MultiStickSSDwithRealSense.py

import sys
if sys.version_info.major < 3 or sys.version_info.minor < 4:
    print("Please using python3.4 or greater!")
    sys.exit(1)
import pyrealsense2 as rs
import numpy as np
import cv2, io, time, argparse, re
from mvnc import mvncapi as mvnc
from os import system
from os.path import isfile, join
from time import sleep
import multiprocessing as mp

pipeline = None
lastresults = None
threads = []
processes = []
frameBuffer = None
results = None
fps = ""
detectfps = ""
framecount = 0
detectframecount = 0
time1 = 0
time2 = 0
graph_folder = ""
cam = None
camera_mode = 0
camera_width = 320
camera_height = 240
window_name = ""
background_transparent_mode = 0
ssd_detection_mode = 1
face_detection_mode = 0
elapsedtime = 0.0
temperature = 0.0
max_temperature = 0.0
active_stick_pointer = 0
mp_active_stick_number = None
stick_num_of_cluster = 0
cluster_switch_cycle = 10000
cluster_switch_temperature = 65.0
background_img = None
depth_sensor = None
depth_scale = 1.0
align_to = None
align = None

LABELS = [['background',
           'aeroplane', 'bicycle', 'bird', 'boat',
           'bottle', 'bus', 'car', 'cat', 'chair',
           'cow', 'diningtable', 'dog', 'horse',
           'motorbike', 'person', 'pottedplant',
           'sheep', 'sofa', 'train', 'tvmonitor'],
          ['background', 'face']]

def camThread(LABELS, results, frameBuffer, camera_mode, camera_width, camera_height, background_transparent_mode, background_img):
    global fps
    global detectfps
    global lastresults
    global framecount
    global detectframecount
    global time1
    global time2
    global cam
    global window_name
    global depth_scale
    global align_to
    global align

    # Configure depth and color streams
    #  Or
    # Open USB Camera streams
    if camera_mode == 0:
        pipeline = rs.pipeline()
        config = rs.config()
        config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
        config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
        profile = pipeline.start(config)
        depth_sensor = profile.get_device().first_depth_sensor()
        depth_scale = depth_sensor.get_depth_scale()
        align_to = rs.stream.color
        align = rs.align(align_to)
        window_name = "RealSense"
    elif camera_mode == 1:
        cam = cv2.VideoCapture(0)
        if cam.isOpened() != True:
            print("USB Camera Open Error!!!")
            sys.exit(0)
        cam.set(cv2.CAP_PROP_FPS, 30)
        cam.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
        cam.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
        window_name = "USB Camera"

    cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE)

    while True:
        t1 = time.perf_counter()

        # 0:= RealSense Mode
        # 1:= USB Camera Mode

        if camera_mode == 0:
            # Wait for a coherent pair of frames: depth and color
            frames = pipeline.wait_for_frames()
            depth_frame = frames.get_depth_frame()
            color_frame = frames.get_color_frame()
            if not depth_frame or not color_frame:
                continue
            if frameBuffer.full():
                frameBuffer.get()
            color_image = np.asanyarray(color_frame.get_data())

        elif camera_mode == 1:
            # USB Camera Stream Read
            s, color_image = cam.read()
            if not s:
                continue
            if frameBuffer.full():
                frameBuffer.get()
            frames = color_image

        height = color_image.shape[0]
        width = color_image.shape[1]
        frameBuffer.put(color_image.copy())
        res = None

        if not results.empty():
            res = results.get(False)
            detectframecount += 1
            imdraw = overlay_on_image(frames, res, LABELS, camera_mode, background_transparent_mode,
                                      background_img, depth_scale=depth_scale, align=align)
            lastresults = res
        else:
            imdraw = overlay_on_image(frames, lastresults, LABELS, camera_mode, background_transparent_mode,
                                      background_img, depth_scale=depth_scale, align=align)

        cv2.imshow(window_name, cv2.resize(imdraw, (width, height)))

        if cv2.waitKey(1)&0xFF == ord('q'):
            # Stop streaming
            if pipeline != None:
                pipeline.stop()
            sys.exit(0)

