I’m going to take the work i did from my first post, setting up a Raspberry pi, and the second post, image detection in AWS Rekognition, and put it all together.
First steps is to move the code from the ImageDectect.py in to Detector class. I ended up breaking this in to 3 classes, and set up in to the init function which looks something like this.
def __init__(self):
#4 = the pin on the Rasberry pi that the MotionSensor is connected to
self.pir = MotionSensor(4, threshold=0.5)
self.camera = PiCamera()
self.source_photo = 'test.jpg'
with open('new_user_credentials.csv', 'r') as input:
csvreader = csv.DictReader(input)
for row in csvreader:
self.access_key_id = row['Access key ID']
self.secret_key = row['Secret access key']
def aws_rekognition_image(self, photo):
client = boto3.client('rekognition',
aws_access_key_id=self.access_key_id,
aws_secret_access_key=self.secret_key,
region_name='us-west-2')
return client.detect_labels(Image={'Bytes': photo})
def covert_img_to_bytes(self):
with open(self.source_photo, 'rb') as photo:
return photo.read()
def print_results(self, results):
for each in results['Labels']:
print(each['Name'] + ": " + str(each['Confidence']))
Next we need to add function for to take a picture instead of a video. This is just a simple function that starts the camera, stores the image, and closed the camera
def take_picture(self):
self.camera.resolution = (1920, 1080)
self.camera.rotation = 180
self.camera.start_preview()
sleep(2)
self.camera.capture(self.source_photo)
self.camera.stop_preview()
Finally we need to update start function to do all the steps.
- Check for motion
- Take a picture
- Wait for motion to send
- Covert the image to bytes
- Sent the bytes to AWS
- print the results
The start code is pretty simple as it call all the functions in order (currently not loop the program ends after it runs
def start(self):
self.wait_for_motion()
self.take_picture()
self.wait_for_no_motion()
photo = self.covert_img_to_bytes()
results = self.aws_rekognition_image(photo)
self.print_results(results)
The end results are the following. It took the following picture of my living room and it was able to see with the precent confidence in each results. I was hoping it would see the water bottle i put directly in the middle of the shot, but it did not. But the rest was pretty accurate.
>>> %Run detector.py
Motion detect!
No Motion
Furniture: 99.96051025390625
Couch: 99.90579986572266
Chair: 99.3740234375
Living Room: 98.54119873046875
Room: 98.54119873046875
Indoors: 98.54119873046875
Interior Design: 97.71133422851562
Shelf: 96.75226593017578
Cushion: 93.28191375732422
Screen: 90.99018859863281
Electronics: 90.99018859863281
Monitor: 90.56035614013672
Display: 90.56035614013672
Bookcase: 78.97013092041016
LCD Screen: 70.0054702758789
Flooring: 65.52447509765625
Entertainment Center: 64.22875213623047
TV: 63.16238021850586
Television: 63.16238021850586
Wood: 59.266143798828125
You can view the entire source code for the above here https://github.com/carchi8py/Raspberry-pi-detector/tree/0.3
For part 4 i’m going to see if we can get the bounding box for each item AWS return to appear on the image.
carchi8py 