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Add web cam motion detector

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Urmi Jana 2022-10-19 00:42:52 +05:30 committed by GitHub
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85
scripts/WebCam Motion Detector/Graph.html Normal file
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.bokeh.org/bokeh/release/bokeh-2.3.1.min.js" integrity="sha384-YF85VygJKMVnHE+lLv2AM93Vbstr0yo2TbIu5v8se5Rq3UQAUmcuh4aaJwNlpKwa" crossorigin="anonymous"></script>
<script type="text/javascript">
Bokeh.set_log_level("info");
</script>
</head>
<body>
<div class="bk-root" id="eb4b3ef5-8cf1-4f01-b94d-8609433fea5d" data-root-id="1002"></div>
<script type="application/json" id="1263">
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</script>
<script type="text/javascript">
(function() {
var fn = function() {
Bokeh.safely(function() {
(function(root) {
function embed_document(root) {
var docs_json = document.getElementById('1263').textContent;
var render_items = [{"docid":"e3880aee-fea0-4f54-b28d-0163e9e0386b","root_ids":["1002"],"roots":{"1002":"eb4b3ef5-8cf1-4f01-b94d-8609433fea5d"}}];
root.Bokeh.embed.embed_items(docs_json, render_items);
}
if (root.Bokeh !== undefined) {
embed_document(root);
} else {
var attempts = 0;
var timer = setInterval(function(root) {
if (root.Bokeh !== undefined) {
clearInterval(timer);
embed_document(root);
} else {
attempts++;
if (attempts > 100) {
clearInterval(timer);
console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
}
}
}, 10, root)
}
})(window);
});
};
if (document.readyState != "loading") fn();
else document.addEventListener("DOMContentLoaded", fn);
})();
</script>
</body>
</html>

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scripts/WebCam Motion Detector/MotionDetector.py Normal file
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import cv2,time,pandas
from datetime import datetime
first_frame = None
status_list = [None, None]
time = []
df = pandas.DataFrame(columns=["Start", "End"])
video = cv2.VideoCapture(0)
while True:
check, frame = video.read()
status = 0
grey = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
grey = cv2.GaussianBlur(grey, (21, 21), 0) #blurring for accuracy
if first_frame is None: #capturing the background or the initial frame
first_frame = grey
continue
delta_frame = cv2.absdiff(first_frame, grey) #to
thresh_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]
#returns a tuple and converts the moving pixels to white
thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)
(cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for cont in cnts:
if cv2.contourArea(cont) < 10000: #excluding negligble objects
continue
status = 1
(x, y, w, h) = cv2.boundingRect(cont)
cv2.rectangle(frame, (x, y), (x+w, y+h), (0,255,0), 3,)
status_list.append(status)
status_list = status_list[-2:]
if status_list[-1] == 1 and status_list[-2] == 0:
time.append(datetime.now())
if status_list[-1] == 0 and status_list[-2] == 1:
time.append(datetime.now())
cv2.imshow("captured", grey)
cv2.imshow("delta", delta_frame)
cv2.imshow("Threshold", thresh_frame)
cv2.imshow("color frame", frame)
key = cv2.waitKey(1)
if key == ord('q'):
if status == 1:
time.append(datetime.now())
break
print(status)
print(status_list)
print(time)
for i in range(0, len(time), 2):
df = df.append({"Start" : time[i], "End": time[i+1]}, ignore_index=True)
df.to_csv("Times.csv")
video.release()
cv2.destroyAllWindows()