import cv2
import time
import numpy
from multiprocessing import Process
from multiprocessing import Queue
from picamera.array import PiRGBArray
from picamera import PiCamera
from sys import getsizeof
#hacked from:
#https://software.intel.com/articles/OpenVINO-Install-RaspberryPI
#https://opencv2-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html
#https://github.com/PINTO0309/MobileNet-SSD-RealSense/blob/master/SingleStickSSDwithUSBCamera_OpenVINO_NCS2.py
#https://raspberrypi.stackexchange.com/questions/87062/overhead-counter
#Les Wright Dec 24 2018 (modified to support picam 30 Dec 2018)
#refined to warp speed (30 fps video, 28 fps inferencing 20 Feb 2019)
#Note cv2.dnn.blobFromImage, the size is present in the XML files, we could write a preamble to go get that data,
#Then we dont have to explicitly set it!
# Load the model
net = cv2.dnn.readNet('models/MobileNetSSD_deploy.xml', 'models/MobileNetSSD_deploy.bin')
# Specify target device
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
#Misc vars
font = cv2.FONT_HERSHEY_SIMPLEX
frameWidth = 1024
frameHeight = 768
queuepulls = 0.0
detections = 0
fps = 0.0
qfps = 0.0
confThreshold = 0.4
#initialize the camera and grab a reference to the raw camera capture
#well this is interesting, we can closely match the input of the network!
#this 'seems' to have improved accuracy!
camera = PiCamera()
camera.resolution = (1024,768)
camera.framerate = 32
camera.hflip = True
# camera.vflip = True
camera.annotate_text = " Raspberry with OpenVINO and Movidius "
rawCapture = PiRGBArray(camera, size=(1024, 768))
# allow the camera to warmup
time.sleep(0.1)
labels_file = 'models/labels.txt'
with open(labels_file, 'r') as f:
labels = [x.strip() for x in f]
print(labels)
#define the function that handles our processing thread
def classify_frame(net, inputQueue, outputQueue):
# keep looping
while True:
# check to see if there is a frame in our input queue
if not inputQueue.empty():
# grab the frame from the input queue, resize it, and
# construct a blob from it
frame = inputQueue.get()
#resframe = cv2.resize(frame, (300, 300))
blob = cv2.dnn.blobFromImage(frame, 0.007843, size=(1024, 768),mean=(127.5,127.5,127.5), swapRB=False, crop=False)
#blob = cv2.dnn.blobFromImage(frame, 0.007843, (1024, 768), 127.5)
net.setInput(blob)
out = net.forward()
data_out = []
for detection in out.reshape(-1, 7):
inference = []
obj_type = int(detection[1]-1)
confidence = float(detection[2])
xmin = int(detection[3] * frame.shape[1])
ymin = int(detection[4] * frame.shape[0])
xmax = int(detection[5] * frame.shape[1])
ymax = int(detection[6] * frame.shape[0])
if confidence > 0: #ignore garbage
inference.extend((obj_type,confidence,xmin,ymin,xmax,ymax))
data_out.append(inference)
outputQueue.put(data_out)
# initialize the input queue (frames), output queue (out),
# and the list of actual detections returned by the child process
inputQueue = Queue(maxsize=1)
outputQueue = Queue(maxsize=1)
out = None
# construct a child process *indepedent* from our main process of
# execution
print("[INFO] starting process...")
p = Process(target=classify_frame, args=(net,inputQueue,outputQueue,))
p.daemon = True
p.start()
print("[INFO] starting capture...")
#time the frame rate....
timer1 = time.time()
frames = 0
queuepulls = 0
timer2 = 0
t2secs = 0
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
if queuepulls ==1:
timer2 = time.time()
# Capture frame-by-frame
frame = frame.array
# if the input queue *is* empty, give the current frame to
# classify
if inputQueue.empty():
inputQueue.put(frame)
# if the output queue *is not* empty, grab the detections
if not outputQueue.empty():
out = outputQueue.get()
queuepulls += 1
# print(len(out))
# print(getsizeof(out))
# check to see if 'out' is not empty
if out is not None:
# loop over the detections
for detection in out:
#print(detection)
#print("
")
objID = detection[0]
confidence = detection[1]
xmin = detection[2]
ymin = detection[3]
xmax = detection[4]
ymax = detection[5]
if confidence > confThreshold:
#bounding box
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))
#label
cv2.rectangle(frame, (xmin-1, ymin-1),(xmin+70, ymin-10), (0,255,0), -1)
#labeltext
cv2.putText(frame,' '+labels[objID]+' '+str(round(confidence,2)),(xmin,ymin-2), font, 0.3,(0,0,0),1,cv2.LINE_AA)
detections +=1 #positive detections
# Display the resulting frame
cv2.rectangle(frame, (0, 0),(90, 15), (0,0,0), -1)
cv2.putText(frame,'Threshold: '+str(round(confThreshold,1)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0, 255, 0), 1, cv2.LINE_AA)
cv2.rectangle(frame, (220, 0),(300, 25), (0,0,0), -1)
cv2.putText(frame,'VID FPS: '+str(fps), (225, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0, 255, 0), 1, cv2.LINE_AA)
cv2.putText(frame,'NCS FPS: '+str(qfps), (225, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0, 255, 0), 1, cv2.LINE_AA)
cv2.rectangle(frame, (0, 265),(170, 300), (0,0,0), -1)
cv2.putText(frame,'Positive detections: '+str(detections), (10, 280), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0, 255, 0), 1, cv2.LINE_AA)
cv2.putText(frame,'Elapsed time: '+str(round(t2secs,2)), (10, 290), cv2.FONT_HERSHEY_SIMPLEX, 0.3,(0, 255, 0), 1, cv2.LINE_AA)
cv2.namedWindow('Raspberry Pi with Movidius',cv2.WINDOW_NORMAL)
cv2.resizeWindow('Raspberry Pi with Movidius',frameWidth,frameHeight)
cv2.imshow('Raspberry Pi with Movidius',frame)
# FPS calculation
frames += 1
if frames >= 1:
end1 = time.time()
t1secs = end1-timer1
fps = round(frames/t1secs,2)
if queuepulls > 1:
end2 = time.time()
t2secs = end2-timer2
qfps = round(queuepulls/t2secs,2)
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
keyPress = cv2.waitKey(1)
if keyPress == ord("q"):
break
if keyPress == 82:
confThreshold += 0.1
if keyPress == 84:
confThreshold -= 0.1
if confThreshold >1:
confThreshold = 1
if confThreshold <0:
confThreshold = 0
cv2.destroyAllWindows()