class VideoModule(Thread):
def __init__(self, vnum=0, vnum2=1):
Thread.__init__(self)
self.vs = None
self.vs2 = None
self.view = Viewer()
#self.distance = DistanceDetect()
#self.distance.callibrationFromCamera()
self.text = TextDetect('eng')
self.data = videomodule_t()
classes = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant", "sheep", "sofa", "train",
"tvmonitor"
]
self.view.setClasses(classes)
self.view.setNet(
"C:\\BPHS_python_prototype-\\MobileNetSSD_deploy.prototxt.txt",
"C:\\BPHS_python_prototype-\\MobileNetSSD_deploy.caffemodel")
self.running = True
self.operation_type = 'ObjectDetection'
self.videonum = vnum
self.videonum2 = vnum2
def setOperationType(self, operation):
self.operation_type = operation
if self.operation_type != 'TextReading' and self.text.focusing:
self.text.stopFocusing()
def getData(self):
return self.data
def textDetectionMode(self):
self.data.setText(self.text.textDetect())
print self.data.text
if len(self.data.text) > 0:
self.text.voiceText(self.data.text)
#self.setOperationType('Waiting')
#self.waitForOperation('TextReading')
def waitForOperation(self, operation_name):
while self.operation_type == operation_name and self.running:
pass
def objectDetectionMode(self, wd=600):
COLORS = np.random.uniform(0, 255, size=(21, 3))
print("[INFO] starting video stream...")
time.sleep(2.0)
#fps = FPS().start()
data = data_arr_t()
while self.running and self.operation_type == 'ObjectDetection':
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = self.view.getFrame(self.vs, wd)
#frameR = self.view.getFrame(self.vs2, wd) # little bit weird
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = self.view.convertToBlob(frame)
# # pass the blob through the network and obtain the detections and
# # predictions
detections = self.view.makeDetections(blob)
# # loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > 0.2:
# 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
self.view.printPrediction(idx, confidence, frame, startX,
startY, endX, endY, COLORS)
#dist = self.distance.getDistance(frame, frameR, startX, startY, endX, endY)
#print dist
data.appendElem(viewer_t(self.view.classes[idx], 0))
#print data.arr[0].object
# show the output frame
cv2.imshow("Frame", frame)
#fps.update()
self.data = videomodule_t(copy.deepcopy(data), '', 2, 0)
#self.data.printModData()
data.freeArr()
key = cv2.waitKey(20)
if key == 27: # exit on ESC
break
# 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()
def stop(self):
self.running = False
def run(self):
print "start"
self.vs = VideoStream(self.videonum).start()
#self.vs2 = VideoStream(self.videonum2).start()
print self.vs
print self.vs2
self.view.setVideoStream(self.vs)
self.text.setVideoStream(self.vs)
self.text.start()
print "text started"
#self.distance.preprocessDepthMap()
print "start while"
while self.running:
#print "thread iteration"
if self.operation_type == 'TextReading':
# change string comparing later
self.textDetectionMode()
elif self.operation_type == 'ObjectDetection':
self.objectDetectionMode()
else:
continue
self.vs.stop()
# ob = VideoModule()
# ob.run()
# ob = VideoModule(1, 0)
# ob.start()
# ob.setOperationType('TextReading')
# i = 0
# while True:
# time.sleep(2)
# i += 1
# ob.getData().printModData()
# print "__________________"
# if i % 10 == 0:
# ob.setOperationType('TextReading')
# if i % 20 == 0:
# ob.setOperationType('ObjectDetection')