        ## Print FPS
        framecount += 1
        if framecount >= 15:
            fps       = "(Playback) {:.1f} FPS".format(time1/15)
            detectfps = "(Detection) {:.1f} FPS".format(detectframecount/time2)
            framecount = 0
            detectframecount = 0
            time1 = 0
            time2 = 0
        t2 = time.perf_counter()
        elapsedTime = t2-t1
        time1 += 1/elapsedTime
        time2 += elapsedTime



def inferencer(results, frameBuffer, ssd_detection_mode, face_detection_mode, devnum, mp_active_stick_number, mp_stick_temperature):

    graphs = []
    graph_buffers = []
    graphHandles = []
    graphHandle0 = None
    graphHandle1 = None

    mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 4)
    devices = mvnc.enumerate_devices()
    if len(devices) == 0:
        print("No NCS devices found")
        sys.exit(1)
    print(len(devices))

    # 1:= Enabled MobileNet-SSD Model
    if ssd_detection_mode == 1:
        with open(join(graph_folder, "graph"), mode="rb") as f:
            graph_buffers.append(f.read())
        graphs.append(mvnc.Graph('MobileNet-SSD'))

    # 1:= Enabled Fullweight FaceDetection Model
    if face_detection_mode == 1:
        with open(join(graph_folder, "graph.fullfacedetection"), mode="rb") as f:
            graph_buffers.append(f.read())
        graphs.append(mvnc.Graph('FullFaceDetection'))

    # 2:= Enabled Lightweight FaceDetection Model
    if face_detection_mode == 2:
        with open(join(graph_folder, "graph.shortfacedetection"), mode="rb") as f:
            graph_buffers.append(f.read())
        graphs.append(mvnc.Graph('ShortFaceDetection'))

    devopen = False
    for device in devices:
        try:
            device = mvnc.Device(device)
            device.open()
            for (graph, graph_buffer) in zip(graphs, graph_buffers):
                graphHandles.append(graph.allocate_with_fifos(device, graph_buffer))
            devopen = True
            break
        except:
            continue

    if devopen == False:
        print("NCS Devices open Error!!!")
        sys.exit(1)

    print("Loaded Graphs!!! ")

    THERMAL_STATS = mvnc.DeviceOption.RO_THERMAL_STATS
    temperature = device.get_option

    while True:
        # 0:= Inactive stick, 1:= Active stick
        if mp_active_stick_number[devnum] == 0:
            continue

        # Measure the temperature inside the stick
        mp_stick_temperature[devnum] = temperature(THERMAL_STATS)[0]

        try:
            if frameBuffer.empty():
                continue

            color_image = frameBuffer.get()
            prepimg = preprocess_image(color_image)
            res = None
            for (graph, graphHandle) in zip(graphs, graphHandles):
                graphHandle0 = graphHandle[0]
                graphHandle1 = graphHandle[1]
                graph.queue_inference_with_fifo_elem(graphHandle0, graphHandle1, prepimg.astype(np.float32), None)
                out, _ = graphHandle1.read_elem()
                num_valid_boxes = int(out[0])
                if num_valid_boxes > 0:
                    if isinstance(res, type(None)):
                        res = [out]
                    else:
                        res = np.append(res, [out], axis=0)
            results.put(res)
        except:
            import traceback
            traceback.print_exc()



def preprocess_image(src):

    try:
        img = cv2.resize(src, (300, 300))
        img = img - 127.5
        img = img * 0.007843
        return img
    except:
        import traceback
        traceback.print_exc()



def overlay_on_image(frames, object_infos, LABELS, camera_mode, background_transparent_mode, background_img, depth_scale=1.0, align=None):

    try:

