Processor

class Processor(object):
    #Constructor
    def __init__(self,exec_net,input_blob,out_blob,n,c,h,w):
        self.exec_net=exec_net
        self.input_blob=input_blob
        self.out_blob=out_blob
        self.n=n
        self.c=c
        self.h=h
        self.w=w

    def process_frame(self,cur_request_id,next_request_id,frame,frame_height,frame_width,is_async_mode):
              
        in_frame = cv2.resize(frame, (self.w, self.h))
        in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
        in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))
        
        if is_async_mode:
            self.exec_net.start_async(request_id=next_request_id, inputs={self.input_blob: in_frame})
        else:
            self.exec_net.start_async(request_id=cur_request_id, inputs={self.input_blob: in_frame})
        
        if self.exec_net.requests[cur_request_id].wait(-1) == 0:
            #Parse detection results of the current request
            res = self.exec_net.requests[cur_request_id].outputs[self.out_blob]
            return res

    
    def placeBoxes(self,res, labels_map, prob_threshold, frame, initial_w, initial_h, is_async_mode, cur_request_id):
        for obj in res[0][0]:
            # Draw only objects when probability more than specified threshold
            if obj[2] > prob_threshold:
                xmin = int(obj[3] * initial_w)
                ymin = int(obj[4] * initial_h)
                xmax = int(obj[5] * initial_w)
                ymax = int(obj[6] * initial_h)
                class_id = int(obj[1])
                # Draw box and label\class_id
                '''
                inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
                        "Inference time: {:.3f} ms".format(det_time * 1000)
                async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                        "Async mode is off. Processing request {}".format(cur_request_id)
                '''
                color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
                cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
                det_label = labels_map[class_id] if labels_map else str(class_id)
                cv2.putText(frame, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, (0,0,255), 1)
                #cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
                #cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,(10, 10, 200), 1)

        return frame