pi-object.py

from imutils.video import VideoStream
from imutils.video import FPS
from multiprocessing import Process
from multiprocessing import Queue
import numpy as np
import argparse
import imutils
import time
import cv2

def classify_frame(net, inputQueue, outputQueue):
	while True:
		if not inputQueue.empty():
			frame = inputQueue.get()
			frame = cv2.resize(frame, (300, 300))
			blob = cv2.dnn.blobFromImage(frame, 0.007843,
				(300, 300), 127.5)

			net.setInput(blob)
			detections = net.forward()

			outputQueue.put(detections)

ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
	help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
	help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
	help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
	"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

inputQueue = Queue(maxsize=1)
outputQueue = Queue(maxsize=1)
detections = None


print("[INFO] starting process...")
p = Process(target=classify_frame, args=(net, inputQueue,
	outputQueue,))
p.daemon = True
p.start()

print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
fps = FPS().start()

while True:
	frame = vs.read()
	frame = imutils.resize(frame, width=400)
	(fH, fW) = frame.shape[:2]

	if inputQueue.empty():
		inputQueue.put(frame)
	if not outputQueue.empty():
		detections = outputQueue.get()

	if detections is not None:
		for i in np.arange(0, detections.shape[2]):
			confidence = detections[0, 0, i, 2]

			if confidence < args["confidence"]:
				continue

			idx = int(detections[0, 0, i, 1])
			dims = np.array([fW, fH, fW, fH])
			box = detections[0, 0, i, 3:7] * dims
			(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),
				cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

	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()