heimbsle69869/smarthome-presence-detect
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Fix video, rename image, add images to gitignore

Browse Source
This commit is contained in:
lheimbs 2019-08-01 10:00:51 +02:00
parent 57f8e55d2a
commit a89e27abfc
6 changed files with 50 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.gitignore vendored
View File

@ -1,2 +1,4 @@
*.pyc
camera/venv
camera/images
camera/videos

21
camera/.vscode/launch.json vendored Normal file
View File

@ -0,0 +1,21 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "Python: Current File",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal"
},
{
"name": "Python: Current File with args",
"type": "python",
"request": "launch",
"program": "${file}",
"console": "integratedTerminal",
"args": ["-v", "~/Videos/video.h264"]
}
]
}

3
camera/.vscode/settings.json vendored Normal file
View File

@ -0,0 +1,3 @@
{
"python.pythonPath": "venv/bin/python"
}

BIN
camera/ML-Models/MobileNetSSD_deploy.caffemodel
View File

Binary file not shown.

2
camera/person-detection.py → camera/image_presence.py Normal file → Executable file
View File

@ -1,3 +1,5 @@
#!/usr/bin/env python
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression

33
camera/video_presence.py Normal file → Executable file
View File

@ -1,3 +1,5 @@
#!/usr/bin/env python
from imutils.video import VideoStream
from imutils.video import FPS
import argparse
@ -87,8 +89,12 @@ while True:
# dilate the thresholded image to fill in holes, then find contours on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
thresh = np.uint8(thresh)
cnts, im2 = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
#cnts = cnts if imutils.is_cv2() else im2
#print(len(cnts))
#if len(cnts) > 1:
#cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours identified
contourcount = 0
@ -103,8 +109,8 @@ while True:
(x, y, w, h) = cv2.boundingRect(c)
initBB2 =(x,y,w,h)
prott1 = r'ML-Models\MobileNetSSD_deploy.prototxt'
prott2 = r'ML-Models\MobileNetSSD_deploy.caffemodel'
prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
net = cv2.dnn.readNetFromCaffe(prott1, prott2)
CLASSES = ["person"]
@ -129,13 +135,17 @@ while True:
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
#label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
label = "{}: {:.2f}%".format(CLASSES[0], confidence * 100)
#cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[0], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
#cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[0], 2)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
# Start tracker
@ -183,8 +193,7 @@ while True:
# draw the text and timestamp on the frame
now2 = datetime.now()
time_passed_seconds = str((now2-now).seconds)
cv2.putText(frame, 'Detecting persons',(10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, 'Detecting persons',(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# show the frame and record if the user presses a key
cv2.imshow("Video stream", frame)