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I'm trying to run a Python program in Terminal, but I face the following error
> pi#raspberrypi:~ $ python3 /home/pi/Cascades2/03_face_recognition.py
Traceback (most recent call last):
File "/home/pi/Cascades2/03_face_recognition.py", line 15, in <module>
recognizer.read('trainer/trainer.yml')
cv2.error: OpenCV(4.1.0) /home/pi/opencv-python/opencv_contrib/modules/face/src/facerec.cpp:61: error: (-2:Unspecified error) File can't be opened for reading! in function 'read'
The Python program works well when I run it with Thonny.
What's the problem?
Here's the program (03_face_recognition.py) code:
import cv2
import numpy as np
import os
import time
import datetime
import RPi.GPIO as GPIO
relay = 23
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
GPIO.setup(relay, GPIO.OUT)
GPIO.output(relay, 0)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
font = cv2.FONT_HERSHEY_SIMPLEX
#initiate id counter
id = 0
# names related to ids: example ==> Jenifer: id=1, etc
names = ['None', 'Jenifer', 'Luciola']
# Initialize and start realtime video capture
cam = cv2.VideoCapture('ipcamera')
frame_rate = 1
prev = 0
# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
while True:
time_elapsed = time.time() - prev
res, image = cam.read()
if time_elapsed > 1./frame_rate:
prev = time.time()
ret, img =cam.read()
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.2,
minNeighbors = 5,
minSize = (int(minW), int(minH)),
)
for(x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
# Check if confidence is less than 100 ==> "0" is perfect match
if (confidence < 80):
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
GPIO.output(relay, 1)
print("Ouverture du portail", datetime.datetime.now(), id, confidence)
time.sleep(1)
GPIO.output(relay, 0)
cam.release()
time.sleep(30)
cam = cv2.VideoCapture('ipcamera')
else:
GPIO.output(relay, 0)
cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
cv2.imshow('camera',img)
k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()
Thank you in advance for your help!
It’s likely to do with what the current working directory is.
In your error you are in your home directory. If you run the program with ‘thonny’ I imagine that ‘thonny’ will set the script’s folder.
When you specify the file to open you are using a relative path. Maybe you could use the absolute path instead.
It is an easy fix, I had to replace trainer/trainer.yml by /home/pi/Cascades2/trainer/trainer.yml, the absolute path.
I tried to use OpenCV with face_recognition to get real-time face recognition, but it end up being slow and laggy. Any help please?
Code:
import face_recognition
import os
import cv2
from PIL import Image
appdata = os.getenv('APPDATA') + "/Project/"
face_cascade = cv2.CascadeClassifier((appdata + "data/face_cascade.xml").replace("\\", "/"))
cap = cv2.VideoCapture(0) #Capture video (OpenCV)
def Main():
while True: #OpenCV start video capture from webcam
ret, frame = cap.read()
gray_scale = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray_scale, 1.5, 5)
for x, y, w, h in faces:
#Start face recognition (face_recognition)
roi_color = frame[y: (y + h) - 15, x: (x + w) - 15] #This cuts the background leaving only the face
cv2.rectangle(frame, (x, y), ((x + w) - 15, (y + h) - 15), (255, 35, 36), 2)
face_encodings = face_recognition.face_encodings(roi_color) #This encodes face features and causes lag
cv2.imshow('sysPy', frame)
if cv2.waitKey(20) == 27:
break
cap.release()
cv2.destroyAllWindows()
Main()
Also I was thinking about "face_encodings = face_recognition.face_encodings(roi_color)" to not encode every frame, skip frames to reduce the lag. Thanks!
I am learning using GOTURN openCV api to track objects. I am following guide from learnopenCV. After initialize tracker and come into the loop, I got the error when update tracker
ok, bbox = tracker.update(frame)
Traceback (most recent call last):
File "<ipython-input-64-e7c5a34c2f7a>", line 1, in <module>
ok, bbox = tracker.update(frame)
error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\dnn\src\layers\convolution_layer.cpp:282: error: (-2:Unspecified error) Number of input channels should be multiple of 3 but got 1 in function 'cv::dnn::ConvolutionLayerImpl::getMemoryShapes'
I am not sure what channel means. I got the frame from video file and the shape is (row, width, 3). I thought channel is 3 but it does not work. I did try with convert frame to gray scale with shape (row, width), but it still not work either.
