added object detection async sample app

Author: yas-sim

iewrap.py

@@ -75,12 +75,12 @@ def createInputBlobDict(self, inputData):
 
  def asyncInfer(self, img):
  status = None
- while status!=0 and status!=-11:
+ while status!=0 and status!=-11: # Find an idle infer_request
  req = self.execNet.requests[self.inferSlot]
  self.inferSlot = (self.inferSlot+1) % self.numRequests
  status = req.wait(-1)
  infID = self.inferenceID
- 
+
  inBlobDict = self.createInputBlobDict(img)
  req.set_completion_callback(self.callback, (infID, req)) 
  req.async_infer(inputs=inBlobDict)
@@ -109,3 +109,7 @@ def blockInfer(self, img):
  else:
  return res # if the model has multiple outputs, return the result in dictionary
  # e.g. ( {'prob': array[], 'data': array[]})
+
+ def dummyInfer(self):
+ for req in self.execNet.requests:
+ req.infer()

iewrap_object_detection_async.py

@@ -0,0 +1,52 @@
+import iewrap
+
+import time
+
+import cv2
+import numpy as np
+
+imgBuf = {}
+label = []
+
+def callback(infId, output):
+ global imgBuf, label
+
+ # Draw bounding boxes and labels onto the image
+ output = output.reshape((100,7))
+ img = imgBuf.pop(infId)
+ img_h, img_w, _ = img.shape
+ for obj in output:
+ imgid, clsid, confidence, x1, y1, x2, y2 = obj
+ if confidence>0.6: # Draw a bounding box and label when confidence>0.8
+ x1 = int(x1 * img_w)
+ y1 = int(y1 * img_h)
+ x2 = int(x2 * img_w)
+ y2 = int(y2 * img_h)
+ cv2.rectangle(img, (x1, y1), (x2, y2), (0,255,255), thickness=4 )
+ cv2.putText(img, label[int(clsid)][:-1], (x1, y1), cv2.FONT_HERSHEY_PLAIN, fontScale=4, color=(0,255,255), thickness=4)
+ cv2.imshow('result', img)
+ cv2.waitKey(1)
+
+def main():
+ global imgBuf, label
+ label = open('voc_labels.txt').readlines()
+
+ cap = cv2.VideoCapture(0)
+ cap.set(cv2.CAP_PROP_FRAME_WIDTH , 640)
+ cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+ 
+ #cap = cv2.VideoCapture('../sample-videos/people-detection.mp4')
+
+ ie = iewrap.ieWrapper('public/mobilenet-ssd/FP16/mobilenet-ssd.xml', 'CPU', 10)
+ ie.setCallback(callback)
+
+ while True:
+ ret, img = cap.read()
+ if ret==False:
+ break
+ refId = ie.asyncInfer(img) # Inference
+ imgBuf[refId]=img
+ #time.sleep(1/30)
+
+if __name__ == '__main__':
+ main()