47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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#! /usr/bin/python3
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from picamera.array import PiRGBArray
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from picamera import PiCamera
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import time
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import cv2
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import imutils
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import numpy as np
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import requests
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protopath = "MobileNetSSD_deploy.prototxt"
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modelpath = "MobileNetSSD_deploy.caffemodel"
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detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
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person_counter = 0
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CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
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"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
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"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
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"sofa", "train", "tvmonitor"]
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# initialize the camera and grab a reference to the raw camera capture
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camera = PiCamera()
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rawCapture = PiRGBArray(camera)
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# allow the camera to warmup
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time.sleep(0.1)
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# grab an image from the camera
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while True:
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camera.capture(rawCapture, format="bgr")
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image = rawCapture.array
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image = imutils.resize(image, width=1024, height=1024)
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(H, W) = image.shape[:2]
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blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)
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detector.setInput(blob)
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person_detections = detector.forward()
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for i in np.arange(0, person_detections.shape[2]):
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confidence = person_detections[0, 0, i, 2]
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if confidence > 0.2:
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idx = int(person_detections[0, 0, i, 1])
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if CLASSES[idx] == "person":
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person_counter += 1
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r = requests.post("http://127.0.0.1:8000/update_sensor", json={"name": "pocet ludi", "value": str(person_counter)})
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time.sleep(60)
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