def process_facerecog():
input_video = 0
# By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
detection_graph, category_index = backbone.set_model('ssd_mobilenet_v1_coco_2017_11_17')
targeted_objects = "person"
fps = 24 # change it with your input video fps
width = 640 # change it with your input video width
height = 480 # change it with your input vide height
is_color_recognition_enabled = 0 # set it to 1 for enabling the color prediction for the detected objects
roi = 350 # roi line position
deviation = 3 # the constant that represents the object counting area
object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, targeted_objects,fps, width, height, roi, deviation) # counting all the objects
#---------------------------------------------- #--- Author : Ahmet Ozlu #--- Mail : [email protected] #--- Date : 27th January 2018 #---------------------------------------------- # Imports import tensorflow as tf # Object detection imports from utils import backbone from api import object_counting_api input_video = "./input_images_and_videos/vehicle_survaillance.mp4" # By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies. detection_graph, category_index = backbone.set_model('ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt') is_color_recognition_enabled = 1 # set it to 1 for enabling the color prediction for the detected objects roi = 185 # roi line position deviation = 2 # the constant that represents the object counting area custom_object_name = 'Vehicle' # set it to your custom object name object_counting_api.cumulative_object_counting_y_axis(input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation, custom_object_name) # counting all the objects
# Imports
import tensorflow as tf
# Object detection imports
from utils import backbone
from api import object_counting_api
# if tf.__version__ < '1.4.0':
# raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')
input_video = "demovideo.mp4"
# By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
detection_graph, category_index = backbone.set_model('fcc')
#fps = 24 # change it with your input video fps
#width = 640 # change it with your input video width
#height = 352 # change it with your input vide height
fps = 30 # change it with your input video fps
width = 670 # change it with your input video width
height = 360 # change it with your input vide height
is_color_recognition_enabled = 0 # set it to 1 for enabling the color prediction for the detected objects
#roi = 200 # roi line position
roi = 180
deviation = 3 # the constant that represents the object counting area
object_counting_api.cumulative_object_counting_y_axis(
input_video, detection_graph, category_index, is_color_recognition_enabled,
fps, width, height, roi, deviation) # counting all the objects
import tensorflow as tf
from utils import backbone
from api import object_counting_api
input_video = "./input_images_and_videos/object_test.mp4"
detection_graph, category_index = backbone.set_model(
'ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt')
is_color_recognition_enabled = 0
roi = 185
deviation = 2
object_counting_api.cumulative_object_counting_y_axis(
input_video, detection_graph, category_index, is_color_recognition_enabled,
roi, deviation)
#object_counting_api.object_counting_webcam(detection_graph, category_index, is_color_recognition_enabled)
