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multiprocessingV2.py
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multiprocessingV2.py
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import cv2
import depthai as dai
import numpy as np
import blobconverter
import CoDrone
import queue
from threading import Thread
from multiprocessing import Process, Value, Queue, freeze_support
import time
height_threshold = 180 # height before drone.land()
depth_upper_threshold = 1350
depth_lower_threshold = 500
left_threshold = -250
right_threshold = 250
drone_movement_start = 0
drone_movement_end = 0
data_request_start = 0
data_request_end = 0
def drone_thread_function(q,data_flag):
# set up drone connection and take off before trying to land
print('Creating Drone Object')
drone = CoDrone.CoDrone()
print("Getting Ready to Pair")
drone.pair(drone.Nearest)
print("Paired")
data_flag.value = 1
drone.takeoff()
drone.set_throttle(50)
drone_start_timer = time.time()
print("Taking Off")
print(drone.get_height())
data_request_timer_counter = 0
while True:
if data_flag.value == 0 and not q.empty():
start_coords = q.get(0)
data_request_end = time.time()
if data_request_timer_counter != 0:
print('data request time', data_request_end - data_request_start)
data_request_timer_counter += 1
print('from drone',start_coords)
# x:left-right, y:height, z:depth in mm
xs,ys,zs = start_coords
xl,yl,zl = [0,0,0]
x = xs - xl
z = zs - zl
drone_movement_start = time.time()
drone_land(drone,x,z,drone_start_timer)
drone_movement_end = time.time()
print('drone movement time', drone_movement_end - drone_movement_start)
data_flag.value = 1
data_request_start = time.time()
# else:
# print('q is empty')
data_flag.value = 0
def camera_thread_function(q,data_flag):
# camera setup
labelMap = [
"person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train",
"truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
"bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie",
"suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
"fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
"orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor",
"laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
"teddy bear", "hair drier", "toothbrush"
]
syncNN = True
# Create pipeline
pipeline = dai.Pipeline()
# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
spatialDetectionNetwork = pipeline.create(dai.node.YoloSpatialDetectionNetwork)
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
stereo = pipeline.create(dai.node.StereoDepth)
xoutRgb = pipeline.create(dai.node.XLinkOut)
xoutNN = pipeline.create(dai.node.XLinkOut)
xoutBoundingBoxDepthMapping = pipeline.create(dai.node.XLinkOut)
xoutDepth = pipeline.create(dai.node.XLinkOut)
xoutRgb.setStreamName("rgb")
xoutNN.setStreamName("detections")
xoutBoundingBoxDepthMapping.setStreamName("boundingBoxDepthMapping")
xoutDepth.setStreamName("depth")
# Properties
camRgb.setPreviewSize(416, 416)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
# setting node configs
stereo.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
#stereo.setExtendedDisparity(False)
stereo.setLeftRightCheck(True)
stereo.setSubpixel(True)
spatialDetectionNetwork.setBlobPath(blobconverter.from_zoo(name='yolo-v4-tiny-tf', shaves=6))
spatialDetectionNetwork.setConfidenceThreshold(0.5)
spatialDetectionNetwork.input.setBlocking(False)
spatialDetectionNetwork.setBoundingBoxScaleFactor(0.5)
spatialDetectionNetwork.setDepthLowerThreshold(100)
spatialDetectionNetwork.setDepthUpperThreshold(5000)
# Yolo specific parameters
spatialDetectionNetwork.setNumClasses(80)
spatialDetectionNetwork.setCoordinateSize(4)
spatialDetectionNetwork.setAnchors(np.array([10,14, 23,27, 37,58, 81,82, 135,169, 344,319]))
spatialDetectionNetwork.setAnchorMasks({ "side26": np.array([1,2,3]), "side13": np.array([3,4,5]) })
spatialDetectionNetwork.setIouThreshold(0.5)
# Linking
monoLeft.out.link(stereo.left)
monoRight.out.link(stereo.right)
camRgb.preview.link(spatialDetectionNetwork.input)
if syncNN:
spatialDetectionNetwork.passthrough.link(xoutRgb.input)
else:
camRgb.preview.link(xoutRgb.input)
spatialDetectionNetwork.out.link(xoutNN.input)
spatialDetectionNetwork.boundingBoxMapping.link(xoutBoundingBoxDepthMapping.input)
stereo.depth.link(spatialDetectionNetwork.inputDepth)
spatialDetectionNetwork.passthroughDepth.link(xoutDepth.input)
# Connect to device and start pipeline
with dai.Device(pipeline) as device:
# Output queues will be used to get the rgb frames and nn data from the outputs defined above
previewQueue = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
detectionNNQueue = device.getOutputQueue(name="detections", maxSize=4, blocking=False)
xoutBoundingBoxDepthMapping = device.getOutputQueue(name="boundingBoxDepthMapping", maxSize=4, blocking=False)
