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Commit 35534c45 authored by Vikrant Rajan's avatar Vikrant Rajan
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Bokeh Motion Detection

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Face Detection/Motion Detection App/Graph.html 0 → 100644
View file @ 35534c45
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js"></script>
<script type="text/javascript">
Bokeh.set_log_level("info");
</script>
</head>
<body>
<div class="bk-root" id="b03d2ed8-a7a1-499f-aa5f-c22c979ff227" data-root-id="1001"></div>
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<script type="text/javascript">
(function() {
var fn = function() {
Bokeh.safely(function() {
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else document.addEventListener("DOMContentLoaded", fn);
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</script>
</body>
</html>
\ No newline at end of file
Face Detection/Motion Detection App/Graph1.html 0 → 100644
View file @ 35534c45
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Bokeh Plot</title>
<script type="text/javascript" src="https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js"></script>
<script type="text/javascript">
Bokeh.set_log_level("info");
</script>
</head>
<body>
<div class="bk-root" id="c856ed81-fc51-4993-a543-b1953e0f27f1" data-root-id="1002"></div>
<script type="application/json" id="1283">
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<script type="text/javascript">
(function() {
var fn = function() {
Bokeh.safely(function() {
(function(root) {
function embed_document(root) {
var docs_json = document.getElementById('1283').textContent;
var render_items = [{"docid":"1e600da5-6854-4e5d-8c7a-fbc2592df22a","roots":{"1002":"c856ed81-fc51-4993-a543-b1953e0f27f1"}}];
root.Bokeh.embed.embed_items(docs_json, render_items);
}
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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>
\ No newline at end of file
Face Detection/Motion Detection App/Times.csv 0 → 100644
View file @ 35534c45
,Start,End
0,2019-12-27 12:34:18.106721,2019-12-27 12:34:21.697614
Face Detection/Motion Detection App/capture.py 0 → 100644
View file @ 35534c45
"""
MOTION DETECTION APPLICATION
"""
import cv2, time, pandas
from datetime import datetime
first_frame = None
status_list = [None, None]
times = []
df = pandas.DataFrame(columns=["Start", "End"])
video = cv2.VideoCapture(0)
while True:
check, frame = video.read()
# 0 = nothing is moving, 1 = motion detected
status = 0
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if first_frame is None:
first_frame = gray
continue
delta_frame = cv2.absdiff(first_frame, gray)
thresh_frame = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)
(cnts,__) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
# checking the area of the objects in camera
if cv2.contourArea(contour) < 10000:
continue
status = 1
(x, y, w, h) = cv2.boundingRect(contour)
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:
times.append(datetime.now())
if status_list[-1] == 0 and status_list[-2] == 1:
times.append(datetime.now())
cv2.imshow("Gray Frame", gray)
cv2.imshow("Delta Frame",delta_frame)
cv2.imshow("Threshold Frame",thresh_frame)
cv2.imshow("Color Frame", frame)
key = cv2.waitKey(1)
if key == ord('q'):
if status == 1:
times.append(datetime.now())
break
print(status_list)
print(times)
for i in range(0, len(times), 2):
df = df.append({"Start":times[i], "End": times[i+1]}, ignore_index=True)
df.to_csv("Times.csv")
video.release()
cv2.destroyAllWindows
\ No newline at end of file
Face Detection/Motion Detection App/plotting.py 0 → 100644
View file @ 35534c45
from capture import df
from bokeh.plotting import figure, show, output_file
from bokeh.models import HoverTool, ColumnDataSource
df["Start_string"] = df["Start"].dt.strftime("%Y-%m-%d %H:%M:%S")
df["End_string"] = df["End"].dt.strftime("%Y-%m-%d %H:%M:%S")
cds = ColumnDataSource(df)
p = figure(x_axis_type = "datetime", height = 300, width = 1000, title = "Motion Graph")
p.yaxis.minor_tick_line_color = None
p.ygrid[0].ticker.desired_num_ticks = 1
hover = HoverTool(tooltips = [("Start", "@Start_string"),("End", "@End_string")])
p.add_tools(hover)
q = p.quad(left="Start", right = "End", bottom = 0, top = 1, color = "green", source = cds)
output_file("Graph1.html")
show(p)
\ No newline at end of file
bokeh/.ipynb_checkpoints/Basic Graph-checkpoint.ipynb 0 → 100644
View file @ 35534c45
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
bokeh/Basic Graph.ipynb 0 → 100644
View file @ 35534c45
%% Cell type:code id: tags:
``` python
# Making a basic Bokeh Line Graph
from bokeh.plotting import figure
from bokeh.io import output_file, show
# getting some data
x = [3, 4, 5]
y = [6, 2, 7]
# creating an output file
output_file("datavisualization.html")
# creating a figure object
f = figure()
# creating a line plot with ^ data above ^
f.line(x, y)
show(f)
```
%% Cell type:code id: tags:
``` python
# Making a basic Bokeh Line Graph
from bokeh.plotting import figure
from bokeh.io import output_file, show
import pandas
# getting some data
df = pandas.read_csv("data.csv")
x = df["x"]
y = df["y"]