def count_objects(top, bottom, right, left, crop_img, roi_position, y_min,
y_max, deviation):
direction = "n.a." # means not available, it is just initialization
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
if (abs(((bottom + top) / 2) - roi_position) < deviation):
is_vehicle_detected.insert(0, 1)
update_csv = True
image_saver.save_image(crop_img) # save detected object image
print("Passei pela condição de centro do box na rotina OBJECT_COUNTER")
else:
if (abs(((right + left) / 2) - roi_position)) >= deviation and (abs((
(right + left) / 2) - roi_position)) <= deviation + .5:
print("OBJECT COUTNER Distancia do centro do box",
(abs(((right + left) / 2) - roi_position)), "Botton:",
bottom, "bottom referencia:",
bottom_position_of_detected_vehicle[0])
if (bottom > bottom_position_of_detected_vehicle[0]):
direction = "down"
else:
direction = "up"
bottom_position_of_detected_vehicle.insert(0, (bottom))
return direction, is_vehicle_detected, update_csv
def predict_speed(
top,
bottom,
right,
left,
current_frame_number,
crop_img,
roi_position,
):
speed = 0 # means not available, it is just initialization
direction = 'n.a.' # means not available, it is just initialization
scale_constant = 1 # manual scaling because we did not performed camera calibration
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
if bottom < 100:
scale_constant = 1 # scale_constant is used for manual scaling because we did not performed camera calibration
elif bottom > 100 and bottom < 400:
scale_constant = 2 # scale_constant is used for manual scaling because we did not performed camera calibration
else:
isInROI = False
if len(bottom_position_of_detected_vehicle) != 0 and bottom \
- bottom_position_of_detected_vehicle[0] > 0 and 170 \
< bottom_position_of_detected_vehicle[0] \
and bottom_position_of_detected_vehicle[0] < 520 \
and roi_position < bottom:
# print('contrue')
is_vehicle_detected.insert(0, 1)
update_csv = True
image_saver.save_image(crop_img) # save detected vehicle image
# for debugging
# print("bottom_position_of_detected_vehicle[0]: " + str(bottom_position_of_detected_vehicle[0]))
# print("bottom: " + str(bottom))
if bottom > bottom_position_of_detected_vehicle[0]:
direction = 'down'
else:
direction = 'up'
if isInROI:
pixel_length = bottom - bottom_position_of_detected_vehicle[0]
#################################################################################################################
# We will change this scale to detect near accurate speed, it should probably be more than 44 and there should be a correcting factor for drone speed effect.
scale_real_length = (
pixel_length * 44
) # multiplied by 44 to convert pixel length to real length in meters (chenge 44 to get length in meters for your case)
total_time_passed = current_frame_number - current_frame_number_list[0]
scale_real_time_passed = total_time_passed * 24 # get the elapsed total time for a vehicle to pass through ROI area (24 = fps)
print(scale_real_time_passed)
if scale_real_time_passed != 0:
speed = scale_real_length / scale_real_time_passed / scale_constant # performing manual scaling because we have not performed camera calibration
speed = speed / 6 * 40 # use reference constant to get vehicle speed prediction in kilometer unit
current_frame_number_list.insert(0, current_frame_number)
bottom_position_of_detected_vehicle.insert(0, bottom)
return (direction, speed, is_vehicle_detected, update_csv)
def predict_speed(
top,
bottom,
right,
left,
current_frame_number,
crop_img,
roi_position,
):
speed = 'n.a.' # means not available, it is just initialization
direction = 'n.a.' # means not available, it is just initialization
scale_constant = 1 # manual scaling because we did not performed camera calibration
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