        # 0:=RealSense Mode, 1:=USB Camera Mode
        if camera_mode == 0:
            # 0:= No background transparent, 1:= Background transparent
            if background_transparent_mode == 0:
                depth_frame = frames.get_depth_frame()
                color_frame = frames.get_color_frame()

            elif background_transparent_mode == 1:
                aligned_frames = align.process(frames)
                depth_frame = aligned_frames.get_depth_frame()
                color_frame = aligned_frames.get_color_frame()

            depth_dist  = depth_frame.as_depth_frame()
            depth_image = np.asanyarray(depth_frame.get_data())
            color_image = np.asanyarray(color_frame.get_data())

        elif camera_mode == 1:
            color_image = frames

        if isinstance(object_infos, type(None)):
            # 0:= No background transparent, 1:= Background transparent
            if background_transparent_mode == 0:
                return color_image
            elif background_transparent_mode == 1:
                return background_img

        # Show images
        height = color_image.shape[0]
        width = color_image.shape[1]
        entire_pixel = height * width
        occupancy_threshold = 0.9

        if background_transparent_mode == 0:
            img_cp = color_image.copy()
        elif background_transparent_mode == 1:
            img_cp = background_img.copy()

        for (object_info, LABEL) in zip(object_infos, LABELS):
            num_valid_boxes = int(object_info[0])

            if num_valid_boxes > 0:

                drawing_initial_flag = True

                for box_index in range(num_valid_boxes):
                    base_index = 7 + box_index * 7
                    if (not np.isfinite(object_info[base_index]) or
                        not np.isfinite(object_info[base_index + 1]) or
                        not np.isfinite(object_info[base_index + 2]) or
                        not np.isfinite(object_info[base_index + 3]) or
                        not np.isfinite(object_info[base_index + 4]) or
                        not np.isfinite(object_info[base_index + 5]) or
                        not np.isfinite(object_info[base_index + 6])):
                        continue

                    x1 = max(0, int(object_info[base_index + 3] * height))
                    y1 = max(0, int(object_info[base_index + 4] * width))
                    x2 = min(height, int(object_info[base_index + 5] * height))
                    y2 = min(width, int(object_info[base_index + 6] * width))

                    object_info_overlay = object_info[base_index:base_index + 7]

                    # 0:= No background transparent, 1:= Background transparent
                    if background_transparent_mode == 0:
                        min_score_percent = 60
                    elif background_transparent_mode == 1:
                        min_score_percent = 20

                    source_image_width = width
                    source_image_height = height

                    base_index = 0
                    class_id = object_info_overlay[base_index + 1]
                    percentage = int(object_info_overlay[base_index + 2] * 100)
                    if (percentage <= min_score_percent):
                        continue

                    box_left = int(object_info_overlay[base_index + 3] * source_image_width)
                    box_top = int(object_info_overlay[base_index + 4] * source_image_height)
                    box_right = int(object_info_overlay[base_index + 5] * source_image_width)
                    box_bottom = int(object_info_overlay[base_index + 6] * source_image_height)

                    # 0:=RealSense Mode, 1:=USB Camera Mode
                    if camera_mode == 0:
                        meters = depth_dist.get_distance(box_left+int((box_right-box_left)/2), box_top+int((box_bottom-box_top)/2))
                        label_text = LABEL[int(class_id)] + " (" + str(percentage) + "%)"+ " {:.2f}".format(meters) + " meters away"
                    elif camera_mode == 1:
                        label_text = LABEL[int(class_id)] + " (" + str(percentage) + "%)"

                    # 0:= No background transparent, 1:= Background transparent
                    if background_transparent_mode == 0:
                        box_color = (255, 128, 0)
                        box_thickness = 1
                        cv2.rectangle(img_cp, (box_left, box_top), (box_right, box_bottom), box_color, box_thickness)
                        label_background_color = (125, 175, 75)
                        label_text_color = (255, 255, 255)
                        label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]
                        label_left = box_left
                        label_top = box_top - label_size[1]
                        if (label_top < 1):
                            label_top = 1
                        label_right = label_left + label_size[0]
                        label_bottom = label_top + label_size[1]
                        cv2.rectangle(img_cp, (label_left - 1, label_top - 1), (label_right + 1, label_bottom + 1), label_background_color, -1)
                        cv2.putText(img_cp, label_text, (label_left, label_bottom), cv2.FONT_HERSHEY_SIMPLEX, 0.5, label_text_color, 1)

                    elif background_transparent_mode == 1:
                        clipping_distance = (meters+0.05) / depth_scale
                        depth_image_3d = np.dstack((depth_image, depth_image, depth_image))
                        fore = np.where((depth_image_3d > clipping_distance) | (depth_image_3d <= 0), 0, color_image)