Below is my code:
import cv2
import sys
background_path = 'images/images_G1_323/background.png'
background_img = cv2.imread(background_path,cv2.IMREAD_GRAYSCALE)
#cv2.imshow('background image',background_img)
tracker = cv2.TrackerGOTURN_create()
video_path = 'videos/G1_323.avi'
cap = cv2.VideoCapture(video_path)
#fgbg = cv2.createBackgroundSubtractorMOG2()
if cap.isOpened() == False:
print('ERROR FILE NOT FOUND OR WRONG CODEC USED!')
sys.exit()
# Read first frame
ok, frame = cap.read()
ok, frame = cap.read()
ok, frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if not ok:
print('Cannot read video file')
sys.exit()
#motion = fgbg.apply(frame)
motion = cv2.absdiff(background_img,frame_gray)
_, thresh1 = cv2.threshold(motion, 10, 255, cv2.THRESH_BINARY)
#gray = cv2.cvtColor(thresh1, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(thresh1, (5, 5), 0)
thresh = cv2.threshold(blurred, 30, 255, cv2.THRESH_BINARY)[1]
erosion_size = 10
dilate_size = 14
thresh = cv2.erode(thresh, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (erosion_size, erosion_size)))
thresh = cv2.dilate(thresh, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilate_size, dilate_size)))
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = contours[0]
(x, y, w, h) = cv2.boundingRect(c)
bbox = (x, y, w, h)
# Initialize tracker with first frame and bounding box
ok = tracker.init(frame,bbox)
while (cap.isOpened):
#
#if ret is true than no error with cap.isOpened
ok, frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if ok==True:
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(frame_gray, p1, p2, (255,0,0), 2, 1)
else :
# Tracking failure
cv2.putText(frame_gray, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
# Display tracker type on frame
cv2.putText(frame_gray, "GOTURN Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
# Display FPS on frame
cv2.putText(frame_gray, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
# Display result
cv2.imshow("Tracking", frame_gray)
# Exit if ESC pressed
if cv2.waitKey(100) & 0xFF == ord("q"):
break
else:
break
cap.release()
cv2.destroyAllWindows()
I'm working on the same kind of things, so I hope maybe some of the things I encountered can help you (otherwise I'm sorry for giving a shit answer). I think you should not put it in gray, as it's asking for 3 channels (RGB instead of gray/BW). When you keep the "convert2gray" out, still you're putting: "thresh = cv2.threshold(blurred, 30, 255, cv2.THRESH_BINARY)[1]", which will give you also only 1 channel. Delete the "[1]" at the end and the conversion to gray and maybe it will then work?
I've been following an opencv tutorial from Sir Adrian Rosebrock for a home surveillance system. This is working. I also have an analog sensor that is using an analog to digital converter which is ADS1115. This is also working.
the problem is once I insert the ADS library inside the surveillance code I get an error.
Error:
Traceback (most recent call last):
File "ss_security.py", line 17, in <module>
import Adafruit_ADS1x15
ImportError: No module named Adafruit_ADS1x15
This is the code:
# import the necessary packages
from pyimagesearch.tempimage import TempImage
from picamera.array import PiRGBArray
from picamera import PiCamera
from imutils.video import VideoStream
import warnings
import dropbox
import json
import datetime
import argparse
import imutils
import time
import cv2
import math
# Import the ADS1x15 module.
import Adafruit_ADS1x15
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
help="path to the JSON configuration file")
ap.add_argument("-p", "--picamera", type=int, default=-1,
help="whether or not the Raspberry Pi camera should be used")
args = vars(ap.parse_args())
# filter warnings, load the configuration and initialize the Dropbox
# client
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None
# check to see if the Dropbox should be used
if conf["use_dropbox"]:
# connect to dropbox and start the session authorization process
client = dropbox.Dropbox(conf["dropbox_access_token"])
print("[SUCCESS] dropbox account linked")
# initialize the video stream and allow the cammera sensor to warmup
vs = VideoStream(usePiCamera=args["picamera"] > 0).start()
vs.resolution = tuple(conf["resolution"])
vs.framerate = conf["fps"]
rawCapture = PiRGBArray(vs, size=tuple(conf["resolution"]))
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=500)
text = "Unoccupied"
# draw the timestamp on the frame
timestamp = datetime.datetime.now()
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# convert it to grayscale, and blur it
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the average frame is None, initialize it
if avg is None:
print("[INFO] starting background model...")
avg = gray.copy().astype("float")
rawCapture.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# threshold the delta image, dilate the thresholded image to fill
# in holes, then find contours on thresholded image
thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
cv2.THRESH_BINARY)[1]
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]
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
# draw the text and timestamp on the frame
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# display temp
cv2.putText(frame, "Temp: 30 C".format(text), (250, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# check to see if the room is occupied
if text == "Occupied":
# check to see if enough time has passed between uploads
if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
# increment the motion counter
motionCounter += 1
# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= conf["min_motion_frames"]:
# check to see if dropbox sohuld be used
if conf["use_dropbox"]:
# write the image to temporary file
t = TempImage()
cv2.imwrite(t.path, frame)
# upload the image to Dropbox and cleanup the tempory image
print("[UPLOAD] {}".format(ts))
path = "/{base_path}/{timestamp}.jpg".format(
base_path=conf["dropbox_base_path"], timestamp=ts)
client.files_upload(open(t.path, "rb").read(), path)
t.cleanup()
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
# otherwise, the room is not occupied
else:
motionCounter = 0
# check to see if the frames should be displayed to screen
if conf["show_video"]:
# display the security feed
cv2.imshow("Security Feed", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the lop
if key == ord("q"):
break
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
I have just inserted the library. I w\haven't done any computations/readings. As you can see I placed a default temp value.
