Create mqtt_pump_focus_image_v2.py
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scripts/mqtt_pump_focus_image_v2.py
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389
scripts/mqtt_pump_focus_image_v2.py
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import paho.mqtt.client as mqtt
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from picamera import PiCamera
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from datetime import datetime, timedelta
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from adafruit_motor import stepper
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from adafruit_motorkit import MotorKit
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from time import sleep
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import json
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import os
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import subprocess
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from skimage.util import img_as_ubyte
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from morphocut import Call
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from morphocut.contrib.ecotaxa import EcotaxaWriter
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from morphocut.contrib.zooprocess import CalculateZooProcessFeatures
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from morphocut.core import Pipeline
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from morphocut.file import Find
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from morphocut.image import (
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ExtractROI,
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FindRegions,
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ImageReader,
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ImageWriter,
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RescaleIntensity,
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RGB2Gray,
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)
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from morphocut.stat import RunningMedian
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from morphocut.str import Format
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from morphocut.stream import TQDM, Enumerate, FilterVariables
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from skimage.feature import canny
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from skimage.color import rgb2gray, label2rgb
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from skimage.morphology import disk
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from skimage.morphology import erosion, dilation, closing
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from skimage.measure import label, regionprops
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import cv2, shutil
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import smbus
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#fan
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bus = smbus.SMBus(1)
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################################################################################
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kit = MotorKit()
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pump_stepper = kit.stepper1
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pump_stepper.release()
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focus_stepper = kit.stepper2
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focus_stepper.release()
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################################################################################
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camera = PiCamera()
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camera.resolution = (3280, 2464)
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camera.iso = 60
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camera.shutter_speed = 500
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camera.exposure_mode = 'fixedfps'
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################################################################################
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message = ''
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topic = ''
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count=''
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################################################################################
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def on_connect(client, userdata, flags, rc):
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print("Connected! - " + str(rc))
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client.subscribe("actuator/#")
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rgb(0,255,0)
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def on_subscribe(client, obj, mid, granted_qos):
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print("Subscribed! - "+str(mid)+" "+str(granted_qos))
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def on_message(client, userdata, msg):
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print(msg.topic+" "+str(msg.qos)+" "+str(msg.payload))
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global message
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global topic
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global count
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message=str(msg.payload.decode())
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topic=msg.topic.split("/")[1]
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count=0
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def on_log(client, obj, level, string):
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print(string)
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def rgb(R,G,B):
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bus.write_byte_data(0x0d, 0x00, 0)
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bus.write_byte_data(0x0d, 0x01, R)
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bus.write_byte_data(0x0d, 0x02, G)
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bus.write_byte_data(0x0d, 0x03, B)
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bus.write_byte_data(0x0d, 0x00, 1)
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bus.write_byte_data(0x0d, 0x01, R)
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bus.write_byte_data(0x0d, 0x02, G)
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bus.write_byte_data(0x0d, 0x03, B)
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bus.write_byte_data(0x0d, 0x00, 2)
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bus.write_byte_data(0x0d, 0x01, R)
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bus.write_byte_data(0x0d, 0x02, G)
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bus.write_byte_data(0x0d, 0x03, B)
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cmd="i2cdetect -y 1"
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subprocess.Popen(cmd.split(),stdout=subprocess.PIPE)
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################################################################################
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client = mqtt.Client()
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client.connect("127.0.0.1",1883,60)
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client.on_connect = on_connect
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client.on_subscribe = on_subscribe
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client.on_message = on_message
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client.on_log = on_log
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client.loop_start()
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local_metadata = {
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"process_datetime": datetime.now(),
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"acq_camera_resolution" : camera.resolution,
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"acq_camera_iso" : camera.iso,
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"acq_camera_shutter_speed" : camera.shutter_speed
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}
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config_txt = open('/home/pi/PlanktonScope/config.txt','r')
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node_red_metadata = json.loads(config_txt.read())
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global_metadata = {**local_metadata, **node_red_metadata}
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archive_fn = os.path.join("/home/pi/PlanktonScope/","export", "ecotaxa_export.zip")
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# Define processing pipeline
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with Pipeline() as p:
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# Recursively find .jpg files in import_path.
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# Sort to get consective frames.
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abs_path = Find("/home/pi/PlanktonScope/tmp", [".jpg"], sort=True, verbose=True)
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# Extract name from abs_path
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name = Call(lambda p: os.path.splitext(os.path.basename(p))[0], abs_path)
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Call(rgb, 0,255,0)
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# Read image
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img = ImageReader(abs_path)
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# Show progress bar for frames
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#TQDM(Format("Frame {name}", name=name))
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# Apply running median to approximate the background image
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flat_field = RunningMedian(img, 5)
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# Correct image
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img = img / flat_field
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# Rescale intensities and convert to uint8 to speed up calculations
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img = RescaleIntensity(img, in_range=(0, 1.1), dtype="uint8")
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# frame_fn = Format(os.path.join("/home/pi/PlanktonScope/tmp","CLEAN", "{name}.jpg"), name=name)
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# ImageWriter(frame_fn, img)
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# Convert image to uint8 gray
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img_gray = RGB2Gray(img)
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# ?