depthQueue = device.getOutputQueue(name="depth", maxSize=4, blocking=False)
startTime = time.monotonic()
counter = 0
fps = 0
color = (255, 255, 255)
while True:
person_detection_timer_start = time.time()
# xs,ys,zs = 0,0,0
inPreview = previewQueue.get()
inDet = detectionNNQueue.get()
depth = depthQueue.get()
frame = inPreview.getCvFrame()
depthFrame = depth.getFrame()
depthFrameColor = cv2.normalize(depthFrame, None, 255, 0, cv2.NORM_INF, cv2.CV_8UC1)
depthFrameColor = cv2.equalizeHist(depthFrameColor)
depthFrameColor = cv2.applyColorMap(depthFrameColor, cv2.COLORMAP_HOT)
counter+=1
current_time = time.monotonic()
if (current_time - startTime) > 1 :
fps = counter / (current_time - startTime)
counter = 0
startTime = current_time
detections = inDet.detections
if len(detections) != 0:
boundingBoxMapping = xoutBoundingBoxDepthMapping.get()
roiDatas = boundingBoxMapping.getConfigData()
for roiData in roiDatas:
roi = roiData.roi
roi = roi.denormalize(depthFrameColor.shape[1], depthFrameColor.shape[0])
topLeft = roi.topLeft()
bottomRight = roi.bottomRight()
xmin = int(topLeft.x)
ymin = int(topLeft.y)
xmax = int(bottomRight.x)
ymax = int(bottomRight.y)
cv2.rectangle(depthFrameColor, (xmin, ymin), (xmax, ymax), 255, cv2.FONT_HERSHEY_SCRIPT_SIMPLEX)
# If the frame is available, draw bounding boxes on it and show the frame
height = frame.shape[0]
width = frame.shape[1]
for detection in detections:
# Denormalize bounding box
x1 = int(detection.xmin * width)
x2 = int(detection.xmax * width)
y1 = int(detection.ymin * height)
y2 = int(detection.ymax * height)
try:
label = labelMap[detection.label]
except:
label = detection.label
cv2.putText(frame, str(label), (x1 + 10, y1 + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, "{:.2f}".format(detection.confidence * 100), (x1 + 10, y1 + 35),
cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"X: {int(detection.spatialCoordinates.x)} mm", (x1 + 10, y1 + 50),
cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Y: {int(detection.spatialCoordinates.y)} mm", (x1 + 10, y1 + 65),
cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.putText(frame, f"Z: {int(detection.spatialCoordinates.z)} mm", (x1 + 10, y1 + 80),
cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), cv2.FONT_HERSHEY_SIMPLEX)
if str(label) == 'person':
person_detection_timer_end = time.time()
# print('Time taken to detect a person:', person_detection_timer_end - person_detection_timer_start)
xs = int(detection.spatialCoordinates.x)
ys = int(detection.spatialCoordinates.y)
zs = int(detection.spatialCoordinates.z)
# str(label) == "person" and
if data_flag.value == 1 and str(label) == "person":
q.put([xs,0,zs])
data_flag.value = 0
# data_request_end = time.time()
# print('data request time', data_request_end - data_request_start)
cv2.putText(frame, "NN fps: {:.2f}".format(fps), (2, frame.shape[0] - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4,
(255, 255, 255))
cv2.imshow("preview", frame)
if cv2.waitKey(1) == ord('q'):
break
def drone_move(drone, x,z):
#startTime = time.time()
pitch_power = 30 # 20 is the scale factor for pitch, assume while loop repeat 3 times
roll_power = 20 # moves left 20
if x < left_threshold:
drone.set_pitch(0)
drone.set_throttle(0)
print('move right')
drone.set_roll(-roll_power)
drone.move(1)
elif x > right_threshold:
drone.set_pitch(0)
drone.set_throttle(0)
print('move left')
drone.set_roll(roll_power)
drone.move(1)
else:
drone.set_roll(0)
drone.set_throttle(0)
drone.move(0)
if z > depth_upper_threshold:
drone.set_roll(0)
drone.set_throttle(0)
print('move forward')
drone.set_pitch(pitch_power)
drone.move(1)
elif z < depth_lower_threshold:
drone.set_roll(0)
drone.set_throttle(0)
print('move backward')
drone.set_pitch(-pitch_power)
drone.move(1)
else:
drone.set_pitch(0)
drone.set_throttle(0)
drone.move(0)
def drone_land(drone, x,z,drone_start_timer):
if depth_lower_threshold <= z <= depth_upper_threshold and left_threshold <= x <= right_threshold:
drone.go_to_height(height_threshold)
drone_end_timer = time.time()
print('Total time taken from takeoff to landing:', drone_end_timer - drone_start_timer)
print('coords before landing', x, z)
# drone.set_pitch(5)
# drone.move(1)
print('landing')
drone.land()
print("landing")
drone.close()
print('drone closed')
drone.disconnect()
print('drone disconnected')
else:
drone_move(drone,x,z)
# executionTime = (time.time() - startTime)
# print('Execution time in seconds: ' + str(executionTime))
if __name__ == '__main__':
freeze_support()
data_flag = Value('i',0)
locationQ = Queue()
camera = Process(target=camera_thread_function, args=(locationQ,data_flag))
camera.start()
drone1 = Process(target=drone_thread_function, args=(locationQ,data_flag))
drone1.start()
camera.join()
drone1.join()