The library is located inside pi/Adafruit_Python_ADS1x15
Steps taken:
Installed the ADS library inside the cv environment (Error: destination path already exist)
Tried from Adafruit_Python_ADS1x15 import Adafruit_ADS1x15 (Error: No module named Adafruit_Python_ADS1x15)
My goal of this script is to start recording to a video file when motion is detected. When motion is no longer detected, the writer will be released and the script will create a new video the next time that motion is detected...and so on.
With the below script, I'm able to start writing to a video file once motion is detected, but I have to press the q button to release the writer and make the video playable. If I don't press q, the recording will stop, but the next time there's motion it will just be added to the existing video. I've tried writer.release() in a few places without success.
# import the necessary packages
from pyimagesearch.tempimage import TempImage
import argparse
import warnings
import datetime
import imutils
import json
import numpy as np
import time
import cv2
print("[INFO] Kicking off script - " +
datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S"))
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
help="path to the JSON configuration file")
args = vars(ap.parse_args())
# filter warnings, load the configuration and initialize the Dropbox
# client
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None
# initialize the camera and grab a reference to the raw camera capture
# if the video argument is None, then we are reading from webcam
if not conf["use_ip_cam"]:
camera = cv2.VideoCapture(0)
time.sleep(0.25)
# otherwise, we are reading from a video input
else:
camera = cv2.VideoCapture(conf["ip_cam_addr"])
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
fourcc = 0x00000020 # a little hacky, but works for now
writer = None
(h, w) = (None, None)
zeros = None
output = None
# capture frames from the camera
# for f in camera.capture_continuous(rawCapture, format="bgr",
# use_video_port=True):
while True:
# grab the raw NumPy array representing the image and initialize
# the timestamp and occupied/unoccupied text
(grabbed, frame) = camera.read()
# frame = f.array
timestamp = datetime.datetime.now()
motion_detected = False
# if the frame could not be grabbed, then we have reached the end
# of the video
if not grabbed:
break
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the average frame is None, initialize it
if avg is None:
print("[INFO] starting background model...")
avg = gray.copy().astype("float")
# frame.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# threshold the delta image, dilate the thresholded image to fill
# in holes, then find contours on thresholded image
thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
(_, cnts, _) = cv2.findContours(thresh.copy(),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w1, h1) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w1, y + h1), (0, 255, 0), 2)
motion_detected = True
fps = camera.get(cv2.CAP_PROP_FPS)
ts = timestamp.strftime("%Y-%m-%d_%H_%M_%S")
time_and_fps = ts + " - fps: " + str(fps)
# draw the text and timestamp on the frame
cv2.putText(frame, "Motion Detected: {}".format(motion_detected), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, time_and_fps, (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# check to see if the room is occupied
if motion_detected:
motionCounter += 1
# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= conf["min_motion_frames"]:
# check if the writer is None
if writer is None:
print("hitting writer is none")
# store the image dimensions, initialzie the video
# writer, and construct the zeros array
(h2, w2) = frame.shape[:2]
writer = cv2.VideoWriter("/Users/user/Library/Mobile Documents/com~apple~CloudDocs/testMotionDetection/" +
datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S") + ".mp4",
fourcc, fps,
(w2, h2), True)
zeros = np.zeros((h2, w2), dtype="uint8")
# construct the final output frame, storing the
# original frame
output = np.zeros((h2, w2, 3), dtype="uint8")
output[0:h2, 0:w2] = frame
# write the output frame to file
writer.write(output)
# otherwise, there is no motion
else:
writer.release()
# Traceback (most recent call last):
# File "pi_surveillance.py", line 178, in <module>
# writer.release()
# AttributeError: 'NoneType' object has no attribute 'release'
motionCounter = 0
# check to see if the frames should be displayed to screen
if conf["show_video"]:
# display the security feed
cv2.imshow("Security Feed", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
print("[INFO] cleaning up...")
camera.release()
cv2.destroyAllWindows()
# writer.release() - only releases writer when q is pressed
I was able to get this working correctly. This script creates a VideoWriter object for every frame with a temporary file. If motion is no detected within that frame, the writer is released and the file that was created with VideoWriter gets deleted.
If motion is detected, the file is kept and written to. Once motion is no longer detected, a countdown timer is started so it continues recording as long as you set. If motion is detected before the timer hits 0, then it continues to record and resets the timer and so on.
This is still being tuned, but works pretty well.
