From eddbc487b6fb6e1a68fe85e52c295c76fff367ef Mon Sep 17 00:00:00 2001
From: Michael Peters <michael@styx>
Date: Mon, 1 Aug 2022 19:39:28 -0500
Subject: [PATCH] initial commit. original pre-2020 (Freshman Year)

---
 .gitignore                  |     4 +
 ard.aliases                 |     2 +
 ard.exe                     |   Bin 0 -> 187904 bytes
 ard.ini                     |    12 +
 begone_thot.mp3             |   Bin 0 -> 30489 bytes
 begone_thot.wav             |   Bin 0 -> 1006800 bytes
 blank.jpg                   |   Bin 0 -> 2051 bytes
 face_cascade.xml            | 33314 ++++++++++++++++++++++++++++++++++
 female_downloader.py        |     0
 grayscaler.py               |    12 +
 not_thot_downloader.py      |    38 +
 thot.mp3                    |   Bin 0 -> 4800 bytes
 thot_cascade.xml            |  1328 ++
 thot_detector.py            |    66 +
 thot_detector_from_input.py |    28 +
 15 files changed, 34804 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 ard.aliases
 create mode 100644 ard.exe
 create mode 100644 ard.ini
 create mode 100644 begone_thot.mp3
 create mode 100644 begone_thot.wav
 create mode 100644 blank.jpg
 create mode 100644 face_cascade.xml
 create mode 100644 female_downloader.py
 create mode 100644 grayscaler.py
 create mode 100644 not_thot_downloader.py
 create mode 100644 thot.mp3
 create mode 100644 thot_cascade.xml
 create mode 100644 thot_detector.py
 create mode 100644 thot_detector_from_input.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e240574
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+formatted_thots
+saved_thots
+thots
+thots_false
diff --git a/ard.aliases b/ard.aliases
new file mode 100644
index 0000000..20d90f1
--- /dev/null
+++ b/ard.aliases
@@ -0,0 +1,2 @@
+[My Computer]
+My Computer="localhost"
\ No newline at end of file
diff --git a/ard.exe b/ard.exe
new file mode 100644
index 0000000000000000000000000000000000000000..f24cd27eaf422e291dbbfa9819bc5b391fe6d0e0
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HcmV?d00001

diff --git a/ard.ini b/ard.ini
new file mode 100644
index 0000000..edbb29a
--- /dev/null
+++ b/ard.ini
@@ -0,0 +1,12 @@
+[ard]
+server.app.propertiesEnabled=True
+server.ole.enabled=True
+server.tcp.paranoid=True
+server.tcp.serviceName="My Computer/VI Server"
+server.vi.callsEnabled=True
+server.vi.propertiesEnabled=True
+WebServer.TcpAccess="c+*"
+WebServer.ViAccess="+*"
+DebugServerEnabled=False
+DebugServerWaitOnLaunch=False
+
diff --git a/begone_thot.mp3 b/begone_thot.mp3
new file mode 100644
index 0000000000000000000000000000000000000000..67ef2e4895270f978868fc3f03a7bf342d41b8e4
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diff --git a/face_cascade.xml b/face_cascade.xml
new file mode 100644
index 0000000..e7a7df8
--- /dev/null
+++ b/face_cascade.xml
@@ -0,0 +1,33314 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 24x24 discrete(?) adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
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+  <height>24</height>
+  <width>24</width>
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diff --git a/female_downloader.py b/female_downloader.py
new file mode 100644
index 0000000..e69de29
diff --git a/grayscaler.py b/grayscaler.py
new file mode 100644
index 0000000..72d718a
--- /dev/null
+++ b/grayscaler.py
@@ -0,0 +1,12 @@
+import cv2
+import os
+
+lines = ""
+for file_name in os.listdir("thots_false"):
+    path = "thots_false/" + file_name
+    img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+    #cv2.imwrite(path, img)
+    lines += path+ "\n"
+f = open("thots_false/bg.txt", "w")
+f.write(lines)
+f.close()
diff --git a/not_thot_downloader.py b/not_thot_downloader.py
new file mode 100644
index 0000000..4667e5a
--- /dev/null
+++ b/not_thot_downloader.py
@@ -0,0 +1,38 @@
+import urllib.request
+
+import cv2
+import numpy as np
+import os
+
+def store_raw_images():
+    blank_img = cv2.imread("thots_false/blank.jpg", cv2.IMREAD_GRAYSCALE)
+    
+    #thots
+    #lnk = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n09972458"
+    #not thots
+    #lnk = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n00017222"
+    lnk = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n04081281"
+    neg_image_urls = urllib.request.urlopen(lnk).read().decode()
+    n = 1273
