person_detection/person_detection.py

"""
SPDX-FileCopyrightText: 2020-present Open Networking Foundation <info@opennetworking.org>
SPDX-License-Identifier: LicenseRef-ONF-Member-1.01
"""

from __future__ import print_function

import logging as log
import os
import sys
import time
from argparse import ArgumentParser, SUPPRESS

import cv2
from imutils import build_montages
from openvino.inference_engine import 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("-i2", "--input2",
    #                   help="Optional. Path to second video file or image. 'cam' for capturing video stream from camera",
    #                   default=None, 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("-pp", "--plugin_dir", help="Optional. Path to a plugin folder", 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("-ns", help='No show output', action='store_true')

    return parser


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"
    # Plugin initialization for specified device and load extensions library if specified
    log.info("Initializing plugin for {} device...".format(args.device))
    # plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
    # if args.cpu_extension and 'CPU' in args.device:
    #   plugin.add_cpu_extension(args.cpu_extension)
    # Read IR
    log.info("Reading IR...")
    net = IECore().read_network(model=model_xml, weights=model_bin)

    assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
    assert len(net.outputs) == 1, "Demo supports only single output topologies"
    input_blob = next(iter(net.inputs))
    out_blob = next(iter(net.outputs))

    # input_blob2 = next(iter(net.inputs))
    # out_blob2 = next(iter(net.outputs))

    log.info("Loading IR to the plugin...")
    # exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=2)
    exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=1)
    # Read and pre-process input image
    n, c, h, w = net.inputs[input_blob].shape
    # n2, c2, h2, w2 = net.inputs[input_blob2].shape
    del net
    if args.input == 'cam':
        input_stream = 0
    elif args.input == 'gstreamer':
        # gst rtp sink
        input_stream = 'udpsrc port=5000 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
        #input_stream = 'udpsrc port=5000 caps = "application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264, payload=(int)96" ! rtph264depay ! decodebin ! videoconvert ! appsink'
    else:
        input_stream = args.input
        assert os.path.isfile(args.input), "Specified input file doesn't exist"

    if input_stream == 'gstreamer':
        cap = cv2.VideoCapture(input_stream, cv2.CAP_GSTREAMER)
    else:
        cap = cv2.VideoCapture(input_stream)

    # if args.input2 == 'cam':
    #     input_stream2 = 0
    # elif args.input2 == 'gstreamer':
    #     input_stream2 = 'udpsrc port=5001 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
    # else:
    #     input_stream2 = args.input2
    #     assert os.path.isfile(args.input2), "Specified input file doesn't exist"
    if args.labels:
        with open(args.labels, 'r') as f:
            labels_map = [x.strip() for x in f]
    else:
        labels_map = None

    # if input_stream2 == 'gstreamer':
    #     cap2 = cv2.VideoCapture(input_stream2, cv2.CAP_GSTREAMER)
    # else:
    #     cap2 = cv2.VideoCapture(input_stream2)

    cur_request_id = 0
    next_request_id = 1

    # cur_request_id2 = 1
    # next_request_id2 = 0

    log.info("Starting inference in async mode...")
    log.info("To switch between sync and async modes press Tab button")
    log.info("To stop the demo execution press Esc button")

    # Async doesn't work if True
    # Request issues = Runtime Error: [REQUEST BUSY]
    is_async_mode = False
    render_time = 0
    ret, frame = cap.read()
    # ret2, frame2 = cap2.read()

    # Montage width and height
    # In this case means 2x1 boxes
    mW = 2
    mH = 1

    frameList = []

    print("To close the application, press 'CTRL+C' or any key with focus on the output window")
    # while cap.isOpened() or cap2.isOpened():
    while cap.isOpened():
        if is_async_mode:
            ret, next_frame = cap.read()
            # ret2, next_frame2 = cap2.read()
        else:
            ret, frame = cap.read()
            # ret2, frame2 = cap2.read()
        #if not (ret and ret2):
        if not ret:
            break
        initial_w = cap.get(3)
        initial_h = cap.get(4)
        # initial_w2 = cap2.get(3)
        # initial_h2 = cap2.get(4)
        # 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:
            # if ret and ret2:
            if ret:
                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))
                exec_net.start_async(request_id=next_request_id, inputs={input_blob: in_frame})

                # in_frame2 = cv2.resize(next_frame2, (w2, h2))
                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
                # exec_net.start_async(request_id=next_request_id2, inputs={input_blob2: in_frame2})

        else:
            # if (ret and ret2):
            if ret:
                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))
                exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})

                # in_frame2 = cv2.resize(frame2, (w2, h2))
                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
                # exec_net.start_async(request_id=cur_request_id2, inputs={input_blob2: in_frame2})

        # if exec_net.requests[cur_request_id].wait(-1) == 0 and exec_net.requests[cur_request_id2].wait(-1) == 0:
        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]
            # res2 = exec_net.requests[cur_request_id2].outputs[out_blob2]

            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] * 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
                    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, color, 1)
                    print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
                          'xmax:', xmax, 'ymax:', ymax)

            # for obj in res2[0][0]:
            #     # Draw only objects when probability more than specified threshold
            #     if obj[2] > args.prob_threshold:
            #         xmin = int(obj[3] * initial_w2)
            #         ymin = int(obj[4] * initial_h2)
            #         xmax = int(obj[5] * initial_w2)
            #         ymax = int(obj[6] * initial_h2)
            #         class_id = int(obj[1])
            #         # Draw box and label\class_id
            #         color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
            #         cv2.rectangle(frame2, (xmin, ymin), (xmax, ymax), color, 2)
            #         det_label = labels_map[class_id] if labels_map else str(class_id)
            #         cv2.putText(frame2, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
            #                     cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
            #         print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
            #               'xmax:', xmax, 'ymax:', ymax)

            # Draw performance stats
            inf_time_message = "Inference time: Not applicable 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)
            if is_async_mode:
                async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id)
            else:
                async_mode_message = "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(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
                        (10, 10, 200), 1)

            # cv2.putText(frame2, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            # cv2.putText(frame2, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            # cv2.putText(frame2, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
            #             (10, 10, 200), 1)

        render_start = time.time()

        if not args.ns:
            # if ret and ret2:
            if ret:
            #     frameList.append(frame)
            #     # frameList.append(frame2)
            # montages = build_montages(frameList, (640, 480), (mW, mH))
            # for montage in montages:
            #     cv2.imshow("Detection results", montage)
                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
            # frame2 = next_frame2
        key = cv2.waitKey(1)
        if key == 27:
            break
        if 9 == key:
            is_async_mode = not is_async_mode
            log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))

    cap.release()
    # cap2.release()
    cv2.destroyAllWindows()


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