                        area = abs(box_bottom - box_top) * abs(box_right - box_left)
                        occupancy = area / entire_pixel

                        if occupancy <= occupancy_threshold:
                            if drawing_initial_flag == True:
                                img_cp = fore
                                drawing_initial_flag = False
                            else:
                                img_cp[box_top:box_bottom, box_left:box_right] = cv2.addWeighted(img_cp[box_top:box_bottom, box_left:box_right],
                                                                                                 0.85,
                                                                                                 fore[box_top:box_bottom, box_left:box_right],
                                                                                                 0.85,
                                                                                                 0)

        cv2.putText(img_cp, fps,       (width-170,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
        cv2.putText(img_cp, detectfps, (width-170,30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
        return img_cp

    except:
        import traceback
        traceback.print_exc()






if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('-grp','--graph',dest='graph_folder',type=str,default='./',help='MVNC graphs Path. (Default=./)')
    parser.add_argument('-mod','--mode',dest='camera_mode',type=int,default=0,help='Camera Mode. (0:=RealSense Mode, 1:=USB Camera Mode. Defalut=0)')
    parser.add_argument('-wd','--width',dest='camera_width',type=int,default=320,help='Width of the frames in the video stream. (USB Camera Mode Only. Default=320)')
    parser.add_argument('-ht','--height',dest='camera_height',type=int,default=240,help='Height of the frames in the video stream. (USB Camera Mode Only. Default=240)')
    parser.add_argument('-tp','--transparent',dest='background_transparent_mode',type=int,default=0,help='TransparentMode. (RealSense Mode Only. 0:=No background transparent, 1:=Background transparent)')
    parser.add_argument('-sd','--ssddetection',dest='ssd_detection_mode',type=int,default=1,help='SSDDetectionMode. (0:=Disabled, 1:=Enabled Default=1)')
    parser.add_argument('-fd','--facedetection',dest='face_detection_mode',type=int,default=0,help='FaceDetectionMode. (0:=Disabled, 1:=Full, 2:=Short Default=0)')
    parser.add_argument('-snc','--sticknumofcluster',dest='stick_num_of_cluster',type=int,default=0,help='Number of sticks to be clustered. (0:=Clustering invalid, n:=Number of sticks Default=0)')
    parser.add_argument('-csc','--clusterswitchcycle',dest='cluster_switch_cycle',type=int,default=10000,help='Cycle of switching active cluster. (n:=millisecond Default=10000)')
    parser.add_argument('-cst','--clusterswittemperature',dest='cluster_switch_temperature',type=float,default=65.0,help='Temperature threshold to switch active cluster. (n.n:=temperature(Celsius) Default=65.0)')
    args = parser.parse_args()

    graph_folder  = args.graph_folder
    camera_mode   = args.camera_mode
    camera_width  = args.camera_width
    camera_height = args.camera_height
    background_transparent_mode = args.background_transparent_mode
    ssd_detection_mode = args.ssd_detection_mode
    face_detection_mode = args.face_detection_mode
    stick_num_of_cluster = args.stick_num_of_cluster
    cluster_switch_cycle = args.cluster_switch_cycle
    cluster_switch_temperature = args.cluster_switch_temperature