+    for image_url in neg_image_urls.split("\n"):
+        try:
+            n += 1
+            if n >= 2000:
+                break
+            path = "thots_false/" + str(n) + ".jpg"
+            print("\n")
+            print(str(n) + "/" + str(len(neg_image_urls.split("\n"))))
+            print(image_url)
+            urllib.request.urlretrieve(image_url, path)
+            img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+            if (img.shape == blank_img.shape):
+                print("Blank Image Detected, deleting...")
+                os.remove(path)
+                continue
+            rs_img = cv2.resize(img, (100, 100))
+            cv2.imwrite("thots_false/" + str(n) + ".jpg", rs_img)
+            print("Success")
+        except Exception as e:
+            print(str(e))
+
+store_raw_images()
diff --git a/thot.mp3 b/thot.mp3
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diff --git a/thot_cascade.xml b/thot_cascade.xml
new file mode 100644
index 0000000..5f7e7ed
--- /dev/null
+++ b/thot_cascade.xml
@@ -0,0 +1,1328 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>BASIC</mode></featureParams>
+  <stageNum>10</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>7</maxWeakCount>
+      <stageThreshold>-1.3274219036102295e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 37 1.0469606146216393e-02</internalNodes>
+          <leafValues>
+            -7.4285715818405151e-01 7.8308457136154175e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 79 1.5145290642976761e-02</internalNodes>
+          <leafValues>
+            -4.4043844938278198e-01 7.8867870569229126e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 4 1.0507825762033463e-02</internalNodes>
+          <leafValues>
+            -3.6847728490829468e-01 7.6782244443893433e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 66 2.2796669509261847e-03</internalNodes>
+          <leafValues>
+            -5.0867736339569092e-01 5.0471705198287964e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 97 1.8995638936758041e-02</internalNodes>
+          <leafValues>
+            -4.1426384449005127e-01 6.7326050996780396e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 9 -6.1324327252805233e-03</internalNodes>
+          <leafValues>
+            6.0510784387588501e-01 -4.5688426494598389e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 -2.1730922162532806e-03</internalNodes>
+          <leafValues>
+            5.4218435287475586e-01 -4.3977108597755432e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-1.6357795000076294e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 60 -5.5220201611518860e-03</internalNodes>
+          <leafValues>
+            6.9913208484649658e-01 -6.4432990550994873e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 55 -1.9900221377611160e-02</internalNodes>
+          <leafValues>
+            5.9260463714599609e-01 -5.1867133378982544e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 36 3.8695689290761948e-03</internalNodes>
+          <leafValues>
+            -4.2546752095222473e-01 5.6908625364303589e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 13 9.0772267431020737e-03</internalNodes>
+          <leafValues>
+            -2.7465942502021790e-01 7.7663981914520264e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 8 -8.0915261059999466e-03</internalNodes>
+          <leafValues>
+            4.7079926729202271e-01 -4.3494972586631775e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 91 1.3856826350092888e-02</internalNodes>
+          <leafValues>
+            -5.0266206264495850e-01 5.0381177663803101e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 22 -6.3862828537821770e-03</internalNodes>
+          <leafValues>
+            4.4686800241470337e-01 -3.9493373036384583e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 0 2.0199924707412720e-02</internalNodes>
+          <leafValues>
+            -2.9429426789283752e-01 6.1475795507431030e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 83 4.5295134186744690e-02</internalNodes>
+          <leafValues>
+            -2.6356130838394165e-01 6.8496376276016235e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-1.0928578376770020e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 29 7.5111049227416515e-03</internalNodes>
+          <leafValues>
