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Real time completion module of MES manufacturing system

Openvino artificial intelligence visual recognition is used to complete the manual manufacturing production of MES system

IntermediateFull instructions provided12 hours376
Real time completion module of MES manufacturing system

Things used in this project

Hardware components

DFRobot LattePanda Delta 432 – Tiny Ultimate Windows / Linux Device 4GB/32GB
USB binocular camera module
Movidius Neural Compute Stick
Intel Movidius Neural Compute Stick

Software apps and online services

OpenVINO toolkit
Intel OpenVINO toolkit


Read more


#!/usr/bin/env python
 TensorFlow Object Decetion APIssd_inception_v2_coco_2018_01_28


from __future__ import print_function
import sys
import os
from argparse import ArgumentParser, SUPPRESS
import cv2
import time
import logging as log
import pymssql  #
import datetime #

from openvino.inference_engine import IENetwork, IECore

def build_argparser():
    parser = ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
                      required=True, type=str)
    args.add_argument("-i", "--input",
                      help="Required. Path to video file or image. 'cam' for capturing video stream from camera",
                      required=True, type=str)
    args.add_argument("-l", "--cpu_extension",
                      help="Optional. Required for CPU custom layers. Absolute path to a shared library with the "
                           "kernels implementations.", type=str, default=None)
    args.add_argument("-d", "--device",
                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is "
                           "acceptable. The demo will look for a suitable plugin for device specified. "
                           "Default value is CPU", default="CPU", type=str)
    args.add_argument("--labels", help="Optional. Path to labels mapping file", default=None, type=str)
    args.add_argument("-pt", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
                      default=0.5, type=float)
    args.add_argument("--no_show", help="Optional. Don't show output", action='store_true')

    return parser

def update_db(prdt_no,qty):
    sql_coon = pymssql.connect(host ='',user='sa',password='',database='DB_MES',charset='UTF8')
    cur = sql_coon.cursor()
    cur.execute("insert into handmade(prd_no,qty,prd_time) VALUES('%s','%s','%s')"%(prdt_no,qty,'%Y-%m-%d %H:%M:%S')))

def main():
    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
    args = build_argparser().parse_args()
    model_xml = args.model
    model_bin = os.path.splitext(model_xml)[0] + ".bin""Creating Inference Engine...")
    ie = IECore()
    if args.cpu_extension and 'CPU' in args.device:
        ie.add_extension(args.cpu_extension, "CPU")
    # Read IR"Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
    net = IENetwork(model=model_xml, weights=model_bin)

    if "CPU" in args.device:
        supported_layers = ie.query_network(net, "CPU")
        not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
        if len(not_supported_layers) != 0:
            log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
                      format(args.device, ', '.join(not_supported_layers)))
            log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
                      "or --cpu_extension command line argument")

    img_info_input_blob = None
    feed_dict = {}
    for blob_name in net.inputs:
        if len(net.inputs[blob_name].shape) == 4:
            input_blob = blob_name
        elif len(net.inputs[blob_name].shape) == 2:
            img_info_input_blob = blob_name
            raise RuntimeError("Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported"
                               .format(len(net.inputs[blob_name].shape), blob_name))

    assert len(net.outputs) == 1, "Demo supports only single output topologies"

    out_blob = next(iter(net.outputs))"Loading IR to the plugin...")
    exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device)
    # Read and pre-process input image
    n, c, h, w = net.inputs[input_blob].shape
    if img_info_input_blob:
        feed_dict[img_info_input_blob] = [h, w, 1]

    if args.input == 'cam':
        input_stream = 0
        input_stream = args.input
    cap = cv2.VideoCapture(input_stream)
    assert cap.isOpened(), "Can't open " + input_stream

    if args.labels:
        with open(args.labels, 'r') as f:
            labels_map = [x.strip() for x in f]
        labels_map = None

    cur_request_id = 0
    next_request_id = 1"Starting inference in async mode...")
    is_async_mode = True
    render_time = 0
    if is_async_mode:
        ret, frame =
        frame_h, frame_w = frame.shape[:2]

    print("To close the application, press 'CTRL+C' here or switch to the output window and press ESC key")
    print("To switch between sync/async modes, press TAB key in the output window")
    prd_qty = 0  # 
    while cap.isOpened():
        if is_async_mode:
            ret, next_frame =
            ret, frame =
            if ret:
                frame_h, frame_w = frame.shape[:2]
        if not ret:
            break  # abandons the last frame in case of async_mode
        # Main sync point:
        # in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
        # in the regular mode we start the CURRENT request and immediately wait for it's completion
        inf_start = time.time()
        if is_async_mode:
            in_frame = cv2.resize(next_frame, (w, h))
            in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            feed_dict[input_blob] = in_frame
            exec_net.start_async(request_id=next_request_id, inputs=feed_dict)
            in_frame = cv2.resize(frame, (w, h))
            in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            feed_dict[input_blob] = in_frame
            exec_net.start_async(request_id=cur_request_id, inputs=feed_dict)
        if exec_net.requests[cur_request_id].wait(-1) == 0:
            inf_end = time.time()
            det_time = inf_end - inf_start

            # Parse detection results of the current request
            res = exec_net.requests[cur_request_id].outputs[out_blob]

            for obj in res[0][0]:
                # Draw only objects when probability more than specified threshold
                if obj[2] > args.prob_threshold:
                    xmin = int(obj[3] * frame_w)
                    ymin = int(obj[4] * frame_h)
                    xmax = int(obj[5] * frame_w)
                    ymax = int(obj[6] * frame_h)
                    class_id = int(obj[1])
                    # Draw box and label\class_id
                    color = (min(class_id * 100, 255), min(class_id * 30, 255), min(class_id * 50, 255))
                    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 5)
                    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:'+str(xmin)+ ' Ymax:'+str(xmax), (xmin, ymin - 7),
                                cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
                    if xmin < 201:#X
                        prd_qty = prd_qty + 1
                        update_db(det_label, prd_qty)

            # Draw performance stats
            inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
                "Inference time: {:.3f} ms".format(det_time * 1000)
            render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_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)

            cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            cv2.putText(frame, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            cv2.putText(frame, async_mode_message, (10, int(frame_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
                        (10, 10, 200), 1)

        render_start = time.time()
        if not args.no_show:
            cv2.imshow("Detection Results", frame)
        render_end = time.time()
        render_time = render_end - render_start

        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame
            frame_h, frame_w = frame.shape[:2]

        if not args.no_show:
            key = cv2.waitKey(1)
            if key == 27:
            if (9 == key):
                is_async_mode = not is_async_mode
      "Switched to {} mode".format("async" if is_async_mode else "sync"))


if __name__ == '__main__':
    sys.exit(main() or 0)




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