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OpenVINO_Thread.py
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OpenVINO_Thread.py
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#! /usr/bin/python3
import numpy as np
import cv2
import datetime
from PIL import Image
from io import BytesIO
from imutils.video import FPS
## *** OpenVINO NCS/NCS2 (aka MYRIAD) AI Thread ***
#******************************************************************************************************************
#******************************************************************************************************************
def AI_thread(results, inframe, net, tnum, cameraLock, nextCamera, Ncameras,
PREPROCESS_DIMS, confidence, noVerifyNeeded, verifyConf, dbg, dnnTarget, QUITf, blobThreshold, SSDv1):
print("[INFO] OpenVINO " + dnnTarget + " AI thread" + str(tnum) + " is running...")
fcnt=0
waits=0
drops=0
prevDetections=list()
for i in range(Ncameras):
prevDetections.append(0)
if tnum > 0:
dnnTarget = dnnTarget + str(tnum)
cfps = FPS().start()
while not QUITf():
cameraLock.acquire()
cq=nextCamera
nextCamera = (nextCamera+1)%Ncameras
cameraLock.release()
# get a frame
try:
(image, cam) = inframe[cq].get(True,0.100)
except:
image = None
waits+=1
continue
if image is None:
continue
(h, w) = image.shape[:2]
zoom=image.copy() # for zoomed in verification run
if SSDv1:
blob = cv2.dnn.blobFromImage(cv2.resize(image, PREPROCESS_DIMS), 0.007843, PREPROCESS_DIMS, 127.5)
personIdx=15
else:
blob = cv2.dnn.blobFromImage(image, size=PREPROCESS_DIMS)
personIdx=1
# pass the blob through the network and obtain the detections and predictions
net.setInput(blob)
detections = net.forward()
imageDT = datetime.datetime.now()
# loop over the detections, pretty much straight from the PyImageSearch sample code.
personDetected = False
ndetected=0
fcnt+=1
cfps.update() # update the FPS counter
boxPoints=(0,0, 0,0, 0,0, 0,0) # startX, startY, endX, endY, Xcenter, Ycenter, Xlength, Ylength
for i in np.arange(0, detections.shape[2]):
conf = detections[0, 0, i, 2] # extract the confidence (i.e., probability)
idx = int(detections[0, 0, i, 1]) # extract the index of the class label
# filter out weak detections by ensuring the `confidence` is greater than the minimum confidence
if conf > confidence and idx == personIdx and not np.array_equal(prevDetections[cam], detections):
# then compute the (x, y)-coordinates of the bounding box for the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
startX=max(1, startX)
startY=max(1, startY)
endX=min(endX, w-1)
endY=min(endY,h-1)
xlen=endX-startX
ylen=endY-startY
xcen=int((startX+endX)/2)
ycen=int((startY+endY)/2)
boxPoints=(startX,startY, endX,endY, xcen,ycen, xlen,ylen)
# adhoc "fix" for out of focus blobs close to the camera
# out of focus blobs sometimes falsely detect -- insects walking on camera, etc.
# In my real world use I have some static false detections, mostly under IR or mixed lighting -- hanging plants etc.
# I put camera specific adhoc filters here based on (xlen,ylen,xcenter,ycenter)
# TODO: come up with better way to do it, probably return (xlen,ylen,xcenter,ycenter) and filter at saving or Notify step.
if float(xlen*ylen)/(w*h) > blobThreshold: # detection filling too much of the frame is bogus
continue
# display and label the prediction
label = "{:.1f}% C:{},{} W:{} H:{} UL:{},{} LR:{},{} {}".format(conf * 100,
str(xcen), str(ycen), str(xlen), str(ylen), str(startX), str(startY), str(endX), str(endY), dnnTarget)
cv2.putText(image, label, (2, (h-5)-(ndetected*28)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv2.LINE_AA)
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
personDetected = True
initialConf=conf
ndetected+=1
break # one is enough
prevDetections[cam]=detections
if personDetected and initialConf < noVerifyNeeded:
personDetected = False # repeat on zoomed detection box
try:
# expand detection box by 10% for verification
startY=int(0.9*startY)
startX=int(0.9*startX)
endY=min(int(1.1*endY),h-1)
endX=min(int(1.1*endX),w-1)
img = cv2.resize(zoom[startY:endY, startX:endX], PREPROCESS_DIMS, interpolation = cv2.INTER_AREA)
except Exception as e:
print(dnnTarget + " crop region ERROR: ", startY, endY, startX, endX)
continue
(h, w) = img.shape[:2]
if SSDv1:
blob = cv2.dnn.blobFromImage(img, 0.007843, PREPROCESS_DIMS, 127.5)
else:
blob = cv2.dnn.blobFromImage(img, size=PREPROCESS_DIMS)
net.setInput(blob)
detections = net.forward()
imgDT = datetime.datetime.now()
cfps.update() # update the FPS counter
tlabel = "{:.1f}% ".format(initialConf * 100) + dnnTarget
cv2.putText(img, tlabel, (2, 28), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv2.LINE_AA)
boxPointsV = (0,0, 0,0, 0,0, 0,0) # startX, startY, endX, endY, 0, 0, 0, 0 only first four are used for dbg plots
for i in np.arange(0, detections.shape[2]):
conf = detections[0, 0, i, 2]
idx = int(detections[0, 0, i, 1])
if idx == personIdx:
if dbg:
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
boxPointsV = (startX,startY, endX,endY, 0,0, 0,0)
label = "{:.1f}% ".format(conf * 100) + dnnTarget +"V"
cv2.rectangle(img, (startX, startY), (endX, endY), (0, 255, 0), 2)
cv2.putText(img, label, (2, (h-5)), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv2.LINE_AA)
if conf > verifyConf:
text = "Verify: {:.1f}%".format(conf * 100) # show verification confidence
cv2.putText(image, text, (2, 28), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2)
personDetected = True
break
else:
ndetected = 0 # flag that no verification was needed
# Queue results
try:
if personDetected:
results.put((image, cam, personDetected, imageDT, dnnTarget, boxPoints), True, 1.0) # try not to drop frames with detections
else:
if dbg is True and ndetected == 1: # I want to see what the "zoom" has rejected
results.put((img, cam, True, imgDT, dnnTarget+"V", boxPointsV), True, 1.0) # force zoom rejection file write
results.put((image, cam, personDetected, imageDT, dnnTarget, boxPoints), True, 0.016)
except:
# presumably outptut queue was full, main thread too slow.
drops+=1
continue
# Thread exits
cfps.stop() # stop the FPS counter timer
print("OpenVINO " + dnnTarget + " AI thread" + str(tnum) + ", waited: " + str(waits) + " dropped: " + str(drops) + " out of "
+ str(fcnt) + " images. AI: {:.2f} inferences/sec".format(cfps.fps()))