print('entered')
if bottom < 250:
scale_constant = 1 # scale_constant is used for manual scaling because we did not performed camera calibration
elif bottom > 250 and bottom < 320:
scale_constant = 2 # scale_constant is used for manual scaling because we did not performed camera calibration
else:
isInROI = False
print(len(bottom_position_of_detected_vehicle))
print('position of Detected_vehicle=', bottom_position_of_detected_vehicle[0])
if len(bottom_position_of_detected_vehicle) != 0 and bottom \
- bottom_position_of_detected_vehicle[0] > 0 and 160 \
< bottom_position_of_detected_vehicle[0] \
and bottom_position_of_detected_vehicle[0] < 310 \
and roi_position < bottom: #Initially 205->160 and 210->310
is_vehicle_detected.insert(0, 1)
update_csv = True
image_saver.save_image(crop_img) # save detected vehicle image
import time
time.sleep(1)
# for debugging
# print("bottom_position_of_detected_vehicle[0]: " + str(bottom_position_of_detected_vehicle[0]))
# print("bottom: " + str(bottom))
if bottom > bottom_position_of_detected_vehicle[0]:
direction = 'down'
else:
direction = 'up'
if isInROI:
pixel_length = bottom - bottom_position_of_detected_vehicle[0]
scale_real_length = pixel_length * 44 # multiplied by 44 to convert pixel length to real length in meters (chenge 44 to get length in meters for your case)
total_time_passed = current_frame_number - current_frame_number_list[0]
scale_real_time_passed = total_time_passed * 24 # get the elapsed total time for a vehicle to pass through ROI area (24 = fps)
if scale_real_time_passed != 0:
speed = scale_real_length / scale_real_time_passed / scale_constant # performing manual scaling because we have not performed camera calibration
speed = speed / 6 * 40 # use reference constant to get vehicle speed prediction in kilometer unit
current_frame_number_list.insert(0, current_frame_number)
bottom_position_of_detected_vehicle.insert(0, bottom)
return (direction, speed, is_vehicle_detected, update_csv)
def predict_speed(
top,
bottom,
right,
left,
current_frame_number,
crop_img,
roi_position,
):
speed = 'n.a.' # Significa no disponible, es solo inicialización.
direction = 'n.a.' # Significa no disponible, es solo inicialización.
scale_constant = 1 # Escala manual porque no realizamos la calibración de la cámara.
isInROI = True #Es el objeto que se encuentra dentro de Región de Interés.
update_csv = False
if bottom < 250:
scale_constant = 1 # scale_constant se usa para la escala manual porque no realizamos la calibración de la cámara
elif bottom > 250 and bottom < 320:
scale_constant = 2 # scale_constant se usa para la escala manual porque no realizamos la calibración de la cámara
else:
isInROI = False
if len(bottom_position_of_detected_vehicle) != 0 and bottom \
- bottom_position_of_detected_vehicle[0] > 0 and 205 \
< bottom_position_of_detected_vehicle[0] \
and bottom_position_of_detected_vehicle[0] < 210 \
and roi_position < bottom:
is_vehicle_detected.insert(0, 1)
update_csv = True
image_saver.save_image(
crop_img) # guardar la imagen del vhiculo detectado
if bottom > bottom_position_of_detected_vehicle[0]:
direction = 'Abajo'
else:
direction = 'Arriba'
if isInROI:
pixel_length = bottom - bottom_position_of_detected_vehicle[0]
scale_real_length = pixel_length * 44 # multiplicado por 44 para convertir la longitud del píxel en longitud real en metros (chenge 44 para obtener la longitud en metros para su caso)
total_time_passed = current_frame_number - current_frame_number_list[0]
scale_real_time_passed = total_time_passed * 24 # obtener el tiempo total transcurrido para que un vehículo pase por el área de ROI (24 = fps)
if scale_real_time_passed != 0:
speed = scale_real_length / scale_real_time_passed / scale_constant # Realización de escala manual porque no hemos realizado calibración de cámara.
