"Entschuldigung, ich habe erkannt ..." oder Himbeeren und Controller mithilfe der Tensorflow-Objekterkennungs-API erkannt

# USAGE
# based on this code https://proglib.io/p/real-time-object-detection/
# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import imutils
import time
import cv2

prototxt="graph.pbtxt"
model="frozen_inference_graph.pb"
min_confidence = 0.5

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "duino","raspb"]
COLORS = [(40,50,60),((140,55,130)),(240,150,25)]

# load our serialized model from disk
print("[INFO] loading model...")

net =cv2.dnn.readNetFromTensorflow(model,prototxt)

# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(0.5)
fps = FPS().start()

# 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=300)

	# grab the frame dimensions and convert it to a blob
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, size=(300, 300), swapRB=True)

	# pass the blob through the network and obtain the detections and
	# predictions
	net.setInput(blob)
	detections = net.forward()

	# loop over the detections
	for i in np.arange(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with
		# the prediction
		print (detections)
		confidence = detections[0, 0, i, 2]

		if confidence > min_confidence:
			# extract the index of the class label from the
			# `detections`, then compute the (x, y)-coordinates of
			# the bounding box for the object
			idx = int(detections[0, 0, i, 1])
			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)
			y = startY - 15 if startY - 15 > 15 else startY + 15
			cv2.putText(frame, label, (startX, y+3),
				cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 1)

	# show the output frame
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF

	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break

	# update the FPS counter
	fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()