jpg") or filename.endswith(".png"): 40 | # print(os.path.join(directory, filename)) 41 | image_data = open(indir+'/'+filename, "rb").read() 42 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 43 | response.raise_for_status() 44 | analysis = response.json() 45 | 46 | for i in analysis: 47 | for j,value in i["faceAttributes"]["emotion"].items(): 48 | 49 | if(value > dic[j]['max']): 50 | dic[j]['max'] = i["faceAttributes"]["emotion"][j] 51 | if(value < dic[j]['min']): 52 | dic[j]['min'] = i["faceAttributes"]["emotion"][j] 53 | #print(i["faceAttributes"]["emotion"]["contempt"]) 54 | 55 | else: 56 | print('invalid image') 57 | print(dic) 58 | 59 | f = open('analysed.txt', 'w') 60 | f.write(json.dumps(dic)) 61 | f.close() 62 | #faces = response.json() 63 | #HTML("Detected %d faces in the image"%len(faces)) -------------------------------------------------------------------------------- /range/meta-chart.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cseas/crowd-analytics/3911d47c9aa02b75823787cebe53026bddf764f8/range/meta-chart.jpeg -------------------------------------------------------------------------------- /range/test.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import requests 4 | 5 | # free, 20 calls/min, limit 30K calls/month 6 | subscription_key = "bf5951c5f4934e2e90bc11c48ffb57fa" 7 | # premium, 10 calls/sec, no limit, 66 INR / 1000 calls 8 | # subscription_key = "02726400482345229652709041c698ba" 9 | assert subscription_key 10 | 11 | face_api_url = 'https://southeastasia.api.cognitive.microsoft.com/face/v1.0/detect' 12 | 13 | headers = { 'Ocp-Apim-Subscription-Key': subscription_key, "Content-Type": "application/octet-stream"} 14 | params = { 15 | 'returnFaceId': 'false', 16 | 'returnFaceLandmarks': 'false', 17 | 'returnFaceAttributes': 'emotion', 18 | } 19 | 20 | # load the stored dictionary data from txt file 21 | f = open('analysed.txt', 'r') 22 | dic = json.loads(f.readline()) 23 | f.close() 24 | 25 | # testing test images with analysed emotion values 26 | indir = '/home/abhijeet/Documents/python/faceapi/test' 27 | for root, dirs, filenames in os.walk(indir): 28 | for filename in filenames: 29 | if filename.endswith(".jpeg") or filename.endswith(".jpg") or filename.endswith(".png"): 30 | image_data = open(indir+'/'+filename, "rb").read() 31 | response = requests.post(face_api_url, params=params, headers=headers, data=image_data) 32 | response.raise_for_status() 33 | analysis = response.json() 34 | 35 | for i in analysis: 36 | flag = 0 37 | for j,value in i["faceAttributes"]["emotion"].items(): 38 | if(j == "neutral"): 39 | continue 40 | # print (j) 41 | # print (value) 42 | # print ("max",dic[j]['max']) 43 | # print ("min",dic[j]['min']) 44 | if(value > dic[j]['max'] or value < dic[j]['min']): 45 | print("Not bored") 46 | flag = 1 47 | break 48 | if(flag == 0): 49 | print("Bored") -------------------------------------------------------------------------------- /testImages/attentive.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cseas/crowd-analytics/3911d47c9aa02b75823787cebe53026bddf764f8/testImages/attentive.jpeg -------------------------------------------------------------------------------- /testImages/bored.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cseas/crowd-analytics/3911d47c9aa02b75823787cebe53026bddf764f8/testImages/bored.jpeg -------------------------------------------------------------------------------- /testImages/serious.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cseas/crowd-analytics/3911d47c9aa02b75823787cebe53026bddf764f8/testImages/serious.jpg --------------------------------------------------------------------------------