Update mqtt_pump_focus_image.py
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tpollina
GitHub
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c237534368
commit
af90680da7
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@ -1,11 +1,39 @@
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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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import os
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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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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
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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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################################################################################
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kit = MotorKit()
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pump_stepper = kit.stepper1
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@ -61,6 +89,7 @@ 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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################################################################################
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while True:
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@ -68,17 +97,20 @@ while True:
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if (topic=="pump"):
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direction=message.split(" ")[0]
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flowrate=message.split(" ")[1]
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volume=message.split(" ")[2]
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nb_step=int(volume)*507
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duration=(int(volume)*60)/float(flowrate)
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delay=(duration/nb_step)-0.005
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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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if direction == "BACKWARD":
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direction=stepper.direction
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if direction == "FORWARD":
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direction=stepper.FORWARD
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while True:
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count+=1
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print(count,nb_step)
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print("pump_stepper.onestep(direction=+action+, style=stepper.DOUBLE")
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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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@ -102,20 +134,25 @@ while True:
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nb_step=int(message.split(" ")[1])
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client.publish("receiver/focus", "Start");
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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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while True:
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count+=1
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print(count,nb_step)
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print("stage.onestep(direction=+action+, style=stepper.microstep")
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sleep(0.001)
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focus_stepper.onestep(direction=direction, style=stepper.MICROSTEP)
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if topic!="focus":
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pump_focus.release()
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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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break
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if count>nb_step:
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pump_focus.release()
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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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@ -125,41 +162,161 @@ while True:
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elif (topic=="image"):
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delay=int(message.split(" ")[0])
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volume_before=int(message.split(" ")[1])
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nb_frame=int(message.split(" ")[2])
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wait_duration=int(message.split(" ")[3])
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path=str(message.split(" ")[4])
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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(delay)
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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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nb_step=int(volume)*507
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for i in range(nb_step):
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print("pump_stepper.onestep(direction=+action+, style=stepper.DOUBLE")
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time.sleep(0.01)
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pump_stepper.onestep(direction=stepper.FORWARD, style=stepper.DOUBLE)
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sleep(0.01)
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directory = os.path.join(path, "PlanktonScope")
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os.makedirs(directory, exist_ok=True)
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date=datetime.now().strftime("%m_%d_%Y")
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time=datetime.now().strftime("%H_%M")
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path_date = os.path.join(directory, datetime.now().strftime("%m_%d_%Y"))
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path_date = os.path.join(directory, date)
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os.makedirs(path_date, exist_ok=True)
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path_hour = os.path.join(path_date,datetime.now().strftime("%H"))
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os.makedirs(path_hour, exist_ok=True)
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path_time = os.path.join(path_date,time)
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os.makedirs(path_time, exist_ok=True)
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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(path_hour,datetime.now().strftime("%M_%S_%f")+".jpg")
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filename = os.path.join(path_time,datetime.now().strftime("%M_%S_%f")+".jpg")
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camera.capture(filename)
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print("pump_stepper.onestep(direction=+action+, style=stepper.DOUBLE")
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client.publish("receiver/image", datetime.now().strftime("%M_%S_%f")+".jpg has been imaged.");
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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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if(count>nb_frame):
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# Meta data that is added to every object
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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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import_path = path_time
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archive_fn = os.path.join(directory, str(date)+"_"+str(time)+"_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(import_path, [".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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# Read image
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img = ImageReader(abs_path)
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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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# Convert image to uint8 gray
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img_gray = RGB2Gray(img)
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img_gray = Call(img_as_ubyte, img_gray)
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img_canny = Call(canny, img_gray, sigma=0.3)
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img_dilate = Call(dilation, img_canny)
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img_closing = Call(closing, img_dilate)
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mask = Call(erosion, img_closing)
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# Show progress bar for frames
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TQDM(Format("Frame {name}", name=name))
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# Apply threshold find objects
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#threshold = 204 # Call(skimage.filters.threshold_otsu, img_gray)
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#mask = img_gray < threshold
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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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# 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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roi_orig
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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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# 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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# If CalculateZooProcessFeatures is not used, we need to copy global_metadata into the stream:
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# meta = Call(lambda: global_metadata.copy())
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# https://github.com/morphocut/morphocut/issues/51
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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/image", object_id)
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p.run()
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sleep(sleep_during)
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count=0
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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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if topic!="image":
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pump_focus.release()
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@ -167,12 +324,8 @@ while True:
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client.publish("receiver/image", "Interrompted");
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break
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if count>nb_frame:
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print("The imaging is done.")
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topic="wait"
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client.publish("receiver/image", "Done");
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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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