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
CLASSES = ["background", "duino","raspb"]
COLORS = [(40,50,60),((140,55,130)),(240,150,25)]
print("[INFO] loading model...")
net =cv2.dnn.readNetFromTensorflow(model,prototxt)
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(0.5)
fps = FPS().start()
while True:
frame = vs.read()
frame = imutils.resize(frame, width=300)
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, size=(300, 300), swapRB=True)
net.setInput(blob)
detections = net.forward()
for i in np.arange(0, detections.shape[2]):
print (detections)
confidence = detections[0, 0, i, 2]
if confidence > min_confidence:
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")
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)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
fps.update()
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
vs.stop()