def capture_image():
ret, image = cap.read()
print("capturing video")
currentTime = datetime.datetime.fromtimestamp(time.time()).strftime('%d-%m-%Y %H:%M:%S')
print(currentTime)
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(grayImage, 1.1, 5 )
print(faces)
#print(faces.shape)
for (x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),1)
cv2.imshow('Image with faces',image)
face_detect = len(faces)
if face_detect >= 1:
maskStatus = 'no'
print("lenfaces is more than 1")
elif face_detect == 0:
print("lenfaces is 0")
maskStatus = 'yes'
print("frame number is: " + str(frame))
fileLoc = "/home/pi/Images/image{}.jpg".format(frame)
#print(fileLoc)
img_flip = cv2.flip(image, 1) # flip image horizontally on y axis before saving
cv2.imwrite(fileLoc, img_flip)
storeFileFB.store_file(fileLoc)
storeFileFB.push_db(fileLoc, currentTime, face_detect, maskStatus)
cv2.destroyAllWindows()
print("Number of faces detected: " + str(face_detect))
return face_detect
def capture_image():
image = cap.read()
# ret, image = cap.read()
print("capturing video")
currentTime = datetime.datetime.fromtimestamp(
time.time()).strftime('%d-%m-%Y %H:%M:%S')
print(currentTime)
grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert to grayscale
faces = face_cascade.detectMultiScale(grayImage, 1.1, 6) # detect faces
maskStatus = 'No' # initialize mask status to no mask
if (len(faces)
) == 0: # if length of faces numpy array is 0 no faces detected
maskStatus = 'No' # Pir activated, No face detected
print("No faces detected")
print("Mask Status: " + str(maskStatus))
else:
for (x, y, w, h) in faces:
image = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0),
1)
roi_grayImage = grayImage[y:y + h, x:x + w]
roi_color = image[y:y + h, x:x + w]
smile = smile_cascade.detectMultiScale(roi_grayImage, 1.7, 20)
# print("print smile co-ords ")
# print(smile.shape)
if (len(smile)) == 0: # face detected but no smile detected
maskStatus = 'Yes' # Pir activated, face detected, no smile
print("face detected but no smile")
print("Mask Status: " + str(maskStatus))
else:
for (sx, sy, sw, sh) in smile:
cv2.rectangle(roi_color, (sx, sy), (sx + sw, sy + sh),
(255, 0, 0), 1)
print("face and smile detected")
maskStatus = 'No' # Pir activated, face & smile detected
print("Mask Status: " + str(maskStatus))
cv2.imshow('Image with faces', image)
face_detect = len(faces)
print("frame number is: " + str(frame))
fileLoc = "/home/pi/Images/image{}.jpg".format(frame)
#print(fileLoc)
img_flip = cv2.flip(image,
1) # flip image horizontally on y axis before saving
cv2.imwrite(fileLoc, img_flip)
storeFileFB.store_file(fileLoc)
storeFileFB.push_db(fileLoc, currentTime, face_detect, maskStatus)
cv2.destroyAllWindows()
print("Number of faces detected: " + str(face_detect))
print("Mask Status being returned: " + str(maskStatus))
# return face_detect
return str(maskStatus)
def SNDEventHandler(sensor): #creates the SNDEventHandler method which takes in the value of sensor
print ("Sound Detected!") #prints message to the console
SNDdetected = 1 #sets SNDdetected value to 1
buzzer.play(Tone(note='A5')) #sends message to the passive buzzer to play note A5
if (SNDdetected == 1): #if a sound is detected
camera.capture(fileLoc) #camera takes a photo and saves the image to the fileLoc folder defined above
storeFileFB.store_file(fileLoc) #triggers store_file method from storeFileFB script using value of fileLoc which stores the captured image in firebase storage
storeFileFB.push_db(fileLoc, currentTime) #triggers push_db method from storeFileFB script using value of fileLoc and current time which pushes the captured image to the firebase realtime database
time.sleep(5) #sleep for 5 seconds while buzzer is ringing away
buzzer.stop() #stops the buzzer
conn = urlopen(baseURL + '&field1=%s' % (SNDdetected)) #triggers a URL using the Thingspeak URL above which reads in the new value of SNDdetected triggering the ThingTweet to be posted.
