import os
import cv2
import base64
import glob
from celery import Celery
from functools import reduce
from dotenv import load_dotenv
from importlib import import_module
from datetime import datetime, timedelta
from backend.centroidtracker import CentroidTracker
from backend.base_camera import BaseCamera
from backend.utils import reduce_tracking
load_dotenv()
Detector = import_module('backend.' + os.environ['DETECTION_MODEL']).Detector
celery = Celery("app")
celery.conf.update(
broker_url='redis://localhost:6379/0',
result_backend='redis://localhost:6379/0',
beat_schedule={
"photos_SO": {
"task": "backend.camera_opencv.CaptureContinous",
"schedule": timedelta(
seconds=int(str(os.environ['BEAT_INTERVAL']))
),
"args": []
}
}
)
IMAGE_FOLDER = "imgs"
class Camera(BaseCamera):
video_source = 0
@staticmethod
def set_video_source(source):
Camera.video_source = source
@staticmethod
def frames():
camera = cv2.VideoCapture(Camera.video_source)
if not camera.isOpened():
raise RuntimeError('Could not start camera.')
while True:
# read current frame
_, img = camera.read()
yield img
class Predictor(object):
"""Docstring for Predictor. """
def __init__(self):
self.detector = Detector()
self.ct = CentroidTracker(maxDisappeared=50)
def prediction(self, img, conf_th=0.3, conf_class=[]):
output = self.detector.prediction(img)
df = self.detector.filter_prediction(output, img, conf_th=conf_th, conf_class=conf_class)
img = self.detector.draw_boxes(img, df)
return img
def object_track(self, img, conf_th=0.3, conf_class=[]):
output = self.detector.prediction(img)
df = self.detector.filter_prediction(output, img, conf_th=conf_th, conf_class=conf_class)
img = self.detector.draw_boxes(img, df)
boxes = df[['x1', 'y1', 'x2', 'y2']].values
objects = self.ct.update(boxes)
if len(boxes) > 0 and (df['class_name'].str.contains('person').any()):
for (objectID, centroid) in objects.items():
text = "ID {}".format(objectID)
cv2.putText(img, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(img, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
return img
def img_to_base64(self, img):
"""encode as a jpeg image and return it"""
buffer = cv2.imencode('.jpg', img)[1].tobytes()
jpg_as_text = base64.b64encode(buffer)
base64_string = jpg_as_text.decode('utf-8')
return base64_string
@celery.task(bind=True)
def CaptureContinous(self, detector):
cap = cv2.VideoCapture(0)
_, image = cap.read()
cap.release()
output = detector.prediction(image)
df = detector.filter_prediction(output, image)
if len(df) > 0:
if (df['class_name']
.str
.contains('person|bird|cat|wine glass|cup|sandwich')
.any()):
day = datetime.now().strftime("%Y%m%d")
directory = os.path.join(IMAGE_FOLDER, 'webcam', day)
if not os.path.exists(directory):
os.makedirs(directory)
image = detector.draw_boxes(image, df)
classes = df['class_name'].unique().tolist()
hour = datetime.now().strftime("%H%M%S")
filename_output = os.path.join(
directory, "{}_{}_.jpg".format(hour, "-".join(classes))
)
cv2.imwrite(filename_output, image)
@celery.task(bind=True)
def ObjectTracking(self):
detector = Detector()
myiter = glob.iglob(os.path.join(IMAGE_FOLDER, '**', '*.jpg'),
recursive=True)
newdict = reduce(lambda a, b: reduce_tracking(a,b), myiter, dict())
startID = max(map(int, newdict.keys()), default=0) + 1
ct = CentroidTracker(startID=startID)
camera = cv2.VideoCapture(0)
if not camera.isOpened():
raise RuntimeError('Could not start camera.')
try:
while True:
_, img = camera.read()
output = detector.prediction(img)
df = detector.filter_prediction(output, img)
img = detector.draw_boxes(img, df)
boxes = df[['x1', 'y1', 'x2', 'y2']].values
previous_object_ID = ct.nextObjectID
#self.update_state(state='PROGRESS',
# meta={
# 'object_id': previous_object_ID,
# })
objects = ct.update(boxes)
if len(boxes) > 0 and (df['class_name'].str.contains('person').any()) and previous_object_ID in list(objects.keys()):
for (objectID, centroid) in objects.items():
text = "ID {}".format(objectID)
cv2.putText(img, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(img, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
day = datetime.now().strftime("%Y%m%d")
directory = os.path.join(IMAGE_FOLDER, 'pi', day)
if not os.path.exists(directory):
os.makedirs(directory)
ids = "-".join(list([str(i) for i in objects.keys()]))
hour = datetime.now().strftime("%H%M%S")
filename_output = os.path.join(
directory, "{}_person_{}_.jpg".format(hour, ids)
)
cv2.imwrite(filename_output, img)
#time.sleep(0.100)
except KeyboardInterrupt:
print('interrupted!')
camera.release()
print(type(objects))
print(objects)
except Exception as e:
print('interrupted! by:')
print(e)
camera.release()
print(type(objects))
print(objects)
if __name__ == '__main__':
ObjectTracking()