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img_gray = Call(img_as_ubyte, img_gray)
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#Canny edge detection
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img_canny = Call(cv2.Canny, img_gray, 50,100)
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#Dilate
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kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (15, 15))
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img_dilate = Call(cv2.dilate, img_canny, kernel, iterations=2)
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#Close
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kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (5, 5))
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img_close = Call(cv2.morphologyEx, img_dilate, cv2.MORPH_CLOSE, kernel, iterations=1)
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#Erode
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kernel = Call(cv2.getStructuringElement, cv2.MORPH_ELLIPSE, (15, 15))
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mask = Call(cv2.erode, img_close, kernel, iterations=2)
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# Find objects
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regionprops = FindRegions(
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mask, img_gray, min_area=1000, padding=10, warn_empty=name
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)
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Call(rgb, 255,0,255)
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# For an object, extract a vignette/ROI from the image
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roi_orig = ExtractROI(img, regionprops, bg_color=255)
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# Generate an object identifier
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i = Enumerate()
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#Call(print,i)
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object_id = Format("{name}_{i:d}", name=name, i=i)
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#Call(print,object_id)
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object_fn = Format(os.path.join("/home/pi/PlanktonScope/","OBJECTS", "{name}.jpg"), name=object_id)
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ImageWriter(object_fn, roi_orig)
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# Calculate features. The calculated features are added to the global_metadata.
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# Returns a Variable representing a dict for every object in the stream.
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meta = CalculateZooProcessFeatures(
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regionprops, prefix="object_", meta=global_metadata
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)
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# json_meta = Call(json.dumps,meta, sort_keys=True, default=str)
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# Call(client.publish, "receiver/segmentation/metric", json_meta)
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# Add object_id to the metadata dictionary
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meta["object_id"] = object_id
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# Generate object filenames
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orig_fn = Format("{object_id}.jpg", object_id=object_id)
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# Write objects to an EcoTaxa archive:
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# roi image in original color, roi image in grayscale, metadata associated with each object
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EcotaxaWriter(archive_fn, (orig_fn, roi_orig), meta)
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# Progress bar for objects
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TQDM(Format("Object {object_id}", object_id=object_id))
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# Call(client.publish, "receiver/segmentation/object_id", object_id)
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camera.start_preview(fullscreen=False, window = (160, 0, 640, 480))
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################################################################################
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while True:
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################################################################################
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if (topic=="pump"):
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rgb(0,0,255)
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direction=message.split(" ")[0]
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delay=float(message.split(" ")[1])
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nb_step=int(message.split(" ")[2])
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client.publish("receiver/pump", "Start");
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while True:
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if direction == "BACKWARD":
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direction=stepper.BACKWARD
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if direction == "FORWARD":
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direction=stepper.FORWARD
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count+=1
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# print(count,nb_step)
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pump_stepper.onestep(direction=direction, style=stepper.DOUBLE)
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sleep(delay)
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if topic!="pump":
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pump_stepper.release()
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print("The pump has been interrompted.")
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client.publish("receiver/pump", "Interrompted");
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rgb(0,255,0)
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break
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if count>nb_step:
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pump_stepper.release()
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print("The pumping is done.")
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topic="wait"
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client.publish("receiver/pump", "Done");
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rgb(0,255,0)
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break
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################################################################################
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elif (topic=="focus"):
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rgb(255,255,0)
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direction=message.split(" ")[0]
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nb_step=int(message.split(" ")[1])
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client.publish("receiver/focus", "Start");
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while True:
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if direction == "FORWARD":
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direction=stepper.FORWARD
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if direction == "BACKWARD":
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direction=stepper.BACKWARD
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count+=1
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# print(count,nb_step)
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focus_stepper.onestep(direction=direction, style=stepper.MICROSTEP)
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if topic!="focus":
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focus_stepper.release()
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print("The stage has been interrompted.")
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client.publish("receiver/focus", "Interrompted");
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rgb(0,255,0)
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break
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if count>nb_step:
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focus_stepper.release()
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print("The focusing is done.")
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topic="wait"
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client.publish("receiver/focus", "Done");
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rgb(0,255,0)
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break
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################################################################################
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elif (topic=="image"):
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sleep_before=int(message.split(" ")[0])
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nb_step=int(message.split(" ")[1])
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path=str(message.split(" ")[2])
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nb_frame=int(message.split(" ")[3])
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sleep_during=int(message.split(" ")[4])
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#sleep a duration before to start
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sleep(sleep_before)
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client.publish("receiver/image", "Start");
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#flushing before to begin
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rgb(0,0,255)
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for i in range(nb_step):
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pump_stepper.onestep(direction=stepper.FORWARD, style=stepper.DOUBLE)
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sleep(0.01)
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rgb(0,255,0)
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while True:
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count+=1
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# print(count,nb_frame)
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filename = os.path.join("/home/pi/PlanktonScope/tmp",datetime.now().strftime("%M_%S_%f")+".jpg")
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rgb(0,255,255)
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camera.capture(filename)
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rgb(0,255,0)
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client.publish("receiver/image", datetime.now().strftime("%M_%S_%f")+".jpg has been imaged.");
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rgb(0,0,255)
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for i in range(10):
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pump_stepper.onestep(direction=stepper.FORWARD, style=stepper.DOUBLE)
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sleep(0.01)
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sleep(0.5)
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rgb(0,255,0)
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if(count>nb_frame):
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# camera.stop_preview()
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client.publish("receiver/image", "Completed");
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# Meta data that is added to every object
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client.publish("receiver/segmentation", "Start");
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# Define processing pipeline
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p.run()
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#remove directory
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#shutil.rmtree(import_path)
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client.publish("receiver/segmentation", "Completed");
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command = os.popen("rm -rf /home/pi/PlanktonScope/tmp/*.jpg")
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rgb(255,255,255)
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sleep(sleep_during)
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rgb(0,255,0)
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rgb(0,0,255)
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for i in range(nb_step):
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pump_stepper.onestep(direction=stepper.FORWARD, style=stepper.DOUBLE)
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sleep(0.01)
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rgb(0,255,0)
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count=0
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if topic!="image":
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pump_stepper.release()
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print("The imaging has been interrompted.")
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client.publish("receiver/image", "Interrompted");
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rgb(0,255,0)
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count=0
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break
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else:
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# print("Waiting")
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sleep(1)
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