print(conn.read()) #information is printed to the URL thus triggering the ThingTweet
conn.close() #temp connection to trigger URL is then closed
def write_gp_pin_handler(pin, value):
frame = 1
if value[0]=="1":
print("Camera on")
print (value)
currentTime = datetime.datetime.now().strftime("%H:%M:%S")
camera.rotation = 90
camera.start_preview()
sleep(3)
fileLoc = f'/home/pi/labs/week9/blynk/img/frame{frame}.jpg'
camera.capture(fileLoc)
camera.stop_preview()
print(f'frame taken at {currentTime}') # print frame number to console
storeFileFB.store_file(fileLoc)
storeFileFB.push_db(fileLoc, currentTime)
frame+=1
def collision(stdDevX, stdDevY, stdDevZ, maxX, maxY, maxZ, x, y, z):
if (x > (maxX + (stdDevX * 2))) or (y > (maxY + (stdDevY * 2))) or (z > (maxZ + (stdDevZ *2))):
sense.clear(255, 0, 0)
time.sleep(2)
print("Collision Detected")
camera.stop_recording()
convertFiles.updateVideos()
botbox2.postTelegram()
for file in glob.glob('*.mp4'):
timeStamp = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
print("copying: ", file)
shutil.copy(file, './backup')
storeFileFB.push_db('./backup', timeStamp)
storeFileFB.store_file(file)
sense.clear()
sys.exit("Closing blackbox...")
else:
print("No accident detected")
return True
from picamera import PiCamera
import datetime
import storeFileFB
camera = PiCamera()
camera.start_preview()
frame = 1
fileLoc = f'/home/pi/week10/img/frame{frame}.jpg' # set location of image file and current time
currentTime = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
camera.capture(fileLoc) # capture image and store in fileLoc
print(f'frame {frame} taken at {currentTime}') # print frame number to console
storeFileFB.store_file(fileLoc)
storeFileFB.push_db(fileLoc, currentTime)
frame += 1
def pet_detector(frame):
# Use globals for the control variables so they retain their value after function exits
global detected_inside, detected_outside
global inside_counter, outside_counter
global pause, pause_counter
frame_expanded = np.expand_dims(frame, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.40)
# Draw couch box
cv2.rectangle(frame,TL_inside,BR_inside,(20,20,255),3)
cv2.putText(frame,"Couch box",(TL_inside[0]+10,TL_inside[1]-10),font,1,(20,255,255),3,cv2.LINE_AA)
# Check the class of the top detected object by looking at classes[0][0].
# If the top detected object is a dog (18),
# find its center coordinates by looking at the boxes[0][0] variable.
# boxes[0][0] variable holds coordinates of detected objects as (ymin, xmin, ymax, xmax)
if (((int(classes[0][0]) == 17) or (int(classes[0][0] == 18) or (int(classes[0][0]) == 88))) and (pause == 0)):
x = int(((boxes[0][0][1]+boxes[0][0][3])/2)*IM_WIDTH)
y = int(((boxes[0][0][0]+boxes[0][0][2])/2)*IM_HEIGHT)
# Draw a circle at center of object
cv2.circle(frame,(x,y), 5, (75,13,180), -1)
# If object is in inside box, increment inside counter variable
if ((x > TL_inside[0]) and (x < BR_inside[0]) and (y > TL_inside[1]) and (y < BR_inside[1])):
inside_counter = inside_counter + 1
# If dog has been detected inside for more than 1 frames, set detected_inside flag
# and send a text to the phone.
if inside_counter >= 1:
detected_inside = True
#camera.start_preview()
pic = 1
fileLoc = f'/home/pi/frame{pic}.jpg' # set location of image file and current time
currentTime = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
camera.capture(fileLoc) # capture image and store in fileLoc
print(f'frame {pic} taken at {currentTime}') # print frame number to console
storeFileFB.store_file(fileLoc)
storeFileFB.push_db(fileLoc, currentTime)
pic += 1
message = client.messages.create(
body = 'Fugitive sighted... http://www.down-boy.glitch.me/',
from_=twilio_number,
to=my_number
)