+            -6.5168541669845581e-01 6.6136574745178223e-01</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -4.0361735224723816e-01 4.6414566040039062e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 99 7.1363905444741249e-03</internalNodes>
+          <leafValues>
+            -2.9460573196411133e-01 5.7261008024215698e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 10 -1.3243447756394744e-03</internalNodes>
+          <leafValues>
+            3.5529056191444397e-01 -4.8852556943893433e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 48 1.9855855498462915e-03</internalNodes>
+          <leafValues>
+            1.8858212232589722e-01 -7.9904526472091675e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 70 -8.6571613792330027e-04</internalNodes>
+          <leafValues>
+            3.1570643186569214e-01 -5.1330500841140747e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 58 2.5003281189128757e-04</internalNodes>
+          <leafValues>
+            -6.2882810831069946e-01 2.8079953789710999e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>11</maxWeakCount>
+      <stageThreshold>-1.2214441299438477e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 31 7.3169162496924400e-03</internalNodes>
+          <leafValues>
+            -5.4644811153411865e-01 6.3739377260208130e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 78 2.4933800101280212e-02</internalNodes>
+          <leafValues>
+            -3.8869139552116394e-01 6.4082938432693481e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 6.6593214869499207e-03</internalNodes>
+          <leafValues>
+            -4.5500367879867554e-01 4.8629078269004822e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 98 -9.6643082797527313e-03</internalNodes>
+          <leafValues>
+            7.1257179975509644e-01 -2.8475266695022583e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 24 -4.9876440316438675e-03</internalNodes>
+          <leafValues>
+            6.2009280920028687e-01 -2.7415782213211060e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 49 -1.6450429335236549e-02</internalNodes>
+          <leafValues>
+            6.1194187402725220e-01 -2.3630347847938538e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 12 5.1880376413464546e-03</internalNodes>
+          <leafValues>
+            -1.6924737393856049e-01 6.9377225637435913e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 34 -2.7812439948320389e-02</internalNodes>
+          <leafValues>
+            -7.3967283964157104e-01 1.8909685313701630e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 96 9.5337665698025376e-05</internalNodes>
+          <leafValues>
+            2.3305855691432953e-01 -6.9120371341705322e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 54 2.6016458868980408e-03</internalNodes>
+          <leafValues>
+            -2.9739683866500854e-01 5.0571995973587036e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 65 -8.3387624472379684e-03</internalNodes>
+          <leafValues>
+            -8.6222010850906372e-01 1.6015651822090149e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>10</maxWeakCount>
+      <stageThreshold>-9.4507956504821777e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 42 2.7271145954728127e-02</internalNodes>
+          <leafValues>
+            -4.6397694945335388e-01 5.8998143672943115e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 86 1.0541098192334175e-02</internalNodes>
+          <leafValues>
+            -5.1467216014862061e-01 4.7240531444549561e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 17 -9.4400290399789810e-03</internalNodes>
+          <leafValues>
+            6.2930542230606079e-01 -2.8486761450767517e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            4.1537266969680786e-01 -4.4202288985252380e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -3.0915626883506775e-01 5.0157052278518677e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 1.5131932683289051e-02</internalNodes>
+          <leafValues>
+            -2.8238829970359802e-01 5.3224080801010132e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 88 5.7480123359709978e-04</internalNodes>
+          <leafValues>
+            2.2724473476409912e-01 -6.7207753658294678e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 13 9.5301354303956032e-03</internalNodes>
