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)
camera = cv2.VideoCapture(
'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=3280, height=2464, format=(string)NV12, framerate=(fraction)20/1 ! nvvidconv ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink',
cv2.CAP_GSTREAMER)
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)
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(gstreamer_pipeline(flip_method=0),
cv2.CAP_GSTREAMER)
if not camera.isOpened():
raise RuntimeError('Could not start camera.')
try:
while True:
_, img = camera.read()
boxes, confs, clss = detector.prediction(img,
conf_th=0.8,
conf_class=[1])
img = detector.draw_boxes(img, boxes, confs, clss)
previous_object_ID = ct.nextObjectID
objects = ct.update(boxes)
if len(boxes) > 0 and 1 in clss and previous_object_ID in list(
objects.keys()):
print("detected {} {} {} {}".format(ct.nextObjectID, confs,
objects, boxes))
# loop over the tracked objects
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)
class Predictor(object):
"""Docstring for Predictor. """
def __init__(self):
self.detector = Detector()
self.ct = CentroidTracker(maxDisappeared=20)
def prediction(self, img, conf_th=0.3, conf_class=[]):
output = self.detector.prediction(img)
boxes, confs, clss = self.detector.filter_prediction(
output, img, conf_th=conf_th, conf_class=conf_class)
img = self.detector.draw_boxes(img, boxes, confs, clss)
return img
def object_track(self, img, conf_th=0.3, conf_class=[]):
output = self.detector.prediction(img)
boxes, confs, clss = self.detector.filter_prediction(
output, img, conf_th=conf_th, conf_class=conf_class)
img = self.detector.draw_boxes(img, boxes, confs, clss)
objects = self.ct.update(boxes)
if len(boxes) > 0 and 1 in clss:
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
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)
with PiCamera() as camera:
camera.resolution = (1280, 960) # twice height and widht
camera.rotation = int(str(os.environ['CAMERA_ROTATION']))
camera.framerate = 10
with PiRGBArray(camera, size=(WIDTH, HEIGHT)) as output:
while True:
camera.capture(output, 'bgr', resize=(WIDTH, HEIGHT))
img = output.array
result = detector.prediction(img)
df = detector.filter_prediction(result, img)
img = detector.draw_boxes(img, df)
boxes = df[['x1', 'y1', 'x2', 'y2']].values
previous_object_ID = ct.nextObjectID
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(0.300)
def __init__(self):
self.detector = Detector()
self.ct = CentroidTracker(maxDisappeared=20)
def load_detector():
global detector, ct
detector = Detector()
ct = CentroidTracker(maxDisappeared=50)