inside_counter = 0
outside_counter = 0
# Pause pet detection by setting "pause" flag
pause = 1
# If pause flag is set, draw message on screen.
if pause == 1:
if detected_inside == True:
cv2.putText(frame,'He is on the couch again!',(int(IM_WIDTH*.1),int(IM_HEIGHT*.5)),font,3,(0,0,0),7,cv2.LINE_AA)
cv2.putText(frame,'He is on the couch again!',(int(IM_WIDTH*.1),int(IM_HEIGHT*.5)),font,3,(95,176,23),5,cv2.LINE_AA)
# Increment pause counter until it reaches 30 (for a framerate of 1.5 FPS, this is about 20 seconds),
# then unpause the application (set pause flag to 0).
pause_counter = pause_counter + 1
if pause_counter > 30:
pause = 0
pause_counter = 0
detected_inside = False
detected_outside = False
# Draw counter info
cv2.putText(frame,'Detection counter: ' + str(max(inside_counter,outside_counter)),(10,100),font,0.5,(255,255,0),1,cv2.LINE_AA)
cv2.putText(frame,'Pause counter: ' + str(pause_counter),(10,150),font,0.5,(255,255,0),1,cv2.LINE_AA)
return frame
q = 0
def writeData(dB):
# Sending the data to thingspeak in the query string
conn = urlopen(baseURL + '&field1=%s' % (dB))
conn.close()
while True:
ret = dev.ctrl_transfer(0xC0,4,0,0,200)
dB = round((ret[0]+((ret[1]&3)*256))*0.1+30,2)
writeData(dB)
currentTime = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
time.sleep(1)
msg="{'dB':'"+str(dB)+"'}"
print (msg)
storeFileFB.push_db(dB, currentTime)
myArray.pop(0)
myArray.append(dB)
print(myArray)
avg = sum(myArray)/len(myArray)
if q > 4:
pythonArray.avgVol(avg)
q = 0
else:
q += 1
print(q)
COLORDEPTH - 1)])
# scale and save
image.resize(
(NX * args.scale, NY * args.scale),
Image.BICUBIC,
).save(args.output)
# This reopens image adds highest temp text and saves the new file
img = Image.open('/home/pi/Assignment2/amg88xx_still.jpg')
draw = ImageDraw.Draw(img)
draw.text((10, 10),
'Highest Temp = {0:0.2f} *C'.format(hot),
font=fnt,
fill=(0, 0, 0))
currentTime = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
# changes the filename after text is added and increments the file name by 1
filename = "images/frame{}.jpg"
counter = 0
while os.path.isfile(filename.format(counter)):
counter += 1
filename = filename.format(counter)
img.save(filename, quality=100, subsampling=0)
# Cretes and sends email
emailServer.text = f'Hi,\n the attached image was taken today at {currentTime} \n Remember In these Uncertain times please proceed with cation. \n Your Visitors temperature is = {hot} *C. \n Our View the image Click https://near-candied-munchkin.glitch.me'
emailServer.send_mail('*****@*****.**', '*****@*****.**',
'Door Event', emailServer.text, filename)
print(f'frame {counter} taken at {currentTime}')
# Pushes file to firebase
storeFileFB.store_file(filename)
storeFileFB.push_db(filename, currentTime)
camera.stop_recording()
print(f'latestToday taken at {currentTime}'
) # print frame number to console
#----------------------------------------------------------------------------
# Convert the video from .h264 to MP4 format with a shell command
#----------------------------------------------------------------------------
command = "MP4Box -add " + videoLoc + " " + videoLocMp4
call([command], shell=True)
print("Video converted")
#----------------------------------------------------------------------------
# Store the MP4 video on firebase storage, and the
# name and time of the file the firebase realtime DB
#----------------------------------------------------------------------------
videoURL = storeFileFB.store_file(videoLocMp4) + URL_token
storeFileFB.push_db(videoLocMp4, currentTime, child.childInRoom,
adult.adultInRoom)
print("files saved on firebase")
#----------------------------------------------------------------------------
# Remove both video formats from the local Pi storage
#----------------------------------------------------------------------------
commandDel = "rm" + " " + videoLocMp4 + " " + videoLoc
call([commandDel], shell=True)
print("Files deleted from local img folder")
time.sleep(30)
time.sleep(10)