+          <leafValues>
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+        <_>
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+          <leafValues>
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+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>12</maxWeakCount>
+      <stageThreshold>-1.1014319658279419e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -3.3333334326744080e-01 6.1477047204971313e-01</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            5.1617169380187988e-01 -3.2136338949203491e-01</leafValues></_>
+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
+            4.6130463480949402e-01 -2.8589373826980591e-01</leafValues></_>
+        <_>
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+            0 -1 84 3.4340843558311462e-02</internalNodes>
+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>11</maxWeakCount>
+      <stageThreshold>-8.2595127820968628e-01</stageThreshold>
+      <weakClassifiers>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+        <_>
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+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-7.8072935342788696e-01</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 8 -->
+    <_>
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diff --git a/thot_detector.py b/thot_detector.py
new file mode 100644
index 0000000..2243a00
--- /dev/null
+++ b/thot_detector.py
@@ -0,0 +1,66 @@
+#code modified from https://pythonprogramming.net/haar-cascade-object-detection-python-opencv-tutorial/
+
+import numpy as np
+import cv2
+import os
+
+from pygame import mixer
+
+mixer.init()
+mixer.music.load("begone_thot.mp3")
+
+thot_cascade = cv2.CascadeClassifier('face_cascade.xml')
+
+cap = cv2.VideoCapture(0)
+
+launched = False
+capturing = False
+saved = 0
+recent_saved = 0
+
+while True:
+    ret, img = cap.read()
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    
+    # add this
+    # image, reject levels level weights.
+    thots = thot_cascade.detectMultiScale(gray, 1.3, 5)
+    
+    # add this
+    for (x,y,w,h) in thots:
+        #speech.say("Thought Detected")
+        #speech.runAndWait()
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        if not launched:
+            pass
+            #os.system("ard.exe")
+        capturing = True
+        recent_saved = 0
+        if (not mixer.music.get_busy()):
+            mixer.music.play()
+        cv2.putText(img, 'THOT',(x+w,y+h), font, 0.5, (11,255,255), 2, cv2.LINE_AA)
+        cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
+    if capturing and recent_saved < 60:
+        cv2.imwrite("M:/Pictures/saved_thots/detected_thot_" + str(saved) + ".jpg", img)
+        saved += 1
+        recent_saved += 1
+
+    """
+    for (x,y,w,h) in faces:
+        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
+
+        
+        roi_gray = gray[y:y+h, x:x+w]
+        roi_color = img[y:y+h, x:x+w]
+        eyes = eye_cascade.detectMultiScale(roi_gray)
+        for (ex,ey,ew,eh) in eyes:
+            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
+    """
+
+    cv2.imshow('Thot Detector',img)
+    k = cv2.waitKey(30) & 0xff
+    if k == 27:
+        break
+
+cap.release()
+cv2.destroyAllWindows()
diff --git a/thot_detector_from_input.py b/thot_detector_from_input.py
new file mode 100644
index 0000000..b771036
--- /dev/null
+++ b/thot_detector_from_input.py
@@ -0,0 +1,28 @@
+#code modified from https://pythonprogramming.net/haar-cascade-object-detection-python-opencv-tutorial/
+
+import numpy as np
+import cv2
+import os
+
+thot_cascade = cv2.CascadeClassifier('face_cascade.xml')
+
+launched = False
+capturing = False
+saved = 0
+recent_saved = 0
+
+img = cv2.imread("IMG_2619.jpg", 0)
+print(img)
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+thots = thot_cascade.detectMultiScale(gray, 1.3, 5)
+for (x,y,w,h) in thots:
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    if not launched:
+        pass
+        #os.system("ard.exe")
+    capturing = True
+    recent_saved = 0
+    cv2.putText(img, 'THOT',(x+w,y+h), font, 0.5, (11,255,255), 2, cv2.LINE_AA)
+    cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
+    cv2.imwrite("detected_thot_" + str(saved) + ".jpg", img)
+