speed = speed / 6 * 40 # use la constante de referencia para obtener la predicción de la velocidad del vehículo en unidades de kilómetro
current_frame_number_list.insert(0, current_frame_number)
bottom_position_of_detected_vehicle.insert(0, bottom)
return (direction, speed, is_vehicle_detected, update_csv)
def predict_speed(top, bottom, right, left, current_frame_number, crop_img,
roi_position):
speed = "n.a." # means not available, it is just initialization
direction = "n.a." # means not available, it is just initialization
scale_constant = 1 # manual scaling because we did not performed camera calibration
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
if ((bottom) < 250):
scale_constant = 1 # scale_constant is used for manual scaling because we did not performed camera calibration
elif ((bottom > 250) and (bottom < 320)):
scale_constant = 2 # scale_constant is used for manual scaling because we did not performed camera calibration
else:
isInROI = False
if ((len(bottom_position_of_detected_vehicle) != 0)
and bottom - bottom_position_of_detected_vehicle[0] > 0
and 205 < bottom_position_of_detected_vehicle[0]
and bottom_position_of_detected_vehicle[0] < 210
and roi_position < bottom):
is_vehicle_detected.insert(0, 1)
update_csv = True
image_saver.save_image(crop_img) # save detected vehicle image
# for debugging
# print("bottom_position_of_detected_vehicle[0]: " + str(bottom_position_of_detected_vehicle[0]))
# print("bottom: " + str(bottom))
if (bottom > bottom_position_of_detected_vehicle[0]):
direction = "down"
else:
direction = "up"
if (isInROI):
pixel_length = ((bottom)) - bottom_position_of_detected_vehicle[0]
scale_real_length = pixel_length * 44 # multiplied by 44 to convert pixel length to real length in meters (chenge 44 to get length in meters for your case)
total_time_passed = (current_frame_number -
current_frame_number_list[0])
scale_real_time_passed = total_time_passed * 24 # get the elapsed total time for a vehicle to pass through ROI area (24 = fps)
if (scale_real_time_passed != 0):
speed = (
scale_real_length / scale_real_time_passed
) / scale_constant # performing manual scaling because we have not performed camera calibration
speed = speed / 6 * 40 # use reference constant to get vehicle speed prediction in kilometer unit
current_frame_number_list.insert(0, current_frame_number)
bottom_position_of_detected_vehicle.insert(0, (bottom))
return direction, speed, is_vehicle_detected, update_csv
def count_objects_x_axis(top, bottom, right, left, crop_img, roi_position, y_min, y_max, deviation):
direction = "n.a." # means not available, it is just initialization
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
if (abs(((right+left)/2)-roi_position) < deviation):
is_vehicle_detected.insert(0,1)
update_csv = True
image_saver.save_image(crop_img) # save detected object image
if(bottom > bottom_position_of_detected_vehicle[0]):
direction = "down"
else:
direction = "up"
bottom_position_of_detected_vehicle.insert(0,(bottom))
return direction, is_vehicle_detected, update_csv
def count_objects(r, name1,top, bottom, right, left, crop_img, roi_position, y_min, y_max, deviation):
#print(current_frame_number)
direction = "n.a." # means not available, it is just initialization
speed = 0
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
#print(width)
if (r==crop_img):
update_csv = True
is_vehicle_detected.insert(0,2)
print("Detection.............")
print(name1)
veh.insert(0,name1)
#update_csv = True
#print(crop_img)
print("##################")
image_saver.save_image(crop_img) # save detected object image
return (direction, speed, is_vehicle_detected, update_csv, veh)
def count_objects_x_axis(top, bottom, right, left, crop_img, roi_position, y_min, y_max, deviation,roiArea):
direction = "n.a." # means not available, it is just initialization
isInROI = True # is the object that is inside Region Of Interest
update_csv = False
if (abs(((right+left)/2)-roi_position) < deviation and last_frame_is_vehicle_detected[0] !=1 ):
is_vehicle_detected.insert(0,1)
#Added for testing
last_frame_is_vehicle_detected.insert(0,1)
update_csv = True
image_saver.save_image(crop_img) # save detected object image
else:
last_frame_is_vehicle_detected.insert(0,0)
if(left > left_position_of_detected_vehicle[0]):
direction = "right"
else:
direction = "left"
# This to keep track of last frame detection
bottom_position_of_detected_vehicle.insert(0,(bottom))
left_position_of_detected_vehicle.insert(0,(left))
return direction, is_vehicle_detected, update_csv