    # 0:=RealSense Mode, 1:=USB Camera Mode
    if camera_mode != 0 and camera_mode != 1:
        print("Camera Mode Error!! " + str(camera_mode))
        sys.exit(0)

    if camera_mode != 0 and background_transparent_mode == 1:
        background_transparent_mode = 0

    if background_transparent_mode == 1:
        background_img = np.zeros((camera_height, camera_width, 3), dtype=np.uint8)

        if face_detection_mode != 0:
            ssd_detection_mode = 0

    if ssd_detection_mode == 0 and face_detection_mode != 0:
        del(LABELS[0])

    devices = None
    try:

        mp.set_start_method('forkserver')
        frameBuffer = mp.Queue(10)
        results = mp.Queue()

        # Start streaming
        p = mp.Process(target=camThread,
                       args=(LABELS, results, frameBuffer, camera_mode, camera_width, camera_height, background_transparent_mode, background_img),
                       daemon=True)
        p.start()
        processes.append(p)

        # Start detection MultiStick
        devices = mvnc.enumerate_devices()

        if len(devices) == 0:
            print("No devices found")
            sys.exit(0)
        device_count = len(devices)

        if stick_num_of_cluster > 0 and stick_num_of_cluster > (device_count // 2):
            print("`stick_num_of_cluster` must be less than half of the total number of sticks.")
            sys.exit(0)

        # Initialization of clustering stick
        mp_active_stick_number = mp.Array('i', device_count)
        mp_stick_temperature   = mp.Array('f', device_count)
        # 0:= Clustering invalid, n:= Number of sticks to be clustered
        if stick_num_of_cluster > 0:
            # Activate only the sticks in the cluster
            for devnum in range(stick_num_of_cluster):
                # 0:= Inactive, 1:= Active
                mp_active_stick_number[devnum] = 1
        else:
            # Activate all sticks
            for devnum in range(device_count):
                # 0:= Inactive, 1:= Active
                mp_active_stick_number[devnum] = 1

        # Activation of inferencer
        for devnum in range(device_count):
            p = mp.Process(target=inferencer,
                           args=(results, frameBuffer, ssd_detection_mode, face_detection_mode, devnum, mp_active_stick_number, mp_stick_temperature),
                           daemon=True)
            p.start()
            processes.append(p)

        # Cluster switching determination
        t1 = time.perf_counter() * 1000
        while True:
            # Switch cluster
            if stick_num_of_cluster > 0:
                # Measure inside temperature of stick
                relative_pointer = active_stick_pointer
                counta = 0
                max_temperature = 0.0
                while True:
                    temperature = mp_stick_temperature[relative_pointer]
                    if max_temperature < temperature:
                        max_temperature = temperature
                    relative_pointer += 1
                    counta += 1
                    if relative_pointer > (device_count - 1):
                        relative_pointer = 0
                    if counta >= stick_num_of_cluster:
                        break

                # Cluster switching judgment
                if (cluster_switch_cycle > 0 and elapsedtime >= cluster_switch_cycle) or max_temperature >= cluster_switch_temperature:
                    # Cluster inactivate
                    counta = 0
                    while True:
                        mp_active_stick_number[active_stick_pointer] = 0
                        active_stick_pointer += 1
                        counta += 1
                        if active_stick_pointer > (device_count - 1):
                            active_stick_pointer = 0
                        if counta >= stick_num_of_cluster:
                            break

                    # Cluster activate
                    relative_pointer = active_stick_pointer
                    counta = 0
                    while True:
                        mp_active_stick_number[relative_pointer] = 1
                        relative_pointer += 1
                        counta += 1
                        if relative_pointer > (device_count - 1):
                            relative_pointer = 0
                        if counta >= stick_num_of_cluster:
                            break
                    elapsedtime = 0.0
                    t1 = time.perf_counter() * 1000
                t2 = time.perf_counter() * 1000
                elapsedtime = (t2-t1)
                print("Active Sticks =", mp_active_stick_number[:],
                      "elapsedtime(millisec) = {:.1f}".format(elapsedtime),
                      "max_temperature = {:.1f}".format(max_temperature))
            else:
                sleep(1)

    except:
        import traceback
        traceback.print_exc()
    finally:
        for p in range(len(processes)):
            processes[p].terminate()

        print("\n\nFinished\n\n")