#!/usr/local/bin/python3
import argparse
import warnings
import datetime
import imutils
import json
import numpy as np
import os
import time
import cv2
print("[INFO] Kicking off script - " +
datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S"))
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True,
help="path to the JSON configuration file")
args = vars(ap.parse_args())
# filter warnings, load the configuration
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
# initialize the camera and grab a reference to the raw camera capture
# if the video argument is None, then we are reading from webcam
if not conf["use_ip_cam"]:
camera = cv2.VideoCapture(0)
time.sleep(0.25)
# otherwise, we are reading from a video input
else:
camera = cv2.VideoCapture(conf["ip_cam_addr"])
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motion_counter = 0
non_motion_timer = conf["nonMotionTimer"]
fourcc = 0x00000020 # a little hacky, but works for now
writer = None
(h, w) = (None, None)
zeros = None
output = None
made_recording = False
# capture frames from the camera
while True:
# grab the raw NumPy array representing the image and initialize
# the timestamp and occupied/unoccupied text
(grabbed, frame) = camera.read()
timestamp = datetime.datetime.now()
motion_detected = False
# if the frame could not be grabbed, then we have reached the end
# of the video
if not grabbed:
print("[INFO] Frame couldn't be grabbed. Breaking - " +
datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S"))
break
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=conf["resizeWidth"])
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the average frame is None, initialize it
if avg is None:
print("[INFO] starting background model...")
avg = gray.copy().astype("float")
# frame.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# threshold the delta image, dilate the thresholded image to fill
# in holes, then find contours on thresholded image
thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
(_, cnts, _) = cv2.findContours(thresh.copy(),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w1, h1) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w1, y + h1), (0, 255, 0), 2)
motion_detected = True
fps = int(round(camera.get(cv2.CAP_PROP_FPS)))
record_fps = 10
ts = timestamp.strftime("%Y-%m-%d_%H_%M_%S")
time_and_fps = ts + " - fps: " + str(fps)
# draw the text and timestamp on the frame
cv2.putText(frame, "Motion Detected: {}".format(motion_detected), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, time_and_fps, (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
# Check if writer is None TODO: make path configurable
if writer is None:
filename = datetime.datetime.now().strftime("%Y-%m-%d_%H_%M_%S")
file_path = (conf["userDir"] + "/Library/Mobile Documents/"
"com~apple~CloudDocs/testMotionDetection/testing/"
"{filename}.mp4")
file_path = file_path.format(filename=filename)
(h2, w2) = frame.shape[:2]
writer = cv2.VideoWriter(file_path, fourcc, record_fps, (w2, h2), True)
zeros = np.zeros((h2, w2), dtype="uint8")
def record_video():
# construct the final output frame, storing the original frame
output = np.zeros((h2, w2, 3), dtype="uint8")
output[0:h2, 0:w2] = frame
# write the output frame to file
writer.write(output)
# print("[DEBUG] Recording....")
if motion_detected:
# increment the motion counter
motion_counter += 1
# check to see if the number of frames with motion is high enough
if motion_counter >= conf["min_motion_frames"]:
if conf["create_image"]:
# create image TODO: make path configurable
image_path = (conf["userDir"] + "/Library/Mobile Documents/"
"com~apple~CloudDocs/testMotionDetection/testing"
"/{filename}.jpg").format(filename=filename)
cv2.imwrite(image_path, frame)
record_video()
made_recording = True
non_motion_timer = conf["nonMotionTimer"]
# If there is no motion, continue recording until timer reaches 0
# Else clean everything up
else: # TODO: implement a max recording time
# print("[DEBUG] no motion")
if made_recording is True and non_motion_timer > 0:
non_motion_timer -= 1
# print("[DEBUG] first else and timer: " + str(non_motion_timer))
record_video()
else:
# print("[DEBUG] hit else")
motion_counter = 0
if writer is not None:
# print("[DEBUG] hit if 1")
writer.release()
writer = None
if made_recording is False:
# print("[DEBUG] hit if 2")
os.remove(file_path)
made_recording = False
non_motion_timer = conf["nonMotionTimer"]
# check to see if the frames should be displayed to screen
if conf["show_video"]:
cv2.imshow("Security Feed", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the loop
if key == ord("q"):
break
# cleanup the camera and close any open windows
print("[INFO] cleaning up...")
camera.release()
cv2.destroyAllWindows()
Example config file:
{
"show_video": true,
"use_ip_cam": false,
"ip_cam_addr": "rtsp://<ip>/live0.264",
"create_image": true,
"min_upload_seconds": 5,
"min_motion_frames": 12,
"camera_warmup_time": 2.5,
"delta_thresh": 5,
"resolution": [640, 480],
"fps": 16,
"min_area": 500,
"userDir": "/Path/to/user",
"resizeWidth": 500,
"nonMotionTimer": 36
}